diff --git a/masked_tiles_nodata.ipynb b/masked_tiles_nodata.ipynb new file mode 100644 index 0000000..84fdd6f --- /dev/null +++ b/masked_tiles_nodata.ipynb @@ -0,0 +1,120 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "id": "4b4eced1", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "OK: C:\\Users\\leoni\\kirgis_repo\\segmenteverygrain\\leonieexamples\\2509_with_vegetation_mask_nodataset\\inputtiles_masked\\tile_r000006_c000024_masked.tif\n", + "OK: C:\\Users\\leoni\\kirgis_repo\\segmenteverygrain\\leonieexamples\\2509_with_vegetation_mask_nodataset\\inputtiles_masked\\tile_r000006_c000025_masked.tif\n", + "OK: C:\\Users\\leoni\\kirgis_repo\\segmenteverygrain\\leonieexamples\\2509_with_vegetation_mask_nodataset\\inputtiles_masked\\tile_r000006_c000028_masked.tif\n", + "OK: C:\\Users\\leoni\\kirgis_repo\\segmenteverygrain\\leonieexamples\\2509_with_vegetation_mask_nodataset\\inputtiles_masked\\tile_r000008_c000015_masked.tif\n", + "OK: C:\\Users\\leoni\\kirgis_repo\\segmenteverygrain\\leonieexamples\\2509_with_vegetation_mask_nodataset\\inputtiles_masked\\tile_r000008_c000026_masked.tif\n", + "OK: C:\\Users\\leoni\\kirgis_repo\\segmenteverygrain\\leonieexamples\\2509_with_vegetation_mask_nodataset\\inputtiles_masked\\tile_r000008_c000031_masked.tif\n", + "OK: C:\\Users\\leoni\\kirgis_repo\\segmenteverygrain\\leonieexamples\\2509_with_vegetation_mask_nodataset\\inputtiles_masked\\tile_r000008_c000032_masked.tif\n" + ] + } + ], + "source": [ + "import numpy as np\n", + "import rasterio\n", + "from pathlib import Path\n", + "from rasterio.windows import from_bounds\n", + "from rasterio.warp import reproject, Resampling\n", + "\n", + "tiles_dir = Path(r\"C:\\Users\\leoni\\kirgis_repo\\segmenteverygrain\\leonieexamples\\2509_with_vegetation_mask_nodataset\\inputtiles\")\n", + "\n", + "veg_mask_path = r\"C:\\Users\\leoni\\kirgis_repo\\veg-shad-wat-filtering\\1_uav-filters\\vegetation\\veg_masks_rgbvi_fallback\\25092025_Aktal_gravel_2_10m_OM_RGB_utm_coregistered_ws128_clipwater_vegmask_rgbvi.tif\"\n", + "shadow_mask_path = r\"C:\\Users\\leoni\\kirgis_repo\\veg-shad-wat-filtering\\1_uav-filters\\shadow\\0-02\\25092025_Aktal_gravel_2_10m_OM_RGB_utm_coregistered_ws128_clipwater_shadowmask.tif\"\n", + "\n", + "out_dir = tiles_dir.parent / (tiles_dir.name + \"_masked\")\n", + "out_dir.mkdir(parents=True, exist_ok=True)\n", + "\n", + "\n", + "def read_mask_aligned(mask_src, dst_src):\n", + " \"\"\"\n", + " Liest den Masken-Ausschnitt passend zum Tile (dst_src) und bringt ihn\n", + " per nearest-neighbor auf exakt das Tile-Grid (falls nötig).\n", + " \"\"\"\n", + " # Window im Maskenraster, basierend auf den Bounds des Tiles\n", + " b = dst_src.bounds\n", + " win = from_bounds(b.left, b.bottom, b.right, b.top, transform=mask_src.transform)\n", + " win = win.round_offsets().round_lengths()\n", + "\n", + " mask_crop = mask_src.read(1, window=win, boundless=True, fill_value=0)\n", + "\n", + " aligned = np.zeros((dst_src.height, dst_src.width), dtype=np.uint8)\n", + "\n", + " reproject(\n", + " source=mask_crop,\n", + " destination=aligned,\n", + " src_transform=mask_src.window_transform(win),\n", + " src_crs=mask_src.crs,\n", + " dst_transform=dst_src.transform,\n", + " dst_crs=dst_src.crs,\n", + " resampling=Resampling.nearest,\n", + " )\n", + " return aligned\n", + "\n", + "\n", + "with rasterio.open(veg_mask_path) as veg_src, rasterio.open(shadow_mask_path) as shad_src:\n", + " for tile_path in sorted(tiles_dir.glob(\"*.tif\")):\n", + " with rasterio.open(tile_path) as src_rgb:\n", + " rgb = src_rgb.read() # (bands, h, w)\n", + " profile = src_rgb.profile.copy()\n", + "\n", + " veg_aligned = read_mask_aligned(veg_src, src_rgb)\n", + " shad_aligned = read_mask_aligned(shad_src, src_rgb)\n", + "\n", + " # robust: alles >0 gilt als \"maskiert\" (egal ob 1 oder 255)\n", + " invalid = (veg_aligned > 0) | (shad_aligned > 0)\n", + "\n", + " # NoData Wert: bei uint8 typischerweise 0\n", + " nodata_value = 0\n", + " profile.update(\n", + " nodata=nodata_value,\n", + " compress=\"deflate\",\n", + " tiled=True,\n", + " driver=\"GTiff\",\n", + " )\n", + "\n", + " # Pixel überschreiben (für Tools, die keine Masken beachten)\n", + " rgb[:, invalid] = nodata_value\n", + "\n", + " out_path = out_dir / tile_path.name.replace(\".tif\", \"_masked.tif\")\n", + " with rasterio.open(out_path, \"w\", **profile) as dst:\n", + " dst.write(rgb)\n", + " # Zusätzlich eine echte GDAL-Maske schreiben (QGIS liebt das)\n", + " dst.write_mask(np.where(invalid, 0, 255).astype(\"uint8\"))\n", + "\n", + " print(f\"OK: {out_path}\")" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "seg_2", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.19" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/notebooks/01_SEG_leonie_loopingtiles.ipynb b/notebooks/01_SEG_leonie_loopingtiles.ipynb new file mode 100644 index 0000000..ed39e9f --- /dev/null +++ b/notebooks/01_SEG_leonie_loopingtiles.ipynb @@ -0,0 +1,58373 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "623da314", + "metadata": {}, + "source": [ + "# Import packages" + ] + }, + { + "cell_type": "markdown", + "id": "2f3b877d", + "metadata": {}, + "source": [ + "Quick memory chceks:" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "f86592c6", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "TF GPUs: []\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2026-03-06 08:34:25.588876: E external/local_xla/xla/stream_executor/cuda/cuda_platform.cc:51] failed call to cuInit: INTERNAL: CUDA error: Failed call to cuInit: CUDA_ERROR_UNKNOWN: unknown error\n", + "2026-03-06 08:34:25.588936: I external/local_xla/xla/stream_executor/cuda/cuda_diagnostics.cc:160] env: CUDA_VISIBLE_DEVICES=\"0\"\n", + "2026-03-06 08:34:25.588942: I external/local_xla/xla/stream_executor/cuda/cuda_diagnostics.cc:171] verbose logging is disabled. Rerun with verbose logging (usually --v=1 or --vmodule=cuda_diagnostics=1) to get more diagnostic output from this module\n", + "2026-03-06 08:34:25.588953: I external/local_xla/xla/stream_executor/cuda/cuda_diagnostics.cc:176] retrieving CUDA diagnostic information for host: hpdar01c03s04\n", + "2026-03-06 08:34:25.588956: I external/local_xla/xla/stream_executor/cuda/cuda_diagnostics.cc:183] hostname: hpdar01c03s04\n", + "2026-03-06 08:34:25.589056: I external/local_xla/xla/stream_executor/cuda/cuda_diagnostics.cc:190] libcuda reported version is: 570.124.6\n", + "2026-03-06 08:34:25.589072: I external/local_xla/xla/stream_executor/cuda/cuda_diagnostics.cc:194] kernel reported version is: 570.124.6\n", + "2026-03-06 08:34:25.589074: I external/local_xla/xla/stream_executor/cuda/cuda_diagnostics.cc:284] kernel version seems to match DSO: 570.124.6\n" + ] + } + ], + "source": [ + "# to prevent blocking all memory\n", + "import os\n", + "os.environ[\"TF_FORCE_GPU_ALLOW_GROWTH\"] = \"true\"\n", + "\n", + "import tensorflow as tf\n", + "for gpu in tf.config.list_physical_devices(\"GPU\"):\n", + " tf.config.experimental.set_memory_growth(gpu, True)\n", + "\n", + "print(\"TF GPUs:\", tf.config.list_physical_devices(\"GPU\"))" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "ff94825b", + "metadata": {}, + "outputs": [], + "source": [ + "import cv2\n", + "from keras.utils import load_img\n", + "from keras.saving import load_model\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "import pandas as pd\n", + "from sam2.build_sam import build_sam2\n", + "from sam2.sam2_image_predictor import SAM2ImagePredictor\n", + "from skimage.measure import regionprops, regionprops_table\n", + "from tqdm import trange, tqdm\n", + "\n", + "import segmenteverygrain as seg\n", + "import segmenteverygrain.interactions as si\n", + "import os\n", + "import rasterio\n", + "import time, traceback\n", + "import pandas as pd\n", + "import torch\n", + "\n", + "# %matplotlib qt" + ] + }, + { + "cell_type": "markdown", + "id": "44e817f1", + "metadata": {}, + "source": [ + "## Load models" + ] + }, + { + "cell_type": "markdown", + "id": "442d52dc", + "metadata": {}, + "source": [ + "Maybe you need to clone first SAM2. Then add the yaml file in the bottom of the next chunk" + ] + }, + { + "cell_type": "markdown", + "id": "7fdb5f2a", + "metadata": {}, + "source": [ + "For terrabyte:" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "3965da5f", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "UNet loaded\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsshome1/0B/di54doz/.local/share/mamba/envs/segmenteverygrain/lib/python3.10/site-packages/torch/cuda/__init__.py:184: UserWarning: CUDA initialization: CUDA unknown error - this may be due to an incorrectly set up environment, e.g. changing env variable CUDA_VISIBLE_DEVICES after program start. Setting the available devices to be zero. (Triggered internally at /pytorch/c10/cuda/CUDAFunctions.cpp:119.)\n", + " return torch._C._cuda_getDeviceCount() > 0\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "SAM2 loaded on cpu\n" + ] + }, + { + "ename": "RuntimeError", + "evalue": "CUDA unknown error - this may be due to an incorrectly set up environment, e.g. changing env variable CUDA_VISIBLE_DEVICES after program start. Setting the available devices to be zero.", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mRuntimeError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[4], line 18\u001b[0m\n\u001b[1;32m 15\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mSAM2 loaded on\u001b[39m\u001b[38;5;124m\"\u001b[39m, device)\n\u001b[1;32m 17\u001b[0m \u001b[38;5;66;03m# 3) kurzer Speicher-Check\u001b[39;00m\n\u001b[0;32m---> 18\u001b[0m free, total \u001b[38;5;241m=\u001b[39m \u001b[43mtorch\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcuda\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmem_get_info\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 19\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mtorch reports free \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mfree\u001b[38;5;241m/\u001b[39m\u001b[38;5;241m1024\u001b[39m\u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39m\u001b[38;5;241m3\u001b[39m\u001b[38;5;132;01m:\u001b[39;00m\u001b[38;5;124m.1f\u001b[39m\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m GB / total \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mtotal\u001b[38;5;241m/\u001b[39m\u001b[38;5;241m1024\u001b[39m\u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39m\u001b[38;5;241m3\u001b[39m\u001b[38;5;132;01m:\u001b[39;00m\u001b[38;5;124m.1f\u001b[39m\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m GB\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n", + "File \u001b[0;32m~/.local/share/mamba/envs/segmenteverygrain/lib/python3.10/site-packages/torch/cuda/memory.py:894\u001b[0m, in \u001b[0;36mmem_get_info\u001b[0;34m(device)\u001b[0m\n\u001b[1;32m 882\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124mr\u001b[39m\u001b[38;5;124;03m\"\"\"Return the global free and total GPU memory for a given device using cudaMemGetInfo.\u001b[39;00m\n\u001b[1;32m 883\u001b[0m \n\u001b[1;32m 884\u001b[0m \u001b[38;5;124;03mArgs:\u001b[39;00m\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 891\u001b[0m \u001b[38;5;124;03m details about GPU memory management.\u001b[39;00m\n\u001b[1;32m 892\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 893\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m device \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m--> 894\u001b[0m device \u001b[38;5;241m=\u001b[39m \u001b[43mtorch\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcuda\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcurrent_device\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 895\u001b[0m \u001b[38;5;66;03m# optional=True allows `device = torch.device('cuda')` for which device.index is None\u001b[39;00m\n\u001b[1;32m 896\u001b[0m device \u001b[38;5;241m=\u001b[39m _get_device_index(device, optional\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m)\n", + "File \u001b[0;32m~/.local/share/mamba/envs/segmenteverygrain/lib/python3.10/site-packages/torch/cuda/__init__.py:1094\u001b[0m, in \u001b[0;36mcurrent_device\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1092\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21mcurrent_device\u001b[39m() \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m \u001b[38;5;28mint\u001b[39m:\n\u001b[1;32m 1093\u001b[0m \u001b[38;5;250m \u001b[39m\u001b[38;5;124mr\u001b[39m\u001b[38;5;124;03m\"\"\"Return the index of a currently selected device.\"\"\"\u001b[39;00m\n\u001b[0;32m-> 1094\u001b[0m \u001b[43m_lazy_init\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1095\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m torch\u001b[38;5;241m.\u001b[39m_C\u001b[38;5;241m.\u001b[39m_cuda_getDevice()\n", + "File \u001b[0;32m~/.local/share/mamba/envs/segmenteverygrain/lib/python3.10/site-packages/torch/cuda/__init__.py:424\u001b[0m, in \u001b[0;36m_lazy_init\u001b[0;34m()\u001b[0m\n\u001b[1;32m 419\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mAssertionError\u001b[39;00m(\n\u001b[1;32m 420\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mlibcudart functions unavailable. It looks like you have a broken build?\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 421\u001b[0m )\n\u001b[1;32m 422\u001b[0m \u001b[38;5;66;03m# This function throws if there's a driver initialization error, no GPUs\u001b[39;00m\n\u001b[1;32m 423\u001b[0m \u001b[38;5;66;03m# are found or any other error occurs\u001b[39;00m\n\u001b[0;32m--> 424\u001b[0m \u001b[43mtorch\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_C\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_cuda_init\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 425\u001b[0m \u001b[38;5;66;03m# Some of the queued calls may reentrantly call _lazy_init();\u001b[39;00m\n\u001b[1;32m 426\u001b[0m \u001b[38;5;66;03m# we need to just return without initializing in that case.\u001b[39;00m\n\u001b[1;32m 427\u001b[0m \u001b[38;5;66;03m# However, we must not let any *other* threads in!\u001b[39;00m\n\u001b[1;32m 428\u001b[0m _tls\u001b[38;5;241m.\u001b[39mis_initializing \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mTrue\u001b[39;00m\n", + "\u001b[0;31mRuntimeError\u001b[0m: CUDA unknown error - this may be due to an incorrectly set up environment, e.g. changing env variable CUDA_VISIBLE_DEVICES after program start. Setting the available devices to be zero." + ] + } + ], + "source": [ + "import torch\n", + "\n", + "# 1) UNet laden (TF)\n", + "unet = load_model(\n", + " \"../models/seg_model.keras\",\n", + " custom_objects={\"weighted_crossentropy\": seg.weighted_crossentropy},\n", + ")\n", + "print(\"UNet loaded\")\n", + "\n", + "# 2) SAM2 laden (Torch)\n", + "device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n", + "cfg = \"configs/sam2.1/sam2.1_hiera_l.yaml\"\n", + "ckpt = \"/dss/dsshome1/0B/di54doz/segmenteverygrain/models/sam2.1_hiera_large.pt\"\n", + "sam = build_sam2(cfg, ckpt, device=device)\n", + "print(\"SAM2 loaded on\", device)\n", + "\n", + "# 3) kurzer Speicher-Check\n", + "free, total = torch.cuda.mem_get_info()\n", + "print(f\"torch reports free {free/1024**3:.1f} GB / total {total/1024**3:.1f} GB\")" + ] + }, + { + "cell_type": "markdown", + "id": "65b1894f", + "metadata": {}, + "source": [ + "For private Laptop:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "da8020af", + "metadata": {}, + "outputs": [], + "source": [ + "# UNET model\n", + "unet = load_model(\n", + " \"../models/seg_model.keras\",\n", + " custom_objects={\"weighted_crossentropy\": seg.weighted_crossentropy},\n", + ")\n", + "print(\"UNet loaded\")\n", + "\n", + "# Auto-detect device: CUDA for NVIDIA, MPS for Apple Silicon, CPU as fallback\n", + "import torch\n", + "if torch.cuda.is_available():\n", + " device = \"cuda\"\n", + "elif torch.backends.mps.is_available():\n", + " device = \"mps\"\n", + "else:\n", + " device = \"cpu\"\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "7b1dd05d", + "metadata": {}, + "outputs": [], + "source": [ + "# # SAM 2.1 checkpoints. Download from:\n", + "# # https://dl.fbaipublicfiles.com/segment_anything_2/092824/sam2.1_hiera_large.pt\n", + "\n", + "# sam = build_sam2(r\"C:\\Users\\leoni\\sam2\\sam2\\configs\\sam2.1\\sam2.1_hiera_l.yaml\", r\"C:\\Users\\leoni\\segmenteverygrain\\models\\sam2.1_hiera_large.pt\", device=device)\n" + ] + }, + { + "cell_type": "markdown", + "id": "93a0c303", + "metadata": {}, + "source": [ + "## Set your directories" + ] + }, + { + "cell_type": "markdown", + "id": "659098c3", + "metadata": {}, + "source": [ + "Change here in Terrabyte !" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "6856ddaf", + "metadata": {}, + "outputs": [], + "source": [ + "dir = os.chdir(\"/dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F8/\")\n", + "dir = os.getcwd()\n", + "os.makedirs(\"ouputgpkg\", exist_ok=True)\n", + "os.makedirs(\"inputtiles\", exist_ok=True)\n", + "os.makedirs(\"pebbleplots\", exist_ok=True)\n", + "os.makedirs(\"histplots\", exist_ok=True)\n", + "os.makedirs(\"csv_stats\", exist_ok=True)\n", + "\n", + "inputtiledir = os.path.join(dir, \"inputtiles/\")\n", + "ouputgpkg = os.path.join(dir, \"ouputgpkg/\")\n", + "csvdir = os.path.join(dir, \"csv_stats\")\n", + "plotdirgravel = os.path.join(dir, \"pebbleplots/\")\n", + "plotdirhist = os.path.join(dir, \"histplots/\")" + ] + }, + { + "cell_type": "markdown", + "id": "f9193000", + "metadata": {}, + "source": [ + "# Run segmentation" + ] + }, + { + "cell_type": "markdown", + "id": "df758347", + "metadata": {}, + "source": [ + "## Not including Masks and saving all statistiks, plots and images and gpkgs" + ] + }, + { + "cell_type": "markdown", + "id": "84acffd9", + "metadata": {}, + "source": [ + "If working not in terrabyte, then adjust your promt number maybe to 2500." + ] + }, + { + "cell_type": "markdown", + "id": "308f8fdb", + "metadata": {}, + "source": [ + "The old version without saving speed stats:" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "035ef937", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.i3WQjSp5j6/ipykernel_48606/2477574397.py:61: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Found 1332 tiles\n", + "CRS: EPSG:32643\n", + "Pixel size: 0.004259 units/px (~4.259 mm/px)\n", + "2 cm => minor_px=4.70 px -> min_area_px=17.3 px^2\n", + "[1/1332] tile_r000004_c000004\n", + " -> skipped (100% Nodata)\n", + "[2/1332] tile_r000004_c000005\n", + " -> skipped (100% Nodata)\n", + "[3/1332] tile_r000004_c000006\n", + " -> skipped (100% Nodata)\n", + "[4/1332] tile_r000004_c000007\n", + " -> skipped (100% Nodata)\n", + "[5/1332] tile_r000004_c000008\n", + " -> skipped (100% Nodata)\n", + "[6/1332] tile_r000004_c000009\n", + " -> skipped (100% Nodata)\n", + "[7/1332] tile_r000004_c000010\n", + " -> skipped (100% Nodata)\n", + "[8/1332] tile_r000004_c000011\n", + " -> skipped (100% Nodata)\n", + "[9/1332] tile_r000004_c000012\n", + " -> skipped (100% Nodata)\n", + "[10/1332] tile_r000004_c000013\n", + " -> skipped (100% Nodata)\n", + "[11/1332] tile_r000004_c000014\n", + " -> skipped (100% Nodata)\n", + "[12/1332] tile_r000004_c000015\n", + " -> skipped (100% Nodata)\n", + "[13/1332] tile_r000004_c000016\n", + " -> skipped (100% Nodata)\n", + "[14/1332] tile_r000004_c000017\n", + " -> skipped (100% Nodata)\n", + "[15/1332] tile_r000004_c000018\n", + " -> skipped (100% Nodata)\n", + "[16/1332] tile_r000004_c000019\n", + " -> skipped (100% Nodata)\n", + "[17/1332] tile_r000004_c000020\n", + " -> skipped (100% Nodata)\n", + "[18/1332] tile_r000004_c000021\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + " 0%| | 0/4 [00:00 skipping histogram plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.i3WQjSp5j6/ipykernel_48606/2477574397.py:61: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F8/ouputgpkg/tile_r000004_c000021_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F8/csv_stats/tile_r000004_c000021_grain_stats.csv\n", + "done with segmentation + export\n", + "[19/1332] tile_r000004_c000022\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.84it/s]\n", + "100%|██████████| 3/3 [00:00<00:00, 3.70it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 20/20 [00:02<00:00, 7.11it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "6it [00:00, 1227.54it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "8it [00:00, 1344.33it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:00<00:00, 526.81it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:00<00:00, 275.76it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "labels shape: (587, 587) dtype: int64\n", + "mask_all shape: (587, 587) dtype: float64\n", + "n grains: 4\n", + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.i3WQjSp5j6/ipykernel_48606/2477574397.py:61: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F8/ouputgpkg/tile_r000004_c000022_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F8/csv_stats/tile_r000004_c000022_grain_stats.csv\n", + "done with segmentation + export\n", + "[20/1332] tile_r000004_c000023\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.79it/s]\n", + "100%|██████████| 3/3 [00:00<00:00, 3.85it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 57/57 [00:08<00:00, 6.78it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "46it [00:00, 330.80it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "53it [00:00, 336.51it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 19/19 [00:00<00:00, 79.27it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 21/21 [00:00<00:00, 200.16it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "labels shape: (587, 587) dtype: int64\n", + "mask_all shape: (587, 587) dtype: float64\n", + "n grains: 21\n", + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.i3WQjSp5j6/ipykernel_48606/2477574397.py:61: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F8/ouputgpkg/tile_r000004_c000023_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F8/csv_stats/tile_r000004_c000023_grain_stats.csv\n", + "done with segmentation + export\n", + "[21/1332] tile_r000004_c000024\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.78it/s]\n", + "100%|██████████| 3/3 [00:00<00:00, 3.71it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 207/207 [00:30<00:00, 6.70it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "179it [00:00, 329.98it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "185it [00:00, 337.75it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 72/72 [00:00<00:00, 98.06it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 73/73 [00:00<00:00, 197.25it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "labels shape: (587, 587) dtype: int64\n", + "mask_all shape: (587, 587) dtype: float64\n", + "n grains: 73\n", + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.i3WQjSp5j6/ipykernel_48606/2477574397.py:61: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F8/ouputgpkg/tile_r000004_c000024_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F8/csv_stats/tile_r000004_c000024_grain_stats.csv\n", + "done with segmentation + export\n", + "[22/1332] tile_r000004_c000025\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.79it/s]\n", + "100%|██████████| 3/3 [00:00<00:00, 3.82it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 280/280 [00:40<00:00, 6.93it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "249it [00:01, 164.25it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "245it [00:01, 179.73it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 87/87 [00:01<00:00, 48.21it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 101/101 [00:00<00:00, 207.98it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "labels shape: (587, 587) dtype: int64\n", + "mask_all shape: (587, 587) dtype: float64\n", + "n grains: 101\n", + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.i3WQjSp5j6/ipykernel_48606/2477574397.py:61: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F8/ouputgpkg/tile_r000004_c000025_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F8/csv_stats/tile_r000004_c000025_grain_stats.csv\n", + "done with segmentation + export\n", + "[23/1332] tile_r000004_c000026\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.82it/s]\n", + "100%|██████████| 3/3 [00:00<00:00, 3.83it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 212/212 [00:33<00:00, 6.41it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "146it [00:00, 346.52it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "145it [00:00, 417.84it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 43/43 [00:00<00:00, 73.16it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 65/65 [00:00<00:00, 207.47it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "labels shape: (587, 587) dtype: int64\n", + "mask_all shape: (587, 587) dtype: float64\n", + "n grains: 65\n", + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.i3WQjSp5j6/ipykernel_48606/2477574397.py:61: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F8/ouputgpkg/tile_r000004_c000026_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F8/csv_stats/tile_r000004_c000026_grain_stats.csv\n", + "done with segmentation + export\n", + "[24/1332] tile_r000004_c000027\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.82it/s]\n", + "100%|██████████| 3/3 [00:00<00:00, 3.88it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 290/290 [00:42<00:00, 6.90it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "228it [00:00, 439.08it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "242it [00:00, 430.01it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 94/94 [00:00<00:00, 109.55it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 97/97 [00:00<00:00, 120.34it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "labels shape: (587, 587) dtype: int64\n", + "mask_all shape: (587, 587) dtype: float64\n", + "n grains: 97\n", + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.i3WQjSp5j6/ipykernel_48606/2477574397.py:61: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F8/ouputgpkg/tile_r000004_c000027_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F8/csv_stats/tile_r000004_c000027_grain_stats.csv\n", + "done with segmentation + export\n", + "[25/1332] tile_r000004_c000028\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.75it/s]\n", + "100%|██████████| 3/3 [00:00<00:00, 3.85it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 330/330 [00:45<00:00, 7.28it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "257it [00:02, 106.96it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "22it [00:00, 39.29it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 1/1 [00:01<00:00, 1.16s/it]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 13/13 [00:00<00:00, 97.34it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "labels shape: (587, 587) dtype: int64\n", + "mask_all shape: (587, 587) dtype: float64\n", + "n grains: 13\n", + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.i3WQjSp5j6/ipykernel_48606/2477574397.py:61: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F8/ouputgpkg/tile_r000004_c000028_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F8/csv_stats/tile_r000004_c000028_grain_stats.csv\n", + "done with segmentation + export\n", + "[26/1332] tile_r000004_c000029\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.80it/s]\n", + "100%|██████████| 3/3 [00:00<00:00, 3.81it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 316/316 [00:46<00:00, 6.80it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "265it [00:00, 422.20it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "281it [00:00, 408.19it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 111/111 [00:01<00:00, 105.70it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 115/115 [00:00<00:00, 205.60it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "labels shape: (587, 587) dtype: int64\n", + "mask_all shape: (587, 587) dtype: float64\n", + "n grains: 115\n", + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.i3WQjSp5j6/ipykernel_48606/2477574397.py:61: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F8/ouputgpkg/tile_r000004_c000029_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F8/csv_stats/tile_r000004_c000029_grain_stats.csv\n", + "done with segmentation + export\n", + "[27/1332] tile_r000004_c000030\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.78it/s]\n", + "100%|██████████| 3/3 [00:00<00:00, 3.85it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 311/311 [00:44<00:00, 7.05it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "254it [00:00, 360.81it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "272it [00:00, 366.67it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 102/102 [00:01<00:00, 91.62it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 103/103 [00:00<00:00, 201.70it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "labels shape: (587, 587) dtype: int64\n", + "mask_all shape: (587, 587) dtype: float64\n", + "n grains: 103\n", + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.i3WQjSp5j6/ipykernel_48606/2477574397.py:61: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F8/ouputgpkg/tile_r000004_c000030_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F8/csv_stats/tile_r000004_c000030_grain_stats.csv\n", + "done with segmentation + export\n", + "[28/1332] tile_r000004_c000031\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.75it/s]\n", + "100%|██████████| 3/3 [00:00<00:00, 3.79it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 234/234 [00:33<00:00, 7.08it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "178it [00:00, 302.33it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "192it [00:00, 308.73it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 71/71 [00:00<00:00, 86.24it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 74/74 [00:00<00:00, 210.93it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "labels shape: (587, 587) dtype: int64\n", + "mask_all shape: (587, 587) dtype: float64\n", + "n grains: 74\n", + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.i3WQjSp5j6/ipykernel_48606/2477574397.py:61: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F8/ouputgpkg/tile_r000004_c000031_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F8/csv_stats/tile_r000004_c000031_grain_stats.csv\n", + "done with segmentation + export\n", + "[29/1332] tile_r000004_c000032\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.79it/s]\n", + "100%|██████████| 3/3 [00:00<00:00, 3.72it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 188/188 [00:26<00:00, 7.19it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "129it [00:00, 505.01it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "147it [00:00, 484.94it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 62/62 [00:00<00:00, 134.48it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 63/63 [00:00<00:00, 195.93it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "labels shape: (587, 587) dtype: int64\n", + "mask_all shape: (587, 587) dtype: float64\n", + "n grains: 63\n", + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.i3WQjSp5j6/ipykernel_48606/2477574397.py:61: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F8/ouputgpkg/tile_r000004_c000032_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F8/csv_stats/tile_r000004_c000032_grain_stats.csv\n", + "done with segmentation + export\n", + "[30/1332] tile_r000004_c000033\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.82it/s]\n", + "100%|██████████| 3/3 [00:00<00:00, 3.84it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 117/117 [00:17<00:00, 6.74it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "89it [00:00, 371.82it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "103it [00:00, 370.58it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 41/41 [00:00<00:00, 89.24it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 44/44 [00:00<00:00, 194.32it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "labels shape: (587, 587) dtype: int64\n", + "mask_all shape: (587, 587) dtype: float64\n", + "n grains: 44\n", + "Saved segmentation plot\n", + "Warning: len(gdf)=44 != len(grain_stats)=43 -> truncating to 43\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.i3WQjSp5j6/ipykernel_48606/2477574397.py:61: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F8/ouputgpkg/tile_r000004_c000033_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F8/csv_stats/tile_r000004_c000033_grain_stats.csv\n", + "done with segmentation + export\n", + "[31/1332] tile_r000004_c000034\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.72it/s]\n", + "100%|██████████| 3/3 [00:00<00:00, 3.80it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 15/15 [00:02<00:00, 6.75it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "6it [00:00, 172.06it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "6it [00:00, 177.10it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 1/1 [00:00<00:00, 12.98it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 2/2 [00:00<00:00, 148.99it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "labels shape: (587, 587) dtype: int64\n", + "mask_all shape: (587, 587) dtype: float64\n", + "n grains: 2\n", + "Saved segmentation plot\n", + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F8/ouputgpkg/tile_r000004_c000034_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F8/csv_stats/tile_r000004_c000034_grain_stats.csv\n", + "done with segmentation + export\n", + "[32/1332] tile_r000004_c000035\n", + " -> skipped (100% Nodata)\n", + "[33/1332] tile_r000004_c000036\n", + " -> skipped (100% Nodata)\n", + "[34/1332] tile_r000004_c000037\n", + " -> skipped (100% Nodata)\n", + "[35/1332] tile_r000004_c000038\n", + " -> skipped (100% Nodata)\n", + "[36/1332] tile_r000004_c000039\n", + " -> skipped (100% Nodata)\n", + "[37/1332] tile_r000004_c000040\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.i3WQjSp5j6/ipykernel_48606/2477574397.py:61: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " -> skipped (100% Nodata)\n", + "[38/1332] tile_r000004_c000041\n", + " -> skipped (100% Nodata)\n", + "[39/1332] tile_r000004_c000042\n", + " -> skipped (100% Nodata)\n", + "[40/1332] tile_r000004_c000043\n", + " -> skipped (100% Nodata)\n", + "[41/1332] tile_r000004_c000044\n", + " -> skipped (100% Nodata)\n", + "[42/1332] tile_r000004_c000045\n", + " -> skipped (100% Nodata)\n", + "[43/1332] tile_r000004_c000046\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.80it/s]\n", + "100%|██████████| 3/3 [00:00<00:00, 3.82it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 12/12 [00:01<00:00, 6.48it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "4it [00:00, 888.29it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "4it [00:00, 1049.43it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 1/1 [00:00<00:00, 180.21it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 1/1 [00:00<00:00, 289.00it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "labels shape: (587, 587) dtype: int64\n", + "mask_all shape: (587, 587) dtype: float64\n", + "n grains: 1\n", + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.i3WQjSp5j6/ipykernel_48606/2477574397.py:61: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F8/ouputgpkg/tile_r000004_c000046_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F8/csv_stats/tile_r000004_c000046_grain_stats.csv\n", + "done with segmentation + export\n", + "[44/1332] tile_r000004_c000047\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.81it/s]\n", + "100%|██████████| 3/3 [00:00<00:00, 3.82it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 24/24 [00:03<00:00, 6.66it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2it [00:00, 1603.94it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2it [00:00, 2130.71it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 1/1 [00:00<00:00, 968.44it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 1/1 [00:00<00:00, 410.36it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "labels shape: (587, 587) dtype: int64\n", + "mask_all shape: (587, 587) dtype: float64\n", + "n grains: 1\n", + "Saved segmentation plot\n", + "Saved histogram plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.i3WQjSp5j6/ipykernel_48606/2477574397.py:61: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F8/ouputgpkg/tile_r000004_c000047_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F8/csv_stats/tile_r000004_c000047_grain_stats.csv\n", + "done with segmentation + export\n", + "[45/1332] tile_r000004_c000048\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.80it/s]\n", + "100%|██████████| 3/3 [00:00<00:00, 3.88it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 18/18 [00:02<00:00, 6.04it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "0it [00:00, ?it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "0it [00:00, ?it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "0it [00:00, ?it/s]\n", + "0it [00:00, ?it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "labels shape: (587, 587) dtype: uint8\n", + "mask_all shape: (587, 587, 3) dtype: uint8\n", + "n grains: 0\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.i3WQjSp5j6/ipykernel_48606/2477574397.py:61: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n", + "No grains found -> skipping histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F8/ouputgpkg/tile_r000004_c000048_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F8/csv_stats/tile_r000004_c000048_grain_stats.csv\n", + "done with segmentation + export\n", + "[46/1332] tile_r000004_c000049\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.78it/s]\n", + "100%|██████████| 3/3 [00:00<00:00, 3.87it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 66/66 [00:10<00:00, 6.36it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "15it [00:00, 2386.20it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "20it [00:00, 1887.33it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 10/10 [00:00<00:00, 750.79it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 10/10 [00:00<00:00, 362.76it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "labels shape: (587, 587) dtype: int64\n", + "mask_all shape: (587, 587) dtype: float64\n", + "n grains: 10\n", + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.i3WQjSp5j6/ipykernel_48606/2477574397.py:61: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F8/ouputgpkg/tile_r000004_c000049_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F8/csv_stats/tile_r000004_c000049_grain_stats.csv\n", + "done with segmentation + export\n", + "[47/1332] tile_r000004_c000050\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.79it/s]\n", + "100%|██████████| 3/3 [00:00<00:00, 3.87it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 75/75 [00:11<00:00, 6.29it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "23it [00:00, 2009.65it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "31it [00:00, 1517.26it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 15/15 [00:00<00:00, 549.98it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 15/15 [00:00<00:00, 280.89it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "labels shape: (587, 587) dtype: int64\n", + "mask_all shape: (587, 587) dtype: float64\n", + "n grains: 15\n", + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.i3WQjSp5j6/ipykernel_48606/2477574397.py:61: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F8/ouputgpkg/tile_r000004_c000050_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F8/csv_stats/tile_r000004_c000050_grain_stats.csv\n", + "done with segmentation + export\n", + "[48/1332] tile_r000004_c000051\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.79it/s]\n", + "100%|██████████| 3/3 [00:00<00:00, 3.86it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 66/66 [00:09<00:00, 6.74it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "25it [00:00, 1638.76it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "36it [00:00, 1457.03it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 17/17 [00:00<00:00, 380.17it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 17/17 [00:00<00:00, 315.10it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "labels shape: (587, 587) dtype: int64\n", + "mask_all shape: (587, 587) dtype: float64\n", + "n grains: 17\n", + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.i3WQjSp5j6/ipykernel_48606/2477574397.py:61: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F8/ouputgpkg/tile_r000004_c000051_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F8/csv_stats/tile_r000004_c000051_grain_stats.csv\n", + "done with segmentation + export\n", + "[49/1332] tile_r000004_c000052\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.83it/s]\n", + "100%|██████████| 3/3 [00:00<00:00, 3.76it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "0it [00:00, ?it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "0it [00:00, ?it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "0it [00:00, ?it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "0it [00:00, ?it/s]\n", + "0it [00:00, ?it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "labels shape: (587, 587) dtype: uint8\n", + "mask_all shape: (587, 587, 3) dtype: uint8\n", + "n grains: 0\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\n", + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.i3WQjSp5j6/ipykernel_48606/2477574397.py:61: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n", + "No grains found -> skipping histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F8/ouputgpkg/tile_r000004_c000052_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F8/csv_stats/tile_r000004_c000052_grain_stats.csv\n", + "done with segmentation + export\n", + "[50/1332] tile_r000004_c000053\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.83it/s]\n", + "100%|██████████| 3/3 [00:00<00:00, 3.70it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "0it [00:00, ?it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "0it [00:00, ?it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "0it [00:00, ?it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "0it [00:00, ?it/s]\n", + "0it [00:00, ?it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "labels shape: (587, 587) dtype: uint8\n", + "mask_all shape: (587, 587, 3) dtype: uint8\n", + "n grains: 0\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n", + "No grains found -> skipping histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F8/ouputgpkg/tile_r000004_c000053_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F8/csv_stats/tile_r000004_c000053_grain_stats.csv\n", + "done with segmentation + export\n", + "[51/1332] tile_r000004_c000054\n", + " -> skipped (100% Nodata)\n", + "[52/1332] tile_r000004_c000055\n", + " -> skipped (100% Nodata)\n", + "[53/1332] tile_r000004_c000056\n", + " -> skipped (100% Nodata)\n", + "[54/1332] tile_r000004_c000057\n", + " -> skipped (100% Nodata)\n", + "[55/1332] tile_r000004_c000058\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.i3WQjSp5j6/ipykernel_48606/2477574397.py:61: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " -> skipped (100% Nodata)\n", + "[56/1332] tile_r000004_c000059\n", + " -> skipped (100% Nodata)\n", + "[57/1332] tile_r000004_c000060\n", + " -> skipped (100% Nodata)\n", + "[58/1332] tile_r000004_c000061\n", + " -> skipped (100% Nodata)\n", + "[59/1332] tile_r000004_c000062\n", + " -> skipped (100% Nodata)\n", + "[60/1332] tile_r000004_c000063\n", + " -> skipped (100% Nodata)\n", + "[61/1332] tile_r000004_c000064\n", + " -> skipped (100% Nodata)\n", + "[62/1332] tile_r000004_c000065\n", + " -> skipped (100% Nodata)\n", + "[63/1332] tile_r000004_c000066\n", + " -> skipped (100% Nodata)\n", + "[64/1332] tile_r000004_c000067\n", + " -> skipped (100% Nodata)\n", + "[65/1332] tile_r000004_c000068\n", + " -> skipped (100% Nodata)\n", + "[66/1332] tile_r000004_c000069\n", + " -> skipped (100% Nodata)\n", + "[67/1332] tile_r000004_c000070\n", + " -> skipped (100% Nodata)\n", + "[68/1332] tile_r000004_c000071\n", + " -> skipped (100% Nodata)\n", + "[69/1332] tile_r000004_c000072\n", + " -> skipped (100% Nodata)\n", + "[70/1332] tile_r000004_c000073\n", + " -> skipped (100% Nodata)\n", + "[71/1332] tile_r000004_c000074\n", + " -> skipped (100% Nodata)\n", + "[72/1332] tile_r000004_c000075\n", + " -> skipped (100% Nodata)\n", + "[73/1332] tile_r000004_c000076\n", + " -> skipped (100% Nodata)\n", + "[74/1332] tile_r000004_c000077\n", + " -> skipped (100% Nodata)\n", + "[75/1332] tile_r000005_c000004\n", + " -> skipped (100% Nodata)\n", + "[76/1332] tile_r000005_c000005\n", + " -> skipped (100% Nodata)\n", + "[77/1332] tile_r000005_c000006\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.81it/s]\n", + "100%|██████████| 3/3 [00:00<00:00, 3.79it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 71/71 [00:09<00:00, 7.19it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "58it [00:00, 348.49it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "58it [00:00, 350.83it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 19/19 [00:00<00:00, 66.66it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 22/22 [00:00<00:00, 161.35it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "labels shape: (587, 587) dtype: int64\n", + "mask_all shape: (587, 587) dtype: float64\n", + "n grains: 22\n", + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.i3WQjSp5j6/ipykernel_48606/2477574397.py:61: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F8/ouputgpkg/tile_r000005_c000006_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F8/csv_stats/tile_r000005_c000006_grain_stats.csv\n", + "done with segmentation + export\n", + "[78/1332] tile_r000005_c000007\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.76it/s]\n", + "100%|██████████| 3/3 [00:00<00:00, 3.85it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 164/164 [00:23<00:00, 7.09it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "125it [00:00, 262.05it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "133it [00:00, 268.88it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 47/47 [00:00<00:00, 61.33it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 47/47 [00:00<00:00, 190.68it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "labels shape: (587, 587) dtype: int64\n", + "mask_all shape: (587, 587) dtype: float64\n", + "n grains: 47\n", + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.i3WQjSp5j6/ipykernel_48606/2477574397.py:61: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F8/ouputgpkg/tile_r000005_c000007_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F8/csv_stats/tile_r000005_c000007_grain_stats.csv\n", + "done with segmentation + export\n", + "[79/1332] tile_r000005_c000008\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.76it/s]\n", + "100%|██████████| 3/3 [00:00<00:00, 3.79it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 129/129 [00:18<00:00, 6.88it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "97it [00:00, 234.48it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "96it [00:00, 250.13it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 30/30 [00:00<00:00, 49.11it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 34/34 [00:00<00:00, 168.34it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "labels shape: (587, 587) dtype: int64\n", + "mask_all shape: (587, 587) dtype: float64\n", + "n grains: 34\n", + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.i3WQjSp5j6/ipykernel_48606/2477574397.py:61: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F8/ouputgpkg/tile_r000005_c000008_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F8/csv_stats/tile_r000005_c000008_grain_stats.csv\n", + "done with segmentation + export\n", + "[80/1332] tile_r000005_c000009\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.78it/s]\n", + "100%|██████████| 3/3 [00:00<00:00, 3.82it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 148/148 [00:22<00:00, 6.67it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "120it [00:00, 295.09it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "123it [00:00, 294.97it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 42/42 [00:00<00:00, 58.82it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 42/42 [00:00<00:00, 179.55it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "labels shape: (587, 587) dtype: int64\n", + "mask_all shape: (587, 587) dtype: float64\n", + "n grains: 42\n", + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.i3WQjSp5j6/ipykernel_48606/2477574397.py:61: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F8/ouputgpkg/tile_r000005_c000009_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F8/csv_stats/tile_r000005_c000009_grain_stats.csv\n", + "done with segmentation + export\n", + "[81/1332] tile_r000005_c000010\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.80it/s]\n", + "100%|██████████| 3/3 [00:00<00:00, 3.79it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 139/139 [00:20<00:00, 6.81it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "114it [00:00, 296.02it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "117it [00:00, 297.41it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 39/39 [00:00<00:00, 60.75it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 39/39 [00:00<00:00, 178.07it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "labels shape: (587, 587) dtype: int64\n", + "mask_all shape: (587, 587) dtype: float64\n", + "n grains: 39\n", + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.i3WQjSp5j6/ipykernel_48606/2477574397.py:61: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F8/ouputgpkg/tile_r000005_c000010_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F8/csv_stats/tile_r000005_c000010_grain_stats.csv\n", + "done with segmentation + export\n", + "[82/1332] tile_r000005_c000011\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.75it/s]\n", + "100%|██████████| 3/3 [00:00<00:00, 3.78it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 155/155 [00:24<00:00, 6.37it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "133it [00:00, 325.23it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "137it [00:00, 325.66it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 49/49 [00:00<00:00, 83.48it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 49/49 [00:00<00:00, 194.63it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "labels shape: (587, 587) dtype: int64\n", + "mask_all shape: (587, 587) dtype: float64\n", + "n grains: 49\n", + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.i3WQjSp5j6/ipykernel_48606/2477574397.py:61: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F8/ouputgpkg/tile_r000005_c000011_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F8/csv_stats/tile_r000005_c000011_grain_stats.csv\n", + "done with segmentation + export\n", + "[83/1332] tile_r000005_c000012\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.81it/s]\n", + "100%|██████████| 3/3 [00:00<00:00, 3.85it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 175/175 [00:24<00:00, 7.11it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "143it [00:00, 340.64it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "149it [00:00, 347.08it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 53/53 [00:00<00:00, 74.80it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 54/54 [00:00<00:00, 198.59it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "labels shape: (587, 587) dtype: int64\n", + "mask_all shape: (587, 587) dtype: float64\n", + "n grains: 54\n", + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.i3WQjSp5j6/ipykernel_48606/2477574397.py:61: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F8/ouputgpkg/tile_r000005_c000012_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F8/csv_stats/tile_r000005_c000012_grain_stats.csv\n", + "done with segmentation + export\n", + "[84/1332] tile_r000005_c000013\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.84it/s]\n", + "100%|██████████| 3/3 [00:00<00:00, 3.86it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 208/208 [00:31<00:00, 6.58it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "162it [00:00, 484.82it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "173it [00:00, 485.58it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 71/71 [00:00<00:00, 143.93it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 71/71 [00:00<00:00, 211.59it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "labels shape: (587, 587) dtype: int64\n", + "mask_all shape: (587, 587) dtype: float64\n", + "n grains: 71\n", + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.i3WQjSp5j6/ipykernel_48606/2477574397.py:61: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F8/ouputgpkg/tile_r000005_c000013_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F8/csv_stats/tile_r000005_c000013_grain_stats.csv\n", + "done with segmentation + export\n", + "[85/1332] tile_r000005_c000014\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.82it/s]\n", + "100%|██████████| 3/3 [00:00<00:00, 3.77it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 203/203 [00:28<00:00, 7.07it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "161it [00:00, 370.24it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "165it [00:00, 373.27it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 59/59 [00:00<00:00, 83.72it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 60/60 [00:00<00:00, 186.47it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "labels shape: (587, 587) dtype: int64\n", + "mask_all shape: (587, 587) dtype: float64\n", + "n grains: 60\n", + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.i3WQjSp5j6/ipykernel_48606/2477574397.py:61: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F8/ouputgpkg/tile_r000005_c000014_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F8/csv_stats/tile_r000005_c000014_grain_stats.csv\n", + "done with segmentation + export\n", + "[86/1332] tile_r000005_c000015\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 2.59it/s]\n", + "100%|██████████| 3/3 [00:00<00:00, 3.77it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 215/215 [00:26<00:00, 8.06it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "153it [00:00, 291.59it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "162it [00:00, 296.29it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 62/62 [00:00<00:00, 82.38it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 63/63 [00:00<00:00, 164.02it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "labels shape: (587, 587) dtype: int64\n", + "mask_all shape: (587, 587) dtype: float64\n", + "n grains: 63\n", + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.i3WQjSp5j6/ipykernel_48606/2477574397.py:61: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F8/ouputgpkg/tile_r000005_c000015_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F8/csv_stats/tile_r000005_c000015_grain_stats.csv\n", + "done with segmentation + export\n", + "[87/1332] tile_r000005_c000016\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.75it/s]\n", + "100%|██████████| 3/3 [00:00<00:00, 3.79it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 268/268 [00:39<00:00, 6.79it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "235it [00:00, 306.88it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "245it [00:00, 313.14it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 92/92 [00:01<00:00, 76.40it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 93/93 [00:00<00:00, 194.12it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "labels shape: (587, 587) dtype: int64\n", + "mask_all shape: (587, 587) dtype: float64\n", + "n grains: 93\n", + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.i3WQjSp5j6/ipykernel_48606/2477574397.py:61: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F8/ouputgpkg/tile_r000005_c000016_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F8/csv_stats/tile_r000005_c000016_grain_stats.csv\n", + "done with segmentation + export\n", + "[88/1332] tile_r000005_c000017\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.85it/s]\n", + "100%|██████████| 3/3 [00:00<00:00, 3.83it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 270/270 [00:41<00:00, 6.58it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "224it [00:00, 307.80it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "241it [00:00, 314.13it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 92/92 [00:01<00:00, 86.43it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 95/95 [00:00<00:00, 194.88it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "labels shape: (587, 587) dtype: int64\n", + "mask_all shape: (587, 587) dtype: float64\n", + "n grains: 95\n", + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.i3WQjSp5j6/ipykernel_48606/2477574397.py:61: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F8/ouputgpkg/tile_r000005_c000017_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F8/csv_stats/tile_r000005_c000017_grain_stats.csv\n", + "done with segmentation + export\n", + "[89/1332] tile_r000005_c000018\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.78it/s]\n", + "100%|██████████| 3/3 [00:00<00:00, 3.90it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 252/252 [00:38<00:00, 6.47it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "195it [00:00, 327.30it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "201it [00:00, 331.10it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 69/69 [00:00<00:00, 70.55it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 70/70 [00:00<00:00, 178.96it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "labels shape: (587, 587) dtype: int64\n", + "mask_all shape: (587, 587) dtype: float64\n", + "n grains: 70\n", + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.i3WQjSp5j6/ipykernel_48606/2477574397.py:61: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F8/ouputgpkg/tile_r000005_c000018_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F8/csv_stats/tile_r000005_c000018_grain_stats.csv\n", + "done with segmentation + export\n", + "[90/1332] tile_r000005_c000019\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.85it/s]\n", + "100%|██████████| 3/3 [00:00<00:00, 3.80it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 315/315 [00:46<00:00, 6.76it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "250it [00:00, 351.93it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "261it [00:00, 357.50it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 94/94 [00:01<00:00, 79.88it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 98/98 [00:00<00:00, 198.48it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "labels shape: (587, 587) dtype: int64\n", + "mask_all shape: (587, 587) dtype: float64\n", + "n grains: 98\n", + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.i3WQjSp5j6/ipykernel_48606/2477574397.py:61: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F8/ouputgpkg/tile_r000005_c000019_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F8/csv_stats/tile_r000005_c000019_grain_stats.csv\n", + "done with segmentation + export\n", + "[91/1332] tile_r000005_c000020\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.76it/s]\n", + "100%|██████████| 3/3 [00:00<00:00, 3.87it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 344/344 [00:49<00:00, 6.95it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "275it [00:01, 194.28it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "289it [00:01, 200.15it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 102/102 [00:01<00:00, 65.17it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 105/105 [00:00<00:00, 189.59it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "labels shape: (587, 587) dtype: int64\n", + "mask_all shape: (587, 587) dtype: float64\n", + "n grains: 105\n", + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.i3WQjSp5j6/ipykernel_48606/2477574397.py:61: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F8/ouputgpkg/tile_r000005_c000020_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F8/csv_stats/tile_r000005_c000020_grain_stats.csv\n", + "done with segmentation + export\n", + "[92/1332] tile_r000005_c000021\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.85it/s]\n", + "100%|██████████| 3/3 [00:00<00:00, 3.82it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 336/336 [00:47<00:00, 7.04it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "282it [00:00, 379.17it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "290it [00:00, 375.55it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 107/107 [00:01<00:00, 90.66it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 110/110 [00:01<00:00, 100.03it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "labels shape: (587, 587) dtype: int64\n", + "mask_all shape: (587, 587) dtype: float64\n", + "n grains: 110\n", + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.i3WQjSp5j6/ipykernel_48606/2477574397.py:61: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F8/ouputgpkg/tile_r000005_c000021_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F8/csv_stats/tile_r000005_c000021_grain_stats.csv\n", + "done with segmentation + export\n", + "[93/1332] tile_r000005_c000022\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.79it/s]\n", + "100%|██████████| 3/3 [00:00<00:00, 3.79it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 368/368 [00:52<00:00, 6.96it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "303it [00:00, 354.24it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "322it [00:00, 365.34it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 122/122 [00:01<00:00, 92.38it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 124/124 [00:00<00:00, 202.49it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "labels shape: (587, 587) dtype: int64\n", + "mask_all shape: (587, 587) dtype: float64\n", + "n grains: 124\n", + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.i3WQjSp5j6/ipykernel_48606/2477574397.py:61: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F8/ouputgpkg/tile_r000005_c000022_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F8/csv_stats/tile_r000005_c000022_grain_stats.csv\n", + "done with segmentation + export\n", + "[94/1332] tile_r000005_c000023\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.79it/s]\n", + "100%|██████████| 3/3 [00:00<00:00, 3.85it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 373/373 [00:56<00:00, 6.62it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "291it [00:00, 345.13it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "317it [00:00, 354.19it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 119/119 [00:01<00:00, 91.82it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 121/121 [00:00<00:00, 200.74it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "labels shape: (587, 587) dtype: int64\n", + "mask_all shape: (587, 587) dtype: float64\n", + "n grains: 121\n", + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.i3WQjSp5j6/ipykernel_48606/2477574397.py:61: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F8/ouputgpkg/tile_r000005_c000023_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F8/csv_stats/tile_r000005_c000023_grain_stats.csv\n", + "done with segmentation + export\n", + "[95/1332] tile_r000005_c000024\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.86it/s]\n", + "100%|██████████| 3/3 [00:00<00:00, 3.92it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 359/359 [00:49<00:00, 7.25it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "272it [00:00, 447.60it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "291it [00:00, 443.28it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 113/113 [00:00<00:00, 114.62it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 113/113 [00:00<00:00, 213.02it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "labels shape: (587, 587) dtype: int64\n", + "mask_all shape: (587, 587) dtype: float64\n", + "n grains: 113\n", + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.i3WQjSp5j6/ipykernel_48606/2477574397.py:61: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F8/ouputgpkg/tile_r000005_c000024_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F8/csv_stats/tile_r000005_c000024_grain_stats.csv\n", + "done with segmentation + export\n", + "[96/1332] tile_r000005_c000025\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.85it/s]\n", + "100%|██████████| 3/3 [00:00<00:00, 3.78it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 352/352 [00:50<00:00, 6.99it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "260it [00:02, 127.29it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "254it [00:00, 331.89it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 88/88 [00:01<00:00, 79.95it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 108/108 [00:00<00:00, 192.54it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "labels shape: (587, 587) dtype: int64\n", + "mask_all shape: (587, 587) dtype: float64\n", + "n grains: 108\n", + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.i3WQjSp5j6/ipykernel_48606/2477574397.py:61: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F8/ouputgpkg/tile_r000005_c000025_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F8/csv_stats/tile_r000005_c000025_grain_stats.csv\n", + "done with segmentation + export\n", + "[97/1332] tile_r000005_c000026\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.87it/s]\n", + "100%|██████████| 3/3 [00:00<00:00, 3.78it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 401/401 [00:57<00:00, 6.92it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "336it [00:00, 458.79it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "360it [00:00, 464.54it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 142/142 [00:01<00:00, 116.67it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 146/146 [00:00<00:00, 229.63it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "labels shape: (587, 587) dtype: int64\n", + "mask_all shape: (587, 587) dtype: float64\n", + "n grains: 146\n", + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.i3WQjSp5j6/ipykernel_48606/2477574397.py:61: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F8/ouputgpkg/tile_r000005_c000026_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F8/csv_stats/tile_r000005_c000026_grain_stats.csv\n", + "done with segmentation + export\n", + "[98/1332] tile_r000005_c000027\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.84it/s]\n", + "100%|██████████| 3/3 [00:00<00:00, 3.79it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 349/349 [00:51<00:00, 6.71it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "284it [00:00, 353.53it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "290it [00:00, 354.73it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 112/112 [00:01<00:00, 95.02it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 112/112 [00:00<00:00, 207.36it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "labels shape: (587, 587) dtype: int64\n", + "mask_all shape: (587, 587) dtype: float64\n", + "n grains: 112\n", + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.i3WQjSp5j6/ipykernel_48606/2477574397.py:61: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F8/ouputgpkg/tile_r000005_c000027_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F8/csv_stats/tile_r000005_c000027_grain_stats.csv\n", + "done with segmentation + export\n", + "[99/1332] tile_r000005_c000028\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.87it/s]\n", + "100%|██████████| 3/3 [00:00<00:00, 3.80it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 354/354 [00:52<00:00, 6.75it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "301it [00:00, 534.75it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "322it [00:00, 537.29it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 130/130 [00:00<00:00, 142.85it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 130/130 [00:00<00:00, 227.62it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "labels shape: (587, 587) dtype: int64\n", + "mask_all shape: (587, 587) dtype: float64\n", + "n grains: 130\n", + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.i3WQjSp5j6/ipykernel_48606/2477574397.py:61: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F8/ouputgpkg/tile_r000005_c000028_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F8/csv_stats/tile_r000005_c000028_grain_stats.csv\n", + "done with segmentation + export\n", + "[100/1332] tile_r000005_c000029\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.86it/s]\n", + "100%|██████████| 3/3 [00:00<00:00, 3.84it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 332/332 [00:51<00:00, 6.47it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "263it [00:00, 284.31it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "261it [00:00, 421.97it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 92/92 [00:01<00:00, 86.33it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 104/104 [00:00<00:00, 209.62it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "labels shape: (587, 587) dtype: int64\n", + "mask_all shape: (587, 587) dtype: float64\n", + "n grains: 104\n", + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.i3WQjSp5j6/ipykernel_48606/2477574397.py:61: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F8/ouputgpkg/tile_r000005_c000029_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F8/csv_stats/tile_r000005_c000029_grain_stats.csv\n", + "done with segmentation + export\n", + "[101/1332] tile_r000005_c000030\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.83it/s]\n", + "100%|██████████| 3/3 [00:00<00:00, 3.86it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 297/297 [00:43<00:00, 6.75it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "242it [00:00, 439.74it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "263it [00:00, 440.97it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 102/102 [00:00<00:00, 115.88it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 103/103 [00:00<00:00, 203.99it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "labels shape: (587, 587) dtype: int64\n", + "mask_all shape: (587, 587) dtype: float64\n", + "n grains: 103\n", + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.i3WQjSp5j6/ipykernel_48606/2477574397.py:61: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F8/ouputgpkg/tile_r000005_c000030_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F8/csv_stats/tile_r000005_c000030_grain_stats.csv\n", + "done with segmentation + export\n", + "[102/1332] tile_r000005_c000031\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.77it/s]\n", + "100%|██████████| 3/3 [00:00<00:00, 3.83it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 285/285 [00:43<00:00, 6.63it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "211it [00:00, 511.92it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "210it [00:00, 572.48it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 83/83 [00:00<00:00, 144.22it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 94/94 [00:00<00:00, 222.30it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "labels shape: (587, 587) dtype: int64\n", + "mask_all shape: (587, 587) dtype: float64\n", + "n grains: 94\n", + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.i3WQjSp5j6/ipykernel_48606/2477574397.py:61: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F8/ouputgpkg/tile_r000005_c000031_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F8/csv_stats/tile_r000005_c000031_grain_stats.csv\n", + "done with segmentation + export\n", + "[103/1332] tile_r000005_c000032\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.82it/s]\n", + "100%|██████████| 3/3 [00:00<00:00, 3.79it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 358/358 [00:50<00:00, 7.02it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "265it [00:00, 380.09it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "263it [00:00, 404.28it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 80/80 [00:01<00:00, 74.97it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 110/110 [00:00<00:00, 214.41it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "labels shape: (587, 587) dtype: int64\n", + "mask_all shape: (587, 587) dtype: float64\n", + "n grains: 110\n", + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.i3WQjSp5j6/ipykernel_48606/2477574397.py:61: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F8/ouputgpkg/tile_r000005_c000032_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F8/csv_stats/tile_r000005_c000032_grain_stats.csv\n", + "done with segmentation + export\n", + "[104/1332] tile_r000005_c000033\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.86it/s]\n", + "100%|██████████| 3/3 [00:00<00:00, 3.78it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 318/318 [00:44<00:00, 7.22it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "244it [00:00, 436.13it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "266it [00:00, 439.47it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 104/104 [00:00<00:00, 112.49it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 106/106 [00:00<00:00, 219.51it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "labels shape: (587, 587) dtype: int64\n", + "mask_all shape: (587, 587) dtype: float64\n", + "n grains: 106\n", + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.i3WQjSp5j6/ipykernel_48606/2477574397.py:61: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F8/ouputgpkg/tile_r000005_c000033_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F8/csv_stats/tile_r000005_c000033_grain_stats.csv\n", + "done with segmentation + export\n", + "[105/1332] tile_r000005_c000034\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.82it/s]\n", + "100%|██████████| 3/3 [00:00<00:00, 3.81it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 249/249 [00:35<00:00, 7.01it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "199it [00:00, 509.07it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "206it [00:00, 488.79it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 79/79 [00:00<00:00, 117.62it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 81/81 [00:00<00:00, 223.19it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "labels shape: (587, 587) dtype: int64\n", + "mask_all shape: (587, 587) dtype: float64\n", + "n grains: 81\n", + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.i3WQjSp5j6/ipykernel_48606/2477574397.py:61: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F8/ouputgpkg/tile_r000005_c000034_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F8/csv_stats/tile_r000005_c000034_grain_stats.csv\n", + "done with segmentation + export\n", + "[106/1332] tile_r000005_c000035\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.79it/s]\n", + "100%|██████████| 3/3 [00:00<00:00, 3.79it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 253/253 [00:36<00:00, 7.03it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "180it [00:00, 186.43it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "202it [00:01, 192.35it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 75/75 [00:01<00:00, 65.18it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 77/77 [00:00<00:00, 193.84it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "labels shape: (587, 587) dtype: int64\n", + "mask_all shape: (587, 587) dtype: float64\n", + "n grains: 77\n", + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.i3WQjSp5j6/ipykernel_48606/2477574397.py:61: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F8/ouputgpkg/tile_r000005_c000035_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F8/csv_stats/tile_r000005_c000035_grain_stats.csv\n", + "done with segmentation + export\n", + "[107/1332] tile_r000005_c000036\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.84it/s]\n", + "100%|██████████| 3/3 [00:00<00:00, 3.84it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 288/288 [00:42<00:00, 6.83it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "228it [00:00, 232.01it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "227it [00:00, 307.92it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 71/71 [00:01<00:00, 57.38it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 97/97 [00:00<00:00, 202.47it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "labels shape: (587, 587) dtype: int64\n", + "mask_all shape: (587, 587) dtype: float64\n", + "n grains: 97\n", + "Saved segmentation plot\n", + "Warning: len(gdf)=97 != len(grain_stats)=96 -> truncating to 96\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.i3WQjSp5j6/ipykernel_48606/2477574397.py:61: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F8/ouputgpkg/tile_r000005_c000036_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F8/csv_stats/tile_r000005_c000036_grain_stats.csv\n", + "done with segmentation + export\n", + "[108/1332] tile_r000005_c000037\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.79it/s]\n", + "100%|██████████| 3/3 [00:00<00:00, 3.79it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 170/170 [00:27<00:00, 6.23it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "115it [00:00, 272.55it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "114it [00:00, 341.27it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 36/36 [00:00<00:00, 49.29it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 56/56 [00:00<00:00, 173.02it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "labels shape: (587, 587) dtype: int64\n", + "mask_all shape: (587, 587) dtype: float64\n", + "n grains: 56\n", + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.i3WQjSp5j6/ipykernel_48606/2477574397.py:61: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F8/ouputgpkg/tile_r000005_c000037_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F8/csv_stats/tile_r000005_c000037_grain_stats.csv\n", + "done with segmentation + export\n", + "[109/1332] tile_r000005_c000038\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.84it/s]\n", + "100%|██████████| 3/3 [00:00<00:00, 3.85it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 170/170 [00:26<00:00, 6.53it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "128it [00:00, 624.27it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "155it [00:00, 624.11it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 65/65 [00:00<00:00, 153.83it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 66/66 [00:00<00:00, 238.75it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "labels shape: (587, 587) dtype: int64\n", + "mask_all shape: (587, 587) dtype: float64\n", + "n grains: 66\n", + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.i3WQjSp5j6/ipykernel_48606/2477574397.py:61: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F8/ouputgpkg/tile_r000005_c000038_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F8/csv_stats/tile_r000005_c000038_grain_stats.csv\n", + "done with segmentation + export\n", + "[110/1332] tile_r000005_c000039\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.82it/s]\n", + "100%|██████████| 3/3 [00:00<00:00, 3.80it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 134/134 [00:20<00:00, 6.39it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "103it [00:00, 331.01it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "127it [00:00, 345.05it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 55/55 [00:00<00:00, 67.80it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 55/55 [00:00<00:00, 231.19it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "labels shape: (587, 587) dtype: int64\n", + "mask_all shape: (587, 587) dtype: float64\n", + "n grains: 55\n", + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.i3WQjSp5j6/ipykernel_48606/2477574397.py:61: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F8/ouputgpkg/tile_r000005_c000039_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F8/csv_stats/tile_r000005_c000039_grain_stats.csv\n", + "done with segmentation + export\n", + "[111/1332] tile_r000005_c000040\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.79it/s]\n", + "100%|██████████| 3/3 [00:00<00:00, 3.83it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 132/132 [00:20<00:00, 6.60it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "88it [00:00, 984.01it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "116it [00:00, 817.29it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 53/53 [00:00<00:00, 272.12it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 54/54 [00:00<00:00, 220.05it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "labels shape: (587, 587) dtype: int64\n", + "mask_all shape: (587, 587) dtype: float64\n", + "n grains: 54\n", + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.i3WQjSp5j6/ipykernel_48606/2477574397.py:61: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F8/ouputgpkg/tile_r000005_c000040_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F8/csv_stats/tile_r000005_c000040_grain_stats.csv\n", + "done with segmentation + export\n", + "[112/1332] tile_r000005_c000041\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.81it/s]\n", + "100%|██████████| 3/3 [00:00<00:00, 3.79it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 74/74 [00:10<00:00, 7.20it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "51it [00:00, 768.67it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "77it [00:00, 639.37it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 37/37 [00:00<00:00, 132.04it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 42/42 [00:00<00:00, 146.30it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "labels shape: (587, 587) dtype: int64\n", + "mask_all shape: (587, 587) dtype: float64\n", + "n grains: 42\n", + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.i3WQjSp5j6/ipykernel_48606/2477574397.py:61: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F8/ouputgpkg/tile_r000005_c000041_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F8/csv_stats/tile_r000005_c000041_grain_stats.csv\n", + "done with segmentation + export\n", + "[113/1332] tile_r000005_c000042\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.77it/s]\n", + "100%|██████████| 3/3 [00:00<00:00, 3.84it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 130/130 [00:18<00:00, 6.87it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "91it [00:00, 753.76it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "117it [00:00, 721.04it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 50/50 [00:00<00:00, 55.57it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 55/55 [00:00<00:00, 270.36it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "labels shape: (587, 587) dtype: int64\n", + "mask_all shape: (587, 587) dtype: float64\n", + "n grains: 55\n", + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.i3WQjSp5j6/ipykernel_48606/2477574397.py:61: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F8/ouputgpkg/tile_r000005_c000042_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F8/csv_stats/tile_r000005_c000042_grain_stats.csv\n", + "done with segmentation + export\n", + "[114/1332] tile_r000005_c000043\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.85it/s]\n", + "100%|██████████| 3/3 [00:00<00:00, 3.85it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 127/127 [00:19<00:00, 6.58it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "90it [00:00, 932.08it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "130it [00:00, 768.69it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 61/61 [00:00<00:00, 252.59it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 64/64 [00:00<00:00, 232.09it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "labels shape: (587, 587) dtype: int64\n", + "mask_all shape: (587, 587) dtype: float64\n", + "n grains: 64\n", + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.i3WQjSp5j6/ipykernel_48606/2477574397.py:61: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F8/ouputgpkg/tile_r000005_c000043_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F8/csv_stats/tile_r000005_c000043_grain_stats.csv\n", + "done with segmentation + export\n", + "[115/1332] tile_r000005_c000044\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.80it/s]\n", + "100%|██████████| 3/3 [00:00<00:00, 3.72it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 102/102 [00:15<00:00, 6.39it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "74it [00:00, 158.37it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "73it [00:00, 210.01it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 13/13 [00:01<00:00, 12.06it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 54/54 [00:00<00:00, 231.65it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "labels shape: (587, 587) dtype: int64\n", + "mask_all shape: (587, 587) dtype: float64\n", + "n grains: 54\n", + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.i3WQjSp5j6/ipykernel_48606/2477574397.py:61: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F8/ouputgpkg/tile_r000005_c000044_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F8/csv_stats/tile_r000005_c000044_grain_stats.csv\n", + "done with segmentation + export\n", + "[116/1332] tile_r000005_c000045\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.78it/s]\n", + "100%|██████████| 3/3 [00:00<00:00, 3.80it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 128/128 [00:18<00:00, 6.80it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "64it [00:00, 944.73it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "85it [00:00, 882.00it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 39/39 [00:00<00:00, 244.39it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 39/39 [00:00<00:00, 288.26it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "labels shape: (587, 587) dtype: int64\n", + "mask_all shape: (587, 587) dtype: float64\n", + "n grains: 39\n", + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.i3WQjSp5j6/ipykernel_48606/2477574397.py:61: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F8/ouputgpkg/tile_r000005_c000045_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F8/csv_stats/tile_r000005_c000045_grain_stats.csv\n", + "done with segmentation + export\n", + "[117/1332] tile_r000005_c000046\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.82it/s]\n", + "100%|██████████| 3/3 [00:00<00:00, 3.86it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 137/137 [00:21<00:00, 6.49it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "75it [00:00, 1265.56it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "114it [00:00, 657.25it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 54/54 [00:00<00:00, 200.16it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 58/58 [00:00<00:00, 219.21it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "labels shape: (587, 587) dtype: int64\n", + "mask_all shape: (587, 587) dtype: float64\n", + "n grains: 58\n", + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.i3WQjSp5j6/ipykernel_48606/2477574397.py:61: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F8/ouputgpkg/tile_r000005_c000046_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F8/csv_stats/tile_r000005_c000046_grain_stats.csv\n", + "done with segmentation + export\n", + "[118/1332] tile_r000005_c000047\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.86it/s]\n", + "100%|██████████| 3/3 [00:00<00:00, 3.87it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 195/195 [00:30<00:00, 6.37it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "143it [00:00, 303.82it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "142it [00:00, 446.87it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 38/38 [00:00<00:00, 68.48it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 77/77 [00:00<00:00, 244.61it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "labels shape: (587, 587) dtype: int64\n", + "mask_all shape: (587, 587) dtype: float64\n", + "n grains: 77\n", + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.i3WQjSp5j6/ipykernel_48606/2477574397.py:61: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F8/ouputgpkg/tile_r000005_c000047_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F8/csv_stats/tile_r000005_c000047_grain_stats.csv\n", + "done with segmentation + export\n", + "[119/1332] tile_r000005_c000048\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.78it/s]\n", + "100%|██████████| 3/3 [00:00<00:00, 3.84it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 123/123 [00:18<00:00, 6.73it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "89it [00:00, 1365.03it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "117it [00:00, 1257.49it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 55/55 [00:00<00:00, 372.65it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 55/55 [00:00<00:00, 311.22it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "labels shape: (587, 587) dtype: int64\n", + "mask_all shape: (587, 587) dtype: float64\n", + "n grains: 55\n", + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.i3WQjSp5j6/ipykernel_48606/2477574397.py:61: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F8/ouputgpkg/tile_r000005_c000048_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F8/csv_stats/tile_r000005_c000048_grain_stats.csv\n", + "done with segmentation + export\n", + "[120/1332] tile_r000005_c000049\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 2.13it/s]\n", + "100%|██████████| 3/3 [00:00<00:00, 3.78it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 157/157 [00:23<00:00, 6.55it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "115it [00:00, 1457.41it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "147it [00:00, 1324.60it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 68/68 [00:00<00:00, 372.34it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 69/69 [00:00<00:00, 293.18it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "labels shape: (587, 587) dtype: int64\n", + "mask_all shape: (587, 587) dtype: float64\n", + "n grains: 69\n", + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.i3WQjSp5j6/ipykernel_48606/2477574397.py:61: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F8/ouputgpkg/tile_r000005_c000049_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F8/csv_stats/tile_r000005_c000049_grain_stats.csv\n", + "done with segmentation + export\n", + "[121/1332] tile_r000005_c000050\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.77it/s]\n", + "100%|██████████| 3/3 [00:00<00:00, 3.77it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 146/146 [00:21<00:00, 6.70it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "82it [00:00, 347.42it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "81it [00:00, 666.62it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 22/22 [00:00<00:00, 89.08it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 45/45 [00:00<00:00, 243.78it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "labels shape: (587, 587) dtype: int64\n", + "mask_all shape: (587, 587) dtype: float64\n", + "n grains: 45\n", + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.i3WQjSp5j6/ipykernel_48606/2477574397.py:61: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F8/ouputgpkg/tile_r000005_c000050_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F8/csv_stats/tile_r000005_c000050_grain_stats.csv\n", + "done with segmentation + export\n", + "[122/1332] tile_r000005_c000051\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.81it/s]\n", + "100%|██████████| 3/3 [00:00<00:00, 3.81it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 48/48 [00:07<00:00, 6.35it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "18it [00:00, 614.95it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "20it [00:00, 655.26it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 8/8 [00:00<00:00, 149.29it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 8/8 [00:00<00:00, 272.52it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "labels shape: (587, 587) dtype: int64\n", + "mask_all shape: (587, 587) dtype: float64\n", + "n grains: 8\n", + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.i3WQjSp5j6/ipykernel_48606/2477574397.py:61: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F8/ouputgpkg/tile_r000005_c000051_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F8/csv_stats/tile_r000005_c000051_grain_stats.csv\n", + "done with segmentation + export\n", + "[123/1332] tile_r000005_c000052\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.80it/s]\n", + "100%|██████████| 3/3 [00:00<00:00, 3.80it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 22/22 [00:03<00:00, 6.44it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "7it [00:00, 335.68it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "7it [00:00, 345.38it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 2/2 [00:00<00:00, 51.40it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 2/2 [00:00<00:00, 137.34it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "labels shape: (587, 587) dtype: int64\n", + "mask_all shape: (587, 587) dtype: float64\n", + "n grains: 2\n", + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.i3WQjSp5j6/ipykernel_48606/2477574397.py:61: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F8/ouputgpkg/tile_r000005_c000052_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F8/csv_stats/tile_r000005_c000052_grain_stats.csv\n", + "done with segmentation + export\n", + "[124/1332] tile_r000005_c000053\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.85it/s]\n", + "100%|██████████| 3/3 [00:00<00:00, 3.84it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 2/2 [00:00<00:00, 6.18it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "0it [00:00, ?it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "0it [00:00, ?it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "0it [00:00, ?it/s]\n", + "0it [00:00, ?it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "labels shape: (587, 587) dtype: uint8\n", + "mask_all shape: (587, 587, 3) dtype: uint8\n", + "n grains: 0\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.i3WQjSp5j6/ipykernel_48606/2477574397.py:61: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n", + "No grains found -> skipping histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F8/ouputgpkg/tile_r000005_c000053_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F8/csv_stats/tile_r000005_c000053_grain_stats.csv\n", + "done with segmentation + export\n", + "[125/1332] tile_r000005_c000054\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.78it/s]\n", + "100%|██████████| 3/3 [00:00<00:00, 3.81it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 6/6 [00:00<00:00, 6.24it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "0it [00:00, ?it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "0it [00:00, ?it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "0it [00:00, ?it/s]\n", + "0it [00:00, ?it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "labels shape: (587, 587) dtype: uint8\n", + "mask_all shape: (587, 587, 3) dtype: uint8\n", + "n grains: 0\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.i3WQjSp5j6/ipykernel_48606/2477574397.py:61: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n", + "No grains found -> skipping histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F8/ouputgpkg/tile_r000005_c000054_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F8/csv_stats/tile_r000005_c000054_grain_stats.csv\n", + "done with segmentation + export\n", + "[126/1332] tile_r000005_c000055\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.80it/s]\n", + "100%|██████████| 3/3 [00:00<00:00, 3.85it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "0it [00:00, ?it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "0it [00:00, ?it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "0it [00:00, ?it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "0it [00:00, ?it/s]\n", + "0it [00:00, ?it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "labels shape: (587, 587) dtype: uint8\n", + "mask_all shape: (587, 587, 3) dtype: uint8\n", + "n grains: 0\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\n", + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.i3WQjSp5j6/ipykernel_48606/2477574397.py:61: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n", + "No grains found -> skipping histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F8/ouputgpkg/tile_r000005_c000055_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F8/csv_stats/tile_r000005_c000055_grain_stats.csv\n", + "done with segmentation + export\n", + "[127/1332] tile_r000005_c000056\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.84it/s]\n", + "100%|██████████| 3/3 [00:00<00:00, 3.79it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "0it [00:00, ?it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "0it [00:00, ?it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "0it [00:00, ?it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "0it [00:00, ?it/s]\n", + "0it [00:00, ?it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "labels shape: (587, 587) dtype: uint8\n", + "mask_all shape: (587, 587, 3) dtype: uint8\n", + "n grains: 0\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\n", + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.i3WQjSp5j6/ipykernel_48606/2477574397.py:61: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n", + "No grains found -> skipping histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F8/ouputgpkg/tile_r000005_c000056_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F8/csv_stats/tile_r000005_c000056_grain_stats.csv\n", + "done with segmentation + export\n", + "[128/1332] tile_r000005_c000057\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.77it/s]\n", + "100%|██████████| 3/3 [00:00<00:00, 3.86it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 27/27 [00:04<00:00, 6.65it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "0it [00:00, ?it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "0it [00:00, ?it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "0it [00:00, ?it/s]\n", + "0it [00:00, ?it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "labels shape: (587, 587) dtype: uint8\n", + "mask_all shape: (587, 587, 3) dtype: uint8\n", + "n grains: 0\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.i3WQjSp5j6/ipykernel_48606/2477574397.py:61: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n", + "No grains found -> skipping histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F8/ouputgpkg/tile_r000005_c000057_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F8/csv_stats/tile_r000005_c000057_grain_stats.csv\n", + "done with segmentation + export\n", + "[129/1332] tile_r000005_c000058\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.77it/s]\n", + "100%|██████████| 3/3 [00:00<00:00, 3.78it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 65/65 [00:10<00:00, 6.33it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "43it [00:00, 446.41it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "44it [00:00, 454.03it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 16/16 [00:00<00:00, 92.86it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 16/16 [00:00<00:00, 204.26it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "labels shape: (587, 587) dtype: int64\n", + "mask_all shape: (587, 587) dtype: float64\n", + "n grains: 16\n", + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.i3WQjSp5j6/ipykernel_48606/2477574397.py:61: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F8/ouputgpkg/tile_r000005_c000058_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F8/csv_stats/tile_r000005_c000058_grain_stats.csv\n", + "done with segmentation + export\n", + "[130/1332] tile_r000005_c000059\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.74it/s]\n", + "100%|██████████| 3/3 [00:00<00:00, 3.82it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 37/37 [00:05<00:00, 6.43it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "25it [00:00, 695.46it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "28it [00:00, 669.70it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 12/12 [00:00<00:00, 164.36it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 12/12 [00:00<00:00, 208.08it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "labels shape: (587, 587) dtype: int64\n", + "mask_all shape: (587, 587) dtype: float64\n", + "n grains: 12\n", + "Saved segmentation plot\n", + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F8/ouputgpkg/tile_r000005_c000059_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F8/csv_stats/tile_r000005_c000059_grain_stats.csv\n", + "done with segmentation + export\n", + "[131/1332] tile_r000005_c000060\n", + " -> skipped (100% Nodata)\n", + "[132/1332] tile_r000005_c000061\n", + " -> skipped (100% Nodata)\n", + "[133/1332] tile_r000005_c000062\n", + " -> skipped (100% Nodata)\n", + "[134/1332] tile_r000005_c000063\n", + " -> skipped (100% Nodata)\n", + "[135/1332] tile_r000005_c000064\n", + " -> skipped (100% Nodata)\n", + "[136/1332] tile_r000005_c000065\n", + " -> skipped (100% Nodata)\n", + "[137/1332] tile_r000005_c000066\n", + " -> skipped (100% Nodata)\n", + "[138/1332] tile_r000005_c000067\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.i3WQjSp5j6/ipykernel_48606/2477574397.py:61: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " -> skipped (100% Nodata)\n", + "[139/1332] tile_r000005_c000068\n", + " -> skipped (100% Nodata)\n", + "[140/1332] tile_r000005_c000069\n", + " -> skipped (100% Nodata)\n", + "[141/1332] tile_r000005_c000070\n", + " -> skipped (100% Nodata)\n", + "[142/1332] tile_r000005_c000071\n", + " -> skipped (100% Nodata)\n", + "[143/1332] tile_r000005_c000072\n", + " -> skipped (100% Nodata)\n", + "[144/1332] tile_r000005_c000073\n", + " -> skipped (100% Nodata)\n", + "[145/1332] tile_r000005_c000074\n", + " -> skipped (100% Nodata)\n", + "[146/1332] tile_r000005_c000075\n", + " -> skipped (100% Nodata)\n", + "[147/1332] tile_r000005_c000076\n", + " -> skipped (100% Nodata)\n", + "[148/1332] tile_r000005_c000077\n", + " -> skipped (100% Nodata)\n", + "[149/1332] tile_r000006_c000004\n", + " -> skipped (100% Nodata)\n", + "[150/1332] tile_r000006_c000005\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.81it/s]\n", + "100%|██████████| 3/3 [00:00<00:00, 3.88it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "0it [00:00, ?it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "0it [00:00, ?it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "0it [00:00, ?it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "0it [00:00, ?it/s]\n", + "0it [00:00, ?it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "labels shape: (587, 587) dtype: uint8\n", + "mask_all shape: (587, 587, 3) dtype: uint8\n", + "n grains: 0\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\n", + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.i3WQjSp5j6/ipykernel_48606/2477574397.py:61: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n", + "No grains found -> skipping histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F8/ouputgpkg/tile_r000006_c000005_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F8/csv_stats/tile_r000006_c000005_grain_stats.csv\n", + "done with segmentation + export\n", + "[151/1332] tile_r000006_c000006\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.83it/s]\n", + "100%|██████████| 3/3 [00:00<00:00, 3.78it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 84/84 [00:11<00:00, 7.26it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "65it [00:00, 252.70it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "69it [00:00, 256.47it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 21/21 [00:00<00:00, 51.75it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 21/21 [00:00<00:00, 144.35it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "labels shape: (587, 587) dtype: int64\n", + "mask_all shape: (587, 587) dtype: float64\n", + "n grains: 21\n", + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.i3WQjSp5j6/ipykernel_48606/2477574397.py:61: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F8/ouputgpkg/tile_r000006_c000006_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F8/csv_stats/tile_r000006_c000006_grain_stats.csv\n", + "done with segmentation + export\n", + "[152/1332] tile_r000006_c000007\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.83it/s]\n", + "100%|██████████| 3/3 [00:00<00:00, 3.80it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 100/100 [00:14<00:00, 7.01it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "60it [00:00, 374.50it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "70it [00:00, 346.86it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 26/26 [00:00<00:00, 77.78it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 26/26 [00:00<00:00, 152.17it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "labels shape: (587, 587) dtype: int64\n", + "mask_all shape: (587, 587) dtype: float64\n", + "n grains: 26\n", + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.i3WQjSp5j6/ipykernel_48606/2477574397.py:61: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F8/ouputgpkg/tile_r000006_c000007_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F8/csv_stats/tile_r000006_c000007_grain_stats.csv\n", + "done with segmentation + export\n", + "[153/1332] tile_r000006_c000008\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.88it/s]\n", + "100%|██████████| 3/3 [00:00<00:00, 3.80it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 156/156 [00:22<00:00, 6.90it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "98it [00:00, 218.67it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "97it [00:00, 249.21it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 29/29 [00:00<00:00, 44.90it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 37/37 [00:00<00:00, 158.44it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "labels shape: (587, 587) dtype: int64\n", + "mask_all shape: (587, 587) dtype: float64\n", + "n grains: 37\n", + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.i3WQjSp5j6/ipykernel_48606/2477574397.py:61: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F8/ouputgpkg/tile_r000006_c000008_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F8/csv_stats/tile_r000006_c000008_grain_stats.csv\n", + "done with segmentation + export\n", + "[154/1332] tile_r000006_c000009\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.82it/s]\n", + "100%|██████████| 3/3 [00:00<00:00, 3.83it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 196/196 [00:26<00:00, 7.35it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "112it [00:00, 258.10it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "117it [00:00, 259.02it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 38/38 [00:00<00:00, 49.26it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 41/41 [00:00<00:00, 182.14it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "labels shape: (587, 587) dtype: int64\n", + "mask_all shape: (587, 587) dtype: float64\n", + "n grains: 41\n", + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.i3WQjSp5j6/ipykernel_48606/2477574397.py:61: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F8/ouputgpkg/tile_r000006_c000009_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F8/csv_stats/tile_r000006_c000009_grain_stats.csv\n", + "done with segmentation + export\n", + "[155/1332] tile_r000006_c000010\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.84it/s]\n", + "100%|██████████| 3/3 [00:00<00:00, 3.88it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 217/217 [00:30<00:00, 7.18it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "154it [00:00, 350.71it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "168it [00:00, 357.02it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 63/63 [00:00<00:00, 94.19it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 63/63 [00:00<00:00, 188.40it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "labels shape: (587, 587) dtype: int64\n", + "mask_all shape: (587, 587) dtype: float64\n", + "n grains: 63\n", + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.i3WQjSp5j6/ipykernel_48606/2477574397.py:61: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F8/ouputgpkg/tile_r000006_c000010_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F8/csv_stats/tile_r000006_c000010_grain_stats.csv\n", + "done with segmentation + export\n", + "[156/1332] tile_r000006_c000011\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.85it/s]\n", + "100%|██████████| 3/3 [00:00<00:00, 3.84it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 318/318 [00:48<00:00, 6.53it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "244it [00:00, 348.50it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "261it [00:00, 350.29it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 93/93 [00:01<00:00, 72.11it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 97/97 [00:00<00:00, 182.26it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "labels shape: (587, 587) dtype: int64\n", + "mask_all shape: (587, 587) dtype: float64\n", + "n grains: 97\n", + "Saved segmentation plot\n", + "Warning: len(gdf)=97 != len(grain_stats)=95 -> truncating to 95\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.i3WQjSp5j6/ipykernel_48606/2477574397.py:61: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F8/ouputgpkg/tile_r000006_c000011_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F8/csv_stats/tile_r000006_c000011_grain_stats.csv\n", + "done with segmentation + export\n", + "[157/1332] tile_r000006_c000012\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.83it/s]\n", + "100%|██████████| 3/3 [00:00<00:00, 3.90it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 356/356 [00:53<00:00, 6.61it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "273it [00:00, 281.76it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "271it [00:00, 315.59it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 88/88 [00:01<00:00, 66.13it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 100/100 [00:00<00:00, 189.06it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "labels shape: (587, 587) dtype: int64\n", + "mask_all shape: (587, 587) dtype: float64\n", + "n grains: 100\n", + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.i3WQjSp5j6/ipykernel_48606/2477574397.py:61: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F8/ouputgpkg/tile_r000006_c000012_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F8/csv_stats/tile_r000006_c000012_grain_stats.csv\n", + "done with segmentation + export\n", + "[158/1332] tile_r000006_c000013\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.80it/s]\n", + "100%|██████████| 3/3 [00:00<00:00, 3.87it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 349/349 [00:51<00:00, 6.73it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "287it [00:00, 338.93it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "300it [00:00, 342.87it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 111/111 [00:01<00:00, 86.37it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 112/112 [00:00<00:00, 195.77it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "labels shape: (587, 587) dtype: int64\n", + "mask_all shape: (587, 587) dtype: float64\n", + "n grains: 112\n", + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.i3WQjSp5j6/ipykernel_48606/2477574397.py:61: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F8/ouputgpkg/tile_r000006_c000013_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F8/csv_stats/tile_r000006_c000013_grain_stats.csv\n", + "done with segmentation + export\n", + "[159/1332] tile_r000006_c000014\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.77it/s]\n", + "100%|██████████| 3/3 [00:00<00:00, 3.86it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 331/331 [00:45<00:00, 7.34it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "275it [00:00, 363.46it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "296it [00:00, 369.82it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 111/111 [00:01<00:00, 94.24it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 112/112 [00:00<00:00, 202.49it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "labels shape: (587, 587) dtype: int64\n", + "mask_all shape: (587, 587) dtype: float64\n", + "n grains: 112\n", + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.i3WQjSp5j6/ipykernel_48606/2477574397.py:61: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F8/ouputgpkg/tile_r000006_c000014_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F8/csv_stats/tile_r000006_c000014_grain_stats.csv\n", + "done with segmentation + export\n", + "[160/1332] tile_r000006_c000015\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.82it/s]\n", + "100%|██████████| 3/3 [00:00<00:00, 3.78it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 423/423 [01:01<00:00, 6.85it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "345it [00:00, 359.18it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "359it [00:00, 363.99it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 129/129 [00:01<00:00, 83.12it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 133/133 [00:00<00:00, 200.01it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "labels shape: (587, 587) dtype: int64\n", + "mask_all shape: (587, 587) dtype: float64\n", + "n grains: 133\n", + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.i3WQjSp5j6/ipykernel_48606/2477574397.py:61: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F8/ouputgpkg/tile_r000006_c000015_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F8/csv_stats/tile_r000006_c000015_grain_stats.csv\n", + "done with segmentation + export\n", + "[161/1332] tile_r000006_c000016\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.68it/s]\n", + "100%|██████████| 3/3 [00:00<00:00, 3.70it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 312/312 [00:44<00:00, 7.03it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "249it [00:00, 349.08it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "266it [00:00, 361.61it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 97/97 [00:01<00:00, 76.56it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 100/100 [00:00<00:00, 205.03it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "labels shape: (587, 587) dtype: int64\n", + "mask_all shape: (587, 587) dtype: float64\n", + "n grains: 100\n", + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.i3WQjSp5j6/ipykernel_48606/2477574397.py:61: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F8/ouputgpkg/tile_r000006_c000016_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F8/csv_stats/tile_r000006_c000016_grain_stats.csv\n", + "done with segmentation + export\n", + "[162/1332] tile_r000006_c000017\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.70it/s]\n", + "100%|██████████| 3/3 [00:00<00:00, 3.56it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 380/380 [00:56<00:00, 6.70it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "308it [00:00, 377.16it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "333it [00:00, 382.72it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 130/130 [00:01<00:00, 105.15it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 131/131 [00:00<00:00, 210.33it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "labels shape: (587, 587) dtype: int64\n", + "mask_all shape: (587, 587) dtype: float64\n", + "n grains: 131\n", + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.i3WQjSp5j6/ipykernel_48606/2477574397.py:61: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F8/ouputgpkg/tile_r000006_c000017_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F8/csv_stats/tile_r000006_c000017_grain_stats.csv\n", + "done with segmentation + export\n", + "[163/1332] tile_r000006_c000018\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.78it/s]\n", + "100%|██████████| 3/3 [00:00<00:00, 3.85it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 394/394 [00:57<00:00, 6.81it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "326it [00:00, 486.28it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "342it [00:00, 483.55it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 137/137 [00:01<00:00, 125.43it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 140/140 [00:00<00:00, 220.01it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "labels shape: (587, 587) dtype: int64\n", + "mask_all shape: (587, 587) dtype: float64\n", + "n grains: 140\n", + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.i3WQjSp5j6/ipykernel_48606/2477574397.py:61: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F8/ouputgpkg/tile_r000006_c000018_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F8/csv_stats/tile_r000006_c000018_grain_stats.csv\n", + "done with segmentation + export\n", + "[164/1332] tile_r000006_c000019\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.77it/s]\n", + "100%|██████████| 3/3 [00:00<00:00, 3.84it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 361/361 [00:51<00:00, 6.97it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "294it [00:00, 427.03it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "312it [00:00, 429.45it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 116/116 [00:01<00:00, 99.76it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 120/120 [00:00<00:00, 201.16it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "labels shape: (587, 587) dtype: int64\n", + "mask_all shape: (587, 587) dtype: float64\n", + "n grains: 120\n", + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.i3WQjSp5j6/ipykernel_48606/2477574397.py:61: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F8/ouputgpkg/tile_r000006_c000019_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F8/csv_stats/tile_r000006_c000019_grain_stats.csv\n", + "done with segmentation + export\n", + "[165/1332] tile_r000006_c000020\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.79it/s]\n", + "100%|██████████| 3/3 [00:00<00:00, 3.79it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 345/345 [00:50<00:00, 6.79it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "290it [00:00, 400.01it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "312it [00:00, 398.89it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 118/118 [00:01<00:00, 98.05it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 119/119 [00:00<00:00, 215.24it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "labels shape: (587, 587) dtype: int64\n", + "mask_all shape: (587, 587) dtype: float64\n", + "n grains: 119\n", + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.i3WQjSp5j6/ipykernel_48606/2477574397.py:61: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F8/ouputgpkg/tile_r000006_c000020_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F8/csv_stats/tile_r000006_c000020_grain_stats.csv\n", + "done with segmentation + export\n", + "[166/1332] tile_r000006_c000021\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.75it/s]\n", + "100%|██████████| 3/3 [00:00<00:00, 3.83it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 380/380 [00:53<00:00, 7.14it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "317it [00:01, 234.38it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "332it [00:01, 237.35it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 115/115 [00:02<00:00, 53.34it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 117/117 [00:00<00:00, 200.02it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "labels shape: (587, 587) dtype: int64\n", + "mask_all shape: (587, 587) dtype: float64\n", + "n grains: 117\n", + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.i3WQjSp5j6/ipykernel_48606/2477574397.py:61: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F8/ouputgpkg/tile_r000006_c000021_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F8/csv_stats/tile_r000006_c000021_grain_stats.csv\n", + "done with segmentation + export\n", + "[167/1332] tile_r000006_c000022\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.78it/s]\n", + "100%|██████████| 3/3 [00:00<00:00, 3.81it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 340/340 [00:48<00:00, 7.08it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "274it [00:00, 396.11it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "273it [00:00, 451.52it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 85/85 [00:01<00:00, 79.92it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 122/122 [00:00<00:00, 204.78it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "labels shape: (587, 587) dtype: int64\n", + "mask_all shape: (587, 587) dtype: float64\n", + "n grains: 122\n", + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.i3WQjSp5j6/ipykernel_48606/2477574397.py:61: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F8/ouputgpkg/tile_r000006_c000022_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F8/csv_stats/tile_r000006_c000022_grain_stats.csv\n", + "done with segmentation + export\n", + "[168/1332] tile_r000006_c000023\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.81it/s]\n", + "100%|██████████| 3/3 [00:00<00:00, 3.88it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 397/397 [00:56<00:00, 6.99it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "306it [00:01, 252.11it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "304it [00:00, 304.13it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 85/85 [00:01<00:00, 47.70it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 129/129 [00:00<00:00, 196.83it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "labels shape: (587, 587) dtype: int64\n", + "mask_all shape: (587, 587) dtype: float64\n", + "n grains: 129\n", + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.i3WQjSp5j6/ipykernel_48606/2477574397.py:61: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F8/ouputgpkg/tile_r000006_c000023_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F8/csv_stats/tile_r000006_c000023_grain_stats.csv\n", + "done with segmentation + export\n", + "[169/1332] tile_r000006_c000024\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.81it/s]\n", + "100%|██████████| 3/3 [00:00<00:00, 3.81it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 305/305 [00:44<00:00, 6.92it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "241it [00:00, 415.44it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "284it [00:00, 421.13it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 111/111 [00:01<00:00, 101.23it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 115/115 [00:00<00:00, 208.93it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "labels shape: (587, 587) dtype: int64\n", + "mask_all shape: (587, 587) dtype: float64\n", + "n grains: 115\n", + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.i3WQjSp5j6/ipykernel_48606/2477574397.py:61: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F8/ouputgpkg/tile_r000006_c000024_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F8/csv_stats/tile_r000006_c000024_grain_stats.csv\n", + "done with segmentation + export\n", + "[170/1332] tile_r000006_c000025\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.86it/s]\n", + "100%|██████████| 3/3 [00:00<00:00, 3.76it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 315/315 [00:39<00:00, 8.07it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "247it [00:00, 442.83it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "275it [00:00, 450.55it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 103/103 [00:00<00:00, 108.65it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 106/106 [00:00<00:00, 214.06it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "labels shape: (587, 587) dtype: int64\n", + "mask_all shape: (587, 587) dtype: float64\n", + "n grains: 106\n", + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.i3WQjSp5j6/ipykernel_48606/2477574397.py:61: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F8/ouputgpkg/tile_r000006_c000025_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F8/csv_stats/tile_r000006_c000025_grain_stats.csv\n", + "done with segmentation + export\n", + "[171/1332] tile_r000006_c000026\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.81it/s]\n", + "100%|██████████| 3/3 [00:00<00:00, 3.81it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 321/321 [00:43<00:00, 7.37it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "260it [00:00, 410.17it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "259it [00:00, 451.66it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 81/81 [00:01<00:00, 78.83it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 118/118 [00:00<00:00, 213.20it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "labels shape: (587, 587) dtype: int64\n", + "mask_all shape: (587, 587) dtype: float64\n", + "n grains: 118\n", + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.i3WQjSp5j6/ipykernel_48606/2477574397.py:61: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F8/ouputgpkg/tile_r000006_c000026_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F8/csv_stats/tile_r000006_c000026_grain_stats.csv\n", + "done with segmentation + export\n", + "[172/1332] tile_r000006_c000027\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.84it/s]\n", + "100%|██████████| 3/3 [00:00<00:00, 3.87it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 332/332 [00:48<00:00, 6.85it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "260it [00:00, 515.07it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "306it [00:00, 515.24it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 125/125 [00:00<00:00, 130.95it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 128/128 [00:00<00:00, 212.64it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "labels shape: (587, 587) dtype: int64\n", + "mask_all shape: (587, 587) dtype: float64\n", + "n grains: 128\n", + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.i3WQjSp5j6/ipykernel_48606/2477574397.py:61: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F8/ouputgpkg/tile_r000006_c000027_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F8/csv_stats/tile_r000006_c000027_grain_stats.csv\n", + "done with segmentation + export\n", + "[173/1332] tile_r000006_c000028\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.82it/s]\n", + "100%|██████████| 3/3 [00:00<00:00, 3.82it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 340/340 [00:52<00:00, 6.53it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "261it [00:00, 349.92it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "292it [00:00, 354.77it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 107/107 [00:01<00:00, 72.93it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 117/117 [00:00<00:00, 196.22it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "labels shape: (587, 587) dtype: int64\n", + "mask_all shape: (587, 587) dtype: float64\n", + "n grains: 117\n", + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.i3WQjSp5j6/ipykernel_48606/2477574397.py:61: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F8/ouputgpkg/tile_r000006_c000028_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F8/csv_stats/tile_r000006_c000028_grain_stats.csv\n", + "done with segmentation + export\n", + "[174/1332] tile_r000006_c000029\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.78it/s]\n", + "100%|██████████| 3/3 [00:00<00:00, 3.83it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 286/286 [00:38<00:00, 7.48it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "184it [00:00, 213.76it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "174it [00:00, 418.84it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 43/43 [00:00<00:00, 52.25it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 81/81 [00:00<00:00, 177.35it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "labels shape: (587, 587) dtype: int64\n", + "mask_all shape: (587, 587) dtype: float64\n", + "n grains: 81\n", + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.i3WQjSp5j6/ipykernel_48606/2477574397.py:61: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F8/ouputgpkg/tile_r000006_c000029_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F8/csv_stats/tile_r000006_c000029_grain_stats.csv\n", + "done with segmentation + export\n", + "[175/1332] tile_r000006_c000030\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.78it/s]\n", + "100%|██████████| 3/3 [00:00<00:00, 3.76it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 299/299 [00:43<00:00, 6.81it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "169it [00:00, 291.11it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "193it [00:00, 291.03it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 67/67 [00:01<00:00, 47.05it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 79/79 [00:00<00:00, 153.67it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "labels shape: (587, 587) dtype: int64\n", + "mask_all shape: (587, 587) dtype: float64\n", + "n grains: 79\n", + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.i3WQjSp5j6/ipykernel_48606/2477574397.py:61: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F8/ouputgpkg/tile_r000006_c000030_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F8/csv_stats/tile_r000006_c000030_grain_stats.csv\n", + "done with segmentation + export\n", + "[176/1332] tile_r000006_c000031\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.78it/s]\n", + "100%|██████████| 3/3 [00:00<00:00, 3.76it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 233/233 [00:33<00:00, 6.97it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "145it [00:00, 517.59it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "168it [00:00, 497.16it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 70/70 [00:00<00:00, 121.47it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 72/72 [00:00<00:00, 200.79it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "labels shape: (587, 587) dtype: int64\n", + "mask_all shape: (587, 587) dtype: float64\n", + "n grains: 72\n", + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.i3WQjSp5j6/ipykernel_48606/2477574397.py:61: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F8/ouputgpkg/tile_r000006_c000031_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F8/csv_stats/tile_r000006_c000031_grain_stats.csv\n", + "done with segmentation + export\n", + "[177/1332] tile_r000006_c000032\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.81it/s]\n", + "100%|██████████| 3/3 [00:00<00:00, 3.82it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 273/273 [00:40<00:00, 6.82it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "176it [00:00, 304.66it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "173it [00:00, 398.50it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 51/51 [00:00<00:00, 52.23it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 82/82 [00:00<00:00, 171.83it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "labels shape: (587, 587) dtype: int64\n", + "mask_all shape: (587, 587) dtype: float64\n", + "n grains: 82\n", + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.i3WQjSp5j6/ipykernel_48606/2477574397.py:61: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F8/ouputgpkg/tile_r000006_c000032_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F8/csv_stats/tile_r000006_c000032_grain_stats.csv\n", + "done with segmentation + export\n", + "[178/1332] tile_r000006_c000033\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.81it/s]\n", + "100%|██████████| 3/3 [00:00<00:00, 3.83it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 246/246 [00:38<00:00, 6.46it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "148it [00:00, 203.90it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "158it [00:00, 209.55it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 51/51 [00:01<00:00, 37.28it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 63/63 [00:00<00:00, 133.68it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "labels shape: (587, 587) dtype: int64\n", + "mask_all shape: (587, 587) dtype: float64\n", + "n grains: 63\n", + "Saved segmentation plot\n", + "Warning: len(gdf)=63 != len(grain_stats)=62 -> truncating to 62\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsshome1/0B/di54doz/.local/share/mamba/envs/segmenteverygrain/lib/python3.10/site-packages/segmenteverygrain/segmenteverygrain.py:2217: RuntimeWarning: divide by zero encountered in log2\n", + " phi_major = -np.log2(major_axis_length) # major axis length in phi scale\n", + "/dss/dsshome1/0B/di54doz/.local/share/mamba/envs/segmenteverygrain/lib/python3.10/site-packages/segmenteverygrain/segmenteverygrain.py:2218: RuntimeWarning: divide by zero encountered in log2\n", + " phi_minor = -np.log2(minor_axis_length)\n", + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.i3WQjSp5j6/ipykernel_48606/2477574397.py:61: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F8/ouputgpkg/tile_r000006_c000033_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F8/csv_stats/tile_r000006_c000033_grain_stats.csv\n", + "done with segmentation + export\n", + "[179/1332] tile_r000006_c000034\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.83it/s]\n", + "100%|██████████| 3/3 [00:00<00:00, 3.84it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 230/230 [00:33<00:00, 6.83it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "114it [00:00, 146.46it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "133it [00:00, 155.54it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 47/47 [00:03<00:00, 15.07it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 57/57 [00:00<00:00, 166.72it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "labels shape: (587, 587) dtype: int64\n", + "mask_all shape: (587, 587) dtype: float64\n", + "n grains: 57\n", + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.i3WQjSp5j6/ipykernel_48606/2477574397.py:61: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F8/ouputgpkg/tile_r000006_c000034_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F8/csv_stats/tile_r000006_c000034_grain_stats.csv\n", + "done with segmentation + export\n", + "[180/1332] tile_r000006_c000035\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.79it/s]\n", + "100%|██████████| 3/3 [00:00<00:00, 3.82it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 203/203 [00:29<00:00, 6.77it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "121it [00:00, 396.05it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "147it [00:00, 327.97it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 56/56 [00:00<00:00, 89.23it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 57/57 [00:00<00:00, 173.70it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "labels shape: (587, 587) dtype: int64\n", + "mask_all shape: (587, 587) dtype: float64\n", + "n grains: 57\n", + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.i3WQjSp5j6/ipykernel_48606/2477574397.py:61: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F8/ouputgpkg/tile_r000006_c000035_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F8/csv_stats/tile_r000006_c000035_grain_stats.csv\n", + "done with segmentation + export\n", + "[181/1332] tile_r000006_c000036\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.83it/s]\n", + "100%|██████████| 3/3 [00:00<00:00, 3.82it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 229/229 [00:35<00:00, 6.53it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "173it [00:00, 390.49it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "197it [00:00, 392.90it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 81/81 [00:00<00:00, 98.30it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 84/84 [00:00<00:00, 202.96it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "labels shape: (587, 587) dtype: int64\n", + "mask_all shape: (587, 587) dtype: float64\n", + "n grains: 84\n", + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.i3WQjSp5j6/ipykernel_48606/2477574397.py:61: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F8/ouputgpkg/tile_r000006_c000036_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F8/csv_stats/tile_r000006_c000036_grain_stats.csv\n", + "done with segmentation + export\n", + "[182/1332] tile_r000006_c000037\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.81it/s]\n", + "100%|██████████| 3/3 [00:00<00:00, 3.86it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 268/268 [00:38<00:00, 7.04it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "202it [00:00, 404.78it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "229it [00:00, 415.21it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 89/89 [00:00<00:00, 95.59it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 93/93 [00:00<00:00, 208.45it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "labels shape: (587, 587) dtype: int64\n", + "mask_all shape: (587, 587) dtype: float64\n", + "n grains: 93\n", + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.i3WQjSp5j6/ipykernel_48606/2477574397.py:61: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F8/ouputgpkg/tile_r000006_c000037_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F8/csv_stats/tile_r000006_c000037_grain_stats.csv\n", + "done with segmentation + export\n", + "[183/1332] tile_r000006_c000038\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.78it/s]\n", + "100%|██████████| 3/3 [00:00<00:00, 3.77it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 233/233 [00:36<00:00, 6.35it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "138it [00:00, 528.91it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "170it [00:00, 565.02it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 71/71 [00:00<00:00, 143.54it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 71/71 [00:00<00:00, 259.14it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "labels shape: (587, 587) dtype: int64\n", + "mask_all shape: (587, 587) dtype: float64\n", + "n grains: 71\n", + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.i3WQjSp5j6/ipykernel_48606/2477574397.py:61: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F8/ouputgpkg/tile_r000006_c000038_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F8/csv_stats/tile_r000006_c000038_grain_stats.csv\n", + "done with segmentation + export\n", + "[184/1332] tile_r000006_c000039\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.80it/s]\n", + "100%|██████████| 3/3 [00:00<00:00, 3.83it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 193/193 [00:29<00:00, 6.56it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "143it [00:00, 260.39it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "161it [00:00, 273.51it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 58/58 [00:01<00:00, 47.69it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 61/61 [00:00<00:00, 196.80it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "labels shape: (587, 587) dtype: int64\n", + "mask_all shape: (587, 587) dtype: float64\n", + "n grains: 61\n", + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.i3WQjSp5j6/ipykernel_48606/2477574397.py:61: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F8/ouputgpkg/tile_r000006_c000039_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F8/csv_stats/tile_r000006_c000039_grain_stats.csv\n", + "done with segmentation + export\n", + "[185/1332] tile_r000006_c000040\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.79it/s]\n", + "100%|██████████| 3/3 [00:00<00:00, 3.88it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 180/180 [00:28<00:00, 6.31it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "101it [00:00, 221.15it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100it [00:00, 222.71it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 26/26 [00:00<00:00, 31.96it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 49/49 [00:00<00:00, 207.60it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "labels shape: (587, 587) dtype: int64\n", + "mask_all shape: (587, 587) dtype: float64\n", + "n grains: 49\n", + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.i3WQjSp5j6/ipykernel_48606/2477574397.py:61: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F8/ouputgpkg/tile_r000006_c000040_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F8/csv_stats/tile_r000006_c000040_grain_stats.csv\n", + "done with segmentation + export\n", + "[186/1332] tile_r000006_c000041\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.83it/s]\n", + "100%|██████████| 3/3 [00:00<00:00, 3.83it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 207/207 [00:33<00:00, 6.26it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "127it [00:00, 350.63it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "142it [00:00, 344.80it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 54/54 [00:00<00:00, 94.08it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 54/54 [00:00<00:00, 185.47it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "labels shape: (587, 587) dtype: int64\n", + "mask_all shape: (587, 587) dtype: float64\n", + "n grains: 54\n", + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.i3WQjSp5j6/ipykernel_48606/2477574397.py:61: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F8/ouputgpkg/tile_r000006_c000041_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F8/csv_stats/tile_r000006_c000041_grain_stats.csv\n", + "done with segmentation + export\n", + "[187/1332] tile_r000006_c000042\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.85it/s]\n", + "100%|██████████| 3/3 [00:00<00:00, 3.83it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 184/184 [00:29<00:00, 6.30it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "92it [00:00, 380.16it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "107it [00:00, 394.97it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 42/42 [00:00<00:00, 102.44it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 42/42 [00:00<00:00, 218.38it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "labels shape: (587, 587) dtype: int64\n", + "mask_all shape: (587, 587) dtype: float64\n", + "n grains: 42\n", + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.i3WQjSp5j6/ipykernel_48606/2477574397.py:61: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F8/ouputgpkg/tile_r000006_c000042_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F8/csv_stats/tile_r000006_c000042_grain_stats.csv\n", + "done with segmentation + export\n", + "[188/1332] tile_r000006_c000043\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.83it/s]\n", + "100%|██████████| 3/3 [00:00<00:00, 3.86it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 157/157 [00:24<00:00, 6.40it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "79it [00:00, 299.55it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "93it [00:00, 317.27it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 34/34 [00:00<00:00, 69.15it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 35/35 [00:01<00:00, 24.59it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "labels shape: (587, 587) dtype: int64\n", + "mask_all shape: (587, 587) dtype: float64\n", + "n grains: 35\n", + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.i3WQjSp5j6/ipykernel_48606/2477574397.py:61: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F8/ouputgpkg/tile_r000006_c000043_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F8/csv_stats/tile_r000006_c000043_grain_stats.csv\n", + "done with segmentation + export\n", + "[189/1332] tile_r000006_c000044\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.76it/s]\n", + "100%|██████████| 3/3 [00:00<00:00, 3.75it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 203/203 [00:30<00:00, 6.59it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "141it [00:00, 321.03it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "146it [00:00, 328.34it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 54/54 [00:00<00:00, 83.63it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 54/54 [00:00<00:00, 188.67it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "labels shape: (587, 587) dtype: int64\n", + "mask_all shape: (587, 587) dtype: float64\n", + "n grains: 54\n", + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.i3WQjSp5j6/ipykernel_48606/2477574397.py:61: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F8/ouputgpkg/tile_r000006_c000044_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F8/csv_stats/tile_r000006_c000044_grain_stats.csv\n", + "done with segmentation + export\n", + "[190/1332] tile_r000006_c000045\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.74it/s]\n", + "100%|██████████| 3/3 [00:00<00:00, 3.81it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 202/202 [00:31<00:00, 6.43it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "135it [00:00, 281.83it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "145it [00:00, 281.87it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 53/53 [00:00<00:00, 85.88it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 54/54 [00:00<00:00, 187.68it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "labels shape: (587, 587) dtype: int64\n", + "mask_all shape: (587, 587) dtype: float64\n", + "n grains: 54\n", + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.i3WQjSp5j6/ipykernel_48606/2477574397.py:61: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F8/ouputgpkg/tile_r000006_c000045_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F8/csv_stats/tile_r000006_c000045_grain_stats.csv\n", + "done with segmentation + export\n", + "[191/1332] tile_r000006_c000046\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.76it/s]\n", + "100%|██████████| 3/3 [00:00<00:00, 3.84it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 149/149 [00:20<00:00, 7.43it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "124it [00:00, 395.08it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "137it [00:00, 399.46it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 53/53 [00:00<00:00, 105.97it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 54/54 [00:00<00:00, 198.32it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "labels shape: (587, 587) dtype: int64\n", + "mask_all shape: (587, 587) dtype: float64\n", + "n grains: 54\n", + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.i3WQjSp5j6/ipykernel_48606/2477574397.py:61: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F8/ouputgpkg/tile_r000006_c000046_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F8/csv_stats/tile_r000006_c000046_grain_stats.csv\n", + "done with segmentation + export\n", + "[192/1332] tile_r000006_c000047\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.82it/s]\n", + "100%|██████████| 3/3 [00:00<00:00, 3.83it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 59/59 [00:08<00:00, 7.17it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "44it [00:00, 410.98it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "44it [00:00, 413.37it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 15/15 [00:00<00:00, 74.21it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 15/15 [00:00<00:00, 171.02it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "labels shape: (587, 587) dtype: int64\n", + "mask_all shape: (587, 587) dtype: float64\n", + "n grains: 15\n", + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.i3WQjSp5j6/ipykernel_48606/2477574397.py:61: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F8/ouputgpkg/tile_r000006_c000047_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F8/csv_stats/tile_r000006_c000047_grain_stats.csv\n", + "done with segmentation + export\n", + "[193/1332] tile_r000006_c000048\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.74it/s]\n", + "100%|██████████| 3/3 [00:00<00:00, 3.76it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 22/22 [00:03<00:00, 6.30it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "4it [00:00, 412.28it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "5it [00:00, 225.86it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 2/2 [00:00<00:00, 74.81it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 2/2 [00:00<00:00, 75.12it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "labels shape: (587, 587) dtype: int64\n", + "mask_all shape: (587, 587) dtype: float64\n", + "n grains: 2\n", + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.i3WQjSp5j6/ipykernel_48606/2477574397.py:61: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F8/ouputgpkg/tile_r000006_c000048_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F8/csv_stats/tile_r000006_c000048_grain_stats.csv\n", + "done with segmentation + export\n", + "[194/1332] tile_r000006_c000049\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.81it/s]\n", + "100%|██████████| 3/3 [00:00<00:00, 3.74it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 31/31 [00:04<00:00, 6.38it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "5it [00:00, 922.43it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "6it [00:00, 829.41it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 3/3 [00:00<00:00, 241.77it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 3/3 [00:00<00:00, 164.26it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "labels shape: (587, 587) dtype: int64\n", + "mask_all shape: (587, 587) dtype: float64\n", + "n grains: 3\n", + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.i3WQjSp5j6/ipykernel_48606/2477574397.py:61: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F8/ouputgpkg/tile_r000006_c000049_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F8/csv_stats/tile_r000006_c000049_grain_stats.csv\n", + "done with segmentation + export\n", + "[195/1332] tile_r000006_c000050\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.76it/s]\n", + "100%|██████████| 3/3 [00:00<00:00, 3.77it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 17/17 [00:02<00:00, 6.06it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "1it [00:00, 1369.79it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2it [00:00, 945.41it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 1/1 [00:00<00:00, 478.36it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 1/1 [00:00<00:00, 227.77it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "labels shape: (587, 587) dtype: int64\n", + "mask_all shape: (587, 587) dtype: float64\n", + "n grains: 1\n", + "Saved segmentation plot\n", + "Saved histogram plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.i3WQjSp5j6/ipykernel_48606/2477574397.py:61: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F8/ouputgpkg/tile_r000006_c000050_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F8/csv_stats/tile_r000006_c000050_grain_stats.csv\n", + "done with segmentation + export\n", + "[196/1332] tile_r000006_c000051\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.81it/s]\n", + "100%|██████████| 3/3 [00:00<00:00, 3.79it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 51/51 [00:07<00:00, 6.63it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "8it [00:00, 1180.00it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "13it [00:00, 991.63it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 6/6 [00:00<00:00, 310.70it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 6/6 [00:00<00:00, 256.47it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "labels shape: (587, 587) dtype: int64\n", + "mask_all shape: (587, 587) dtype: float64\n", + "n grains: 6\n", + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.i3WQjSp5j6/ipykernel_48606/2477574397.py:61: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F8/ouputgpkg/tile_r000006_c000051_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F8/csv_stats/tile_r000006_c000051_grain_stats.csv\n", + "done with segmentation + export\n", + "[197/1332] tile_r000006_c000052\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.78it/s]\n", + "100%|██████████| 3/3 [00:00<00:00, 3.85it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 26/26 [00:03<00:00, 6.54it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2it [00:00, 3785.47it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "4it [00:00, 939.32it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 2/2 [00:00<00:00, 384.68it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 2/2 [00:00<00:00, 222.66it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "labels shape: (587, 587) dtype: int64\n", + "mask_all shape: (587, 587) dtype: float64\n", + "n grains: 2\n", + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.i3WQjSp5j6/ipykernel_48606/2477574397.py:61: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F8/ouputgpkg/tile_r000006_c000052_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F8/csv_stats/tile_r000006_c000052_grain_stats.csv\n", + "done with segmentation + export\n", + "[198/1332] tile_r000006_c000053\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.79it/s]\n", + "100%|██████████| 3/3 [00:00<00:00, 3.78it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:00<00:00, 6.39it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "1it [00:00, 1252.03it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2it [00:00, 659.17it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 1/1 [00:00<00:00, 292.78it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 1/1 [00:00<00:00, 176.35it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "labels shape: (587, 587) dtype: int64\n", + "mask_all shape: (587, 587) dtype: float64\n", + "n grains: 1\n", + "Saved segmentation plot\n", + "Saved histogram plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.i3WQjSp5j6/ipykernel_48606/2477574397.py:61: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F8/ouputgpkg/tile_r000006_c000053_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F8/csv_stats/tile_r000006_c000053_grain_stats.csv\n", + "done with segmentation + export\n", + "[199/1332] tile_r000006_c000054\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.79it/s]\n", + "100%|██████████| 3/3 [00:00<00:00, 3.91it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:00<00:00, 6.74it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "0it [00:00, ?it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "0it [00:00, ?it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "0it [00:00, ?it/s]\n", + "0it [00:00, ?it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "labels shape: (587, 587) dtype: uint8\n", + "mask_all shape: (587, 587, 3) dtype: uint8\n", + "n grains: 0\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.i3WQjSp5j6/ipykernel_48606/2477574397.py:61: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n", + "No grains found -> skipping histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F8/ouputgpkg/tile_r000006_c000054_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F8/csv_stats/tile_r000006_c000054_grain_stats.csv\n", + "done with segmentation + export\n", + "[200/1332] tile_r000006_c000055\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.81it/s]\n", + "100%|██████████| 3/3 [00:00<00:00, 3.83it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "0it [00:00, ?it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "0it [00:00, ?it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "0it [00:00, ?it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "0it [00:00, ?it/s]\n", + "0it [00:00, ?it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "labels shape: (587, 587) dtype: uint8\n", + "mask_all shape: (587, 587, 3) dtype: uint8\n", + "n grains: 0\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\n", + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.i3WQjSp5j6/ipykernel_48606/2477574397.py:61: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n", + "No grains found -> skipping histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F8/ouputgpkg/tile_r000006_c000055_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F8/csv_stats/tile_r000006_c000055_grain_stats.csv\n", + "done with segmentation + export\n", + "[201/1332] tile_r000006_c000056\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.74it/s]\n", + "100%|██████████| 3/3 [00:00<00:00, 3.84it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 3/3 [00:00<00:00, 6.53it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "0it [00:00, ?it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "0it [00:00, ?it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "0it [00:00, ?it/s]\n", + "0it [00:00, ?it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "labels shape: (587, 587) dtype: uint8\n", + "mask_all shape: (587, 587, 3) dtype: uint8\n", + "n grains: 0\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.i3WQjSp5j6/ipykernel_48606/2477574397.py:61: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n", + "No grains found -> skipping histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F8/ouputgpkg/tile_r000006_c000056_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F8/csv_stats/tile_r000006_c000056_grain_stats.csv\n", + "done with segmentation + export\n", + "[202/1332] tile_r000006_c000057\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.75it/s]\n", + "100%|██████████| 3/3 [00:00<00:00, 3.84it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 9/9 [00:01<00:00, 6.51it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "1it [00:00, 1249.79it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2it [00:00, 760.25it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 1/1 [00:00<00:00, 374.09it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 1/1 [00:00<00:00, 198.21it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "labels shape: (587, 587) dtype: int64\n", + "mask_all shape: (587, 587) dtype: float64\n", + "n grains: 1\n", + "Saved segmentation plot\n", + "Saved histogram plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.i3WQjSp5j6/ipykernel_48606/2477574397.py:61: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F8/ouputgpkg/tile_r000006_c000057_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F8/csv_stats/tile_r000006_c000057_grain_stats.csv\n", + "done with segmentation + export\n", + "[203/1332] tile_r000006_c000058\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.81it/s]\n", + "100%|██████████| 3/3 [00:00<00:00, 3.78it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:00<00:00, 6.52it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "1it [00:00, 1243.86it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2it [00:00, 503.91it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 1/1 [00:00<00:00, 75.51it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 1/1 [00:00<00:00, 115.29it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "labels shape: (587, 587) dtype: int64\n", + "mask_all shape: (587, 587) dtype: float64\n", + "n grains: 1\n", + "Saved segmentation plot\n", + "Saved histogram plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.i3WQjSp5j6/ipykernel_48606/2477574397.py:61: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F8/ouputgpkg/tile_r000006_c000058_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F8/csv_stats/tile_r000006_c000058_grain_stats.csv\n", + "done with segmentation + export\n", + "[204/1332] tile_r000006_c000059\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.83it/s]\n", + "100%|██████████| 3/3 [00:00<00:00, 3.86it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 267/267 [00:37<00:00, 7.16it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "208it [00:00, 386.15it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "218it [00:00, 393.65it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 85/85 [00:00<00:00, 114.02it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 85/85 [00:00<00:00, 208.19it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "labels shape: (587, 587) dtype: int64\n", + "mask_all shape: (587, 587) dtype: float64\n", + "n grains: 85\n", + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.i3WQjSp5j6/ipykernel_48606/2477574397.py:61: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F8/ouputgpkg/tile_r000006_c000059_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F8/csv_stats/tile_r000006_c000059_grain_stats.csv\n", + "done with segmentation + export\n", + "[205/1332] tile_r000006_c000060\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.77it/s]\n", + "100%|██████████| 3/3 [00:00<00:00, 3.78it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 181/181 [00:25<00:00, 6.96it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "119it [00:00, 351.88it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "128it [00:00, 359.49it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 46/46 [00:00<00:00, 85.00it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 46/46 [00:00<00:00, 194.88it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "labels shape: (587, 587) dtype: int64\n", + "mask_all shape: (587, 587) dtype: float64\n", + "n grains: 46\n", + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.i3WQjSp5j6/ipykernel_48606/2477574397.py:61: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F8/ouputgpkg/tile_r000006_c000060_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F8/csv_stats/tile_r000006_c000060_grain_stats.csv\n", + "done with segmentation + export\n", + "[206/1332] tile_r000006_c000061\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.84it/s]\n", + "100%|██████████| 3/3 [00:00<00:00, 3.84it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 132/132 [00:18<00:00, 7.17it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "98it [00:00, 371.91it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "107it [00:00, 370.71it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 39/39 [00:00<00:00, 90.48it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 39/39 [00:00<00:00, 194.70it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "labels shape: (587, 587) dtype: int64\n", + "mask_all shape: (587, 587) dtype: float64\n", + "n grains: 39\n", + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.i3WQjSp5j6/ipykernel_48606/2477574397.py:61: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F8/ouputgpkg/tile_r000006_c000061_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F8/csv_stats/tile_r000006_c000061_grain_stats.csv\n", + "done with segmentation + export\n", + "[207/1332] tile_r000006_c000062\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.83it/s]\n", + "100%|██████████| 3/3 [00:00<00:00, 3.85it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 164/164 [00:21<00:00, 7.46it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "124it [00:00, 418.42it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "134it [00:00, 401.47it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 52/52 [00:00<00:00, 129.54it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 52/52 [00:00<00:00, 212.98it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "labels shape: (587, 587) dtype: int64\n", + "mask_all shape: (587, 587) dtype: float64\n", + "n grains: 52\n", + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.i3WQjSp5j6/ipykernel_48606/2477574397.py:61: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F8/ouputgpkg/tile_r000006_c000062_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F8/csv_stats/tile_r000006_c000062_grain_stats.csv\n", + "done with segmentation + export\n", + "[208/1332] tile_r000006_c000063\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.78it/s]\n", + "100%|██████████| 3/3 [00:00<00:00, 3.75it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 171/171 [00:22<00:00, 7.60it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "150it [00:00, 347.90it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "159it [00:00, 335.09it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 60/60 [00:00<00:00, 98.76it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 60/60 [00:00<00:00, 208.90it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "labels shape: (587, 587) dtype: int64\n", + "mask_all shape: (587, 587) dtype: float64\n", + "n grains: 60\n", + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.i3WQjSp5j6/ipykernel_48606/2477574397.py:61: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F8/ouputgpkg/tile_r000006_c000063_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F8/csv_stats/tile_r000006_c000063_grain_stats.csv\n", + "done with segmentation + export\n", + "[209/1332] tile_r000006_c000064\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.77it/s]\n", + "100%|██████████| 3/3 [00:00<00:00, 3.87it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 158/158 [00:23<00:00, 6.68it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "132it [00:00, 382.46it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "135it [00:00, 379.90it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 52/52 [00:00<00:00, 106.66it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 52/52 [00:00<00:00, 204.13it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "labels shape: (587, 587) dtype: int64\n", + "mask_all shape: (587, 587) dtype: float64\n", + "n grains: 52\n", + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.i3WQjSp5j6/ipykernel_48606/2477574397.py:61: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F8/ouputgpkg/tile_r000006_c000064_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F8/csv_stats/tile_r000006_c000064_grain_stats.csv\n", + "done with segmentation + export\n", + "[210/1332] tile_r000006_c000065\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.76it/s]\n", + "100%|██████████| 3/3 [00:00<00:00, 3.81it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 182/182 [00:25<00:00, 7.12it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "157it [00:00, 417.01it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "169it [00:00, 419.35it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 66/66 [00:00<00:00, 128.83it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 68/68 [00:00<00:00, 225.41it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "labels shape: (587, 587) dtype: int64\n", + "mask_all shape: (587, 587) dtype: float64\n", + "n grains: 68\n", + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.i3WQjSp5j6/ipykernel_48606/2477574397.py:61: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F8/ouputgpkg/tile_r000006_c000065_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F8/csv_stats/tile_r000006_c000065_grain_stats.csv\n", + "done with segmentation + export\n", + "[211/1332] tile_r000006_c000066\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.84it/s]\n", + "100%|██████████| 3/3 [00:00<00:00, 3.87it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 191/191 [00:27<00:00, 7.00it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "149it [00:00, 330.55it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "156it [00:00, 328.70it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 55/55 [00:00<00:00, 82.11it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 55/55 [00:00<00:00, 201.39it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "labels shape: (587, 587) dtype: int64\n", + "mask_all shape: (587, 587) dtype: float64\n", + "n grains: 55\n", + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.i3WQjSp5j6/ipykernel_48606/2477574397.py:61: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F8/ouputgpkg/tile_r000006_c000066_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F8/csv_stats/tile_r000006_c000066_grain_stats.csv\n", + "done with segmentation + export\n", + "[212/1332] tile_r000006_c000067\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.79it/s]\n", + "100%|██████████| 3/3 [00:00<00:00, 3.79it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 60/60 [00:08<00:00, 6.70it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "51it [00:00, 318.53it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "54it [00:00, 316.78it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 19/19 [00:00<00:00, 92.93it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 19/19 [00:00<00:00, 202.51it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "labels shape: (587, 587) dtype: int64\n", + "mask_all shape: (587, 587) dtype: float64\n", + "n grains: 19\n", + "Saved segmentation plot\n", + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F8/ouputgpkg/tile_r000006_c000067_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F8/csv_stats/tile_r000006_c000067_grain_stats.csv\n", + "done with segmentation + export\n", + "[213/1332] tile_r000006_c000068\n", + " -> skipped (100% Nodata)\n", + "[214/1332] tile_r000006_c000069\n", + " -> skipped (100% Nodata)\n", + "[215/1332] tile_r000006_c000070\n", + " -> skipped (100% Nodata)\n", + "[216/1332] tile_r000006_c000071\n", + " -> skipped (100% Nodata)\n", + "[217/1332] tile_r000006_c000072\n", + " -> skipped (100% Nodata)\n", + "[218/1332] tile_r000006_c000073\n", + " -> skipped (100% Nodata)\n", + "[219/1332] tile_r000006_c000074\n", + " -> skipped (100% Nodata)\n", + "[220/1332] tile_r000006_c000075\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.i3WQjSp5j6/ipykernel_48606/2477574397.py:61: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " -> skipped (100% Nodata)\n", + "[221/1332] tile_r000006_c000076\n", + " -> skipped (100% Nodata)\n", + "[222/1332] tile_r000006_c000077\n", + " -> skipped (100% Nodata)\n", + "[223/1332] tile_r000007_c000004\n", + " -> skipped (100% Nodata)\n", + "[224/1332] tile_r000007_c000005\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.80it/s]\n", + "100%|██████████| 3/3 [00:00<00:00, 3.80it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 47/47 [00:07<00:00, 6.62it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "26it [00:00, 621.08it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "35it [00:00, 460.85it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 16/16 [00:00<00:00, 178.55it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 17/17 [00:00<00:00, 172.07it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "labels shape: (587, 587) dtype: int64\n", + "mask_all shape: (587, 587) dtype: float64\n", + "n grains: 17\n", + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.i3WQjSp5j6/ipykernel_48606/2477574397.py:61: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F8/ouputgpkg/tile_r000007_c000005_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F8/csv_stats/tile_r000007_c000005_grain_stats.csv\n", + "done with segmentation + export\n", + "[225/1332] tile_r000007_c000006\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.84it/s]\n", + "100%|██████████| 3/3 [00:00<00:00, 3.81it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 175/175 [00:25<00:00, 6.80it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "136it [00:00, 367.71it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "144it [00:00, 371.14it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 51/51 [00:00<00:00, 80.07it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 51/51 [00:00<00:00, 196.50it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "labels shape: (587, 587) dtype: int64\n", + "mask_all shape: (587, 587) dtype: float64\n", + "n grains: 51\n", + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.i3WQjSp5j6/ipykernel_48606/2477574397.py:61: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F8/ouputgpkg/tile_r000007_c000006_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F8/csv_stats/tile_r000007_c000006_grain_stats.csv\n", + "done with segmentation + export\n", + "[226/1332] tile_r000007_c000007\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.70it/s]\n", + "100%|██████████| 3/3 [00:00<00:00, 3.90it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 170/170 [00:26<00:00, 6.42it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "122it [00:00, 467.17it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "137it [00:00, 447.83it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 52/52 [00:00<00:00, 113.39it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 52/52 [00:00<00:00, 215.13it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "labels shape: (587, 587) dtype: int64\n", + "mask_all shape: (587, 587) dtype: float64\n", + "n grains: 52\n", + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.i3WQjSp5j6/ipykernel_48606/2477574397.py:61: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F8/ouputgpkg/tile_r000007_c000007_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F8/csv_stats/tile_r000007_c000007_grain_stats.csv\n", + "done with segmentation + export\n", + "[227/1332] tile_r000007_c000008\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.77it/s]\n", + "100%|██████████| 3/3 [00:00<00:00, 3.79it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 163/163 [00:23<00:00, 6.85it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "117it [00:00, 428.43it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "131it [00:00, 434.80it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 52/52 [00:00<00:00, 107.75it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 53/53 [00:00<00:00, 193.23it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "labels shape: (587, 587) dtype: int64\n", + "mask_all shape: (587, 587) dtype: float64\n", + "n grains: 53\n", + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.i3WQjSp5j6/ipykernel_48606/2477574397.py:61: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F8/ouputgpkg/tile_r000007_c000008_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F8/csv_stats/tile_r000007_c000008_grain_stats.csv\n", + "done with segmentation + export\n", + "[228/1332] tile_r000007_c000009\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.79it/s]\n", + "100%|██████████| 3/3 [00:00<00:00, 3.89it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 252/252 [00:36<00:00, 6.81it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "200it [00:00, 493.53it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "220it [00:00, 478.92it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 88/88 [00:00<00:00, 126.99it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 90/90 [00:00<00:00, 214.00it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "labels shape: (587, 587) dtype: int64\n", + "mask_all shape: (587, 587) dtype: float64\n", + "n grains: 90\n", + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.i3WQjSp5j6/ipykernel_48606/2477574397.py:61: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F8/ouputgpkg/tile_r000007_c000009_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F8/csv_stats/tile_r000007_c000009_grain_stats.csv\n", + "done with segmentation + export\n", + "[229/1332] tile_r000007_c000010\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.68it/s]\n", + "100%|██████████| 3/3 [00:00<00:00, 3.84it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 297/297 [00:43<00:00, 6.80it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "244it [00:00, 437.75it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "265it [00:00, 443.67it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 98/98 [00:01<00:00, 95.47it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 101/101 [00:00<00:00, 203.89it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "labels shape: (587, 587) dtype: int64\n", + "mask_all shape: (587, 587) dtype: float64\n", + "n grains: 101\n", + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.i3WQjSp5j6/ipykernel_48606/2477574397.py:61: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F8/ouputgpkg/tile_r000007_c000010_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F8/csv_stats/tile_r000007_c000010_grain_stats.csv\n", + "done with segmentation + export\n", + "[230/1332] tile_r000007_c000011\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.80it/s]\n", + "100%|██████████| 3/3 [00:00<00:00, 3.72it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 322/322 [00:50<00:00, 6.43it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "255it [00:00, 360.86it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "254it [00:00, 381.04it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 77/77 [00:01<00:00, 69.19it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 114/114 [00:00<00:00, 193.47it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "labels shape: (587, 587) dtype: int64\n", + "mask_all shape: (587, 587) dtype: float64\n", + "n grains: 114\n", + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.i3WQjSp5j6/ipykernel_48606/2477574397.py:61: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F8/ouputgpkg/tile_r000007_c000011_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F8/csv_stats/tile_r000007_c000011_grain_stats.csv\n", + "done with segmentation + export\n", + "[231/1332] tile_r000007_c000012\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.82it/s]\n", + "100%|██████████| 3/3 [00:00<00:00, 3.89it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 314/314 [00:45<00:00, 6.90it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "262it [00:00, 306.64it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "280it [00:00, 305.43it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 94/94 [00:01<00:00, 49.74it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 104/104 [00:00<00:00, 192.37it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "labels shape: (587, 587) dtype: int64\n", + "mask_all shape: (587, 587) dtype: float64\n", + "n grains: 104\n", + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.i3WQjSp5j6/ipykernel_48606/2477574397.py:61: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F8/ouputgpkg/tile_r000007_c000012_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F8/csv_stats/tile_r000007_c000012_grain_stats.csv\n", + "done with segmentation + export\n", + "[232/1332] tile_r000007_c000013\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.71it/s]\n", + "100%|██████████| 3/3 [00:00<00:00, 3.71it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 318/318 [00:42<00:00, 7.54it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "244it [00:01, 155.13it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "231it [00:00, 407.56it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 73/73 [00:00<00:00, 76.75it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 103/103 [00:00<00:00, 188.03it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "labels shape: (587, 587) dtype: int64\n", + "mask_all shape: (587, 587) dtype: float64\n", + "n grains: 103\n", + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.i3WQjSp5j6/ipykernel_48606/2477574397.py:61: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F8/ouputgpkg/tile_r000007_c000013_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F8/csv_stats/tile_r000007_c000013_grain_stats.csv\n", + "done with segmentation + export\n", + "[233/1332] tile_r000007_c000014\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.74it/s]\n", + "100%|██████████| 3/3 [00:00<00:00, 3.80it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 359/359 [00:55<00:00, 6.49it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "288it [00:00, 326.66it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "307it [00:00, 331.41it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 106/106 [00:01<00:00, 67.35it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 118/118 [00:00<00:00, 181.41it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "labels shape: (587, 587) dtype: int64\n", + "mask_all shape: (587, 587) dtype: float64\n", + "n grains: 118\n", + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.i3WQjSp5j6/ipykernel_48606/2477574397.py:61: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F8/ouputgpkg/tile_r000007_c000014_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F8/csv_stats/tile_r000007_c000014_grain_stats.csv\n", + "done with segmentation + export\n", + "[234/1332] tile_r000007_c000015\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.73it/s]\n", + "100%|██████████| 3/3 [00:00<00:00, 3.71it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 303/303 [00:42<00:00, 7.18it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "254it [00:00, 410.33it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "286it [00:00, 408.03it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 106/106 [00:01<00:00, 97.96it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 111/111 [00:00<00:00, 190.94it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "labels shape: (587, 587) dtype: int64\n", + "mask_all shape: (587, 587) dtype: float64\n", + "n grains: 111\n", + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.i3WQjSp5j6/ipykernel_48606/2477574397.py:61: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F8/ouputgpkg/tile_r000007_c000015_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F8/csv_stats/tile_r000007_c000015_grain_stats.csv\n", + "done with segmentation + export\n", + "[235/1332] tile_r000007_c000016\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.81it/s]\n", + "100%|██████████| 3/3 [00:00<00:00, 3.78it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 369/369 [00:52<00:00, 7.06it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "288it [00:00, 470.79it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "309it [00:00, 471.07it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 122/122 [00:01<00:00, 121.57it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 127/127 [00:00<00:00, 214.57it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "labels shape: (587, 587) dtype: int64\n", + "mask_all shape: (587, 587) dtype: float64\n", + "n grains: 127\n", + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.i3WQjSp5j6/ipykernel_48606/2477574397.py:61: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F8/ouputgpkg/tile_r000007_c000016_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F8/csv_stats/tile_r000007_c000016_grain_stats.csv\n", + "done with segmentation + export\n", + "[236/1332] tile_r000007_c000017\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.75it/s]\n", + "100%|██████████| 3/3 [00:00<00:00, 3.77it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 360/360 [00:54<00:00, 6.64it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "300it [00:00, 337.43it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "299it [00:00, 450.00it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 96/96 [00:01<00:00, 88.17it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 131/131 [00:00<00:00, 217.85it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "labels shape: (587, 587) dtype: int64\n", + "mask_all shape: (587, 587) dtype: float64\n", + "n grains: 131\n", + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.i3WQjSp5j6/ipykernel_48606/2477574397.py:61: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F8/ouputgpkg/tile_r000007_c000017_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F8/csv_stats/tile_r000007_c000017_grain_stats.csv\n", + "done with segmentation + export\n", + "[237/1332] tile_r000007_c000018\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.77it/s]\n", + "100%|██████████| 3/3 [00:00<00:00, 3.88it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 347/347 [00:52<00:00, 6.66it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "266it [00:00, 377.52it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "290it [00:00, 385.09it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 111/111 [00:01<00:00, 93.49it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 111/111 [00:00<00:00, 203.72it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "labels shape: (587, 587) dtype: int64\n", + "mask_all shape: (587, 587) dtype: float64\n", + "n grains: 111\n", + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.i3WQjSp5j6/ipykernel_48606/2477574397.py:61: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F8/ouputgpkg/tile_r000007_c000018_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F8/csv_stats/tile_r000007_c000018_grain_stats.csv\n", + "done with segmentation + export\n", + "[238/1332] tile_r000007_c000019\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.83it/s]\n", + "100%|██████████| 3/3 [00:00<00:00, 3.81it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 379/379 [00:55<00:00, 6.87it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "314it [00:00, 410.27it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "338it [00:00, 408.33it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 129/129 [00:01<00:00, 95.29it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 131/131 [00:00<00:00, 208.42it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "labels shape: (587, 587) dtype: int64\n", + "mask_all shape: (587, 587) dtype: float64\n", + "n grains: 131\n" + ] + }, + { + "ename": "OSError", + "evalue": "[Errno 122] Disk quota exceeded: '/dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F8/pebbleplots/tile_r000007_c000019.png'", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mOSError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[7], line 115\u001b[0m\n\u001b[1;32m 111\u001b[0m \u001b[38;5;66;03m# --------------------------------------------------\u001b[39;00m\n\u001b[1;32m 112\u001b[0m \u001b[38;5;66;03m# 1) Segmentierungsplot speichern (dein bestehender Plot)\u001b[39;00m\n\u001b[1;32m 113\u001b[0m \u001b[38;5;66;03m# --------------------------------------------------\u001b[39;00m\n\u001b[1;32m 114\u001b[0m seg_plot_path \u001b[38;5;241m=\u001b[39m os\u001b[38;5;241m.\u001b[39mpath\u001b[38;5;241m.\u001b[39mjoin(plotdirgravel, \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mtile_id\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m.png\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m--> 115\u001b[0m \u001b[43mfig\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msavefig\u001b[49m\u001b[43m(\u001b[49m\u001b[43mseg_plot_path\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdpi\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m200\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mbbox_inches\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mtight\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m 116\u001b[0m plt\u001b[38;5;241m.\u001b[39mclose(fig)\n\u001b[1;32m 117\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mSaved segmentation plot\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n", + "File \u001b[0;32m~/.local/share/mamba/envs/segmenteverygrain/lib/python3.10/site-packages/matplotlib/figure.py:3490\u001b[0m, in \u001b[0;36mFigure.savefig\u001b[0;34m(self, fname, transparent, **kwargs)\u001b[0m\n\u001b[1;32m 3488\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m ax \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39maxes:\n\u001b[1;32m 3489\u001b[0m _recursively_make_axes_transparent(stack, ax)\n\u001b[0;32m-> 3490\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcanvas\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mprint_figure\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfname\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m~/.local/share/mamba/envs/segmenteverygrain/lib/python3.10/site-packages/matplotlib/backend_bases.py:2186\u001b[0m, in \u001b[0;36mFigureCanvasBase.print_figure\u001b[0;34m(self, filename, dpi, facecolor, edgecolor, orientation, format, bbox_inches, pad_inches, bbox_extra_artists, backend, **kwargs)\u001b[0m\n\u001b[1;32m 2182\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m 2183\u001b[0m \u001b[38;5;66;03m# _get_renderer may change the figure dpi (as vector formats\u001b[39;00m\n\u001b[1;32m 2184\u001b[0m \u001b[38;5;66;03m# force the figure dpi to 72), so we need to set it again here.\u001b[39;00m\n\u001b[1;32m 2185\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m cbook\u001b[38;5;241m.\u001b[39m_setattr_cm(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mfigure, dpi\u001b[38;5;241m=\u001b[39mdpi):\n\u001b[0;32m-> 2186\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[43mprint_method\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 2187\u001b[0m \u001b[43m \u001b[49m\u001b[43mfilename\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 2188\u001b[0m \u001b[43m \u001b[49m\u001b[43mfacecolor\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mfacecolor\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 2189\u001b[0m \u001b[43m \u001b[49m\u001b[43medgecolor\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43medgecolor\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 2190\u001b[0m \u001b[43m \u001b[49m\u001b[43morientation\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43morientation\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 2191\u001b[0m \u001b[43m \u001b[49m\u001b[43mbbox_inches_restore\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m_bbox_inches_restore\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 2192\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 2193\u001b[0m \u001b[38;5;28;01mfinally\u001b[39;00m:\n\u001b[1;32m 2194\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m bbox_inches \u001b[38;5;129;01mand\u001b[39;00m restore_bbox:\n", + "File \u001b[0;32m~/.local/share/mamba/envs/segmenteverygrain/lib/python3.10/site-packages/matplotlib/backend_bases.py:2042\u001b[0m, in \u001b[0;36mFigureCanvasBase._switch_canvas_and_return_print_method..\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m 2038\u001b[0m optional_kws \u001b[38;5;241m=\u001b[39m { \u001b[38;5;66;03m# Passed by print_figure for other renderers.\u001b[39;00m\n\u001b[1;32m 2039\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mdpi\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mfacecolor\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124medgecolor\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124morientation\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[1;32m 2040\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mbbox_inches_restore\u001b[39m\u001b[38;5;124m\"\u001b[39m}\n\u001b[1;32m 2041\u001b[0m skip \u001b[38;5;241m=\u001b[39m optional_kws \u001b[38;5;241m-\u001b[39m {\u001b[38;5;241m*\u001b[39minspect\u001b[38;5;241m.\u001b[39msignature(meth)\u001b[38;5;241m.\u001b[39mparameters}\n\u001b[0;32m-> 2042\u001b[0m print_method \u001b[38;5;241m=\u001b[39m functools\u001b[38;5;241m.\u001b[39mwraps(meth)(\u001b[38;5;28;01mlambda\u001b[39;00m \u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs: \u001b[43mmeth\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 2043\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43m{\u001b[49m\u001b[43mk\u001b[49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mv\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mfor\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mk\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mv\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01min\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mkwargs\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mitems\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mif\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mk\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01mnot\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;129;43;01min\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mskip\u001b[49m\u001b[43m}\u001b[49m\u001b[43m)\u001b[49m)\n\u001b[1;32m 2044\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m: \u001b[38;5;66;03m# Let third-parties do as they see fit.\u001b[39;00m\n\u001b[1;32m 2045\u001b[0m print_method \u001b[38;5;241m=\u001b[39m meth\n", + "File \u001b[0;32m~/.local/share/mamba/envs/segmenteverygrain/lib/python3.10/site-packages/matplotlib/backends/backend_agg.py:481\u001b[0m, in \u001b[0;36mFigureCanvasAgg.print_png\u001b[0;34m(self, filename_or_obj, metadata, pil_kwargs)\u001b[0m\n\u001b[1;32m 434\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21mprint_png\u001b[39m(\u001b[38;5;28mself\u001b[39m, filename_or_obj, \u001b[38;5;241m*\u001b[39m, metadata\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, pil_kwargs\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m):\n\u001b[1;32m 435\u001b[0m \u001b[38;5;250m \u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 436\u001b[0m \u001b[38;5;124;03m Write the figure to a PNG file.\u001b[39;00m\n\u001b[1;32m 437\u001b[0m \n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 479\u001b[0m \u001b[38;5;124;03m *metadata*, including the default 'Software' key.\u001b[39;00m\n\u001b[1;32m 480\u001b[0m \u001b[38;5;124;03m \"\"\"\u001b[39;00m\n\u001b[0;32m--> 481\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_print_pil\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfilename_or_obj\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mpng\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mpil_kwargs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmetadata\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m~/.local/share/mamba/envs/segmenteverygrain/lib/python3.10/site-packages/matplotlib/backends/backend_agg.py:430\u001b[0m, in \u001b[0;36mFigureCanvasAgg._print_pil\u001b[0;34m(self, filename_or_obj, fmt, pil_kwargs, metadata)\u001b[0m\n\u001b[1;32m 425\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 426\u001b[0m \u001b[38;5;124;03mDraw the canvas, then save it using `.image.imsave` (to which\u001b[39;00m\n\u001b[1;32m 427\u001b[0m \u001b[38;5;124;03m*pil_kwargs* and *metadata* are forwarded).\u001b[39;00m\n\u001b[1;32m 428\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 429\u001b[0m FigureCanvasAgg\u001b[38;5;241m.\u001b[39mdraw(\u001b[38;5;28mself\u001b[39m)\n\u001b[0;32m--> 430\u001b[0m \u001b[43mmpl\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mimage\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mimsave\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 431\u001b[0m \u001b[43m \u001b[49m\u001b[43mfilename_or_obj\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mbuffer_rgba\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mformat\u001b[39;49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mfmt\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43morigin\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mupper\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m 432\u001b[0m \u001b[43m \u001b[49m\u001b[43mdpi\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfigure\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdpi\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmetadata\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmetadata\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mpil_kwargs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mpil_kwargs\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m~/.local/share/mamba/envs/segmenteverygrain/lib/python3.10/site-packages/matplotlib/image.py:1657\u001b[0m, in \u001b[0;36mimsave\u001b[0;34m(fname, arr, vmin, vmax, cmap, format, origin, dpi, metadata, pil_kwargs)\u001b[0m\n\u001b[1;32m 1655\u001b[0m pil_kwargs\u001b[38;5;241m.\u001b[39msetdefault(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mformat\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;28mformat\u001b[39m)\n\u001b[1;32m 1656\u001b[0m pil_kwargs\u001b[38;5;241m.\u001b[39msetdefault(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mdpi\u001b[39m\u001b[38;5;124m\"\u001b[39m, (dpi, dpi))\n\u001b[0;32m-> 1657\u001b[0m \u001b[43mimage\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msave\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfname\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mpil_kwargs\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m~/.local/share/mamba/envs/segmenteverygrain/lib/python3.10/site-packages/PIL/Image.py:2566\u001b[0m, in \u001b[0;36mImage.save\u001b[0;34m(self, fp, format, **params)\u001b[0m\n\u001b[1;32m 2564\u001b[0m fp \u001b[38;5;241m=\u001b[39m builtins\u001b[38;5;241m.\u001b[39mopen(filename, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mr+b\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m 2565\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m-> 2566\u001b[0m fp \u001b[38;5;241m=\u001b[39m \u001b[43mbuiltins\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mopen\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfilename\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mw+b\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m 2567\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 2568\u001b[0m fp \u001b[38;5;241m=\u001b[39m cast(IO[\u001b[38;5;28mbytes\u001b[39m], fp)\n", + "\u001b[0;31mOSError\u001b[0m: [Errno 122] Disk quota exceeded: '/dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F8/pebbleplots/tile_r000007_c000019.png'" + ] + }, + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import os\n", + "import glob\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "from tensorflow.keras.utils import load_img\n", + "import rasterio\n", + "\n", + "# zusätzliche Imports für Nachbearbeitung (ohne deine Library-Zelle zu ändern)\n", + "import geopandas as gpd\n", + "from shapely.geometry import Polygon\n", + "from skimage.measure import regionprops_table\n", + "\n", + "# --- optional: prompt cap for SAM (None = no limit) ---\n", + "MAX_SAM_PROMPTS = 8000\n", + "PROMPT_SUBSAMPLE_MODE = \"random\" # \"random\" oder \"first\"\n", + "RNG_SEED = 42\n", + "rng = np.random.default_rng(RNG_SEED)\n", + "\n", + "# --- collect tiles ---\n", + "tiles = sorted(glob.glob(os.path.join(inputtiledir, \"*.tif\")))\n", + "\n", + "print(f\"Found {len(tiles)} tiles\")\n", + "\n", + "if len(tiles) == 0:\n", + " raise RuntimeError(\"No tiles found.\")\n", + "\n", + "# --- read pixel size once from first tile ---\n", + "with rasterio.open(tiles[0]) as src:\n", + " xres, yres = src.res # CRS units per pixel (usually meters)\n", + " crs = src.crs\n", + "\n", + "gsd_units = float((xres + yres) / 2.0)\n", + "gsd_mm = gsd_units * 1000.0 # assumes CRS units are meters (e.g., UTM)\n", + "\n", + "minor_mm = 20.0 # 2 cm\n", + "minor_px = minor_mm / gsd_mm\n", + "\n", + "# area threshold from minor-axis (circle assumption)\n", + "min_area_px = float(np.pi * (minor_px / 2.0) ** 2)\n", + "\n", + "print(\"CRS:\", crs)\n", + "print(f\"Pixel size: {gsd_units:.6f} units/px (~{gsd_mm:.3f} mm/px)\")\n", + "print(f\"2 cm => minor_px={minor_px:.2f} px -> min_area_px={min_area_px:.1f} px^2\")\n", + "\n", + "\n", + "\n", + "# GPU free before (optional; only if cuda)\n", + "gpu_free_before = None\n", + "if torch.cuda.is_available():\n", + " free, total = torch.cuda.mem_get_info()\n", + " gpu_free_before = free / 1024**3\n", + "\n", + "\n", + "# --- loop ---\n", + "for i, fname in enumerate(tiles, start=1):\n", + " tile_id = os.path.splitext(os.path.basename(fname))[0]\n", + " print(f\"[{i}/{len(tiles)}] {tile_id}\")\n", + "\n", + " # dont calculate nodata tiles (under 100% is allowed)\n", + " with rasterio.open(fname) as src:\n", + " m = src.dataset_mask()\n", + " if not np.any(m):\n", + " print(\" -> skipped (100% Nodata)\")\n", + " continue\n", + "\n", + " # load + predict\n", + " image = np.array(load_img(fname))\n", + " image_pred = seg.predict_image(image, unet, I=256)\n", + "\n", + " # prompts (Unet -> points)\n", + " labels, coords = seg.label_grains(image, image_pred, dbs_max_dist=10.0)\n", + "\n", + " # --- optional: cap number of prompts before SAM ---\n", + " coords = np.asarray(coords)\n", + " if MAX_SAM_PROMPTS is not None and len(coords) > MAX_SAM_PROMPTS:\n", + " n_before = len(coords)\n", + "\n", + " if PROMPT_SUBSAMPLE_MODE == \"first\":\n", + " keep_idx = np.arange(MAX_SAM_PROMPTS)\n", + " else: # random\n", + " keep_idx = np.sort(rng.choice(len(coords), size=MAX_SAM_PROMPTS, replace=False))\n", + "\n", + " coords = coords[keep_idx]\n", + "\n", + " # labels nur mitfiltern, wenn es ein 1D prompt-label array ist\n", + " try:\n", + " labels_arr = np.asarray(labels)\n", + " if labels_arr.ndim == 1 and len(labels_arr) == n_before:\n", + " labels = labels_arr[keep_idx]\n", + " except Exception:\n", + " pass\n", + "\n", + " print(f\"Prompt cap active: reduced prompts from {n_before} -> {len(coords)}\")\n", + "\n", + " # SAM segmentation\n", + " all_grains, labels, mask_all, grain_data, fig, ax = seg.sam_segmentation(\n", + " sam,\n", + " image,\n", + " image_pred,\n", + " coords,\n", + " labels,\n", + " min_area=min_area_px,\n", + " plot_image=True,\n", + " remove_edge_grains=True,\n", + " remove_large_objects=True,\n", + " )\n", + " print(\"labels shape:\", getattr(labels, \"shape\", None), \"dtype:\", getattr(labels, \"dtype\", None))\n", + " print(\"mask_all shape:\", getattr(mask_all, \"shape\", None), \"dtype:\", getattr(mask_all, \"dtype\", None))\n", + " print(\"n grains:\", len(all_grains))\n", + " \n", + " # --------------------------------------------------\n", + " # 1) Segmentierungsplot speichern (dein bestehender Plot)\n", + " # --------------------------------------------------\n", + " seg_plot_path = os.path.join(plotdirgravel, f\"{tile_id}.png\")\n", + " fig.savefig(seg_plot_path, dpi=200, bbox_inches=\"tight\")\n", + " plt.close(fig)\n", + " print(\"Saved segmentation plot\")\n", + "\n", + " # --------------------------------------------------\n", + " # 2) Polygone georeferenzieren (Pixel -> UTM / CRS des Tiles)\n", + " # --------------------------------------------------\n", + " with rasterio.open(fname) as dataset:\n", + " projected_polys = []\n", + " for grain in all_grains:\n", + " # grain.exterior.xy liefert (x_coords, y_coords) in Pixelkoordinaten\n", + " # hier: row = y, col = x\n", + " px_x = np.asarray(grain.exterior.xy[0])\n", + " px_y = np.asarray(grain.exterior.xy[1])\n", + "\n", + " x_proj, y_proj = rasterio.transform.xy(\n", + " dataset.transform,\n", + " px_y, # rows\n", + " px_x # cols\n", + " )\n", + "\n", + " poly = Polygon(np.vstack((x_proj, y_proj)).T)\n", + " projected_polys.append(poly)\n", + "\n", + " # GeoDataFrame erzeugen\n", + " gdf = gpd.GeoDataFrame({\"geometry\": projected_polys}, geometry=\"geometry\", crs=dataset.crs)\n", + "\n", + " # --------------------------------------------------\n", + " # 3) Eigenschaften / Statistiken aus labeled image\n", + " # --------------------------------------------------\n", + " # (intensity_image hier weggelassen, da nur geometrische Properties gebraucht werden)\n", + " props = regionprops_table(\n", + " labels.astype(\"int\"),\n", + " properties=(\"label\", \"area\", \"centroid\", \"major_axis_length\", \"minor_axis_length\"),\n", + " )\n", + " grain_stats = pd.DataFrame(props)\n", + "\n", + " # Falls aus irgendeinem Grund Anzahl nicht exakt passt, robust behandeln\n", + " if len(grain_stats) != len(gdf):\n", + " nmin = min(len(grain_stats), len(gdf))\n", + " print(f\"Warning: len(gdf)={len(gdf)} != len(grain_stats)={len(grain_stats)} -> truncating to {nmin}\")\n", + " gdf = gdf.iloc[:nmin].copy()\n", + " grain_stats = grain_stats.iloc[:nmin].copy()\n", + "\n", + " # Zentroiden (row,col) -> CRS coords\n", + " centroid_x, centroid_y = rasterio.transform.xy(\n", + " dataset.transform,\n", + " grain_stats[\"centroid-0\"].values, # rows\n", + " grain_stats[\"centroid-1\"].values, # cols\n", + " )\n", + "\n", + " # Pixelgröße aus Transform (X-Richtung); für quadratische Pixel ok\n", + " px_size_x = abs(dataset.transform.a)\n", + "\n", + " # Attribute in GDF schreiben\n", + " gdf[\"label\"] = grain_stats[\"label\"].values\n", + " gdf[\"area_px\"] = grain_stats[\"area\"].values\n", + " gdf[\"centroid_x\"] = centroid_x\n", + " gdf[\"centroid_y\"] = centroid_y\n", + " gdf[\"major_axis_px\"] = grain_stats[\"major_axis_length\"].values\n", + " gdf[\"minor_axis_px\"] = grain_stats[\"minor_axis_length\"].values\n", + " gdf[\"major_axis_length\"] = grain_stats[\"major_axis_length\"].values * px_size_x\n", + " gdf[\"minor_axis_length\"] = grain_stats[\"minor_axis_length\"].values * px_size_x\n", + " gdf[\"major_axis_mm\"] = gdf[\"major_axis_length\"] * 1000.0\n", + " gdf[\"minor_axis_mm\"] = gdf[\"minor_axis_length\"] * 1000.0\n", + "\n", + " # --------------------------------------------------\n", + " # 4) Histogramm-Plot speichern (NICHT den Segmentierungsplot überschreiben)\n", + " # --------------------------------------------------\n", + " if len(gdf) > 0:\n", + " fig_hist, ax_hist = seg.plot_histogram_of_axis_lengths(\n", + " gdf[\"major_axis_mm\"],\n", + " gdf[\"minor_axis_mm\"],\n", + " binsize=0.25,\n", + " xlimits=[8, 2 * 256],\n", + " )\n", + " hist_plot_path = os.path.join(plotdirhist, f\"{tile_id}_hist.png\") # eigener Name!\n", + " fig_hist.savefig(hist_plot_path, dpi=200, bbox_inches=\"tight\")\n", + " plt.close(fig_hist)\n", + " print(\"Saved histogram plot\")\n", + " else:\n", + " print(\"No grains found -> skipping histogram plot\")\n", + "\n", + " # --------------------------------------------------\n", + " # 5) GPKG + Statistik-CSV speichern\n", + " # --------------------------------------------------\n", + " gpkg_path = os.path.join(ouputgpkg, f\"{tile_id}_grains.gpkg\")\n", + " csv_path = os.path.join(csvdir, f\"{tile_id}_grain_stats.csv\")\n", + "\n", + " # GPKG\n", + " gdf.to_file(gpkg_path, driver=\"GPKG\")\n", + " print(f\"Saved GPKG: {gpkg_path}\")\n", + "\n", + " # CSV (ohne Geometrie; Geometrie steckt im GPKG)\n", + " stats_df = gdf.drop(columns=\"geometry\").copy()\n", + " stats_df.to_csv(csv_path, index=False)\n", + " print(f\"Saved stats CSV: {csv_path}\")\n", + "\n", + " print(\"done with segmentation + export\")" + ] + }, + { + "cell_type": "markdown", + "id": "7170fddf", + "metadata": {}, + "source": [ + "New version saving also speed stats:" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "id": "76cc409f", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Found 720 tiles\n", + "CRS: EPSG:32643\n", + "Pixel size: 0.004288 units/px (~4.288 mm/px)\n", + "2 cm => minor_px=4.66 px -> min_area_px=17.1 px^2\n", + "[1/720] tile_r000004_c000004\n", + " -> skipped (100% Nodata)\n", + "[2/720] tile_r000004_c000005\n", + " -> skipped (100% Nodata)\n", + "[3/720] tile_r000004_c000006\n", + " -> skipped (100% Nodata)\n", + "[4/720] tile_r000004_c000007\n", + " -> skipped (100% Nodata)\n", + "[5/720] tile_r000004_c000008\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.BpaNNCn48p/ipykernel_53064/3422303653.py:106: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " -> skipped (100% Nodata)\n", + "[6/720] tile_r000004_c000009\n", + " -> skipped (100% Nodata)\n", + "[7/720] tile_r000004_c000010\n", + " -> skipped (100% Nodata)\n", + "[8/720] tile_r000004_c000011\n", + " -> skipped (100% Nodata)\n", + "[9/720] tile_r000004_c000012\n", + " -> skipped (100% Nodata)\n", + "[10/720] tile_r000004_c000013\n", + " -> skipped (100% Nodata)\n", + "[11/720] tile_r000004_c000014\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.68it/s]\n", + "100%|██████████| 3/3 [00:00<00:00, 3.18it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 35/35 [00:00<00:00, 42.06it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "16it [00:00, 435.05it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "16it [00:00, 380.35it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 6/6 [00:00<00:00, 83.17it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 6/6 [00:00<00:00, 175.57it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.BpaNNCn48p/ipykernel_53064/3422303653.py:106: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/ouputgpkg/tile_r000004_c000014_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/csv_stats/tile_r000004_c000014_grain_stats.csv\n", + "done with segmentation + export\n", + "[12/720] tile_r000004_c000015\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.15it/s]\n", + "100%|██████████| 3/3 [00:00<00:00, 3.14it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "0it [00:00, ?it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "0it [00:00, ?it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "0it [00:00, ?it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "0it [00:00, ?it/s]\n", + "0it [00:00, ?it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n", + "No grains found -> skipping histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/ouputgpkg/tile_r000004_c000015_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/csv_stats/tile_r000004_c000015_grain_stats.csv\n", + "done with segmentation + export\n", + "[13/720] tile_r000004_c000016\n", + " -> skipped (100% Nodata)\n", + "[14/720] tile_r000004_c000017\n", + " -> skipped (100% Nodata)\n", + "[15/720] tile_r000004_c000018\n", + " -> skipped (100% Nodata)\n", + "[16/720] tile_r000004_c000019\n", + " -> skipped (100% Nodata)\n", + "[17/720] tile_r000004_c000020\n", + " -> skipped (100% Nodata)\n", + "[18/720] tile_r000004_c000021\n", + " -> skipped (100% Nodata)\n", + "[19/720] tile_r000004_c000022\n", + " -> skipped (100% Nodata)\n", + "[20/720] tile_r000004_c000023\n", + " -> skipped (100% Nodata)\n", + "[21/720] tile_r000004_c000024\n", + " -> skipped (100% Nodata)\n", + "[22/720] tile_r000004_c000025\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.BpaNNCn48p/ipykernel_53064/3422303653.py:106: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " -> skipped (100% Nodata)\n", + "[23/720] tile_r000004_c000026\n", + " -> skipped (100% Nodata)\n", + "[24/720] tile_r000004_c000027\n", + " -> skipped (100% Nodata)\n", + "[25/720] tile_r000004_c000028\n", + " -> skipped (100% Nodata)\n", + "[26/720] tile_r000004_c000029\n", + " -> skipped (100% Nodata)\n", + "[27/720] tile_r000004_c000030\n", + " -> skipped (100% Nodata)\n", + "[28/720] tile_r000004_c000031\n", + " -> skipped (100% Nodata)\n", + "[29/720] tile_r000004_c000032\n", + " -> skipped (100% Nodata)\n", + "[30/720] tile_r000004_c000033\n", + " -> skipped (100% Nodata)\n", + "[31/720] tile_r000005_c000004\n", + " -> skipped (100% Nodata)\n", + "[32/720] tile_r000005_c000005\n", + " -> skipped (100% Nodata)\n", + "[33/720] tile_r000005_c000006\n", + " -> skipped (100% Nodata)\n", + "[34/720] tile_r000005_c000007\n", + " -> skipped (100% Nodata)\n", + "[35/720] tile_r000005_c000008\n", + " -> skipped (100% Nodata)\n", + "[36/720] tile_r000005_c000009\n", + " -> skipped (100% Nodata)\n", + "[37/720] tile_r000005_c000010\n", + " -> skipped (100% Nodata)\n", + "[38/720] tile_r000005_c000011\n", + " -> skipped (100% Nodata)\n", + "[39/720] tile_r000005_c000012\n", + " -> skipped (100% Nodata)\n", + "[40/720] tile_r000005_c000013\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.16it/s]\n", + "100%|██████████| 3/3 [00:00<00:00, 3.29it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 14/14 [00:00<00:00, 36.96it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "4it [00:00, 936.12it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "4it [00:00, 1126.97it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 2/2 [00:00<00:00, 126.20it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 2/2 [00:00<00:00, 197.12it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.BpaNNCn48p/ipykernel_53064/3422303653.py:106: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/ouputgpkg/tile_r000005_c000013_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/csv_stats/tile_r000005_c000013_grain_stats.csv\n", + "done with segmentation + export\n", + "[41/720] tile_r000005_c000014\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.23it/s]\n", + "100%|██████████| 3/3 [00:00<00:00, 3.12it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 288/288 [00:05<00:00, 51.46it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "233it [00:00, 377.56it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "247it [00:00, 372.17it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 100/100 [00:01<00:00, 93.92it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 106/106 [00:00<00:00, 192.53it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.BpaNNCn48p/ipykernel_53064/3422303653.py:106: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/ouputgpkg/tile_r000005_c000014_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/csv_stats/tile_r000005_c000014_grain_stats.csv\n", + "done with segmentation + export\n", + "[42/720] tile_r000005_c000015\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.18it/s]\n", + "100%|██████████| 3/3 [00:00<00:00, 3.17it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 276/276 [00:06<00:00, 45.05it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "219it [00:01, 192.45it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "215it [00:00, 396.40it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 67/67 [00:01<00:00, 57.87it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 98/98 [00:00<00:00, 182.47it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n", + "Warning: len(gdf)=98 != len(grain_stats)=96 -> truncating to 96\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.BpaNNCn48p/ipykernel_53064/3422303653.py:106: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/ouputgpkg/tile_r000005_c000015_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/csv_stats/tile_r000005_c000015_grain_stats.csv\n", + "done with segmentation + export\n", + "[43/720] tile_r000005_c000016\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.15it/s]\n", + "100%|██████████| 3/3 [00:00<00:00, 3.02it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 231/231 [00:05<00:00, 39.82it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "163it [00:00, 236.34it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "175it [00:00, 240.52it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 70/70 [00:01<00:00, 66.42it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 74/74 [00:00<00:00, 187.59it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.BpaNNCn48p/ipykernel_53064/3422303653.py:106: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/ouputgpkg/tile_r000005_c000016_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/csv_stats/tile_r000005_c000016_grain_stats.csv\n", + "done with segmentation + export\n", + "[44/720] tile_r000005_c000017\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 2.59it/s]\n", + "100%|██████████| 3/3 [00:01<00:00, 2.05it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 20/20 [00:00<00:00, 34.80it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "11it [00:00, 476.09it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "15it [00:00, 454.04it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 7/7 [00:00<00:00, 144.52it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 7/7 [00:00<00:00, 200.99it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n", + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/ouputgpkg/tile_r000005_c000017_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/csv_stats/tile_r000005_c000017_grain_stats.csv\n", + "done with segmentation + export\n", + "[45/720] tile_r000005_c000018\n", + " -> skipped (100% Nodata)\n", + "[46/720] tile_r000005_c000019\n", + " -> skipped (100% Nodata)\n", + "[47/720] tile_r000005_c000020\n", + " -> skipped (100% Nodata)\n", + "[48/720] tile_r000005_c000021\n", + " -> skipped (100% Nodata)\n", + "[49/720] tile_r000005_c000022\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.BpaNNCn48p/ipykernel_53064/3422303653.py:106: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " -> skipped (100% Nodata)\n", + "[50/720] tile_r000005_c000023\n", + " -> skipped (100% Nodata)\n", + "[51/720] tile_r000005_c000024\n", + " -> skipped (100% Nodata)\n", + "[52/720] tile_r000005_c000025\n", + " -> skipped (100% Nodata)\n", + "[53/720] tile_r000005_c000026\n", + " -> skipped (100% Nodata)\n", + "[54/720] tile_r000005_c000027\n", + " -> skipped (100% Nodata)\n", + "[55/720] tile_r000005_c000028\n", + " -> skipped (100% Nodata)\n", + "[56/720] tile_r000005_c000029\n", + " -> skipped (100% Nodata)\n", + "[57/720] tile_r000005_c000030\n", + " -> skipped (100% Nodata)\n", + "[58/720] tile_r000005_c000031\n", + " -> skipped (100% Nodata)\n", + "[59/720] tile_r000005_c000032\n", + " -> skipped (100% Nodata)\n", + "[60/720] tile_r000005_c000033\n", + " -> skipped (100% Nodata)\n", + "[61/720] tile_r000006_c000004\n", + " -> skipped (100% Nodata)\n", + "[62/720] tile_r000006_c000005\n", + " -> skipped (100% Nodata)\n", + "[63/720] tile_r000006_c000006\n", + " -> skipped (100% Nodata)\n", + "[64/720] tile_r000006_c000007\n", + " -> skipped (100% Nodata)\n", + "[65/720] tile_r000006_c000008\n", + " -> skipped (100% Nodata)\n", + "[66/720] tile_r000006_c000009\n", + " -> skipped (100% Nodata)\n", + "[67/720] tile_r000006_c000010\n", + " -> skipped (100% Nodata)\n", + "[68/720] tile_r000006_c000011\n", + " -> skipped (100% Nodata)\n", + "[69/720] tile_r000006_c000012\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 2.44it/s]\n", + "100%|██████████| 3/3 [00:01<00:00, 2.24it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 19/19 [00:00<00:00, 37.78it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2it [00:00, 544.26it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2it [00:00, 710.42it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 1/1 [00:00<00:00, 200.23it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 1/1 [00:00<00:00, 148.18it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.BpaNNCn48p/ipykernel_53064/3422303653.py:106: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/ouputgpkg/tile_r000006_c000012_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/csv_stats/tile_r000006_c000012_grain_stats.csv\n", + "done with segmentation + export\n", + "[70/720] tile_r000006_c000013\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 2.68it/s]\n", + "100%|██████████| 3/3 [00:01<00:00, 2.56it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 290/290 [00:06<00:00, 44.69it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "200it [00:01, 117.82it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "198it [00:01, 133.31it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 56/56 [00:02<00:00, 20.29it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 81/81 [00:00<00:00, 171.05it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.BpaNNCn48p/ipykernel_53064/3422303653.py:106: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/ouputgpkg/tile_r000006_c000013_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/csv_stats/tile_r000006_c000013_grain_stats.csv\n", + "done with segmentation + export\n", + "[71/720] tile_r000006_c000014\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 2.53it/s]\n", + "100%|██████████| 3/3 [00:01<00:00, 2.39it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 315/315 [00:06<00:00, 47.51it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "231it [00:01, 156.62it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "220it [00:00, 280.16it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 80/80 [00:01<00:00, 68.62it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 92/92 [00:00<00:00, 181.13it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.BpaNNCn48p/ipykernel_53064/3422303653.py:106: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/ouputgpkg/tile_r000006_c000014_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/csv_stats/tile_r000006_c000014_grain_stats.csv\n", + "done with segmentation + export\n", + "[72/720] tile_r000006_c000015\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 2.83it/s]\n", + "100%|██████████| 3/3 [00:01<00:00, 2.87it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 181/181 [00:04<00:00, 38.03it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "92it [00:00, 250.35it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100it [00:00, 249.93it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 36/36 [00:01<00:00, 32.39it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 41/41 [00:00<00:00, 135.37it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.BpaNNCn48p/ipykernel_53064/3422303653.py:106: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/ouputgpkg/tile_r000006_c000015_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/csv_stats/tile_r000006_c000015_grain_stats.csv\n", + "done with segmentation + export\n", + "[73/720] tile_r000006_c000016\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 2.96it/s]\n", + "100%|██████████| 3/3 [00:00<00:00, 3.12it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 162/162 [00:03<00:00, 48.94it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "98it [00:00, 298.19it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "107it [00:00, 314.84it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 39/39 [00:00<00:00, 60.29it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 44/44 [00:00<00:00, 166.07it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.BpaNNCn48p/ipykernel_53064/3422303653.py:106: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/ouputgpkg/tile_r000006_c000016_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/csv_stats/tile_r000006_c000016_grain_stats.csv\n", + "done with segmentation + export\n", + "[74/720] tile_r000006_c000017\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.23it/s]\n", + "100%|██████████| 3/3 [00:01<00:00, 2.66it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 282/282 [00:05<00:00, 53.24it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "227it [00:01, 186.83it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "222it [00:00, 276.26it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 75/75 [00:01<00:00, 59.59it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 90/90 [00:00<00:00, 182.08it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.BpaNNCn48p/ipykernel_53064/3422303653.py:106: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/ouputgpkg/tile_r000006_c000017_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/csv_stats/tile_r000006_c000017_grain_stats.csv\n", + "done with segmentation + export\n", + "[75/720] tile_r000006_c000018\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 2.72it/s]\n", + "100%|██████████| 3/3 [00:01<00:00, 2.80it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 15/15 [00:00<00:00, 34.28it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "5it [00:00, 450.31it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "7it [00:00, 571.32it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 3/3 [00:00<00:00, 136.37it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 3/3 [00:00<00:00, 195.68it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n", + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/ouputgpkg/tile_r000006_c000018_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/csv_stats/tile_r000006_c000018_grain_stats.csv\n", + "done with segmentation + export\n", + "[76/720] tile_r000006_c000019\n", + " -> skipped (100% Nodata)\n", + "[77/720] tile_r000006_c000020\n", + " -> skipped (100% Nodata)\n", + "[78/720] tile_r000006_c000021\n", + " -> skipped (100% Nodata)\n", + "[79/720] tile_r000006_c000022\n", + " -> skipped (100% Nodata)\n", + "[80/720] tile_r000006_c000023\n", + " -> skipped (100% Nodata)\n", + "[81/720] tile_r000006_c000024\n", + " -> skipped (100% Nodata)\n", + "[82/720] tile_r000006_c000025\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.BpaNNCn48p/ipykernel_53064/3422303653.py:106: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " -> skipped (100% Nodata)\n", + "[83/720] tile_r000006_c000026\n", + " -> skipped (100% Nodata)\n", + "[84/720] tile_r000006_c000027\n", + " -> skipped (100% Nodata)\n", + "[85/720] tile_r000006_c000028\n", + " -> skipped (100% Nodata)\n", + "[86/720] tile_r000006_c000029\n", + " -> skipped (100% Nodata)\n", + "[87/720] tile_r000006_c000030\n", + " -> skipped (100% Nodata)\n", + "[88/720] tile_r000006_c000031\n", + " -> skipped (100% Nodata)\n", + "[89/720] tile_r000006_c000032\n", + " -> skipped (100% Nodata)\n", + "[90/720] tile_r000006_c000033\n", + " -> skipped (100% Nodata)\n", + "[91/720] tile_r000007_c000004\n", + " -> skipped (100% Nodata)\n", + "[92/720] tile_r000007_c000005\n", + " -> skipped (100% Nodata)\n", + "[93/720] tile_r000007_c000006\n", + " -> skipped (100% Nodata)\n", + "[94/720] tile_r000007_c000007\n", + " -> skipped (100% Nodata)\n", + "[95/720] tile_r000007_c000008\n", + " -> skipped (100% Nodata)\n", + "[96/720] tile_r000007_c000009\n", + " -> skipped (100% Nodata)\n", + "[97/720] tile_r000007_c000010\n", + " -> skipped (100% Nodata)\n", + "[98/720] tile_r000007_c000011\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 2.71it/s]\n", + "100%|██████████| 3/3 [00:01<00:00, 2.67it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 19/19 [00:00<00:00, 37.86it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "0it [00:00, ?it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "0it [00:00, ?it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "0it [00:00, ?it/s]\n", + "0it [00:00, ?it/s]\n", + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.BpaNNCn48p/ipykernel_53064/3422303653.py:106: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n", + "No grains found -> skipping histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/ouputgpkg/tile_r000007_c000011_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/csv_stats/tile_r000007_c000011_grain_stats.csv\n", + "done with segmentation + export\n", + "[99/720] tile_r000007_c000012\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 2.72it/s]\n", + "100%|██████████| 3/3 [00:01<00:00, 2.79it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 99/99 [00:04<00:00, 24.58it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "40it [00:06, 5.84it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "22it [00:03, 7.00it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 3/3 [00:06<00:00, 2.20s/it]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 7/7 [00:00<00:00, 63.59it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.BpaNNCn48p/ipykernel_53064/3422303653.py:106: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/ouputgpkg/tile_r000007_c000012_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/csv_stats/tile_r000007_c000012_grain_stats.csv\n", + "done with segmentation + export\n", + "[100/720] tile_r000007_c000013\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 2.70it/s]\n", + "100%|██████████| 3/3 [00:01<00:00, 2.80it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 240/240 [00:07<00:00, 34.17it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "154it [00:00, 162.88it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "152it [00:00, 275.28it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 43/43 [00:01<00:00, 35.57it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 70/70 [00:00<00:00, 166.53it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.BpaNNCn48p/ipykernel_53064/3422303653.py:106: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/ouputgpkg/tile_r000007_c000013_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/csv_stats/tile_r000007_c000013_grain_stats.csv\n", + "done with segmentation + export\n", + "[101/720] tile_r000007_c000014\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 2.68it/s]\n", + "100%|██████████| 3/3 [00:01<00:00, 2.96it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 343/343 [00:06<00:00, 54.77it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "265it [00:01, 251.65it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "276it [00:01, 250.05it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 99/99 [00:01<00:00, 56.23it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 109/109 [00:00<00:00, 171.65it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.BpaNNCn48p/ipykernel_53064/3422303653.py:106: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/ouputgpkg/tile_r000007_c000014_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/csv_stats/tile_r000007_c000014_grain_stats.csv\n", + "done with segmentation + export\n", + "[102/720] tile_r000007_c000015\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 2.70it/s]\n", + "100%|██████████| 3/3 [00:01<00:00, 2.89it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 306/306 [00:05<00:00, 51.10it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "245it [00:00, 345.45it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "261it [00:00, 334.23it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 97/97 [00:01<00:00, 82.41it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 105/105 [00:00<00:00, 183.22it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.BpaNNCn48p/ipykernel_53064/3422303653.py:106: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/ouputgpkg/tile_r000007_c000015_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/csv_stats/tile_r000007_c000015_grain_stats.csv\n", + "done with segmentation + export\n", + "[103/720] tile_r000007_c000016\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 2.67it/s]\n", + "100%|██████████| 3/3 [00:01<00:00, 2.92it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 287/287 [00:05<00:00, 51.80it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "243it [00:02, 89.27it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "229it [00:01, 147.99it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 72/72 [00:02<00:00, 30.14it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 92/92 [00:00<00:00, 159.90it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.BpaNNCn48p/ipykernel_53064/3422303653.py:106: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/ouputgpkg/tile_r000007_c000016_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/csv_stats/tile_r000007_c000016_grain_stats.csv\n", + "done with segmentation + export\n", + "[104/720] tile_r000007_c000017\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 2.72it/s]\n", + "100%|██████████| 3/3 [00:01<00:00, 2.98it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 331/331 [00:06<00:00, 53.90it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "252it [00:00, 256.58it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "250it [00:00, 398.37it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 87/87 [00:01<00:00, 76.69it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 111/111 [00:00<00:00, 209.57it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.BpaNNCn48p/ipykernel_53064/3422303653.py:106: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/ouputgpkg/tile_r000007_c000017_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/csv_stats/tile_r000007_c000017_grain_stats.csv\n", + "done with segmentation + export\n", + "[105/720] tile_r000007_c000018\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 2.71it/s]\n", + "100%|██████████| 3/3 [00:01<00:00, 2.85it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 244/244 [00:05<00:00, 45.00it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "172it [00:00, 199.75it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "180it [00:00, 208.61it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 73/73 [00:01<00:00, 55.65it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 75/75 [00:00<00:00, 171.84it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.BpaNNCn48p/ipykernel_53064/3422303653.py:106: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/ouputgpkg/tile_r000007_c000018_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/csv_stats/tile_r000007_c000018_grain_stats.csv\n", + "done with segmentation + export\n", + "[106/720] tile_r000007_c000019\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 2.79it/s]\n", + "100%|██████████| 3/3 [00:01<00:00, 2.96it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 69/69 [00:01<00:00, 42.07it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "30it [00:00, 512.65it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "34it [00:00, 494.43it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 14/14 [00:00<00:00, 122.66it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\n", + "100%|██████████| 15/15 [00:00<00:00, 208.98it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n", + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/ouputgpkg/tile_r000007_c000019_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/csv_stats/tile_r000007_c000019_grain_stats.csv\n", + "done with segmentation + export\n", + "[107/720] tile_r000007_c000020\n", + " -> skipped (100% Nodata)\n", + "[108/720] tile_r000007_c000021\n", + " -> skipped (100% Nodata)\n", + "[109/720] tile_r000007_c000022\n", + " -> skipped (100% Nodata)\n", + "[110/720] tile_r000007_c000023\n", + " -> skipped (100% Nodata)\n", + "[111/720] tile_r000007_c000024\n", + " -> skipped (100% Nodata)\n", + "[112/720] tile_r000007_c000025\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.BpaNNCn48p/ipykernel_53064/3422303653.py:106: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " -> skipped (100% Nodata)\n", + "[113/720] tile_r000007_c000026\n", + " -> skipped (100% Nodata)\n", + "[114/720] tile_r000007_c000027\n", + " -> skipped (100% Nodata)\n", + "[115/720] tile_r000007_c000028\n", + " -> skipped (100% Nodata)\n", + "[116/720] tile_r000007_c000029\n", + " -> skipped (100% Nodata)\n", + "[117/720] tile_r000007_c000030\n", + " -> skipped (100% Nodata)\n", + "[118/720] tile_r000007_c000031\n", + " -> skipped (100% Nodata)\n", + "[119/720] tile_r000007_c000032\n", + " -> skipped (100% Nodata)\n", + "[120/720] tile_r000007_c000033\n", + " -> skipped (100% Nodata)\n", + "[121/720] tile_r000008_c000004\n", + " -> skipped (100% Nodata)\n", + "[122/720] tile_r000008_c000005\n", + " -> skipped (100% Nodata)\n", + "[123/720] tile_r000008_c000006\n", + " -> skipped (100% Nodata)\n", + "[124/720] tile_r000008_c000007\n", + " -> skipped (100% Nodata)\n", + "[125/720] tile_r000008_c000008\n", + " -> skipped (100% Nodata)\n", + "[126/720] tile_r000008_c000009\n", + " -> skipped (100% Nodata)\n", + "[127/720] tile_r000008_c000010\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 2.84it/s]\n", + "100%|██████████| 3/3 [00:01<00:00, 2.97it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 44/44 [00:01<00:00, 34.93it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "5it [00:00, 1610.34it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "10it [00:00, 400.10it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:00<00:00, 203.52it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:00<00:00, 165.65it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.BpaNNCn48p/ipykernel_53064/3422303653.py:106: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/ouputgpkg/tile_r000008_c000010_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/csv_stats/tile_r000008_c000010_grain_stats.csv\n", + "done with segmentation + export\n", + "[128/720] tile_r000008_c000011\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 2.93it/s]\n", + "100%|██████████| 3/3 [00:01<00:00, 2.96it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 71/71 [00:01<00:00, 38.14it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "24it [00:00, 103.67it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "35it [00:00, 106.98it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 13/13 [00:00<00:00, 13.56it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 13/13 [00:00<00:00, 84.41it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.BpaNNCn48p/ipykernel_53064/3422303653.py:106: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/ouputgpkg/tile_r000008_c000011_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/csv_stats/tile_r000008_c000011_grain_stats.csv\n", + "done with segmentation + export\n", + "[129/720] tile_r000008_c000012\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 2.86it/s]\n", + "100%|██████████| 3/3 [00:01<00:00, 2.93it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 133/133 [00:03<00:00, 35.50it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "54it [00:01, 38.20it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "17it [00:00, 26.43it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 1/1 [00:01<00:00, 1.90s/it]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 12/12 [00:00<00:00, 61.23it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.BpaNNCn48p/ipykernel_53064/3422303653.py:106: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/ouputgpkg/tile_r000008_c000012_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/csv_stats/tile_r000008_c000012_grain_stats.csv\n", + "done with segmentation + export\n", + "[130/720] tile_r000008_c000013\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 2.90it/s]\n", + "100%|██████████| 3/3 [00:01<00:00, 2.84it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 240/240 [00:06<00:00, 36.59it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "150it [00:00, 323.47it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "166it [00:00, 305.15it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 62/62 [00:01<00:00, 46.27it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 75/75 [00:00<00:00, 164.59it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.BpaNNCn48p/ipykernel_53064/3422303653.py:106: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/ouputgpkg/tile_r000008_c000013_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/csv_stats/tile_r000008_c000013_grain_stats.csv\n", + "done with segmentation + export\n", + "[131/720] tile_r000008_c000014\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 2.88it/s]\n", + "100%|██████████| 3/3 [00:01<00:00, 2.84it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 282/282 [00:05<00:00, 51.08it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "217it [00:00, 274.46it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "216it [00:00, 301.08it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 68/68 [00:01<00:00, 35.91it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 93/93 [00:00<00:00, 168.58it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.BpaNNCn48p/ipykernel_53064/3422303653.py:106: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/ouputgpkg/tile_r000008_c000014_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/csv_stats/tile_r000008_c000014_grain_stats.csv\n", + "done with segmentation + export\n", + "[132/720] tile_r000008_c000015\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 2.90it/s]\n", + "100%|██████████| 3/3 [00:01<00:00, 2.80it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 389/389 [00:08<00:00, 44.86it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "289it [00:00, 400.80it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "287it [00:00, 423.82it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 105/105 [00:01<00:00, 96.18it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 123/123 [00:00<00:00, 197.34it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.BpaNNCn48p/ipykernel_53064/3422303653.py:106: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/ouputgpkg/tile_r000008_c000015_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/csv_stats/tile_r000008_c000015_grain_stats.csv\n", + "done with segmentation + export\n", + "[133/720] tile_r000008_c000016\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 2.85it/s]\n", + "100%|██████████| 3/3 [00:01<00:00, 2.76it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 372/372 [00:07<00:00, 50.32it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "306it [00:00, 408.92it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "326it [00:00, 392.66it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 131/131 [00:01<00:00, 78.08it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 134/134 [00:00<00:00, 200.78it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.BpaNNCn48p/ipykernel_53064/3422303653.py:106: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/ouputgpkg/tile_r000008_c000016_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/csv_stats/tile_r000008_c000016_grain_stats.csv\n", + "done with segmentation + export\n", + "[134/720] tile_r000008_c000017\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.00it/s]\n", + "100%|██████████| 3/3 [00:01<00:00, 2.88it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 346/346 [00:06<00:00, 50.85it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "269it [00:00, 415.05it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "289it [00:00, 409.23it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 118/118 [00:01<00:00, 113.71it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 119/119 [00:00<00:00, 199.91it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.BpaNNCn48p/ipykernel_53064/3422303653.py:106: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/ouputgpkg/tile_r000008_c000017_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/csv_stats/tile_r000008_c000017_grain_stats.csv\n", + "done with segmentation + export\n", + "[135/720] tile_r000008_c000018\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 2.95it/s]\n", + "100%|██████████| 3/3 [00:01<00:00, 2.86it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 364/364 [00:07<00:00, 47.60it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "286it [00:00, 465.13it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "306it [00:00, 457.78it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 126/126 [00:01<00:00, 120.34it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 130/130 [00:00<00:00, 217.50it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.BpaNNCn48p/ipykernel_53064/3422303653.py:106: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/ouputgpkg/tile_r000008_c000018_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/csv_stats/tile_r000008_c000018_grain_stats.csv\n", + "done with segmentation + export\n", + "[136/720] tile_r000008_c000019\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 2.98it/s]\n", + "100%|██████████| 3/3 [00:01<00:00, 2.78it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 294/294 [00:05<00:00, 52.58it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "240it [00:00, 368.28it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "239it [00:00, 402.41it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 85/85 [00:00<00:00, 86.74it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 102/102 [00:00<00:00, 197.08it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.BpaNNCn48p/ipykernel_53064/3422303653.py:106: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/ouputgpkg/tile_r000008_c000019_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/csv_stats/tile_r000008_c000019_grain_stats.csv\n", + "done with segmentation + export\n", + "[137/720] tile_r000008_c000020\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 2.97it/s]\n", + "100%|██████████| 3/3 [00:01<00:00, 2.93it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 47/47 [00:01<00:00, 37.25it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "22it [00:00, 616.36it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "30it [00:00, 535.86it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 14/14 [00:00<00:00, 181.85it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 15/15 [00:00<00:00, 205.53it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n", + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/ouputgpkg/tile_r000008_c000020_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/csv_stats/tile_r000008_c000020_grain_stats.csv\n", + "done with segmentation + export\n", + "[138/720] tile_r000008_c000021\n", + " -> skipped (100% Nodata)\n", + "[139/720] tile_r000008_c000022\n", + " -> skipped (100% Nodata)\n", + "[140/720] tile_r000008_c000023\n", + " -> skipped (100% Nodata)\n", + "[141/720] tile_r000008_c000024\n", + " -> skipped (100% Nodata)\n", + "[142/720] tile_r000008_c000025\n", + " -> skipped (100% Nodata)\n", + "[143/720] tile_r000008_c000026\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.BpaNNCn48p/ipykernel_53064/3422303653.py:106: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " -> skipped (100% Nodata)\n", + "[144/720] tile_r000008_c000027\n", + " -> skipped (100% Nodata)\n", + "[145/720] tile_r000008_c000028\n", + " -> skipped (100% Nodata)\n", + "[146/720] tile_r000008_c000029\n", + " -> skipped (100% Nodata)\n", + "[147/720] tile_r000008_c000030\n", + " -> skipped (100% Nodata)\n", + "[148/720] tile_r000008_c000031\n", + " -> skipped (100% Nodata)\n", + "[149/720] tile_r000008_c000032\n", + " -> skipped (100% Nodata)\n", + "[150/720] tile_r000008_c000033\n", + " -> skipped (100% Nodata)\n", + "[151/720] tile_r000009_c000004\n", + " -> skipped (100% Nodata)\n", + "[152/720] tile_r000009_c000005\n", + " -> skipped (100% Nodata)\n", + "[153/720] tile_r000009_c000006\n", + " -> skipped (100% Nodata)\n", + "[154/720] tile_r000009_c000007\n", + " -> skipped (100% Nodata)\n", + "[155/720] tile_r000009_c000008\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.07it/s]\n", + "100%|██████████| 3/3 [00:01<00:00, 2.88it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 7/7 [00:00<00:00, 26.02it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "0it [00:00, ?it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "0it [00:00, ?it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "0it [00:00, ?it/s]\n", + "0it [00:00, ?it/s]\n", + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.BpaNNCn48p/ipykernel_53064/3422303653.py:106: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n", + "No grains found -> skipping histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/ouputgpkg/tile_r000009_c000008_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/csv_stats/tile_r000009_c000008_grain_stats.csv\n", + "done with segmentation + export\n", + "[156/720] tile_r000009_c000009\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 2.96it/s]\n", + "100%|██████████| 3/3 [00:00<00:00, 3.09it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 18/18 [00:00<00:00, 21.15it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2it [00:00, 212.18it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "4it [00:00, 79.38it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 2/2 [00:00<00:00, 29.31it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 2/2 [00:00<00:00, 39.11it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.BpaNNCn48p/ipykernel_53064/3422303653.py:106: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/ouputgpkg/tile_r000009_c000009_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/csv_stats/tile_r000009_c000009_grain_stats.csv\n", + "done with segmentation + export\n", + "[157/720] tile_r000009_c000010\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.15it/s]\n", + "100%|██████████| 3/3 [00:00<00:00, 3.08it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 67/67 [00:01<00:00, 33.71it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "23it [00:00, 330.58it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "32it [00:00, 271.98it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 14/14 [00:00<00:00, 68.25it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 18/18 [00:00<00:00, 102.49it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.BpaNNCn48p/ipykernel_53064/3422303653.py:106: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/ouputgpkg/tile_r000009_c000010_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/csv_stats/tile_r000009_c000010_grain_stats.csv\n", + "done with segmentation + export\n", + "[158/720] tile_r000009_c000011\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.15it/s]\n", + "100%|██████████| 3/3 [00:01<00:00, 2.97it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 79/79 [00:02<00:00, 31.42it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "17it [00:00, 183.25it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "25it [00:00, 197.56it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 12/12 [00:00<00:00, 43.49it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 14/14 [00:00<00:00, 85.94it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.BpaNNCn48p/ipykernel_53064/3422303653.py:106: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/ouputgpkg/tile_r000009_c000011_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/csv_stats/tile_r000009_c000011_grain_stats.csv\n", + "done with segmentation + export\n", + "[159/720] tile_r000009_c000012\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.07it/s]\n", + "100%|██████████| 3/3 [00:01<00:00, 2.98it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 125/125 [00:03<00:00, 37.79it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "70it [00:01, 40.71it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "69it [00:01, 41.99it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 14/14 [00:05<00:00, 2.65it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 33/33 [00:00<00:00, 126.68it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.BpaNNCn48p/ipykernel_53064/3422303653.py:106: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/ouputgpkg/tile_r000009_c000012_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/csv_stats/tile_r000009_c000012_grain_stats.csv\n", + "done with segmentation + export\n", + "[160/720] tile_r000009_c000013\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.05it/s]\n", + "100%|██████████| 3/3 [00:01<00:00, 2.98it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 290/290 [00:07<00:00, 40.05it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "199it [00:00, 479.40it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "217it [00:00, 471.87it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 86/86 [00:00<00:00, 110.20it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 91/91 [00:00<00:00, 193.31it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.BpaNNCn48p/ipykernel_53064/3422303653.py:106: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/ouputgpkg/tile_r000009_c000013_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/csv_stats/tile_r000009_c000013_grain_stats.csv\n", + "done with segmentation + export\n", + "[161/720] tile_r000009_c000014\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 2.88it/s]\n", + "100%|██████████| 3/3 [00:00<00:00, 3.06it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 276/276 [00:05<00:00, 50.41it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "216it [00:02, 101.70it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "198it [00:00, 231.20it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 58/58 [00:02<00:00, 27.24it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 84/84 [00:00<00:00, 181.60it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n", + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/ouputgpkg/tile_r000009_c000014_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/csv_stats/tile_r000009_c000014_grain_stats.csv\n", + "done with segmentation + export\n", + "[162/720] tile_r000009_c000015\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.BpaNNCn48p/ipykernel_53064/3422303653.py:106: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 2.83it/s]\n", + "100%|██████████| 3/3 [00:01<00:00, 2.96it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 344/344 [00:07<00:00, 46.35it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "232it [00:01, 150.59it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "229it [00:01, 163.10it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 66/66 [00:03<00:00, 19.71it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 98/98 [00:00<00:00, 162.19it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.BpaNNCn48p/ipykernel_53064/3422303653.py:106: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/ouputgpkg/tile_r000009_c000015_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/csv_stats/tile_r000009_c000015_grain_stats.csv\n", + "done with segmentation + export\n", + "[163/720] tile_r000009_c000016\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 2.77it/s]\n", + "100%|██████████| 3/3 [00:01<00:00, 2.93it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 353/353 [00:08<00:00, 43.54it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "269it [00:00, 371.35it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "287it [00:00, 348.70it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 115/115 [00:01<00:00, 86.52it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 121/121 [00:00<00:00, 184.74it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.BpaNNCn48p/ipykernel_53064/3422303653.py:106: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/ouputgpkg/tile_r000009_c000016_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/csv_stats/tile_r000009_c000016_grain_stats.csv\n", + "done with segmentation + export\n", + "[164/720] tile_r000009_c000017\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 2.90it/s]\n", + "100%|██████████| 3/3 [00:00<00:00, 3.03it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 384/384 [00:06<00:00, 56.09it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "316it [00:00, 324.29it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "331it [00:01, 312.77it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 123/123 [00:01<00:00, 73.03it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 132/132 [00:00<00:00, 182.43it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.BpaNNCn48p/ipykernel_53064/3422303653.py:106: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/ouputgpkg/tile_r000009_c000017_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/csv_stats/tile_r000009_c000017_grain_stats.csv\n", + "done with segmentation + export\n", + "[165/720] tile_r000009_c000018\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.26it/s]\n", + "100%|██████████| 3/3 [00:00<00:00, 3.40it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 347/347 [00:06<00:00, 50.65it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "272it [00:01, 139.06it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "17it [00:00, 1249.97it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 1/1 [00:00<00:00, 36.00it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 17/17 [00:00<00:00, 177.28it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.BpaNNCn48p/ipykernel_53064/3422303653.py:106: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/ouputgpkg/tile_r000009_c000018_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/csv_stats/tile_r000009_c000018_grain_stats.csv\n", + "done with segmentation + export\n", + "[166/720] tile_r000009_c000019\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 2.83it/s]\n", + "100%|██████████| 3/3 [00:00<00:00, 3.03it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 361/361 [00:06<00:00, 52.63it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "280it [00:00, 485.10it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "297it [00:00, 483.77it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 125/125 [00:00<00:00, 128.92it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 127/127 [00:00<00:00, 215.82it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.BpaNNCn48p/ipykernel_53064/3422303653.py:106: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/ouputgpkg/tile_r000009_c000019_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/csv_stats/tile_r000009_c000019_grain_stats.csv\n", + "done with segmentation + export\n", + "[167/720] tile_r000009_c000020\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 2.87it/s]\n", + "100%|██████████| 3/3 [00:00<00:00, 3.06it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 257/257 [00:06<00:00, 37.13it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "194it [00:00, 496.32it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "206it [00:00, 483.35it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 86/86 [00:00<00:00, 133.48it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 89/89 [00:00<00:00, 212.33it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.BpaNNCn48p/ipykernel_53064/3422303653.py:106: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/ouputgpkg/tile_r000009_c000020_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/csv_stats/tile_r000009_c000020_grain_stats.csv\n", + "done with segmentation + export\n", + "[168/720] tile_r000009_c000021\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 2.67it/s]\n", + "100%|██████████| 3/3 [00:01<00:00, 2.64it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 23/23 [00:00<00:00, 38.08it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "11it [00:00, 846.70it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "14it [00:00, 872.77it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 7/7 [00:00<00:00, 313.53it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 7/7 [00:00<00:00, 224.51it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n", + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/ouputgpkg/tile_r000009_c000021_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/csv_stats/tile_r000009_c000021_grain_stats.csv\n", + "done with segmentation + export\n", + "[169/720] tile_r000009_c000022\n", + " -> skipped (100% Nodata)\n", + "[170/720] tile_r000009_c000023\n", + " -> skipped (100% Nodata)\n", + "[171/720] tile_r000009_c000024\n", + " -> skipped (100% Nodata)\n", + "[172/720] tile_r000009_c000025\n", + " -> skipped (100% Nodata)\n", + "[173/720] tile_r000009_c000026\n", + " -> skipped (100% Nodata)\n", + "[174/720] tile_r000009_c000027\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.BpaNNCn48p/ipykernel_53064/3422303653.py:106: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " -> skipped (100% Nodata)\n", + "[175/720] tile_r000009_c000028\n", + " -> skipped (100% Nodata)\n", + "[176/720] tile_r000009_c000029\n", + " -> skipped (100% Nodata)\n", + "[177/720] tile_r000009_c000030\n", + " -> skipped (100% Nodata)\n", + "[178/720] tile_r000009_c000031\n", + " -> skipped (100% Nodata)\n", + "[179/720] tile_r000009_c000032\n", + " -> skipped (100% Nodata)\n", + "[180/720] tile_r000009_c000033\n", + " -> skipped (100% Nodata)\n", + "[181/720] tile_r000010_c000004\n", + " -> skipped (100% Nodata)\n", + "[182/720] tile_r000010_c000005\n", + " -> skipped (100% Nodata)\n", + "[183/720] tile_r000010_c000006\n", + " -> skipped (100% Nodata)\n", + "[184/720] tile_r000010_c000007\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 2.52it/s]\n", + "100%|██████████| 3/3 [00:01<00:00, 2.51it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 1/1 [00:00<00:00, 7.04it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "0it [00:00, ?it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "0it [00:00, ?it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "0it [00:00, ?it/s]\n", + "0it [00:00, ?it/s]\n", + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.BpaNNCn48p/ipykernel_53064/3422303653.py:106: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n", + "No grains found -> skipping histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/ouputgpkg/tile_r000010_c000007_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/csv_stats/tile_r000010_c000007_grain_stats.csv\n", + "done with segmentation + export\n", + "[185/720] tile_r000010_c000008\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 2.77it/s]\n", + "100%|██████████| 3/3 [00:00<00:00, 3.09it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 37/37 [00:00<00:00, 37.60it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "12it [00:00, 227.83it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "17it [00:00, 134.50it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 8/8 [00:00<00:00, 46.36it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 9/9 [00:00<00:00, 55.09it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.BpaNNCn48p/ipykernel_53064/3422303653.py:106: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/ouputgpkg/tile_r000010_c000008_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/csv_stats/tile_r000010_c000008_grain_stats.csv\n", + "done with segmentation + export\n", + "[186/720] tile_r000010_c000009\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.04it/s]\n", + "100%|██████████| 3/3 [00:01<00:00, 2.99it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 48/48 [00:01<00:00, 29.43it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "19it [00:00, 243.71it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "31it [00:00, 134.75it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 15/15 [00:00<00:00, 46.87it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 16/16 [00:00<00:00, 60.32it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.BpaNNCn48p/ipykernel_53064/3422303653.py:106: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/ouputgpkg/tile_r000010_c000009_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/csv_stats/tile_r000010_c000009_grain_stats.csv\n", + "done with segmentation + export\n", + "[187/720] tile_r000010_c000010\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.02it/s]\n", + "100%|██████████| 3/3 [00:00<00:00, 3.06it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 41/41 [00:01<00:00, 33.45it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "19it [00:00, 146.78it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "28it [00:00, 103.75it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 12/12 [00:00<00:00, 33.59it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 12/12 [00:00<00:00, 52.79it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.BpaNNCn48p/ipykernel_53064/3422303653.py:106: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/ouputgpkg/tile_r000010_c000010_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/csv_stats/tile_r000010_c000010_grain_stats.csv\n", + "done with segmentation + export\n", + "[188/720] tile_r000010_c000011\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.08it/s]\n", + "100%|██████████| 3/3 [00:01<00:00, 2.87it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 12/12 [00:00<00:00, 31.27it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "10it [00:00, 514.08it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "14it [00:00, 250.42it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 6/6 [00:00<00:00, 60.89it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 7/7 [00:00<00:00, 91.05it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.BpaNNCn48p/ipykernel_53064/3422303653.py:106: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/ouputgpkg/tile_r000010_c000011_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/csv_stats/tile_r000010_c000011_grain_stats.csv\n", + "done with segmentation + export\n", + "[189/720] tile_r000010_c000012\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.04it/s]\n", + "100%|██████████| 3/3 [00:00<00:00, 3.00it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 117/117 [00:03<00:00, 38.59it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "51it [00:00, 122.06it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "50it [00:00, 148.80it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 11/11 [00:01<00:00, 10.03it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 27/27 [00:00<00:00, 108.42it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.BpaNNCn48p/ipykernel_53064/3422303653.py:106: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/ouputgpkg/tile_r000010_c000012_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/csv_stats/tile_r000010_c000012_grain_stats.csv\n", + "done with segmentation + export\n", + "[190/720] tile_r000010_c000013\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.06it/s]\n", + "100%|██████████| 3/3 [00:01<00:00, 2.95it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 174/174 [00:04<00:00, 35.80it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "109it [00:05, 18.52it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "89it [00:00, 642.30it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 31/31 [00:00<00:00, 88.32it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 49/49 [00:00<00:00, 169.29it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.BpaNNCn48p/ipykernel_53064/3422303653.py:106: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/ouputgpkg/tile_r000010_c000013_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/csv_stats/tile_r000010_c000013_grain_stats.csv\n", + "done with segmentation + export\n", + "[191/720] tile_r000010_c000014\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.05it/s]\n", + "100%|██████████| 3/3 [00:01<00:00, 2.95it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 263/263 [00:05<00:00, 44.31it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "183it [00:00, 225.22it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "180it [00:00, 456.34it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 63/63 [00:00<00:00, 86.18it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 83/83 [00:00<00:00, 182.46it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.BpaNNCn48p/ipykernel_53064/3422303653.py:106: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/ouputgpkg/tile_r000010_c000014_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/csv_stats/tile_r000010_c000014_grain_stats.csv\n", + "done with segmentation + export\n", + "[192/720] tile_r000010_c000015\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.40it/s]\n", + "100%|██████████| 3/3 [00:00<00:00, 3.19it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 303/303 [00:05<00:00, 59.46it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "217it [00:00, 268.92it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "213it [00:00, 331.06it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 71/71 [00:01<00:00, 61.24it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 93/93 [00:00<00:00, 182.48it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.BpaNNCn48p/ipykernel_53064/3422303653.py:106: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/ouputgpkg/tile_r000010_c000015_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/csv_stats/tile_r000010_c000015_grain_stats.csv\n", + "done with segmentation + export\n", + "[193/720] tile_r000010_c000016\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.56it/s]\n", + "100%|██████████| 3/3 [00:01<00:00, 2.98it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 349/349 [00:07<00:00, 48.70it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "257it [00:00, 399.71it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "273it [00:00, 391.15it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 105/105 [00:01<00:00, 94.90it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 110/110 [00:00<00:00, 191.03it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.BpaNNCn48p/ipykernel_53064/3422303653.py:106: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/ouputgpkg/tile_r000010_c000016_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/csv_stats/tile_r000010_c000016_grain_stats.csv\n", + "done with segmentation + export\n", + "[194/720] tile_r000010_c000017\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.13it/s]\n", + "100%|██████████| 3/3 [00:00<00:00, 3.17it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 373/373 [00:07<00:00, 46.91it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "272it [00:01, 200.21it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "301it [00:01, 203.83it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 120/120 [00:02<00:00, 48.99it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 123/123 [00:00<00:00, 186.92it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.BpaNNCn48p/ipykernel_53064/3422303653.py:106: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/ouputgpkg/tile_r000010_c000017_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/csv_stats/tile_r000010_c000017_grain_stats.csv\n", + "done with segmentation + export\n", + "[195/720] tile_r000010_c000018\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.11it/s]\n", + "100%|██████████| 3/3 [00:00<00:00, 3.07it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 343/343 [00:08<00:00, 38.82it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "235it [00:00, 254.83it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "234it [00:00, 282.44it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 79/79 [00:01<00:00, 60.46it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 95/95 [00:00<00:00, 189.31it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.BpaNNCn48p/ipykernel_53064/3422303653.py:106: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/ouputgpkg/tile_r000010_c000018_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/csv_stats/tile_r000010_c000018_grain_stats.csv\n", + "done with segmentation + export\n", + "[196/720] tile_r000010_c000019\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.02it/s]\n", + "100%|██████████| 3/3 [00:00<00:00, 3.44it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 307/307 [00:06<00:00, 48.45it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "251it [00:00, 468.23it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "262it [00:00, 477.77it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 109/109 [00:00<00:00, 129.13it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 110/110 [00:00<00:00, 206.43it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.BpaNNCn48p/ipykernel_53064/3422303653.py:106: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/ouputgpkg/tile_r000010_c000019_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/csv_stats/tile_r000010_c000019_grain_stats.csv\n", + "done with segmentation + export\n", + "[197/720] tile_r000010_c000020\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.08it/s]\n", + "100%|██████████| 3/3 [00:00<00:00, 3.18it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 283/283 [00:06<00:00, 46.21it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "196it [00:00, 548.13it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "221it [00:00, 582.97it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 94/94 [00:00<00:00, 153.68it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 96/96 [00:00<00:00, 230.34it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.BpaNNCn48p/ipykernel_53064/3422303653.py:106: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/ouputgpkg/tile_r000010_c000020_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/csv_stats/tile_r000010_c000020_grain_stats.csv\n", + "done with segmentation + export\n", + "[198/720] tile_r000010_c000021\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.05it/s]\n", + "100%|██████████| 3/3 [00:00<00:00, 3.10it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 156/156 [00:03<00:00, 39.75it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "111it [00:00, 193.01it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "109it [00:00, 490.04it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 31/31 [00:00<00:00, 59.48it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 62/62 [00:00<00:00, 177.21it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.BpaNNCn48p/ipykernel_53064/3422303653.py:106: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/ouputgpkg/tile_r000010_c000021_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/csv_stats/tile_r000010_c000021_grain_stats.csv\n", + "done with segmentation + export\n", + "[199/720] tile_r000010_c000022\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.23it/s]\n", + "100%|██████████| 3/3 [00:00<00:00, 3.39it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 8/8 [00:00<00:00, 28.70it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "4it [00:00, 536.84it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "5it [00:00, 638.91it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 2/2 [00:00<00:00, 84.58it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 2/2 [00:00<00:00, 136.12it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n", + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/ouputgpkg/tile_r000010_c000022_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/csv_stats/tile_r000010_c000022_grain_stats.csv\n", + "done with segmentation + export\n", + "[200/720] tile_r000010_c000023\n", + " -> skipped (100% Nodata)\n", + "[201/720] tile_r000010_c000024\n", + " -> skipped (100% Nodata)\n", + "[202/720] tile_r000010_c000025\n", + " -> skipped (100% Nodata)\n", + "[203/720] tile_r000010_c000026\n", + " -> skipped (100% Nodata)\n", + "[204/720] tile_r000010_c000027\n", + " -> skipped (100% Nodata)\n", + "[205/720] tile_r000010_c000028\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.BpaNNCn48p/ipykernel_53064/3422303653.py:106: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " -> skipped (100% Nodata)\n", + "[206/720] tile_r000010_c000029\n", + " -> skipped (100% Nodata)\n", + "[207/720] tile_r000010_c000030\n", + " -> skipped (100% Nodata)\n", + "[208/720] tile_r000010_c000031\n", + " -> skipped (100% Nodata)\n", + "[209/720] tile_r000010_c000032\n", + " -> skipped (100% Nodata)\n", + "[210/720] tile_r000010_c000033\n", + " -> skipped (100% Nodata)\n", + "[211/720] tile_r000011_c000004\n", + " -> skipped (100% Nodata)\n", + "[212/720] tile_r000011_c000005\n", + " -> skipped (100% Nodata)\n", + "[213/720] tile_r000011_c000006\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.10it/s]\n", + "100%|██████████| 3/3 [00:00<00:00, 3.10it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 12/12 [00:00<00:00, 32.92it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "0it [00:00, ?it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "0it [00:00, ?it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "0it [00:00, ?it/s]\n", + "0it [00:00, ?it/s]\n", + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.BpaNNCn48p/ipykernel_53064/3422303653.py:106: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n", + "No grains found -> skipping histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/ouputgpkg/tile_r000011_c000006_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/csv_stats/tile_r000011_c000006_grain_stats.csv\n", + "done with segmentation + export\n", + "[214/720] tile_r000011_c000007\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.21it/s]\n", + "100%|██████████| 3/3 [00:00<00:00, 3.07it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 84/84 [00:02<00:00, 38.59it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "59it [00:00, 151.69it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "60it [00:00, 154.36it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 18/18 [00:00<00:00, 26.65it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 20/20 [00:00<00:00, 134.48it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.BpaNNCn48p/ipykernel_53064/3422303653.py:106: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/ouputgpkg/tile_r000011_c000007_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/csv_stats/tile_r000011_c000007_grain_stats.csv\n", + "done with segmentation + export\n", + "[215/720] tile_r000011_c000008\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.10it/s]\n", + "100%|██████████| 3/3 [00:00<00:00, 3.31it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 174/174 [00:03<00:00, 51.70it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "149it [00:00, 299.58it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "162it [00:00, 301.39it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 65/65 [00:00<00:00, 81.07it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 67/67 [00:00<00:00, 175.19it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.BpaNNCn48p/ipykernel_53064/3422303653.py:106: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/ouputgpkg/tile_r000011_c000008_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/csv_stats/tile_r000011_c000008_grain_stats.csv\n", + "done with segmentation + export\n", + "[216/720] tile_r000011_c000009\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 2.96it/s]\n", + "100%|██████████| 3/3 [00:00<00:00, 3.02it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 216/216 [00:04<00:00, 53.43it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "169it [00:00, 417.19it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "186it [00:00, 395.67it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 76/76 [00:00<00:00, 96.28it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 80/80 [00:00<00:00, 184.65it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.BpaNNCn48p/ipykernel_53064/3422303653.py:106: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/ouputgpkg/tile_r000011_c000009_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/csv_stats/tile_r000011_c000009_grain_stats.csv\n", + "done with segmentation + export\n", + "[217/720] tile_r000011_c000010\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.16it/s]\n", + "100%|██████████| 3/3 [00:00<00:00, 3.07it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 151/151 [00:02<00:00, 53.34it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "121it [00:00, 347.49it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "138it [00:00, 345.55it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 51/51 [00:00<00:00, 65.27it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 59/59 [00:00<00:00, 159.50it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.BpaNNCn48p/ipykernel_53064/3422303653.py:106: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/ouputgpkg/tile_r000011_c000010_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/csv_stats/tile_r000011_c000010_grain_stats.csv\n", + "done with segmentation + export\n", + "[218/720] tile_r000011_c000011\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 2.84it/s]\n", + "100%|██████████| 3/3 [00:01<00:00, 2.98it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 178/178 [00:04<00:00, 41.49it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "124it [00:00, 325.86it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "151it [00:00, 288.28it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 59/59 [00:01<00:00, 54.40it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 69/69 [00:00<00:00, 159.93it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.BpaNNCn48p/ipykernel_53064/3422303653.py:106: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/ouputgpkg/tile_r000011_c000011_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/csv_stats/tile_r000011_c000011_grain_stats.csv\n", + "done with segmentation + export\n", + "[219/720] tile_r000011_c000012\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.06it/s]\n", + "100%|██████████| 3/3 [00:00<00:00, 3.24it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 149/149 [00:04<00:00, 33.32it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "93it [00:02, 36.94it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "87it [00:00, 146.16it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 22/22 [00:01<00:00, 15.02it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 45/45 [00:00<00:00, 118.66it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.BpaNNCn48p/ipykernel_53064/3422303653.py:106: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/ouputgpkg/tile_r000011_c000012_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/csv_stats/tile_r000011_c000012_grain_stats.csv\n", + "done with segmentation + export\n", + "[220/720] tile_r000011_c000013\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.01it/s]\n", + "100%|██████████| 3/3 [00:00<00:00, 3.18it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 208/208 [00:06<00:00, 34.43it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "133it [00:02, 59.14it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "123it [00:00, 175.54it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 28/28 [00:02<00:00, 12.24it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 59/59 [00:00<00:00, 136.97it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.BpaNNCn48p/ipykernel_53064/3422303653.py:106: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/ouputgpkg/tile_r000011_c000013_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/csv_stats/tile_r000011_c000013_grain_stats.csv\n", + "done with segmentation + export\n", + "[221/720] tile_r000011_c000014\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.10it/s]\n", + "100%|██████████| 3/3 [00:00<00:00, 3.10it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 209/209 [00:04<00:00, 47.60it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "122it [00:00, 144.65it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "121it [00:00, 188.92it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 26/26 [00:01<00:00, 16.80it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 60/60 [00:00<00:00, 156.15it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.BpaNNCn48p/ipykernel_53064/3422303653.py:106: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/ouputgpkg/tile_r000011_c000014_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/csv_stats/tile_r000011_c000014_grain_stats.csv\n", + "done with segmentation + export\n", + "[222/720] tile_r000011_c000015\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 2.97it/s]\n", + "100%|██████████| 3/3 [00:00<00:00, 3.12it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 221/221 [00:05<00:00, 42.92it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "139it [00:00, 305.22it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "138it [00:00, 328.88it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 39/39 [00:00<00:00, 47.23it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 70/70 [00:00<00:00, 156.96it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.BpaNNCn48p/ipykernel_53064/3422303653.py:106: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/ouputgpkg/tile_r000011_c000015_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/csv_stats/tile_r000011_c000015_grain_stats.csv\n", + "done with segmentation + export\n", + "[223/720] tile_r000011_c000016\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 2.99it/s]\n", + "100%|██████████| 3/3 [00:00<00:00, 3.13it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 247/247 [00:04<00:00, 50.06it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "183it [00:01, 113.14it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "175it [00:00, 215.72it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 50/50 [00:01<00:00, 28.15it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 81/81 [00:00<00:00, 154.88it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.BpaNNCn48p/ipykernel_53064/3422303653.py:106: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/ouputgpkg/tile_r000011_c000016_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/csv_stats/tile_r000011_c000016_grain_stats.csv\n", + "done with segmentation + export\n", + "[224/720] tile_r000011_c000017\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.11it/s]\n", + "100%|██████████| 3/3 [00:00<00:00, 3.15it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 321/321 [00:06<00:00, 50.18it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "247it [00:01, 161.33it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "241it [00:01, 234.17it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 59/59 [00:03<00:00, 17.52it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 100/100 [00:00<00:00, 157.66it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.BpaNNCn48p/ipykernel_53064/3422303653.py:106: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/ouputgpkg/tile_r000011_c000017_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/csv_stats/tile_r000011_c000017_grain_stats.csv\n", + "done with segmentation + export\n", + "[225/720] tile_r000011_c000018\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.03it/s]\n", + "100%|██████████| 3/3 [00:00<00:00, 3.09it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 327/327 [00:07<00:00, 45.38it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "236it [00:01, 235.35it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "234it [00:00, 319.88it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 72/72 [00:01<00:00, 43.80it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 109/109 [00:00<00:00, 193.65it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.BpaNNCn48p/ipykernel_53064/3422303653.py:106: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/ouputgpkg/tile_r000011_c000018_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/csv_stats/tile_r000011_c000018_grain_stats.csv\n", + "done with segmentation + export\n", + "[226/720] tile_r000011_c000019\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.20it/s]\n", + "100%|██████████| 3/3 [00:00<00:00, 3.17it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 314/314 [00:06<00:00, 45.53it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "224it [00:02, 95.54it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "218it [00:01, 151.15it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 60/60 [00:02<00:00, 24.12it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 86/86 [00:00<00:00, 162.46it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.BpaNNCn48p/ipykernel_53064/3422303653.py:106: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/ouputgpkg/tile_r000011_c000019_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/csv_stats/tile_r000011_c000019_grain_stats.csv\n", + "done with segmentation + export\n", + "[227/720] tile_r000011_c000020\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.14it/s]\n", + "100%|██████████| 3/3 [00:00<00:00, 3.16it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 288/288 [00:05<00:00, 53.58it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "239it [00:00, 317.12it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "250it [00:00, 324.55it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 94/94 [00:01<00:00, 73.29it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 102/102 [00:01<00:00, 57.20it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.BpaNNCn48p/ipykernel_53064/3422303653.py:106: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/ouputgpkg/tile_r000011_c000020_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/csv_stats/tile_r000011_c000020_grain_stats.csv\n", + "done with segmentation + export\n", + "[228/720] tile_r000011_c000021\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.11it/s]\n", + "100%|██████████| 3/3 [00:01<00:00, 2.89it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 303/303 [00:06<00:00, 47.62it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "221it [00:00, 316.73it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "220it [00:00, 381.66it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 80/80 [00:01<00:00, 79.27it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 96/96 [00:00<00:00, 197.11it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.BpaNNCn48p/ipykernel_53064/3422303653.py:106: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/ouputgpkg/tile_r000011_c000021_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/csv_stats/tile_r000011_c000021_grain_stats.csv\n", + "done with segmentation + export\n", + "[229/720] tile_r000011_c000022\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.15it/s]\n", + "100%|██████████| 3/3 [00:01<00:00, 2.95it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 150/150 [00:04<00:00, 36.58it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "96it [00:05, 18.57it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "76it [00:00, 301.65it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 19/19 [00:00<00:00, 43.14it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 34/34 [00:00<00:00, 148.84it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.BpaNNCn48p/ipykernel_53064/3422303653.py:106: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/ouputgpkg/tile_r000011_c000022_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/csv_stats/tile_r000011_c000022_grain_stats.csv\n", + "done with segmentation + export\n", + "[230/720] tile_r000011_c000023\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.13it/s]\n", + "100%|██████████| 3/3 [00:01<00:00, 2.94it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 13/13 [00:00<00:00, 36.58it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "5it [00:00, 785.10it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "5it [00:00, 883.01it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 2/2 [00:00<00:00, 133.07it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 2/2 [00:00<00:00, 235.24it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n", + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/ouputgpkg/tile_r000011_c000023_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/csv_stats/tile_r000011_c000023_grain_stats.csv\n", + "done with segmentation + export\n", + "[231/720] tile_r000011_c000024\n", + " -> skipped (100% Nodata)\n", + "[232/720] tile_r000011_c000025\n", + " -> skipped (100% Nodata)\n", + "[233/720] tile_r000011_c000026\n", + " -> skipped (100% Nodata)\n", + "[234/720] tile_r000011_c000027\n", + " -> skipped (100% Nodata)\n", + "[235/720] tile_r000011_c000028\n", + " -> skipped (100% Nodata)\n", + "[236/720] tile_r000011_c000029\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.BpaNNCn48p/ipykernel_53064/3422303653.py:106: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " -> skipped (100% Nodata)\n", + "[237/720] tile_r000011_c000030\n", + " -> skipped (100% Nodata)\n", + "[238/720] tile_r000011_c000031\n", + " -> skipped (100% Nodata)\n", + "[239/720] tile_r000011_c000032\n", + " -> skipped (100% Nodata)\n", + "[240/720] tile_r000011_c000033\n", + " -> skipped (100% Nodata)\n", + "[241/720] tile_r000012_c000004\n", + " -> skipped (100% Nodata)\n", + "[242/720] tile_r000012_c000005\n", + " -> skipped (100% Nodata)\n", + "[243/720] tile_r000012_c000006\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.04it/s]\n", + "100%|██████████| 3/3 [00:01<00:00, 2.94it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 176/176 [00:03<00:00, 48.95it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "138it [00:00, 213.76it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "141it [00:00, 221.88it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 49/49 [00:01<00:00, 46.02it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 50/50 [00:00<00:00, 150.48it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.BpaNNCn48p/ipykernel_53064/3422303653.py:106: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/ouputgpkg/tile_r000012_c000006_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/csv_stats/tile_r000012_c000006_grain_stats.csv\n", + "done with segmentation + export\n", + "[244/720] tile_r000012_c000007\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.18it/s]\n", + "100%|██████████| 3/3 [00:00<00:00, 3.03it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 185/185 [00:03<00:00, 50.30it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "113it [00:00, 310.95it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "123it [00:00, 305.86it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 49/49 [00:00<00:00, 86.07it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 52/52 [00:00<00:00, 143.07it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.BpaNNCn48p/ipykernel_53064/3422303653.py:106: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/ouputgpkg/tile_r000012_c000007_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/csv_stats/tile_r000012_c000007_grain_stats.csv\n", + "done with segmentation + export\n", + "[245/720] tile_r000012_c000008\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.18it/s]\n", + "100%|██████████| 3/3 [00:00<00:00, 3.10it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 180/180 [00:04<00:00, 42.15it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "132it [00:04, 29.75it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "116it [00:00, 244.41it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 37/37 [00:00<00:00, 45.95it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 46/46 [00:00<00:00, 154.06it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.BpaNNCn48p/ipykernel_53064/3422303653.py:106: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/ouputgpkg/tile_r000012_c000008_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/csv_stats/tile_r000012_c000008_grain_stats.csv\n", + "done with segmentation + export\n", + "[246/720] tile_r000012_c000009\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.07it/s]\n", + "100%|██████████| 3/3 [00:01<00:00, 2.96it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 296/296 [00:06<00:00, 45.74it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "230it [00:01, 198.33it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "229it [00:01, 222.34it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 61/61 [00:01<00:00, 32.81it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 92/92 [00:00<00:00, 170.51it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.BpaNNCn48p/ipykernel_53064/3422303653.py:106: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/ouputgpkg/tile_r000012_c000009_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/csv_stats/tile_r000012_c000009_grain_stats.csv\n", + "done with segmentation + export\n", + "[247/720] tile_r000012_c000010\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.17it/s]\n", + "100%|██████████| 3/3 [00:00<00:00, 3.13it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 307/307 [00:05<00:00, 54.79it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "230it [00:00, 236.84it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "229it [00:00, 251.23it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 64/64 [00:01<00:00, 41.93it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 102/102 [00:00<00:00, 155.63it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.BpaNNCn48p/ipykernel_53064/3422303653.py:106: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/ouputgpkg/tile_r000012_c000010_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/csv_stats/tile_r000012_c000010_grain_stats.csv\n", + "done with segmentation + export\n", + "[248/720] tile_r000012_c000011\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.13it/s]\n", + "100%|██████████| 3/3 [00:00<00:00, 3.09it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 221/221 [00:06<00:00, 36.41it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "155it [00:00, 262.59it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "170it [00:00, 261.90it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 62/62 [00:01<00:00, 53.55it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 68/68 [00:00<00:00, 147.84it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.BpaNNCn48p/ipykernel_53064/3422303653.py:106: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/ouputgpkg/tile_r000012_c000011_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/csv_stats/tile_r000012_c000011_grain_stats.csv\n", + "done with segmentation + export\n", + "[249/720] tile_r000012_c000012\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.22it/s]\n", + "100%|██████████| 3/3 [00:00<00:00, 3.18it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 299/299 [00:06<00:00, 42.98it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "208it [00:00, 373.21it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "245it [00:00, 365.89it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 94/94 [00:01<00:00, 69.09it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 104/104 [00:00<00:00, 191.08it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.BpaNNCn48p/ipykernel_53064/3422303653.py:106: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/ouputgpkg/tile_r000012_c000012_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/csv_stats/tile_r000012_c000012_grain_stats.csv\n", + "done with segmentation + export\n", + "[250/720] tile_r000012_c000013\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.37it/s]\n", + "100%|██████████| 3/3 [00:00<00:00, 3.44it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 254/254 [00:05<00:00, 44.48it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "161it [00:00, 236.10it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "158it [00:00, 477.79it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 44/44 [00:00<00:00, 46.13it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 80/80 [00:00<00:00, 200.52it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.BpaNNCn48p/ipykernel_53064/3422303653.py:106: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/ouputgpkg/tile_r000012_c000013_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/csv_stats/tile_r000012_c000013_grain_stats.csv\n", + "done with segmentation + export\n", + "[251/720] tile_r000012_c000014\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.37it/s]\n", + "100%|██████████| 3/3 [00:00<00:00, 3.32it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 271/271 [00:06<00:00, 40.31it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "177it [00:00, 336.57it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "207it [00:00, 346.76it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 79/79 [00:01<00:00, 41.96it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 92/92 [00:00<00:00, 196.53it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.BpaNNCn48p/ipykernel_53064/3422303653.py:106: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/ouputgpkg/tile_r000012_c000014_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/csv_stats/tile_r000012_c000014_grain_stats.csv\n", + "done with segmentation + export\n", + "[252/720] tile_r000012_c000015\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 2.97it/s]\n", + "100%|██████████| 3/3 [00:00<00:00, 3.03it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 284/284 [00:07<00:00, 39.97it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "215it [00:01, 180.22it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "212it [00:00, 295.99it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 59/59 [00:01<00:00, 40.24it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 92/92 [00:00<00:00, 164.34it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n", + "Warning: len(gdf)=92 != len(grain_stats)=91 -> truncating to 91\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.BpaNNCn48p/ipykernel_53064/3422303653.py:106: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/ouputgpkg/tile_r000012_c000015_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/csv_stats/tile_r000012_c000015_grain_stats.csv\n", + "done with segmentation + export\n", + "[253/720] tile_r000012_c000016\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 2.90it/s]\n", + "100%|██████████| 3/3 [00:00<00:00, 3.08it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 277/277 [00:07<00:00, 36.46it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "174it [00:00, 187.77it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "171it [00:00, 273.20it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 47/47 [00:01<00:00, 25.81it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 92/92 [00:00<00:00, 153.56it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.BpaNNCn48p/ipykernel_53064/3422303653.py:106: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/ouputgpkg/tile_r000012_c000016_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/csv_stats/tile_r000012_c000016_grain_stats.csv\n", + "done with segmentation + export\n", + "[254/720] tile_r000012_c000017\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 2.95it/s]\n", + "100%|██████████| 3/3 [00:00<00:00, 3.11it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 342/342 [00:09<00:00, 37.46it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "232it [00:00, 397.50it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "260it [00:00, 367.36it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 97/97 [00:01<00:00, 65.93it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 110/110 [00:00<00:00, 182.80it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.BpaNNCn48p/ipykernel_53064/3422303653.py:106: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/ouputgpkg/tile_r000012_c000017_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/csv_stats/tile_r000012_c000017_grain_stats.csv\n", + "done with segmentation + export\n", + "[255/720] tile_r000012_c000018\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 2.91it/s]\n", + "100%|██████████| 3/3 [00:00<00:00, 3.08it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 350/350 [00:07<00:00, 48.66it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "247it [00:02, 112.15it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "241it [00:01, 156.37it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 65/65 [00:02<00:00, 26.46it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 102/102 [00:00<00:00, 156.30it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.BpaNNCn48p/ipykernel_53064/3422303653.py:106: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/ouputgpkg/tile_r000012_c000018_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/csv_stats/tile_r000012_c000018_grain_stats.csv\n", + "done with segmentation + export\n", + "[256/720] tile_r000012_c000019\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 2.94it/s]\n", + "100%|██████████| 3/3 [00:01<00:00, 2.97it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 322/322 [00:08<00:00, 39.92it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "216it [00:00, 292.71it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "236it [00:00, 314.31it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 88/88 [00:01<00:00, 52.29it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 94/94 [00:00<00:00, 194.51it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.BpaNNCn48p/ipykernel_53064/3422303653.py:106: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/ouputgpkg/tile_r000012_c000019_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/csv_stats/tile_r000012_c000019_grain_stats.csv\n", + "done with segmentation + export\n", + "[257/720] tile_r000012_c000020\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 2.90it/s]\n", + "100%|██████████| 3/3 [00:00<00:00, 3.01it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 273/273 [00:06<00:00, 44.91it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "213it [00:00, 366.87it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "227it [00:00, 364.55it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 87/87 [00:00<00:00, 87.76it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 91/91 [00:00<00:00, 189.72it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n", + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/ouputgpkg/tile_r000012_c000020_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/csv_stats/tile_r000012_c000020_grain_stats.csv\n", + "done with segmentation + export\n", + "[258/720] tile_r000012_c000021\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.BpaNNCn48p/ipykernel_53064/3422303653.py:106: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.18it/s]\n", + "100%|██████████| 3/3 [00:00<00:00, 3.48it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 324/324 [00:06<00:00, 48.51it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "230it [00:00, 319.59it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "226it [00:00, 390.66it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 81/81 [00:01<00:00, 64.34it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 88/88 [00:00<00:00, 201.40it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.BpaNNCn48p/ipykernel_53064/3422303653.py:106: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/ouputgpkg/tile_r000012_c000021_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/csv_stats/tile_r000012_c000021_grain_stats.csv\n", + "done with segmentation + export\n", + "[259/720] tile_r000012_c000022\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.24it/s]\n", + "100%|██████████| 3/3 [00:00<00:00, 3.27it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 226/226 [00:06<00:00, 37.05it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "141it [00:00, 441.48it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "150it [00:00, 432.15it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 59/59 [00:00<00:00, 95.70it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 61/61 [00:00<00:00, 169.08it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.BpaNNCn48p/ipykernel_53064/3422303653.py:106: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/ouputgpkg/tile_r000012_c000022_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/csv_stats/tile_r000012_c000022_grain_stats.csv\n", + "done with segmentation + export\n", + "[260/720] tile_r000012_c000023\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 2.88it/s]\n", + "100%|██████████| 3/3 [00:00<00:00, 3.08it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 83/83 [00:02<00:00, 37.83it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "60it [00:00, 250.74it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "64it [00:00, 226.63it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 21/21 [00:01<00:00, 14.41it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 28/28 [00:00<00:00, 170.37it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.BpaNNCn48p/ipykernel_53064/3422303653.py:106: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/ouputgpkg/tile_r000012_c000023_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/csv_stats/tile_r000012_c000023_grain_stats.csv\n", + "done with segmentation + export\n", + "[261/720] tile_r000012_c000024\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.07it/s]\n", + "100%|██████████| 3/3 [00:00<00:00, 3.05it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "0it [00:00, ?it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "0it [00:00, ?it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "0it [00:00, ?it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "0it [00:00, ?it/s]\n", + "0it [00:00, ?it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n", + "No grains found -> skipping histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/ouputgpkg/tile_r000012_c000024_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/csv_stats/tile_r000012_c000024_grain_stats.csv\n", + "done with segmentation + export\n", + "[262/720] tile_r000012_c000025\n", + " -> skipped (100% Nodata)\n", + "[263/720] tile_r000012_c000026\n", + " -> skipped (100% Nodata)\n", + "[264/720] tile_r000012_c000027\n", + " -> skipped (100% Nodata)\n", + "[265/720] tile_r000012_c000028\n", + " -> skipped (100% Nodata)\n", + "[266/720] tile_r000012_c000029\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.BpaNNCn48p/ipykernel_53064/3422303653.py:106: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " -> skipped (100% Nodata)\n", + "[267/720] tile_r000012_c000030\n", + " -> skipped (100% Nodata)\n", + "[268/720] tile_r000012_c000031\n", + " -> skipped (100% Nodata)\n", + "[269/720] tile_r000012_c000032\n", + " -> skipped (100% Nodata)\n", + "[270/720] tile_r000012_c000033\n", + " -> skipped (100% Nodata)\n", + "[271/720] tile_r000013_c000004\n", + " -> skipped (100% Nodata)\n", + "[272/720] tile_r000013_c000005\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.11it/s]\n", + "100%|██████████| 3/3 [00:00<00:00, 3.14it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 27/27 [00:00<00:00, 45.06it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "16it [00:00, 255.33it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "17it [00:00, 255.58it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:00<00:00, 34.96it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 7/7 [00:00<00:00, 156.13it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.BpaNNCn48p/ipykernel_53064/3422303653.py:106: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/ouputgpkg/tile_r000013_c000005_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/csv_stats/tile_r000013_c000005_grain_stats.csv\n", + "done with segmentation + export\n", + "[273/720] tile_r000013_c000006\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 2.95it/s]\n", + "100%|██████████| 3/3 [00:01<00:00, 2.98it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 319/319 [00:06<00:00, 50.10it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "229it [00:02, 110.71it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "239it [00:02, 111.82it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 67/67 [00:03<00:00, 21.59it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 75/75 [00:00<00:00, 150.10it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.BpaNNCn48p/ipykernel_53064/3422303653.py:106: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/ouputgpkg/tile_r000013_c000006_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/csv_stats/tile_r000013_c000006_grain_stats.csv\n", + "done with segmentation + export\n", + "[274/720] tile_r000013_c000007\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 2.95it/s]\n", + "100%|██████████| 3/3 [00:01<00:00, 2.99it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 338/338 [00:06<00:00, 53.14it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "254it [00:01, 239.46it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "269it [00:01, 238.30it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 102/102 [00:01<00:00, 65.99it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 104/104 [00:00<00:00, 180.92it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.BpaNNCn48p/ipykernel_53064/3422303653.py:106: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/ouputgpkg/tile_r000013_c000007_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/csv_stats/tile_r000013_c000007_grain_stats.csv\n", + "done with segmentation + export\n", + "[275/720] tile_r000013_c000008\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.13it/s]\n", + "100%|██████████| 3/3 [00:00<00:00, 3.15it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 206/206 [00:04<00:00, 42.73it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "117it [00:00, 247.72it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "126it [00:00, 235.37it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 48/48 [00:00<00:00, 64.69it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 49/49 [00:00<00:00, 158.09it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n", + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/ouputgpkg/tile_r000013_c000008_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/csv_stats/tile_r000013_c000008_grain_stats.csv\n", + "done with segmentation + export\n", + "[276/720] tile_r000013_c000009\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.BpaNNCn48p/ipykernel_53064/3422303653.py:106: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.15it/s]\n", + "100%|██████████| 3/3 [00:01<00:00, 2.98it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 290/290 [00:05<00:00, 52.70it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "230it [00:01, 183.48it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "248it [00:01, 199.90it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 90/90 [00:01<00:00, 46.64it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 92/92 [00:00<00:00, 165.07it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.BpaNNCn48p/ipykernel_53064/3422303653.py:106: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/ouputgpkg/tile_r000013_c000009_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/csv_stats/tile_r000013_c000009_grain_stats.csv\n", + "done with segmentation + export\n", + "[277/720] tile_r000013_c000010\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 2.96it/s]\n", + "100%|██████████| 3/3 [00:01<00:00, 2.84it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 355/355 [00:07<00:00, 48.77it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "275it [00:00, 289.64it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "298it [00:01, 278.77it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 109/109 [00:01<00:00, 58.68it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 119/119 [00:00<00:00, 175.72it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.BpaNNCn48p/ipykernel_53064/3422303653.py:106: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/ouputgpkg/tile_r000013_c000010_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/csv_stats/tile_r000013_c000010_grain_stats.csv\n", + "done with segmentation + export\n", + "[278/720] tile_r000013_c000011\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.08it/s]\n", + "100%|██████████| 3/3 [00:01<00:00, 2.87it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 324/324 [00:07<00:00, 45.40it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "257it [00:01, 156.89it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "252it [00:00, 261.40it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 81/81 [00:01<00:00, 50.00it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 106/106 [00:00<00:00, 176.52it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.BpaNNCn48p/ipykernel_53064/3422303653.py:106: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/ouputgpkg/tile_r000013_c000011_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/csv_stats/tile_r000013_c000011_grain_stats.csv\n", + "done with segmentation + export\n", + "[279/720] tile_r000013_c000012\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.15it/s]\n", + "100%|██████████| 3/3 [00:00<00:00, 3.01it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 231/231 [00:05<00:00, 39.67it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "127it [00:01, 104.12it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "145it [00:01, 105.52it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 51/51 [00:01<00:00, 26.86it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 57/57 [00:00<00:00, 151.55it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.BpaNNCn48p/ipykernel_53064/3422303653.py:106: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/ouputgpkg/tile_r000013_c000012_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/csv_stats/tile_r000013_c000012_grain_stats.csv\n", + "done with segmentation + export\n", + "[280/720] tile_r000013_c000013\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.17it/s]\n", + "100%|██████████| 3/3 [00:00<00:00, 3.02it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 264/264 [00:07<00:00, 37.32it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "151it [00:00, 230.40it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "150it [00:00, 242.74it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 36/36 [00:01<00:00, 25.46it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 81/81 [00:00<00:00, 162.18it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.BpaNNCn48p/ipykernel_53064/3422303653.py:106: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/ouputgpkg/tile_r000013_c000013_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/csv_stats/tile_r000013_c000013_grain_stats.csv\n", + "done with segmentation + export\n", + "[281/720] tile_r000013_c000014\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.02it/s]\n", + "100%|██████████| 3/3 [00:01<00:00, 2.98it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 306/306 [00:07<00:00, 43.00it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "225it [00:01, 166.02it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "29it [00:00, 570.04it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 1/1 [00:00<00:00, 8.93it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\n", + "100%|██████████| 25/25 [00:00<00:00, 204.31it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.BpaNNCn48p/ipykernel_53064/3422303653.py:106: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/ouputgpkg/tile_r000013_c000014_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/csv_stats/tile_r000013_c000014_grain_stats.csv\n", + "done with segmentation + export\n", + "[282/720] tile_r000013_c000015\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.17it/s]\n", + "100%|██████████| 3/3 [00:00<00:00, 3.08it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 189/189 [00:04<00:00, 38.47it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "110it [00:00, 261.40it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "108it [00:00, 321.63it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 24/24 [00:00<00:00, 24.04it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 57/57 [00:00<00:00, 147.06it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n", + "Warning: len(gdf)=57 != len(grain_stats)=56 -> truncating to 56\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.BpaNNCn48p/ipykernel_53064/3422303653.py:106: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/ouputgpkg/tile_r000013_c000015_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/csv_stats/tile_r000013_c000015_grain_stats.csv\n", + "done with segmentation + export\n", + "[283/720] tile_r000013_c000016\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.14it/s]\n", + "100%|██████████| 3/3 [00:00<00:00, 3.12it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 262/262 [00:06<00:00, 39.93it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "180it [00:01, 118.88it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "165it [00:00, 214.53it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 45/45 [00:01<00:00, 28.08it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 71/71 [00:00<00:00, 156.84it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.BpaNNCn48p/ipykernel_53064/3422303653.py:106: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/ouputgpkg/tile_r000013_c000016_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/csv_stats/tile_r000013_c000016_grain_stats.csv\n", + "done with segmentation + export\n", + "[284/720] tile_r000013_c000017\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 2.99it/s]\n", + "100%|██████████| 3/3 [00:00<00:00, 3.02it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 303/303 [00:06<00:00, 46.31it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "226it [00:00, 302.79it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "225it [00:00, 322.61it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 62/62 [00:01<00:00, 43.95it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 102/102 [00:00<00:00, 177.97it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.BpaNNCn48p/ipykernel_53064/3422303653.py:106: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/ouputgpkg/tile_r000013_c000017_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/csv_stats/tile_r000013_c000017_grain_stats.csv\n", + "done with segmentation + export\n", + "[285/720] tile_r000013_c000018\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 2.94it/s]\n", + "100%|██████████| 3/3 [00:00<00:00, 3.07it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 302/302 [00:07<00:00, 42.99it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "225it [00:01, 164.04it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "218it [00:00, 230.46it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 58/58 [00:01<00:00, 32.82it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 89/89 [00:00<00:00, 161.46it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.BpaNNCn48p/ipykernel_53064/3422303653.py:106: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/ouputgpkg/tile_r000013_c000018_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/csv_stats/tile_r000013_c000018_grain_stats.csv\n", + "done with segmentation + export\n", + "[286/720] tile_r000013_c000019\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.09it/s]\n", + "100%|██████████| 3/3 [00:00<00:00, 3.26it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 293/293 [00:06<00:00, 48.64it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "235it [00:00, 243.59it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "234it [00:00, 276.39it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 74/74 [00:01<00:00, 49.18it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 101/101 [00:00<00:00, 174.42it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.BpaNNCn48p/ipykernel_53064/3422303653.py:106: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/ouputgpkg/tile_r000013_c000019_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/csv_stats/tile_r000013_c000019_grain_stats.csv\n", + "done with segmentation + export\n", + "[287/720] tile_r000013_c000020\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.02it/s]\n", + "100%|██████████| 3/3 [00:00<00:00, 3.14it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 328/328 [00:06<00:00, 47.50it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "242it [00:00, 260.13it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "241it [00:00, 278.21it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 76/76 [00:01<00:00, 41.20it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 106/106 [00:00<00:00, 202.15it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.BpaNNCn48p/ipykernel_53064/3422303653.py:106: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/ouputgpkg/tile_r000013_c000020_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/csv_stats/tile_r000013_c000020_grain_stats.csv\n", + "done with segmentation + export\n", + "[288/720] tile_r000013_c000021\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.00it/s]\n", + "100%|██████████| 3/3 [00:00<00:00, 3.13it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 287/287 [00:05<00:00, 50.34it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "226it [00:01, 222.81it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "224it [00:00, 342.23it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 78/78 [00:01<00:00, 60.38it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 86/86 [00:00<00:00, 192.91it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.BpaNNCn48p/ipykernel_53064/3422303653.py:106: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/ouputgpkg/tile_r000013_c000021_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/csv_stats/tile_r000013_c000021_grain_stats.csv\n", + "done with segmentation + export\n", + "[289/720] tile_r000013_c000022\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 2.94it/s]\n", + "100%|██████████| 3/3 [00:00<00:00, 3.00it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 256/256 [00:05<00:00, 49.88it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "205it [00:00, 377.37it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "203it [00:00, 499.81it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 76/76 [00:00<00:00, 85.35it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 89/89 [00:00<00:00, 199.05it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.BpaNNCn48p/ipykernel_53064/3422303653.py:106: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/ouputgpkg/tile_r000013_c000022_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/csv_stats/tile_r000013_c000022_grain_stats.csv\n", + "done with segmentation + export\n", + "[290/720] tile_r000013_c000023\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.01it/s]\n", + "100%|██████████| 3/3 [00:00<00:00, 3.12it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 125/125 [00:03<00:00, 39.75it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "91it [00:00, 746.41it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "112it [00:00, 507.57it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 50/50 [00:00<00:00, 144.77it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 52/52 [00:00<00:00, 156.53it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.BpaNNCn48p/ipykernel_53064/3422303653.py:106: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/ouputgpkg/tile_r000013_c000023_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/csv_stats/tile_r000013_c000023_grain_stats.csv\n", + "done with segmentation + export\n", + "[291/720] tile_r000013_c000024\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 2.99it/s]\n", + "100%|██████████| 3/3 [00:01<00:00, 2.95it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 12/12 [00:00<00:00, 23.99it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "3it [00:00, 1003.90it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "4it [00:00, 722.78it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 2/2 [00:00<00:00, 163.80it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 2/2 [00:00<00:00, 189.71it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.BpaNNCn48p/ipykernel_53064/3422303653.py:106: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/ouputgpkg/tile_r000013_c000024_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/csv_stats/tile_r000013_c000024_grain_stats.csv\n", + "done with segmentation + export\n", + "[292/720] tile_r000013_c000025\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.03it/s]\n", + "100%|██████████| 3/3 [00:00<00:00, 3.18it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "0it [00:00, ?it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "0it [00:00, ?it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "0it [00:00, ?it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "0it [00:00, ?it/s]\n", + "0it [00:00, ?it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n", + "No grains found -> skipping histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/ouputgpkg/tile_r000013_c000025_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/csv_stats/tile_r000013_c000025_grain_stats.csv\n", + "done with segmentation + export\n", + "[293/720] tile_r000013_c000026\n", + " -> skipped (100% Nodata)\n", + "[294/720] tile_r000013_c000027\n", + " -> skipped (100% Nodata)\n", + "[295/720] tile_r000013_c000028\n", + " -> skipped (100% Nodata)\n", + "[296/720] tile_r000013_c000029\n", + " -> skipped (100% Nodata)\n", + "[297/720] tile_r000013_c000030\n", + " -> skipped (100% Nodata)\n", + "[298/720] tile_r000013_c000031\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.BpaNNCn48p/ipykernel_53064/3422303653.py:106: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " -> skipped (100% Nodata)\n", + "[299/720] tile_r000013_c000032\n", + " -> skipped (100% Nodata)\n", + "[300/720] tile_r000013_c000033\n", + " -> skipped (100% Nodata)\n", + "[301/720] tile_r000014_c000004\n", + " -> skipped (100% Nodata)\n", + "[302/720] tile_r000014_c000005\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.05it/s]\n", + "100%|██████████| 3/3 [00:00<00:00, 3.13it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 118/118 [00:02<00:00, 44.87it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "69it [00:00, 234.30it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "76it [00:00, 232.42it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 28/28 [00:00<00:00, 59.33it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 28/28 [00:00<00:00, 159.72it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.BpaNNCn48p/ipykernel_53064/3422303653.py:106: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/ouputgpkg/tile_r000014_c000005_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/csv_stats/tile_r000014_c000005_grain_stats.csv\n", + "done with segmentation + export\n", + "[303/720] tile_r000014_c000006\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.03it/s]\n", + "100%|██████████| 3/3 [00:00<00:00, 3.18it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 225/225 [00:04<00:00, 47.74it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "173it [00:01, 149.34it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "172it [00:01, 165.05it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 48/48 [00:02<00:00, 21.59it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 56/56 [00:00<00:00, 158.14it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.BpaNNCn48p/ipykernel_53064/3422303653.py:106: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/ouputgpkg/tile_r000014_c000006_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/csv_stats/tile_r000014_c000006_grain_stats.csv\n", + "done with segmentation + export\n", + "[304/720] tile_r000014_c000007\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.02it/s]\n", + "100%|██████████| 3/3 [00:00<00:00, 3.12it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 285/285 [00:05<00:00, 50.33it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "219it [00:02, 96.83it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "211it [00:00, 233.15it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 76/76 [00:01<00:00, 63.10it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 85/85 [00:00<00:00, 149.19it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.BpaNNCn48p/ipykernel_53064/3422303653.py:106: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/ouputgpkg/tile_r000014_c000007_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/csv_stats/tile_r000014_c000007_grain_stats.csv\n", + "done with segmentation + export\n", + "[305/720] tile_r000014_c000008\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.10it/s]\n", + "100%|██████████| 3/3 [00:00<00:00, 3.10it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 291/291 [00:06<00:00, 48.11it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "209it [00:01, 162.13it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "218it [00:01, 163.11it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 79/79 [00:01<00:00, 48.02it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 82/82 [00:00<00:00, 160.01it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n", + "Warning: len(gdf)=82 != len(grain_stats)=81 -> truncating to 81\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.BpaNNCn48p/ipykernel_53064/3422303653.py:106: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/ouputgpkg/tile_r000014_c000008_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/csv_stats/tile_r000014_c000008_grain_stats.csv\n", + "done with segmentation + export\n", + "[306/720] tile_r000014_c000009\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 2.99it/s]\n", + "100%|██████████| 3/3 [00:00<00:00, 3.15it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 274/274 [00:05<00:00, 46.18it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "204it [00:00, 276.97it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "210it [00:00, 285.46it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 79/79 [00:01<00:00, 72.11it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 80/80 [00:00<00:00, 187.62it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.BpaNNCn48p/ipykernel_53064/3422303653.py:106: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/ouputgpkg/tile_r000014_c000009_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/csv_stats/tile_r000014_c000009_grain_stats.csv\n", + "done with segmentation + export\n", + "[307/720] tile_r000014_c000010\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.12it/s]\n", + "100%|██████████| 3/3 [00:00<00:00, 3.03it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 361/361 [00:07<00:00, 49.82it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "246it [00:01, 149.76it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "236it [00:00, 270.38it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 80/80 [00:01<00:00, 56.49it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 93/93 [00:00<00:00, 170.61it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.BpaNNCn48p/ipykernel_53064/3422303653.py:106: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/ouputgpkg/tile_r000014_c000010_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/csv_stats/tile_r000014_c000010_grain_stats.csv\n", + "done with segmentation + export\n", + "[308/720] tile_r000014_c000011\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.18it/s]\n", + "100%|██████████| 3/3 [00:00<00:00, 3.13it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 377/377 [00:07<00:00, 51.47it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "288it [00:01, 258.09it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "287it [00:01, 285.68it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 97/97 [00:01<00:00, 58.14it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 119/119 [00:00<00:00, 186.31it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.BpaNNCn48p/ipykernel_53064/3422303653.py:106: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/ouputgpkg/tile_r000014_c000011_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/csv_stats/tile_r000014_c000011_grain_stats.csv\n", + "done with segmentation + export\n", + "[309/720] tile_r000014_c000012\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.12it/s]\n", + "100%|██████████| 3/3 [00:00<00:00, 3.10it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 331/331 [00:06<00:00, 52.39it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "270it [00:01, 220.38it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "267it [00:00, 275.14it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 82/82 [00:01<00:00, 47.16it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 118/118 [00:00<00:00, 155.88it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n", + "Warning: len(gdf)=118 != len(grain_stats)=117 -> truncating to 117\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.BpaNNCn48p/ipykernel_53064/3422303653.py:106: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/ouputgpkg/tile_r000014_c000012_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/csv_stats/tile_r000014_c000012_grain_stats.csv\n", + "done with segmentation + export\n", + "[310/720] tile_r000014_c000013\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.14it/s]\n", + "100%|██████████| 3/3 [00:00<00:00, 3.07it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 319/319 [00:06<00:00, 47.31it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "229it [00:01, 220.32it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "226it [00:00, 256.30it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 65/65 [00:01<00:00, 35.89it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 88/88 [00:00<00:00, 176.07it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.BpaNNCn48p/ipykernel_53064/3422303653.py:106: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/ouputgpkg/tile_r000014_c000013_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/csv_stats/tile_r000014_c000013_grain_stats.csv\n", + "done with segmentation + export\n", + "[311/720] tile_r000014_c000014\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.09it/s]\n", + "100%|██████████| 3/3 [00:01<00:00, 2.93it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 327/327 [00:06<00:00, 47.33it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "262it [00:01, 210.15it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "258it [00:00, 412.54it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 95/95 [00:00<00:00, 95.72it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 112/112 [00:02<00:00, 42.42it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.BpaNNCn48p/ipykernel_53064/3422303653.py:106: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/ouputgpkg/tile_r000014_c000014_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/csv_stats/tile_r000014_c000014_grain_stats.csv\n", + "done with segmentation + export\n", + "[312/720] tile_r000014_c000015\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.11it/s]\n", + "100%|██████████| 3/3 [00:01<00:00, 2.88it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 331/331 [00:07<00:00, 45.35it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "258it [00:00, 259.42it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "257it [00:00, 329.77it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 81/81 [00:01<00:00, 55.55it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 111/111 [00:00<00:00, 179.89it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.BpaNNCn48p/ipykernel_53064/3422303653.py:106: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/ouputgpkg/tile_r000014_c000015_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/csv_stats/tile_r000014_c000015_grain_stats.csv\n", + "done with segmentation + export\n", + "[313/720] tile_r000014_c000016\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.23it/s]\n", + "100%|██████████| 3/3 [00:00<00:00, 3.11it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 198/198 [00:04<00:00, 46.11it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "138it [00:01, 123.65it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "137it [00:00, 169.35it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 32/32 [00:03<00:00, 9.29it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 72/72 [00:00<00:00, 133.80it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.BpaNNCn48p/ipykernel_53064/3422303653.py:106: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/ouputgpkg/tile_r000014_c000016_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/csv_stats/tile_r000014_c000016_grain_stats.csv\n", + "done with segmentation + export\n", + "[314/720] tile_r000014_c000017\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.12it/s]\n", + "100%|██████████| 3/3 [00:01<00:00, 2.98it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 248/248 [00:06<00:00, 37.17it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "167it [00:00, 184.99it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "166it [00:00, 251.19it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 46/46 [00:01<00:00, 27.27it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 76/76 [00:00<00:00, 163.77it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.BpaNNCn48p/ipykernel_53064/3422303653.py:106: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/ouputgpkg/tile_r000014_c000017_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/csv_stats/tile_r000014_c000017_grain_stats.csv\n", + "done with segmentation + export\n", + "[315/720] tile_r000014_c000018\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.10it/s]\n", + "100%|██████████| 3/3 [00:01<00:00, 2.97it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 209/209 [00:05<00:00, 34.84it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "112it [00:02, 45.09it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "93it [00:00, 202.30it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 20/20 [00:01<00:00, 14.62it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 42/42 [00:00<00:00, 116.16it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.BpaNNCn48p/ipykernel_53064/3422303653.py:106: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/ouputgpkg/tile_r000014_c000018_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/csv_stats/tile_r000014_c000018_grain_stats.csv\n", + "done with segmentation + export\n", + "[316/720] tile_r000014_c000019\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.23it/s]\n", + "100%|██████████| 3/3 [00:00<00:00, 3.02it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 325/325 [00:07<00:00, 41.56it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "224it [00:00, 241.78it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "222it [00:00, 273.85it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 66/66 [00:01<00:00, 43.94it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 101/101 [00:00<00:00, 181.25it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.BpaNNCn48p/ipykernel_53064/3422303653.py:106: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/ouputgpkg/tile_r000014_c000019_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/csv_stats/tile_r000014_c000019_grain_stats.csv\n", + "done with segmentation + export\n", + "[317/720] tile_r000014_c000020\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.21it/s]\n", + "100%|██████████| 3/3 [00:00<00:00, 3.21it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 255/255 [00:06<00:00, 42.00it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "189it [00:01, 122.79it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "187it [00:01, 137.59it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 62/62 [00:01<00:00, 41.51it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 79/79 [00:00<00:00, 178.62it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.BpaNNCn48p/ipykernel_53064/3422303653.py:106: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/ouputgpkg/tile_r000014_c000020_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/csv_stats/tile_r000014_c000020_grain_stats.csv\n", + "done with segmentation + export\n", + "[318/720] tile_r000014_c000021\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.15it/s]\n", + "100%|██████████| 3/3 [00:00<00:00, 3.22it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 299/299 [00:06<00:00, 43.21it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "215it [00:00, 431.22it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "229it [00:00, 434.33it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 84/84 [00:01<00:00, 78.78it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 95/95 [00:00<00:00, 182.36it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.BpaNNCn48p/ipykernel_53064/3422303653.py:106: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/ouputgpkg/tile_r000014_c000021_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/csv_stats/tile_r000014_c000021_grain_stats.csv\n", + "done with segmentation + export\n", + "[319/720] tile_r000014_c000022\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.09it/s]\n", + "100%|██████████| 3/3 [00:00<00:00, 3.10it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 294/294 [00:05<00:00, 50.41it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "222it [00:00, 453.89it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "233it [00:00, 460.53it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 90/90 [00:01<00:00, 82.55it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 96/96 [00:00<00:00, 197.04it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n", + "Warning: len(gdf)=96 != len(grain_stats)=95 -> truncating to 95\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.BpaNNCn48p/ipykernel_53064/3422303653.py:106: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/ouputgpkg/tile_r000014_c000022_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/csv_stats/tile_r000014_c000022_grain_stats.csv\n", + "done with segmentation + export\n", + "[320/720] tile_r000014_c000023\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.06it/s]\n", + "100%|██████████| 3/3 [00:00<00:00, 3.04it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 350/350 [00:07<00:00, 44.97it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "243it [00:01, 181.92it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "240it [00:00, 256.01it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 75/75 [00:01<00:00, 42.11it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 97/97 [00:00<00:00, 200.45it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.BpaNNCn48p/ipykernel_53064/3422303653.py:106: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/ouputgpkg/tile_r000014_c000023_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/csv_stats/tile_r000014_c000023_grain_stats.csv\n", + "done with segmentation + export\n", + "[321/720] tile_r000014_c000024\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.33it/s]\n", + "100%|██████████| 3/3 [00:00<00:00, 3.47it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 76/76 [00:01<00:00, 39.57it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "47it [00:00, 102.47it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "45it [00:00, 273.14it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 12/12 [00:00<00:00, 17.92it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 24/24 [00:00<00:00, 157.40it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.BpaNNCn48p/ipykernel_53064/3422303653.py:106: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/ouputgpkg/tile_r000014_c000024_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/csv_stats/tile_r000014_c000024_grain_stats.csv\n", + "done with segmentation + export\n", + "[322/720] tile_r000014_c000025\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.42it/s]\n", + "100%|██████████| 3/3 [00:00<00:00, 3.42it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "0it [00:00, ?it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "0it [00:00, ?it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "0it [00:00, ?it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "0it [00:00, ?it/s]\n", + "0it [00:00, ?it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n", + "No grains found -> skipping histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/ouputgpkg/tile_r000014_c000025_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/csv_stats/tile_r000014_c000025_grain_stats.csv\n", + "done with segmentation + export\n", + "[323/720] tile_r000014_c000026\n", + " -> skipped (100% Nodata)\n", + "[324/720] tile_r000014_c000027\n", + " -> skipped (100% Nodata)\n", + "[325/720] tile_r000014_c000028\n", + " -> skipped (100% Nodata)\n", + "[326/720] tile_r000014_c000029\n", + " -> skipped (100% Nodata)\n", + "[327/720] tile_r000014_c000030\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.BpaNNCn48p/ipykernel_53064/3422303653.py:106: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " -> skipped (100% Nodata)\n", + "[328/720] tile_r000014_c000031\n", + " -> skipped (100% Nodata)\n", + "[329/720] tile_r000014_c000032\n", + " -> skipped (100% Nodata)\n", + "[330/720] tile_r000014_c000033\n", + " -> skipped (100% Nodata)\n", + "[331/720] tile_r000015_c000004\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.25it/s]\n", + "100%|██████████| 3/3 [00:00<00:00, 3.46it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 3/3 [00:00<00:00, 18.36it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "0it [00:00, ?it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "0it [00:00, ?it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "0it [00:00, ?it/s]\n", + "0it [00:00, ?it/s]\n", + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.BpaNNCn48p/ipykernel_53064/3422303653.py:106: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n", + "No grains found -> skipping histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/ouputgpkg/tile_r000015_c000004_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/csv_stats/tile_r000015_c000004_grain_stats.csv\n", + "done with segmentation + export\n", + "[332/720] tile_r000015_c000005\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.42it/s]\n", + "100%|██████████| 3/3 [00:00<00:00, 3.66it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 289/289 [00:04<00:00, 63.88it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "241it [00:02, 115.84it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "240it [00:02, 119.89it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 68/68 [00:03<00:00, 22.56it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 91/91 [00:00<00:00, 160.24it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n", + "Warning: len(gdf)=91 != len(grain_stats)=90 -> truncating to 90\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.BpaNNCn48p/ipykernel_53064/3422303653.py:106: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/ouputgpkg/tile_r000015_c000005_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/csv_stats/tile_r000015_c000005_grain_stats.csv\n", + "done with segmentation + export\n", + "[333/720] tile_r000015_c000006\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.05it/s]\n", + "100%|██████████| 3/3 [00:00<00:00, 3.15it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 272/272 [00:05<00:00, 53.08it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "194it [00:00, 203.00it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "204it [00:00, 207.07it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 78/78 [00:01<00:00, 50.26it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 80/80 [00:00<00:00, 169.87it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.BpaNNCn48p/ipykernel_53064/3422303653.py:106: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/ouputgpkg/tile_r000015_c000006_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/csv_stats/tile_r000015_c000006_grain_stats.csv\n", + "done with segmentation + export\n", + "[334/720] tile_r000015_c000007\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.10it/s]\n", + "100%|██████████| 3/3 [00:00<00:00, 3.14it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 126/126 [00:02<00:00, 43.80it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "80it [00:00, 262.96it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "84it [00:00, 263.14it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 32/32 [00:00<00:00, 56.35it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 34/34 [00:00<00:00, 125.66it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.BpaNNCn48p/ipykernel_53064/3422303653.py:106: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/ouputgpkg/tile_r000015_c000007_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/csv_stats/tile_r000015_c000007_grain_stats.csv\n", + "done with segmentation + export\n", + "[335/720] tile_r000015_c000008\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.13it/s]\n", + "100%|██████████| 3/3 [00:00<00:00, 3.30it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 222/222 [00:05<00:00, 41.71it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "126it [00:01, 114.64it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "131it [00:01, 118.91it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 40/40 [00:01<00:00, 27.49it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 44/44 [00:00<00:00, 107.95it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.BpaNNCn48p/ipykernel_53064/3422303653.py:106: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/ouputgpkg/tile_r000015_c000008_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/csv_stats/tile_r000015_c000008_grain_stats.csv\n", + "done with segmentation + export\n", + "[336/720] tile_r000015_c000009\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.09it/s]\n", + "100%|██████████| 3/3 [00:00<00:00, 3.29it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 318/318 [00:06<00:00, 51.84it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "257it [00:04, 53.28it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "29it [00:00, 60.80it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 2/2 [00:01<00:00, 1.67it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 14/14 [00:00<00:00, 92.32it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.BpaNNCn48p/ipykernel_53064/3422303653.py:106: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/ouputgpkg/tile_r000015_c000009_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/csv_stats/tile_r000015_c000009_grain_stats.csv\n", + "done with segmentation + export\n", + "[337/720] tile_r000015_c000010\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.00it/s]\n", + "100%|██████████| 3/3 [00:00<00:00, 3.17it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 371/371 [00:06<00:00, 55.70it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "300it [00:02, 143.24it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "295it [00:01, 174.77it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 93/93 [00:02<00:00, 31.91it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 112/112 [00:00<00:00, 166.15it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.BpaNNCn48p/ipykernel_53064/3422303653.py:106: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/ouputgpkg/tile_r000015_c000010_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/csv_stats/tile_r000015_c000010_grain_stats.csv\n", + "done with segmentation + export\n", + "[338/720] tile_r000015_c000011\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.05it/s]\n", + "100%|██████████| 3/3 [00:00<00:00, 3.19it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 360/360 [00:06<00:00, 53.77it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "280it [00:01, 212.74it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "279it [00:01, 218.87it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 100/100 [00:01<00:00, 60.32it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 111/111 [00:00<00:00, 178.73it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n", + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/ouputgpkg/tile_r000015_c000011_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/csv_stats/tile_r000015_c000011_grain_stats.csv\n", + "done with segmentation + export\n", + "[339/720] tile_r000015_c000012\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.BpaNNCn48p/ipykernel_53064/3422303653.py:106: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.09it/s]\n", + "100%|██████████| 3/3 [00:01<00:00, 2.93it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 376/376 [00:06<00:00, 55.14it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "303it [00:01, 241.03it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "313it [00:01, 247.93it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 115/115 [00:02<00:00, 51.26it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 120/120 [00:00<00:00, 186.54it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.BpaNNCn48p/ipykernel_53064/3422303653.py:106: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/ouputgpkg/tile_r000015_c000012_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/csv_stats/tile_r000015_c000012_grain_stats.csv\n", + "done with segmentation + export\n", + "[340/720] tile_r000015_c000013\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.12it/s]\n", + "100%|██████████| 3/3 [00:00<00:00, 3.09it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 340/340 [00:06<00:00, 51.36it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "236it [00:01, 180.74it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "233it [00:01, 219.20it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 69/69 [00:02<00:00, 27.71it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 98/98 [00:00<00:00, 163.44it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.BpaNNCn48p/ipykernel_53064/3422303653.py:106: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/ouputgpkg/tile_r000015_c000013_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/csv_stats/tile_r000015_c000013_grain_stats.csv\n", + "done with segmentation + export\n", + "[341/720] tile_r000015_c000014\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.26it/s]\n", + "100%|██████████| 3/3 [00:00<00:00, 3.26it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 317/317 [00:07<00:00, 42.72it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "236it [00:03, 74.79it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "226it [00:00, 375.67it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 69/69 [00:01<00:00, 61.31it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 113/113 [00:00<00:00, 172.16it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.BpaNNCn48p/ipykernel_53064/3422303653.py:106: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/ouputgpkg/tile_r000015_c000014_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/csv_stats/tile_r000015_c000014_grain_stats.csv\n", + "done with segmentation + export\n", + "[342/720] tile_r000015_c000015\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.22it/s]\n", + "100%|██████████| 3/3 [00:00<00:00, 3.10it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 344/344 [00:06<00:00, 52.77it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "256it [00:01, 195.08it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "250it [00:00, 328.22it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 80/80 [00:01<00:00, 51.91it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 114/114 [00:00<00:00, 173.04it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.BpaNNCn48p/ipykernel_53064/3422303653.py:106: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/ouputgpkg/tile_r000015_c000015_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/csv_stats/tile_r000015_c000015_grain_stats.csv\n", + "done with segmentation + export\n", + "[343/720] tile_r000015_c000016\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.17it/s]\n", + "100%|██████████| 3/3 [00:00<00:00, 3.16it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 251/251 [00:06<00:00, 38.44it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "158it [00:01, 110.75it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "150it [00:00, 200.73it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 36/36 [00:01<00:00, 19.65it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 72/72 [00:00<00:00, 135.27it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.BpaNNCn48p/ipykernel_53064/3422303653.py:106: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/ouputgpkg/tile_r000015_c000016_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/csv_stats/tile_r000015_c000016_grain_stats.csv\n", + "done with segmentation + export\n", + "[344/720] tile_r000015_c000017\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.24it/s]\n", + "100%|██████████| 3/3 [00:00<00:00, 3.02it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 203/203 [00:04<00:00, 41.46it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "136it [00:00, 303.66it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "165it [00:00, 316.10it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 61/61 [00:00<00:00, 62.59it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 72/72 [00:00<00:00, 166.06it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.BpaNNCn48p/ipykernel_53064/3422303653.py:106: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/ouputgpkg/tile_r000015_c000017_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/csv_stats/tile_r000015_c000017_grain_stats.csv\n", + "done with segmentation + export\n", + "[345/720] tile_r000015_c000018\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.26it/s]\n", + "100%|██████████| 3/3 [00:00<00:00, 3.11it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 224/224 [00:05<00:00, 39.59it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "137it [00:01, 92.87it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "21it [00:00, 254.01it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 1/1 [00:00<00:00, 12.30it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\n", + "100%|██████████| 18/18 [00:00<00:00, 54.50it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.BpaNNCn48p/ipykernel_53064/3422303653.py:106: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/ouputgpkg/tile_r000015_c000018_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/csv_stats/tile_r000015_c000018_grain_stats.csv\n", + "done with segmentation + export\n", + "[346/720] tile_r000015_c000019\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.22it/s]\n", + "100%|██████████| 3/3 [00:00<00:00, 3.20it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 279/279 [00:07<00:00, 39.46it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "157it [00:02, 64.52it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "147it [00:01, 125.44it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 34/34 [00:02<00:00, 15.93it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 62/62 [00:00<00:00, 142.81it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.BpaNNCn48p/ipykernel_53064/3422303653.py:106: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/ouputgpkg/tile_r000015_c000019_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/csv_stats/tile_r000015_c000019_grain_stats.csv\n", + "done with segmentation + export\n", + "[347/720] tile_r000015_c000020\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.18it/s]\n", + "100%|██████████| 3/3 [00:00<00:00, 3.16it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 276/276 [00:06<00:00, 41.65it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "196it [00:01, 166.90it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "195it [00:01, 194.00it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 49/49 [00:01<00:00, 25.31it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 76/76 [00:00<00:00, 139.43it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n", + "Warning: len(gdf)=76 != len(grain_stats)=75 -> truncating to 75\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.BpaNNCn48p/ipykernel_53064/3422303653.py:106: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/ouputgpkg/tile_r000015_c000020_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/csv_stats/tile_r000015_c000020_grain_stats.csv\n", + "done with segmentation + export\n", + "[348/720] tile_r000015_c000021\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.18it/s]\n", + "100%|██████████| 3/3 [00:00<00:00, 3.06it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 302/302 [00:07<00:00, 39.23it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "177it [00:01, 145.08it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "176it [00:01, 155.40it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 55/55 [00:01<00:00, 31.66it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 77/77 [00:00<00:00, 122.83it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.BpaNNCn48p/ipykernel_53064/3422303653.py:106: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/ouputgpkg/tile_r000015_c000021_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/csv_stats/tile_r000015_c000021_grain_stats.csv\n", + "done with segmentation + export\n", + "[349/720] tile_r000015_c000022\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.19it/s]\n", + "100%|██████████| 3/3 [00:00<00:00, 3.01it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 332/332 [00:09<00:00, 33.73it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "189it [00:00, 286.13it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "204it [00:00, 282.67it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 76/76 [00:01<00:00, 60.64it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 80/80 [00:00<00:00, 167.80it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.BpaNNCn48p/ipykernel_53064/3422303653.py:106: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/ouputgpkg/tile_r000015_c000022_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/csv_stats/tile_r000015_c000022_grain_stats.csv\n", + "done with segmentation + export\n", + "[350/720] tile_r000015_c000023\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.23it/s]\n", + "100%|██████████| 3/3 [00:00<00:00, 3.10it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 329/329 [00:08<00:00, 40.31it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "212it [00:00, 351.59it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "210it [00:00, 455.69it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 66/66 [00:01<00:00, 61.83it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 93/93 [00:00<00:00, 196.01it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.BpaNNCn48p/ipykernel_53064/3422303653.py:106: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/ouputgpkg/tile_r000015_c000023_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/csv_stats/tile_r000015_c000023_grain_stats.csv\n", + "done with segmentation + export\n", + "[351/720] tile_r000015_c000024\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.25it/s]\n", + "100%|██████████| 3/3 [00:00<00:00, 3.21it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 263/263 [00:06<00:00, 40.66it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "168it [00:00, 400.10it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "183it [00:00, 375.83it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 70/70 [00:00<00:00, 86.97it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 72/72 [00:00<00:00, 182.99it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.BpaNNCn48p/ipykernel_53064/3422303653.py:106: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/ouputgpkg/tile_r000015_c000024_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/csv_stats/tile_r000015_c000024_grain_stats.csv\n", + "done with segmentation + export\n", + "[352/720] tile_r000015_c000025\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.28it/s]\n", + "100%|██████████| 3/3 [00:00<00:00, 3.22it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 43/43 [00:01<00:00, 40.12it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "18it [00:00, 326.58it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "21it [00:00, 317.46it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 7/7 [00:00<00:00, 41.23it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 8/8 [00:00<00:00, 122.59it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.BpaNNCn48p/ipykernel_53064/3422303653.py:106: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/ouputgpkg/tile_r000015_c000025_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/csv_stats/tile_r000015_c000025_grain_stats.csv\n", + "done with segmentation + export\n", + "[353/720] tile_r000015_c000026\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.16it/s]\n", + "100%|██████████| 3/3 [00:00<00:00, 3.25it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "0it [00:00, ?it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "0it [00:00, ?it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "0it [00:00, ?it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "0it [00:00, ?it/s]\n", + "0it [00:00, ?it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n", + "No grains found -> skipping histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/ouputgpkg/tile_r000015_c000026_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/csv_stats/tile_r000015_c000026_grain_stats.csv\n", + "done with segmentation + export\n", + "[354/720] tile_r000015_c000027\n", + " -> skipped (100% Nodata)\n", + "[355/720] tile_r000015_c000028\n", + " -> skipped (100% Nodata)\n", + "[356/720] tile_r000015_c000029\n", + " -> skipped (100% Nodata)\n", + "[357/720] tile_r000015_c000030\n", + " -> skipped (100% Nodata)\n", + "[358/720] tile_r000015_c000031\n", + " -> skipped (100% Nodata)\n", + "[359/720] tile_r000015_c000032\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.BpaNNCn48p/ipykernel_53064/3422303653.py:106: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " -> skipped (100% Nodata)\n", + "[360/720] tile_r000015_c000033\n", + " -> skipped (100% Nodata)\n", + "[361/720] tile_r000016_c000004\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.23it/s]\n", + "100%|██████████| 3/3 [00:00<00:00, 3.20it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 85/85 [00:01<00:00, 43.39it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "44it [00:00, 243.61it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "52it [00:00, 227.65it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 18/18 [00:00<00:00, 58.03it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 20/20 [00:00<00:00, 156.58it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.BpaNNCn48p/ipykernel_53064/3422303653.py:106: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/ouputgpkg/tile_r000016_c000004_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/csv_stats/tile_r000016_c000004_grain_stats.csv\n", + "done with segmentation + export\n", + "[362/720] tile_r000016_c000005\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.26it/s]\n", + "100%|██████████| 3/3 [00:00<00:00, 3.14it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 321/321 [00:05<00:00, 54.58it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "246it [00:01, 130.69it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "244it [00:01, 146.77it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 73/73 [00:02<00:00, 34.36it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 90/90 [00:03<00:00, 29.48it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.BpaNNCn48p/ipykernel_53064/3422303653.py:106: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/ouputgpkg/tile_r000016_c000005_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/csv_stats/tile_r000016_c000005_grain_stats.csv\n", + "done with segmentation + export\n", + "[363/720] tile_r000016_c000006\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.00it/s]\n", + "100%|██████████| 3/3 [00:00<00:00, 3.02it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 268/268 [00:04<00:00, 54.76it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "215it [00:00, 236.22it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "213it [00:00, 316.58it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 77/77 [00:01<00:00, 75.04it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 90/90 [00:00<00:00, 180.23it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.BpaNNCn48p/ipykernel_53064/3422303653.py:106: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/ouputgpkg/tile_r000016_c000006_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/csv_stats/tile_r000016_c000006_grain_stats.csv\n", + "done with segmentation + export\n", + "[364/720] tile_r000016_c000007\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.17it/s]\n", + "100%|██████████| 3/3 [00:00<00:00, 3.20it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 277/277 [00:05<00:00, 54.76it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "223it [00:00, 226.42it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "228it [00:00, 228.59it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 81/81 [00:01<00:00, 53.23it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 83/83 [00:00<00:00, 177.14it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.BpaNNCn48p/ipykernel_53064/3422303653.py:106: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/ouputgpkg/tile_r000016_c000007_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/csv_stats/tile_r000016_c000007_grain_stats.csv\n", + "done with segmentation + export\n", + "[365/720] tile_r000016_c000008\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.11it/s]\n", + "100%|██████████| 3/3 [00:00<00:00, 3.23it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 314/314 [00:06<00:00, 49.28it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "221it [00:03, 63.76it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "207it [00:01, 126.09it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 75/75 [00:01<00:00, 44.01it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 85/85 [00:00<00:00, 175.28it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.BpaNNCn48p/ipykernel_53064/3422303653.py:106: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/ouputgpkg/tile_r000016_c000008_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/csv_stats/tile_r000016_c000008_grain_stats.csv\n", + "done with segmentation + export\n", + "[366/720] tile_r000016_c000009\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.18it/s]\n", + "100%|██████████| 3/3 [00:00<00:00, 3.25it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 253/253 [00:04<00:00, 53.80it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "180it [00:00, 250.06it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "186it [00:00, 244.01it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 65/65 [00:01<00:00, 55.99it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 68/68 [00:00<00:00, 161.62it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n", + "Warning: len(gdf)=68 != len(grain_stats)=67 -> truncating to 67\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.BpaNNCn48p/ipykernel_53064/3422303653.py:106: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/ouputgpkg/tile_r000016_c000009_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/csv_stats/tile_r000016_c000009_grain_stats.csv\n", + "done with segmentation + export\n", + "[367/720] tile_r000016_c000010\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.17it/s]\n", + "100%|██████████| 3/3 [00:00<00:00, 3.32it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 283/283 [00:05<00:00, 53.48it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "190it [00:02, 78.99it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "180it [00:01, 99.61it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 52/52 [00:03<00:00, 14.16it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 72/72 [00:00<00:00, 124.90it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.BpaNNCn48p/ipykernel_53064/3422303653.py:106: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/ouputgpkg/tile_r000016_c000010_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/csv_stats/tile_r000016_c000010_grain_stats.csv\n", + "done with segmentation + export\n", + "[368/720] tile_r000016_c000011\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.10it/s]\n", + "100%|██████████| 3/3 [00:00<00:00, 3.17it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 391/391 [00:07<00:00, 55.60it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "297it [00:01, 179.41it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "295it [00:01, 191.39it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 93/93 [00:02<00:00, 34.42it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 106/106 [00:00<00:00, 161.95it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.BpaNNCn48p/ipykernel_53064/3422303653.py:106: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/ouputgpkg/tile_r000016_c000011_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/csv_stats/tile_r000016_c000011_grain_stats.csv\n", + "done with segmentation + export\n", + "[369/720] tile_r000016_c000012\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.12it/s]\n", + "100%|██████████| 3/3 [00:00<00:00, 3.19it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 337/337 [00:06<00:00, 54.86it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "259it [00:01, 156.32it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "254it [00:01, 228.64it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 72/72 [00:02<00:00, 34.15it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 101/101 [00:00<00:00, 167.16it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.BpaNNCn48p/ipykernel_53064/3422303653.py:106: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/ouputgpkg/tile_r000016_c000012_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/csv_stats/tile_r000016_c000012_grain_stats.csv\n", + "done with segmentation + export\n", + "[370/720] tile_r000016_c000013\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.08it/s]\n", + "100%|██████████| 3/3 [00:00<00:00, 3.16it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 390/390 [00:07<00:00, 55.49it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "295it [00:01, 191.23it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "293it [00:01, 230.82it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 98/98 [00:01<00:00, 55.84it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 112/112 [00:00<00:00, 184.21it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.BpaNNCn48p/ipykernel_53064/3422303653.py:106: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/ouputgpkg/tile_r000016_c000013_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/csv_stats/tile_r000016_c000013_grain_stats.csv\n", + "done with segmentation + export\n", + "[371/720] tile_r000016_c000014\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.10it/s]\n", + "100%|██████████| 3/3 [00:00<00:00, 3.18it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 331/331 [00:06<00:00, 53.80it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "260it [00:01, 231.66it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "258it [00:01, 255.79it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 89/89 [00:01<00:00, 53.02it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 112/112 [00:00<00:00, 181.96it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.BpaNNCn48p/ipykernel_53064/3422303653.py:106: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/ouputgpkg/tile_r000016_c000014_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/csv_stats/tile_r000016_c000014_grain_stats.csv\n", + "done with segmentation + export\n", + "[372/720] tile_r000016_c000015\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.28it/s]\n", + "100%|██████████| 3/3 [00:00<00:00, 3.10it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 343/343 [00:06<00:00, 54.11it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "281it [00:00, 384.76it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "292it [00:00, 381.60it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 115/115 [00:01<00:00, 100.94it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 118/118 [00:00<00:00, 198.67it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.BpaNNCn48p/ipykernel_53064/3422303653.py:106: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/ouputgpkg/tile_r000016_c000015_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/csv_stats/tile_r000016_c000015_grain_stats.csv\n", + "done with segmentation + export\n", + "[373/720] tile_r000016_c000016\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.23it/s]\n", + "100%|██████████| 3/3 [00:00<00:00, 3.34it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 328/328 [00:06<00:00, 48.73it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "237it [00:01, 233.50it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "233it [00:00, 280.59it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 85/85 [00:01<00:00, 51.63it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 98/98 [00:00<00:00, 192.28it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.BpaNNCn48p/ipykernel_53064/3422303653.py:106: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/ouputgpkg/tile_r000016_c000016_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/csv_stats/tile_r000016_c000016_grain_stats.csv\n", + "done with segmentation + export\n", + "[374/720] tile_r000016_c000017\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.21it/s]\n", + "100%|██████████| 3/3 [00:00<00:00, 3.30it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 252/252 [00:06<00:00, 38.46it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "163it [00:07, 21.22it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "121it [00:00, 212.69it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 30/30 [00:01<00:00, 26.79it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 55/55 [00:00<00:00, 140.00it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.BpaNNCn48p/ipykernel_53064/3422303653.py:106: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/ouputgpkg/tile_r000016_c000017_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/csv_stats/tile_r000016_c000017_grain_stats.csv\n", + "done with segmentation + export\n", + "[375/720] tile_r000016_c000018\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.79it/s]\n", + "100%|██████████| 3/3 [00:00<00:00, 3.80it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 223/223 [00:05<00:00, 42.21it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "143it [00:00, 263.83it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "162it [00:00, 257.17it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 65/65 [00:01<00:00, 57.23it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 69/69 [00:00<00:00, 157.73it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.BpaNNCn48p/ipykernel_53064/3422303653.py:106: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/ouputgpkg/tile_r000016_c000018_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/csv_stats/tile_r000016_c000018_grain_stats.csv\n", + "done with segmentation + export\n", + "[376/720] tile_r000016_c000019\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.28it/s]\n", + "100%|██████████| 3/3 [00:00<00:00, 3.13it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 326/326 [00:07<00:00, 46.49it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "219it [00:01, 203.55it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "211it [00:00, 326.07it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 54/54 [00:01<00:00, 36.23it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 96/96 [00:00<00:00, 168.80it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.BpaNNCn48p/ipykernel_53064/3422303653.py:106: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/ouputgpkg/tile_r000016_c000019_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/csv_stats/tile_r000016_c000019_grain_stats.csv\n", + "done with segmentation + export\n", + "[377/720] tile_r000016_c000020\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.18it/s]\n", + "100%|██████████| 3/3 [00:01<00:00, 2.99it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 200/200 [00:05<00:00, 36.86it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "103it [00:00, 199.28it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "102it [00:00, 298.81it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 21/21 [00:01<00:00, 17.64it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 47/47 [00:00<00:00, 123.88it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.BpaNNCn48p/ipykernel_53064/3422303653.py:106: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/ouputgpkg/tile_r000016_c000020_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/csv_stats/tile_r000016_c000020_grain_stats.csv\n", + "done with segmentation + export\n", + "[378/720] tile_r000016_c000021\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.26it/s]\n", + "100%|██████████| 3/3 [00:00<00:00, 3.08it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 259/259 [00:06<00:00, 41.74it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "133it [00:01, 104.41it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "129it [00:01, 125.35it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 29/29 [00:02<00:00, 13.98it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 53/53 [00:00<00:00, 127.48it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.BpaNNCn48p/ipykernel_53064/3422303653.py:106: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/ouputgpkg/tile_r000016_c000021_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/csv_stats/tile_r000016_c000021_grain_stats.csv\n", + "done with segmentation + export\n", + "[379/720] tile_r000016_c000022\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.27it/s]\n", + "100%|██████████| 3/3 [00:00<00:00, 3.11it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 342/342 [00:08<00:00, 39.72it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "215it [00:04, 52.78it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "199it [00:01, 128.34it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 37/37 [00:03<00:00, 10.46it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 72/72 [00:00<00:00, 108.31it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n", + "Warning: len(gdf)=72 != len(grain_stats)=71 -> truncating to 71\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.BpaNNCn48p/ipykernel_53064/3422303653.py:106: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/ouputgpkg/tile_r000016_c000022_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/csv_stats/tile_r000016_c000022_grain_stats.csv\n", + "done with segmentation + export\n", + "[380/720] tile_r000016_c000023\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.20it/s]\n", + "100%|██████████| 3/3 [00:00<00:00, 3.03it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 349/349 [00:08<00:00, 39.10it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "218it [00:02, 86.71it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "213it [00:02, 104.53it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 51/51 [00:05<00:00, 9.49it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 77/77 [00:00<00:00, 121.76it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.BpaNNCn48p/ipykernel_53064/3422303653.py:106: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/ouputgpkg/tile_r000016_c000023_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/csv_stats/tile_r000016_c000023_grain_stats.csv\n", + "done with segmentation + export\n", + "[381/720] tile_r000016_c000024\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.25it/s]\n", + "100%|██████████| 3/3 [00:00<00:00, 3.03it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 349/349 [00:08<00:00, 41.91it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "234it [00:03, 74.45it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "220it [00:01, 121.77it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 60/60 [00:03<00:00, 19.54it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 85/85 [00:00<00:00, 134.31it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.BpaNNCn48p/ipykernel_53064/3422303653.py:106: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/ouputgpkg/tile_r000016_c000024_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/csv_stats/tile_r000016_c000024_grain_stats.csv\n", + "done with segmentation + export\n", + "[382/720] tile_r000016_c000025\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.33it/s]\n", + "100%|██████████| 3/3 [00:00<00:00, 3.02it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 155/155 [00:04<00:00, 33.66it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "80it [00:00, 223.28it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "79it [00:00, 286.82it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 23/23 [00:00<00:00, 25.67it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 40/40 [00:03<00:00, 13.10it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.BpaNNCn48p/ipykernel_53064/3422303653.py:106: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/ouputgpkg/tile_r000016_c000025_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/csv_stats/tile_r000016_c000025_grain_stats.csv\n", + "done with segmentation + export\n", + "[383/720] tile_r000016_c000026\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 2.97it/s]\n", + "100%|██████████| 3/3 [00:00<00:00, 3.14it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:00<00:00, 20.95it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "1it [00:00, 3328.81it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2it [00:00, 1113.29it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 1/1 [00:00<00:00, 482.27it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 1/1 [00:00<00:00, 235.86it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.BpaNNCn48p/ipykernel_53064/3422303653.py:106: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/ouputgpkg/tile_r000016_c000026_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/csv_stats/tile_r000016_c000026_grain_stats.csv\n", + "done with segmentation + export\n", + "[384/720] tile_r000016_c000027\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.20it/s]\n", + "100%|██████████| 3/3 [00:00<00:00, 3.17it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "0it [00:00, ?it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "0it [00:00, ?it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "0it [00:00, ?it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "0it [00:00, ?it/s]\n", + "0it [00:00, ?it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n", + "No grains found -> skipping histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/ouputgpkg/tile_r000016_c000027_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/csv_stats/tile_r000016_c000027_grain_stats.csv\n", + "done with segmentation + export\n", + "[385/720] tile_r000016_c000028\n", + " -> skipped (100% Nodata)\n", + "[386/720] tile_r000016_c000029\n", + " -> skipped (100% Nodata)\n", + "[387/720] tile_r000016_c000030\n", + " -> skipped (100% Nodata)\n", + "[388/720] tile_r000016_c000031\n", + " -> skipped (100% Nodata)\n", + "[389/720] tile_r000016_c000032\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.BpaNNCn48p/ipykernel_53064/3422303653.py:106: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " -> skipped (100% Nodata)\n", + "[390/720] tile_r000016_c000033\n", + " -> skipped (100% Nodata)\n", + "[391/720] tile_r000017_c000004\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.21it/s]\n", + "100%|██████████| 3/3 [00:00<00:00, 3.14it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 1/1 [00:00<00:00, 7.28it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "0it [00:00, ?it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "0it [00:00, ?it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "0it [00:00, ?it/s]\n", + "0it [00:00, ?it/s]\n", + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.BpaNNCn48p/ipykernel_53064/3422303653.py:106: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n", + "No grains found -> skipping histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/ouputgpkg/tile_r000017_c000004_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/csv_stats/tile_r000017_c000004_grain_stats.csv\n", + "done with segmentation + export\n", + "[392/720] tile_r000017_c000005\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.15it/s]\n", + "100%|██████████| 3/3 [00:00<00:00, 3.10it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 196/196 [00:03<00:00, 52.78it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "139it [00:00, 197.78it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "147it [00:00, 201.91it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 54/54 [00:01<00:00, 52.13it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 57/57 [00:00<00:00, 160.56it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.BpaNNCn48p/ipykernel_53064/3422303653.py:106: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/ouputgpkg/tile_r000017_c000005_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/csv_stats/tile_r000017_c000005_grain_stats.csv\n", + "done with segmentation + export\n", + "[393/720] tile_r000017_c000006\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.26it/s]\n", + "100%|██████████| 3/3 [00:00<00:00, 3.12it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 226/226 [00:04<00:00, 52.54it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "148it [00:00, 191.48it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "156it [00:00, 186.44it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 55/55 [00:01<00:00, 47.68it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 56/56 [00:00<00:00, 151.49it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.BpaNNCn48p/ipykernel_53064/3422303653.py:106: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/ouputgpkg/tile_r000017_c000006_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/csv_stats/tile_r000017_c000006_grain_stats.csv\n", + "done with segmentation + export\n", + "[394/720] tile_r000017_c000007\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.06it/s]\n", + "100%|██████████| 3/3 [00:00<00:00, 3.31it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 334/334 [00:06<00:00, 53.70it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "232it [00:01, 212.67it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "236it [00:01, 212.17it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 80/80 [00:01<00:00, 42.63it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 90/90 [00:00<00:00, 143.02it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n", + "Warning: len(gdf)=90 != len(grain_stats)=89 -> truncating to 89\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.BpaNNCn48p/ipykernel_53064/3422303653.py:106: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/ouputgpkg/tile_r000017_c000007_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/csv_stats/tile_r000017_c000007_grain_stats.csv\n", + "done with segmentation + export\n", + "[395/720] tile_r000017_c000008\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.07it/s]\n", + "100%|██████████| 3/3 [00:00<00:00, 3.20it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 296/296 [00:05<00:00, 53.55it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "216it [00:00, 383.95it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "218it [00:00, 380.18it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 85/85 [00:00<00:00, 89.68it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 85/85 [00:00<00:00, 187.70it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.BpaNNCn48p/ipykernel_53064/3422303653.py:106: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/ouputgpkg/tile_r000017_c000008_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/csv_stats/tile_r000017_c000008_grain_stats.csv\n", + "done with segmentation + export\n", + "[396/720] tile_r000017_c000009\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.14it/s]\n", + "100%|██████████| 3/3 [00:00<00:00, 3.27it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 359/359 [00:06<00:00, 51.38it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "264it [00:01, 254.38it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "271it [00:01, 258.63it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 101/101 [00:01<00:00, 67.41it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 105/105 [00:00<00:00, 178.51it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.BpaNNCn48p/ipykernel_53064/3422303653.py:106: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/ouputgpkg/tile_r000017_c000009_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/csv_stats/tile_r000017_c000009_grain_stats.csv\n", + "done with segmentation + export\n", + "[397/720] tile_r000017_c000010\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.12it/s]\n", + "100%|██████████| 3/3 [00:00<00:00, 3.29it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 342/342 [00:06<00:00, 51.33it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "261it [00:01, 220.03it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "269it [00:01, 219.90it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 93/93 [00:01<00:00, 55.10it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 98/98 [00:00<00:00, 166.34it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.BpaNNCn48p/ipykernel_53064/3422303653.py:106: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/ouputgpkg/tile_r000017_c000010_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/csv_stats/tile_r000017_c000010_grain_stats.csv\n", + "done with segmentation + export\n", + "[398/720] tile_r000017_c000011\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.25it/s]\n", + "100%|██████████| 3/3 [00:00<00:00, 3.27it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 364/364 [00:06<00:00, 56.49it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "291it [00:01, 252.58it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "301it [00:01, 256.07it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 108/108 [00:01<00:00, 56.12it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 113/113 [00:00<00:00, 171.30it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.BpaNNCn48p/ipykernel_53064/3422303653.py:106: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/ouputgpkg/tile_r000017_c000011_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/csv_stats/tile_r000017_c000011_grain_stats.csv\n", + "done with segmentation + export\n", + "[399/720] tile_r000017_c000012\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.22it/s]\n", + "100%|██████████| 3/3 [00:00<00:00, 3.29it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 385/385 [00:06<00:00, 57.12it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "311it [00:01, 217.51it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "310it [00:01, 226.65it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 100/100 [00:02<00:00, 36.59it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 116/116 [00:00<00:00, 166.22it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.BpaNNCn48p/ipykernel_53064/3422303653.py:106: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/ouputgpkg/tile_r000017_c000012_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/csv_stats/tile_r000017_c000012_grain_stats.csv\n", + "done with segmentation + export\n", + "[400/720] tile_r000017_c000013\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.28it/s]\n", + "100%|██████████| 3/3 [00:00<00:00, 3.44it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 368/368 [00:06<00:00, 55.37it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "269it [00:01, 182.48it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "268it [00:01, 197.89it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 88/88 [00:02<00:00, 38.72it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 102/102 [00:00<00:00, 173.67it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.BpaNNCn48p/ipykernel_53064/3422303653.py:106: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/ouputgpkg/tile_r000017_c000013_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/csv_stats/tile_r000017_c000013_grain_stats.csv\n", + "done with segmentation + export\n", + "[401/720] tile_r000017_c000014\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.23it/s]\n", + "100%|██████████| 3/3 [00:00<00:00, 3.37it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 387/387 [00:07<00:00, 54.87it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "300it [00:01, 283.95it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "316it [00:01, 290.46it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 116/116 [00:01<00:00, 66.17it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 125/125 [00:00<00:00, 180.92it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.BpaNNCn48p/ipykernel_53064/3422303653.py:106: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/ouputgpkg/tile_r000017_c000014_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/csv_stats/tile_r000017_c000014_grain_stats.csv\n", + "done with segmentation + export\n", + "[402/720] tile_r000017_c000015\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.04it/s]\n", + "100%|██████████| 3/3 [00:00<00:00, 3.32it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 353/353 [00:06<00:00, 52.46it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "278it [00:02, 118.87it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "272it [00:01, 174.97it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 78/78 [00:02<00:00, 29.04it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 108/108 [00:00<00:00, 184.77it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.BpaNNCn48p/ipykernel_53064/3422303653.py:106: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/ouputgpkg/tile_r000017_c000015_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/csv_stats/tile_r000017_c000015_grain_stats.csv\n", + "done with segmentation + export\n", + "[403/720] tile_r000017_c000016\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.23it/s]\n", + "100%|██████████| 3/3 [00:00<00:00, 3.43it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 371/371 [00:06<00:00, 56.37it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "291it [00:00, 335.04it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "305it [00:00, 327.27it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 114/114 [00:01<00:00, 74.88it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 119/119 [00:00<00:00, 186.68it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.BpaNNCn48p/ipykernel_53064/3422303653.py:106: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/ouputgpkg/tile_r000017_c000016_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/csv_stats/tile_r000017_c000016_grain_stats.csv\n", + "done with segmentation + export\n", + "[404/720] tile_r000017_c000017\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.34it/s]\n", + "100%|██████████| 3/3 [00:00<00:00, 3.26it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 347/347 [00:06<00:00, 54.46it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "267it [00:01, 208.22it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "266it [00:01, 233.23it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 86/86 [00:01<00:00, 43.81it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 103/103 [00:00<00:00, 175.92it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.BpaNNCn48p/ipykernel_53064/3422303653.py:106: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/ouputgpkg/tile_r000017_c000017_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/csv_stats/tile_r000017_c000017_grain_stats.csv\n", + "done with segmentation + export\n", + "[405/720] tile_r000017_c000018\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.25it/s]\n", + "100%|██████████| 3/3 [00:00<00:00, 3.30it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 301/301 [00:05<00:00, 51.80it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "236it [00:00, 257.57it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "250it [00:00, 257.75it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 89/89 [00:01<00:00, 52.09it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 97/97 [00:00<00:00, 175.22it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n", + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/ouputgpkg/tile_r000017_c000018_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/csv_stats/tile_r000017_c000018_grain_stats.csv\n", + "done with segmentation + export\n", + "[406/720] tile_r000017_c000019\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.BpaNNCn48p/ipykernel_53064/3422303653.py:106: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.13it/s]\n", + "100%|██████████| 3/3 [00:00<00:00, 3.18it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 261/261 [00:05<00:00, 48.40it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "175it [00:00, 350.31it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "190it [00:00, 344.90it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 75/75 [00:00<00:00, 83.56it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 78/78 [00:00<00:00, 202.65it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.BpaNNCn48p/ipykernel_53064/3422303653.py:106: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/ouputgpkg/tile_r000017_c000019_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/csv_stats/tile_r000017_c000019_grain_stats.csv\n", + "done with segmentation + export\n", + "[407/720] tile_r000017_c000020\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.32it/s]\n", + "100%|██████████| 3/3 [00:00<00:00, 3.43it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 216/216 [00:04<00:00, 44.99it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "118it [00:00, 449.90it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "140it [00:00, 412.23it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 63/63 [00:00<00:00, 136.41it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 66/66 [00:00<00:00, 192.20it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.BpaNNCn48p/ipykernel_53064/3422303653.py:106: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/ouputgpkg/tile_r000017_c000020_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/csv_stats/tile_r000017_c000020_grain_stats.csv\n", + "done with segmentation + export\n", + "[408/720] tile_r000017_c000021\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.35it/s]\n", + "100%|██████████| 3/3 [00:00<00:00, 3.24it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 243/243 [00:06<00:00, 37.27it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "138it [00:01, 78.21it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "127it [00:00, 162.54it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 30/30 [00:01<00:00, 16.57it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 57/57 [00:00<00:00, 148.90it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.BpaNNCn48p/ipykernel_53064/3422303653.py:106: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/ouputgpkg/tile_r000017_c000021_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/csv_stats/tile_r000017_c000021_grain_stats.csv\n", + "done with segmentation + export\n", + "[409/720] tile_r000017_c000022\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.40it/s]\n", + "100%|██████████| 3/3 [00:00<00:00, 3.29it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 174/174 [00:04<00:00, 39.61it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "82it [00:00, 165.58it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "92it [00:00, 179.58it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 29/29 [00:00<00:00, 33.50it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 32/32 [00:00<00:00, 144.35it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.BpaNNCn48p/ipykernel_53064/3422303653.py:106: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/ouputgpkg/tile_r000017_c000022_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/csv_stats/tile_r000017_c000022_grain_stats.csv\n", + "done with segmentation + export\n", + "[410/720] tile_r000017_c000023\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.62it/s]\n", + "100%|██████████| 3/3 [00:00<00:00, 3.59it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 167/167 [00:04<00:00, 36.58it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "78it [00:00, 145.91it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "75it [00:00, 186.35it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 17/17 [00:01<00:00, 13.50it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 30/30 [00:00<00:00, 97.07it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.BpaNNCn48p/ipykernel_53064/3422303653.py:106: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/ouputgpkg/tile_r000017_c000023_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/csv_stats/tile_r000017_c000023_grain_stats.csv\n", + "done with segmentation + export\n", + "[411/720] tile_r000017_c000024\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.30it/s]\n", + "100%|██████████| 3/3 [00:00<00:00, 3.18it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 356/356 [00:10<00:00, 33.22it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "179it [00:01, 119.27it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "176it [00:01, 153.21it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 39/39 [00:02<00:00, 16.04it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 64/64 [00:00<00:00, 114.69it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n", + "Warning: len(gdf)=64 != len(grain_stats)=63 -> truncating to 63\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.BpaNNCn48p/ipykernel_53064/3422303653.py:106: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/ouputgpkg/tile_r000017_c000024_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/csv_stats/tile_r000017_c000024_grain_stats.csv\n", + "done with segmentation + export\n", + "[412/720] tile_r000017_c000025\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.30it/s]\n", + "100%|██████████| 3/3 [00:00<00:00, 3.12it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 368/368 [00:11<00:00, 30.79it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "165it [00:01, 94.55it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "163it [00:01, 104.99it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 37/37 [00:02<00:00, 12.63it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 59/59 [00:00<00:00, 102.29it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.BpaNNCn48p/ipykernel_53064/3422303653.py:106: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/ouputgpkg/tile_r000017_c000025_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/csv_stats/tile_r000017_c000025_grain_stats.csv\n", + "done with segmentation + export\n", + "[413/720] tile_r000017_c000026\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.20it/s]\n", + "100%|██████████| 3/3 [00:00<00:00, 3.27it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 22/22 [00:00<00:00, 34.61it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "3it [00:00, 264.30it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "3it [00:00, 550.58it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 1/1 [00:00<00:00, 113.46it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 1/1 [00:00<00:00, 176.79it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.BpaNNCn48p/ipykernel_53064/3422303653.py:106: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/ouputgpkg/tile_r000017_c000026_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/csv_stats/tile_r000017_c000026_grain_stats.csv\n", + "done with segmentation + export\n", + "[414/720] tile_r000017_c000027\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.13it/s]\n", + "100%|██████████| 3/3 [00:00<00:00, 3.14it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 3/3 [00:00<00:00, 17.45it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "3it [00:00, 1955.08it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "4it [00:00, 854.32it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 2/2 [00:00<00:00, 166.79it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 2/2 [00:00<00:00, 149.74it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.BpaNNCn48p/ipykernel_53064/3422303653.py:106: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/ouputgpkg/tile_r000017_c000027_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/csv_stats/tile_r000017_c000027_grain_stats.csv\n", + "done with segmentation + export\n", + "[415/720] tile_r000017_c000028\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.26it/s]\n", + "100%|██████████| 3/3 [00:00<00:00, 3.20it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "0it [00:00, ?it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "0it [00:00, ?it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "0it [00:00, ?it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "0it [00:00, ?it/s]\n", + "0it [00:00, ?it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n", + "No grains found -> skipping histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/ouputgpkg/tile_r000017_c000028_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/csv_stats/tile_r000017_c000028_grain_stats.csv\n", + "done with segmentation + export\n", + "[416/720] tile_r000017_c000029\n", + " -> skipped (100% Nodata)\n", + "[417/720] tile_r000017_c000030\n", + " -> skipped (100% Nodata)\n", + "[418/720] tile_r000017_c000031\n", + " -> skipped (100% Nodata)\n", + "[419/720] tile_r000017_c000032\n", + " -> skipped (100% Nodata)\n", + "[420/720] tile_r000017_c000033\n", + " -> skipped (100% Nodata)\n", + "[421/720] tile_r000018_c000004\n", + " -> skipped (100% Nodata)\n", + "[422/720] tile_r000018_c000005\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.BpaNNCn48p/ipykernel_53064/3422303653.py:106: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.23it/s]\n", + "100%|██████████| 3/3 [00:00<00:00, 3.30it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 1/1 [00:00<00:00, 7.43it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "0it [00:00, ?it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "0it [00:00, ?it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "0it [00:00, ?it/s]\n", + "0it [00:00, ?it/s]\n", + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.BpaNNCn48p/ipykernel_53064/3422303653.py:106: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n", + "No grains found -> skipping histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/ouputgpkg/tile_r000018_c000005_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/csv_stats/tile_r000018_c000005_grain_stats.csv\n", + "done with segmentation + export\n", + "[423/720] tile_r000018_c000006\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.27it/s]\n", + "100%|██████████| 3/3 [00:00<00:00, 3.28it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 87/87 [00:02<00:00, 42.86it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "36it [00:00, 492.69it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "42it [00:00, 458.95it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 17/17 [00:00<00:00, 91.33it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 19/19 [00:00<00:00, 155.69it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.BpaNNCn48p/ipykernel_53064/3422303653.py:106: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/ouputgpkg/tile_r000018_c000006_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/csv_stats/tile_r000018_c000006_grain_stats.csv\n", + "done with segmentation + export\n", + "[424/720] tile_r000018_c000007\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.17it/s]\n", + "100%|██████████| 3/3 [00:00<00:00, 3.16it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 263/263 [00:05<00:00, 45.25it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "172it [00:00, 190.03it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "180it [00:00, 189.84it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 63/63 [00:01<00:00, 44.30it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 68/68 [00:00<00:00, 154.02it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.BpaNNCn48p/ipykernel_53064/3422303653.py:106: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/ouputgpkg/tile_r000018_c000007_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/csv_stats/tile_r000018_c000007_grain_stats.csv\n", + "done with segmentation + export\n", + "[425/720] tile_r000018_c000008\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 2.91it/s]\n", + "100%|██████████| 3/3 [00:01<00:00, 2.91it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 343/343 [00:06<00:00, 56.32it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "275it [00:01, 186.97it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "282it [00:01, 187.64it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 97/97 [00:02<00:00, 42.46it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 102/102 [00:00<00:00, 156.95it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.BpaNNCn48p/ipykernel_53064/3422303653.py:106: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/ouputgpkg/tile_r000018_c000008_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/csv_stats/tile_r000018_c000008_grain_stats.csv\n", + "done with segmentation + export\n", + "[426/720] tile_r000018_c000009\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.20it/s]\n", + "100%|██████████| 3/3 [00:00<00:00, 3.30it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 333/333 [00:05<00:00, 61.27it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "240it [00:00, 385.59it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "245it [00:00, 394.76it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 96/96 [00:00<00:00, 96.80it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 98/98 [00:00<00:00, 193.76it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.BpaNNCn48p/ipykernel_53064/3422303653.py:106: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/ouputgpkg/tile_r000018_c000009_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/csv_stats/tile_r000018_c000009_grain_stats.csv\n", + "done with segmentation + export\n", + "[427/720] tile_r000018_c000010\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.25it/s]\n", + "100%|██████████| 3/3 [00:00<00:00, 3.56it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 344/344 [00:05<00:00, 62.92it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "270it [00:00, 311.39it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "281it [00:01, 279.52it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 100/100 [00:01<00:00, 61.72it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 105/105 [00:00<00:00, 170.90it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.BpaNNCn48p/ipykernel_53064/3422303653.py:106: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/ouputgpkg/tile_r000018_c000010_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/csv_stats/tile_r000018_c000010_grain_stats.csv\n", + "done with segmentation + export\n", + "[428/720] tile_r000018_c000011\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.16it/s]\n", + "100%|██████████| 3/3 [00:00<00:00, 3.19it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 338/338 [00:06<00:00, 52.36it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "239it [00:01, 180.65it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "238it [00:01, 195.48it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 76/76 [00:01<00:00, 42.58it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 91/91 [00:00<00:00, 166.04it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.BpaNNCn48p/ipykernel_53064/3422303653.py:106: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/ouputgpkg/tile_r000018_c000011_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/csv_stats/tile_r000018_c000011_grain_stats.csv\n", + "done with segmentation + export\n", + "[429/720] tile_r000018_c000012\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.15it/s]\n", + "100%|██████████| 3/3 [00:00<00:00, 3.11it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 357/357 [00:06<00:00, 54.60it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "273it [00:01, 259.38it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "282it [00:01, 278.37it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 103/103 [00:01<00:00, 64.20it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 109/109 [00:00<00:00, 176.06it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.BpaNNCn48p/ipykernel_53064/3422303653.py:106: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/ouputgpkg/tile_r000018_c000012_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/csv_stats/tile_r000018_c000012_grain_stats.csv\n", + "done with segmentation + export\n", + "[430/720] tile_r000018_c000013\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.09it/s]\n", + "100%|██████████| 3/3 [00:00<00:00, 3.26it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 353/353 [00:06<00:00, 56.79it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "283it [00:02, 117.32it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "267it [00:01, 246.40it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 92/92 [00:01<00:00, 59.55it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 103/103 [00:00<00:00, 188.80it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.BpaNNCn48p/ipykernel_53064/3422303653.py:106: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/ouputgpkg/tile_r000018_c000013_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/csv_stats/tile_r000018_c000013_grain_stats.csv\n", + "done with segmentation + export\n", + "[431/720] tile_r000018_c000014\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.09it/s]\n", + "100%|██████████| 3/3 [00:00<00:00, 3.25it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 303/303 [00:05<00:00, 52.54it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "222it [00:00, 230.86it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "232it [00:01, 224.17it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 82/82 [00:01<00:00, 55.17it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 87/87 [00:00<00:00, 175.04it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.BpaNNCn48p/ipykernel_53064/3422303653.py:106: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/ouputgpkg/tile_r000018_c000014_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/csv_stats/tile_r000018_c000014_grain_stats.csv\n", + "done with segmentation + export\n", + "[432/720] tile_r000018_c000015\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.38it/s]\n", + "100%|██████████| 3/3 [00:00<00:00, 3.30it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 327/327 [00:05<00:00, 55.76it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "254it [00:01, 238.32it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "267it [00:01, 236.16it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 96/96 [00:01<00:00, 58.75it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 105/105 [00:00<00:00, 178.66it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.BpaNNCn48p/ipykernel_53064/3422303653.py:106: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/ouputgpkg/tile_r000018_c000015_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/csv_stats/tile_r000018_c000015_grain_stats.csv\n", + "done with segmentation + export\n", + "[433/720] tile_r000018_c000016\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.29it/s]\n", + "100%|██████████| 3/3 [00:00<00:00, 3.31it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 370/370 [00:06<00:00, 55.05it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "306it [00:01, 254.25it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "305it [00:01, 269.91it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 105/105 [00:01<00:00, 58.78it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 119/119 [00:00<00:00, 195.68it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n", + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/ouputgpkg/tile_r000018_c000016_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/csv_stats/tile_r000018_c000016_grain_stats.csv\n", + "done with segmentation + export\n", + "[434/720] tile_r000018_c000017\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.BpaNNCn48p/ipykernel_53064/3422303653.py:106: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.28it/s]\n", + "100%|██████████| 3/3 [00:00<00:00, 3.29it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 355/355 [00:06<00:00, 52.82it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "275it [00:01, 223.37it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "287it [00:01, 224.44it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 107/107 [00:02<00:00, 51.24it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 110/110 [00:00<00:00, 196.09it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.BpaNNCn48p/ipykernel_53064/3422303653.py:106: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/ouputgpkg/tile_r000018_c000017_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/csv_stats/tile_r000018_c000017_grain_stats.csv\n", + "done with segmentation + export\n", + "[435/720] tile_r000018_c000018\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.37it/s]\n", + "100%|██████████| 3/3 [00:00<00:00, 3.30it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 329/329 [00:06<00:00, 50.81it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "224it [00:01, 219.55it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "222it [00:00, 275.48it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 70/70 [00:01<00:00, 43.93it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 85/85 [00:00<00:00, 176.30it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.BpaNNCn48p/ipykernel_53064/3422303653.py:106: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/ouputgpkg/tile_r000018_c000018_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/csv_stats/tile_r000018_c000018_grain_stats.csv\n", + "done with segmentation + export\n", + "[436/720] tile_r000018_c000019\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.37it/s]\n", + "100%|██████████| 3/3 [00:00<00:00, 3.30it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 352/352 [00:06<00:00, 51.71it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "279it [00:01, 201.23it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "276it [00:00, 322.58it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 88/88 [00:01<00:00, 59.26it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 113/113 [00:00<00:00, 186.00it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.BpaNNCn48p/ipykernel_53064/3422303653.py:106: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/ouputgpkg/tile_r000018_c000019_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/csv_stats/tile_r000018_c000019_grain_stats.csv\n", + "done with segmentation + export\n", + "[437/720] tile_r000018_c000020\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.33it/s]\n", + "100%|██████████| 3/3 [00:00<00:00, 3.35it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 305/305 [00:06<00:00, 44.53it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "205it [00:01, 137.42it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "200it [00:00, 341.42it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 60/60 [00:01<00:00, 53.23it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 91/91 [00:00<00:00, 184.03it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.BpaNNCn48p/ipykernel_53064/3422303653.py:106: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/ouputgpkg/tile_r000018_c000020_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/csv_stats/tile_r000018_c000020_grain_stats.csv\n", + "done with segmentation + export\n", + "[438/720] tile_r000018_c000021\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.17it/s]\n", + "100%|██████████| 3/3 [00:00<00:00, 3.23it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 284/284 [00:06<00:00, 40.59it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "153it [00:07, 20.68it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "54it [00:05, 10.36it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 1/1 [00:23<00:00, 23.72s/it]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 17/17 [00:00<00:00, 105.85it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.BpaNNCn48p/ipykernel_53064/3422303653.py:106: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/ouputgpkg/tile_r000018_c000021_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/csv_stats/tile_r000018_c000021_grain_stats.csv\n", + "done with segmentation + export\n", + "[439/720] tile_r000018_c000022\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.33it/s]\n", + "100%|██████████| 3/3 [00:00<00:00, 3.25it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 201/201 [00:04<00:00, 45.88it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "135it [00:01, 129.93it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "131it [00:00, 178.84it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 29/29 [00:02<00:00, 13.05it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 55/55 [00:00<00:00, 151.20it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.BpaNNCn48p/ipykernel_53064/3422303653.py:106: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/ouputgpkg/tile_r000018_c000022_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/csv_stats/tile_r000018_c000022_grain_stats.csv\n", + "done with segmentation + export\n", + "[440/720] tile_r000018_c000023\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.32it/s]\n", + "100%|██████████| 3/3 [00:00<00:00, 3.23it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 71/71 [00:02<00:00, 34.48it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "28it [00:00, 331.65it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "36it [00:00, 250.84it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 14/14 [00:00<00:00, 32.64it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 18/18 [00:00<00:00, 98.61it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.BpaNNCn48p/ipykernel_53064/3422303653.py:106: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/ouputgpkg/tile_r000018_c000023_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/csv_stats/tile_r000018_c000023_grain_stats.csv\n", + "done with segmentation + export\n", + "[441/720] tile_r000018_c000024\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.35it/s]\n", + "100%|██████████| 3/3 [00:01<00:00, 2.89it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 103/103 [00:03<00:00, 34.05it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "54it [00:00, 64.59it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "61it [00:00, 66.92it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 17/17 [00:03<00:00, 4.32it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 28/28 [00:00<00:00, 93.05it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.BpaNNCn48p/ipykernel_53064/3422303653.py:106: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/ouputgpkg/tile_r000018_c000024_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/csv_stats/tile_r000018_c000024_grain_stats.csv\n", + "done with segmentation + export\n", + "[442/720] tile_r000018_c000025\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.18it/s]\n", + "100%|██████████| 3/3 [00:00<00:00, 3.18it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 257/257 [00:06<00:00, 39.67it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "129it [00:00, 209.21it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "128it [00:00, 220.78it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 32/32 [00:01<00:00, 30.99it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 49/49 [00:00<00:00, 138.88it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.BpaNNCn48p/ipykernel_53064/3422303653.py:106: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/ouputgpkg/tile_r000018_c000025_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/csv_stats/tile_r000018_c000025_grain_stats.csv\n", + "done with segmentation + export\n", + "[443/720] tile_r000018_c000026\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.24it/s]\n", + "100%|██████████| 3/3 [00:00<00:00, 3.21it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 154/154 [00:03<00:00, 43.19it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "93it [00:00, 181.22it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "91it [00:00, 328.48it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 26/26 [00:00<00:00, 48.54it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 44/44 [00:00<00:00, 135.55it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.BpaNNCn48p/ipykernel_53064/3422303653.py:106: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/ouputgpkg/tile_r000018_c000026_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/csv_stats/tile_r000018_c000026_grain_stats.csv\n", + "done with segmentation + export\n", + "[444/720] tile_r000018_c000027\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.26it/s]\n", + "100%|██████████| 3/3 [00:00<00:00, 3.23it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 23/23 [00:00<00:00, 35.18it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "15it [00:00, 508.86it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "16it [00:00, 480.48it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 6/6 [00:00<00:00, 81.00it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 6/6 [00:00<00:00, 146.95it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.BpaNNCn48p/ipykernel_53064/3422303653.py:106: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/ouputgpkg/tile_r000018_c000027_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/csv_stats/tile_r000018_c000027_grain_stats.csv\n", + "done with segmentation + export\n", + "[445/720] tile_r000018_c000028\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.22it/s]\n", + "100%|██████████| 3/3 [00:00<00:00, 3.22it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 1/1 [00:00<00:00, 7.60it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "0it [00:00, ?it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "0it [00:00, ?it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "0it [00:00, ?it/s]\n", + "0it [00:00, ?it/s]\n", + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.BpaNNCn48p/ipykernel_53064/3422303653.py:106: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n", + "No grains found -> skipping histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/ouputgpkg/tile_r000018_c000028_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/csv_stats/tile_r000018_c000028_grain_stats.csv\n", + "done with segmentation + export\n", + "[446/720] tile_r000018_c000029\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.13it/s]\n", + "100%|██████████| 3/3 [00:00<00:00, 3.34it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "0it [00:00, ?it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "0it [00:00, ?it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "0it [00:00, ?it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "0it [00:00, ?it/s]\n", + "0it [00:00, ?it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n", + "No grains found -> skipping histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/ouputgpkg/tile_r000018_c000029_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/csv_stats/tile_r000018_c000029_grain_stats.csv\n", + "done with segmentation + export\n", + "[447/720] tile_r000018_c000030\n", + " -> skipped (100% Nodata)\n", + "[448/720] tile_r000018_c000031\n", + " -> skipped (100% Nodata)\n", + "[449/720] tile_r000018_c000032\n", + " -> skipped (100% Nodata)\n", + "[450/720] tile_r000018_c000033\n", + " -> skipped (100% Nodata)\n", + "[451/720] tile_r000019_c000004\n", + " -> skipped (100% Nodata)\n", + "[452/720] tile_r000019_c000005\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.BpaNNCn48p/ipykernel_53064/3422303653.py:106: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " -> skipped (100% Nodata)\n", + "[453/720] tile_r000019_c000006\n", + " -> skipped (100% Nodata)\n", + "[454/720] tile_r000019_c000007\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.32it/s]\n", + "100%|██████████| 3/3 [00:00<00:00, 3.29it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 16/16 [00:00<00:00, 39.17it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "9it [00:00, 314.96it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "11it [00:00, 240.26it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:00<00:00, 65.60it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:00<00:00, 115.51it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.BpaNNCn48p/ipykernel_53064/3422303653.py:106: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/ouputgpkg/tile_r000019_c000007_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/csv_stats/tile_r000019_c000007_grain_stats.csv\n", + "done with segmentation + export\n", + "[455/720] tile_r000019_c000008\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.21it/s]\n", + "100%|██████████| 3/3 [00:00<00:00, 3.32it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 140/140 [00:02<00:00, 51.85it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "109it [00:00, 281.01it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "108it [00:00, 312.80it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 36/36 [00:00<00:00, 54.18it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 44/44 [00:00<00:00, 149.64it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.BpaNNCn48p/ipykernel_53064/3422303653.py:106: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/ouputgpkg/tile_r000019_c000008_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/csv_stats/tile_r000019_c000008_grain_stats.csv\n", + "done with segmentation + export\n", + "[456/720] tile_r000019_c000009\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.21it/s]\n", + "100%|██████████| 3/3 [00:00<00:00, 3.19it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 304/304 [00:06<00:00, 48.61it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "196it [00:01, 107.03it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "193it [00:00, 270.16it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 59/59 [00:01<00:00, 40.60it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 75/75 [00:00<00:00, 165.71it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.BpaNNCn48p/ipykernel_53064/3422303653.py:106: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/ouputgpkg/tile_r000019_c000009_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/csv_stats/tile_r000019_c000009_grain_stats.csv\n", + "done with segmentation + export\n", + "[457/720] tile_r000019_c000010\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.36it/s]\n", + "100%|██████████| 3/3 [00:00<00:00, 3.31it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 325/325 [00:06<00:00, 53.36it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "225it [00:01, 153.14it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "231it [00:01, 154.33it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 80/80 [00:02<00:00, 35.87it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 81/81 [00:00<00:00, 154.45it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.BpaNNCn48p/ipykernel_53064/3422303653.py:106: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/ouputgpkg/tile_r000019_c000010_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/csv_stats/tile_r000019_c000010_grain_stats.csv\n", + "done with segmentation + export\n", + "[458/720] tile_r000019_c000011\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.32it/s]\n", + "100%|██████████| 3/3 [00:00<00:00, 3.36it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 395/395 [00:07<00:00, 56.39it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "304it [00:01, 205.90it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "311it [00:01, 212.92it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 107/107 [00:02<00:00, 47.32it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 114/114 [00:00<00:00, 169.88it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.BpaNNCn48p/ipykernel_53064/3422303653.py:106: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/ouputgpkg/tile_r000019_c000011_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/csv_stats/tile_r000019_c000011_grain_stats.csv\n", + "done with segmentation + export\n", + "[459/720] tile_r000019_c000012\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.37it/s]\n", + "100%|██████████| 3/3 [00:00<00:00, 3.28it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 375/375 [00:06<00:00, 57.08it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "312it [00:01, 231.18it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "310it [00:01, 301.07it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 114/114 [00:01<00:00, 81.14it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 125/125 [00:00<00:00, 192.40it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.BpaNNCn48p/ipykernel_53064/3422303653.py:106: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/ouputgpkg/tile_r000019_c000012_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/csv_stats/tile_r000019_c000012_grain_stats.csv\n", + "done with segmentation + export\n", + "[460/720] tile_r000019_c000013\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.33it/s]\n", + "100%|██████████| 3/3 [00:00<00:00, 3.45it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 325/325 [00:05<00:00, 58.23it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "254it [00:00, 315.85it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "263it [00:00, 316.02it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 98/98 [00:01<00:00, 77.41it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 102/102 [00:00<00:00, 178.60it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.BpaNNCn48p/ipykernel_53064/3422303653.py:106: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/ouputgpkg/tile_r000019_c000013_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/csv_stats/tile_r000019_c000013_grain_stats.csv\n", + "done with segmentation + export\n", + "[461/720] tile_r000019_c000014\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.36it/s]\n", + "100%|██████████| 3/3 [00:00<00:00, 3.32it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 327/327 [00:06<00:00, 53.92it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "239it [00:00, 253.27it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "249it [00:00, 263.28it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 92/92 [00:01<00:00, 67.57it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 94/94 [00:00<00:00, 178.04it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.BpaNNCn48p/ipykernel_53064/3422303653.py:106: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/ouputgpkg/tile_r000019_c000014_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/csv_stats/tile_r000019_c000014_grain_stats.csv\n", + "done with segmentation + export\n", + "[462/720] tile_r000019_c000015\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.32it/s]\n", + "100%|██████████| 3/3 [00:00<00:00, 3.32it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 371/371 [00:06<00:00, 56.92it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "273it [00:00, 297.67it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "272it [00:00, 321.24it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 96/96 [00:01<00:00, 71.70it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 110/110 [00:00<00:00, 173.21it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.BpaNNCn48p/ipykernel_53064/3422303653.py:106: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/ouputgpkg/tile_r000019_c000015_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/csv_stats/tile_r000019_c000015_grain_stats.csv\n", + "done with segmentation + export\n", + "[463/720] tile_r000019_c000016\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.23it/s]\n", + "100%|██████████| 3/3 [00:00<00:00, 3.39it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 336/336 [00:05<00:00, 56.35it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "244it [00:01, 183.44it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "249it [00:01, 183.66it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 81/81 [00:01<00:00, 42.69it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 85/85 [00:00<00:00, 171.91it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.BpaNNCn48p/ipykernel_53064/3422303653.py:106: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/ouputgpkg/tile_r000019_c000016_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/csv_stats/tile_r000019_c000016_grain_stats.csv\n", + "done with segmentation + export\n", + "[464/720] tile_r000019_c000017\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.53it/s]\n", + "100%|██████████| 3/3 [00:00<00:00, 3.63it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 325/325 [00:05<00:00, 60.15it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "248it [00:00, 251.61it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "243it [00:00, 361.16it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 80/80 [00:01<00:00, 67.52it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 104/104 [00:00<00:00, 189.61it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.BpaNNCn48p/ipykernel_53064/3422303653.py:106: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/ouputgpkg/tile_r000019_c000017_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/csv_stats/tile_r000019_c000017_grain_stats.csv\n", + "done with segmentation + export\n", + "[465/720] tile_r000019_c000018\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 2.80it/s]\n", + "100%|██████████| 3/3 [00:01<00:00, 2.89it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 356/356 [00:06<00:00, 52.66it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "266it [00:00, 268.12it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "284it [00:01, 271.93it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 108/108 [00:01<00:00, 58.84it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 111/111 [00:00<00:00, 183.68it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.BpaNNCn48p/ipykernel_53064/3422303653.py:106: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/ouputgpkg/tile_r000019_c000018_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/csv_stats/tile_r000019_c000018_grain_stats.csv\n", + "done with segmentation + export\n", + "[466/720] tile_r000019_c000019\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.12it/s]\n", + "100%|██████████| 3/3 [00:00<00:00, 3.12it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 291/291 [00:06<00:00, 46.30it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "193it [00:01, 173.11it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "187it [00:00, 281.07it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 55/55 [00:01<00:00, 46.56it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 78/78 [00:00<00:00, 160.97it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.BpaNNCn48p/ipykernel_53064/3422303653.py:106: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/ouputgpkg/tile_r000019_c000019_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/csv_stats/tile_r000019_c000019_grain_stats.csv\n", + "done with segmentation + export\n", + "[467/720] tile_r000019_c000020\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.01it/s]\n", + "100%|██████████| 3/3 [00:00<00:00, 3.13it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 313/313 [00:05<00:00, 52.43it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "217it [00:00, 310.06it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "235it [00:00, 311.43it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 90/90 [00:01<00:00, 80.11it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 97/97 [00:00<00:00, 186.68it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.BpaNNCn48p/ipykernel_53064/3422303653.py:106: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/ouputgpkg/tile_r000019_c000020_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/csv_stats/tile_r000019_c000020_grain_stats.csv\n", + "done with segmentation + export\n", + "[468/720] tile_r000019_c000021\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.09it/s]\n", + "100%|██████████| 3/3 [00:00<00:00, 3.19it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 310/310 [00:05<00:00, 52.53it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "234it [00:00, 359.32it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "254it [00:00, 366.03it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 98/98 [00:01<00:00, 93.12it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 102/102 [00:00<00:00, 183.99it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.BpaNNCn48p/ipykernel_53064/3422303653.py:106: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/ouputgpkg/tile_r000019_c000021_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/csv_stats/tile_r000019_c000021_grain_stats.csv\n", + "done with segmentation + export\n", + "[469/720] tile_r000019_c000022\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.31it/s]\n", + "100%|██████████| 3/3 [00:00<00:00, 3.36it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 277/277 [00:06<00:00, 43.90it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "187it [00:02, 67.37it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "181it [00:01, 96.03it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 45/45 [00:03<00:00, 14.01it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 71/71 [00:00<00:00, 148.39it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.BpaNNCn48p/ipykernel_53064/3422303653.py:106: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/ouputgpkg/tile_r000019_c000022_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/csv_stats/tile_r000019_c000022_grain_stats.csv\n", + "done with segmentation + export\n", + "[470/720] tile_r000019_c000023\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.25it/s]\n", + "100%|██████████| 3/3 [00:00<00:00, 3.22it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 239/239 [00:06<00:00, 36.19it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "146it [00:01, 116.28it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "144it [00:01, 137.41it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 31/31 [00:03<00:00, 8.24it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 67/67 [00:00<00:00, 113.41it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.BpaNNCn48p/ipykernel_53064/3422303653.py:106: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/ouputgpkg/tile_r000019_c000023_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/csv_stats/tile_r000019_c000023_grain_stats.csv\n", + "done with segmentation + export\n", + "[471/720] tile_r000019_c000024\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.35it/s]\n", + "100%|██████████| 3/3 [00:00<00:00, 3.47it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 307/307 [00:06<00:00, 47.02it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "181it [00:01, 140.67it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "196it [00:01, 148.40it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 65/65 [00:02<00:00, 22.19it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 76/76 [00:00<00:00, 165.63it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.BpaNNCn48p/ipykernel_53064/3422303653.py:106: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/ouputgpkg/tile_r000019_c000024_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/csv_stats/tile_r000019_c000024_grain_stats.csv\n", + "done with segmentation + export\n", + "[472/720] tile_r000019_c000025\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.55it/s]\n", + "100%|██████████| 3/3 [00:00<00:00, 3.66it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 234/234 [00:05<00:00, 40.39it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "127it [00:01, 83.99it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "120it [00:00, 140.66it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 26/26 [00:01<00:00, 13.79it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 52/52 [00:00<00:00, 99.06it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.BpaNNCn48p/ipykernel_53064/3422303653.py:106: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/ouputgpkg/tile_r000019_c000025_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/csv_stats/tile_r000019_c000025_grain_stats.csv\n", + "done with segmentation + export\n", + "[473/720] tile_r000019_c000026\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.17it/s]\n", + "100%|██████████| 3/3 [00:00<00:00, 3.16it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 245/245 [00:07<00:00, 34.78it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "123it [00:02, 50.33it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "114it [00:00, 121.66it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 24/24 [00:03<00:00, 7.70it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 47/47 [00:00<00:00, 86.23it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.BpaNNCn48p/ipykernel_53064/3422303653.py:106: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/ouputgpkg/tile_r000019_c000026_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/csv_stats/tile_r000019_c000026_grain_stats.csv\n", + "done with segmentation + export\n", + "[474/720] tile_r000019_c000027\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.18it/s]\n", + "100%|██████████| 3/3 [00:00<00:00, 3.16it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 99/99 [00:02<00:00, 34.51it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "35it [00:00, 226.96it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "42it [00:00, 222.00it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 14/14 [00:00<00:00, 45.13it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 16/16 [00:00<00:00, 128.74it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.BpaNNCn48p/ipykernel_53064/3422303653.py:106: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/ouputgpkg/tile_r000019_c000027_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/csv_stats/tile_r000019_c000027_grain_stats.csv\n", + "done with segmentation + export\n", + "[475/720] tile_r000019_c000028\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.53it/s]\n", + "100%|██████████| 3/3 [00:01<00:00, 2.95it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 38/38 [00:00<00:00, 45.34it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "25it [00:00, 391.46it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "25it [00:00, 387.75it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 10/10 [00:00<00:00, 76.19it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 12/12 [00:00<00:00, 104.99it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.BpaNNCn48p/ipykernel_53064/3422303653.py:106: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/ouputgpkg/tile_r000019_c000028_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/csv_stats/tile_r000019_c000028_grain_stats.csv\n", + "done with segmentation + export\n", + "[476/720] tile_r000019_c000029\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.13it/s]\n", + "100%|██████████| 3/3 [00:00<00:00, 3.17it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 2/2 [00:00<00:00, 12.19it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "0it [00:00, ?it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "0it [00:00, ?it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "0it [00:00, ?it/s]\n", + "0it [00:00, ?it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n", + "No grains found -> skipping histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/ouputgpkg/tile_r000019_c000029_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/csv_stats/tile_r000019_c000029_grain_stats.csv\n", + "done with segmentation + export\n", + "[477/720] tile_r000019_c000030\n", + " -> skipped (100% Nodata)\n", + "[478/720] tile_r000019_c000031\n", + " -> skipped (100% Nodata)\n", + "[479/720] tile_r000019_c000032\n", + " -> skipped (100% Nodata)\n", + "[480/720] tile_r000019_c000033\n", + " -> skipped (100% Nodata)\n", + "[481/720] tile_r000020_c000004\n", + " -> skipped (100% Nodata)\n", + "[482/720] tile_r000020_c000005\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.BpaNNCn48p/ipykernel_53064/3422303653.py:106: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " -> skipped (100% Nodata)\n", + "[483/720] tile_r000020_c000006\n", + " -> skipped (100% Nodata)\n", + "[484/720] tile_r000020_c000007\n", + " -> skipped (100% Nodata)\n", + "[485/720] tile_r000020_c000008\n", + " -> skipped (100% Nodata)\n", + "[486/720] tile_r000020_c000009\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.21it/s]\n", + "100%|██████████| 3/3 [00:00<00:00, 3.15it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 28/28 [00:00<00:00, 40.77it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "8it [00:00, 333.93it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "9it [00:00, 318.54it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 3/3 [00:00<00:00, 46.22it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:00<00:00, 129.02it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.BpaNNCn48p/ipykernel_53064/3422303653.py:106: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/ouputgpkg/tile_r000020_c000009_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/csv_stats/tile_r000020_c000009_grain_stats.csv\n", + "done with segmentation + export\n", + "[487/720] tile_r000020_c000010\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 2.96it/s]\n", + "100%|██████████| 3/3 [00:01<00:00, 2.71it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 102/102 [00:02<00:00, 39.41it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "45it [00:00, 251.31it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "50it [00:00, 256.27it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 16/16 [00:00<00:00, 39.65it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 18/18 [00:00<00:00, 113.15it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.BpaNNCn48p/ipykernel_53064/3422303653.py:106: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/ouputgpkg/tile_r000020_c000010_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/csv_stats/tile_r000020_c000010_grain_stats.csv\n", + "done with segmentation + export\n", + "[488/720] tile_r000020_c000011\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.09it/s]\n", + "100%|██████████| 3/3 [00:00<00:00, 3.12it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 217/217 [00:04<00:00, 53.06it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "150it [00:00, 222.17it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "153it [00:00, 215.87it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 49/49 [00:01<00:00, 42.34it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 51/51 [00:00<00:00, 140.60it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.BpaNNCn48p/ipykernel_53064/3422303653.py:106: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/ouputgpkg/tile_r000020_c000011_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/csv_stats/tile_r000020_c000011_grain_stats.csv\n", + "done with segmentation + export\n", + "[489/720] tile_r000020_c000012\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.40it/s]\n", + "100%|██████████| 3/3 [00:00<00:00, 3.13it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 374/374 [00:07<00:00, 50.74it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "281it [00:05, 54.00it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "32it [00:01, 28.32it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 1/1 [00:02<00:00, 2.74s/it]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 12/12 [00:00<00:00, 92.04it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.BpaNNCn48p/ipykernel_53064/3422303653.py:106: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/ouputgpkg/tile_r000020_c000012_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/csv_stats/tile_r000020_c000012_grain_stats.csv\n", + "done with segmentation + export\n", + "[490/720] tile_r000020_c000013\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.24it/s]\n", + "100%|██████████| 3/3 [00:00<00:00, 3.25it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 419/419 [00:07<00:00, 57.35it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "327it [00:01, 217.36it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "326it [00:01, 235.97it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 108/108 [00:01<00:00, 60.66it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 120/120 [00:00<00:00, 176.09it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.BpaNNCn48p/ipykernel_53064/3422303653.py:106: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/ouputgpkg/tile_r000020_c000013_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/csv_stats/tile_r000020_c000013_grain_stats.csv\n", + "done with segmentation + export\n", + "[491/720] tile_r000020_c000014\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.19it/s]\n", + "100%|██████████| 3/3 [00:00<00:00, 3.13it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 350/350 [00:06<00:00, 57.17it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "289it [00:01, 210.26it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "303it [00:01, 212.59it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 106/106 [00:02<00:00, 51.37it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 110/110 [00:00<00:00, 169.78it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n", + "Warning: len(gdf)=110 != len(grain_stats)=109 -> truncating to 109\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.BpaNNCn48p/ipykernel_53064/3422303653.py:106: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/ouputgpkg/tile_r000020_c000014_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/csv_stats/tile_r000020_c000014_grain_stats.csv\n", + "done with segmentation + export\n", + "[492/720] tile_r000020_c000015\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.22it/s]\n", + "100%|██████████| 3/3 [00:00<00:00, 3.17it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 354/354 [00:06<00:00, 57.68it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "288it [00:01, 277.56it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "297it [00:01, 279.09it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 109/109 [00:01<00:00, 70.19it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 113/113 [00:00<00:00, 185.04it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.BpaNNCn48p/ipykernel_53064/3422303653.py:106: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/ouputgpkg/tile_r000020_c000015_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/csv_stats/tile_r000020_c000015_grain_stats.csv\n", + "done with segmentation + export\n", + "[493/720] tile_r000020_c000016\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.29it/s]\n", + "100%|██████████| 3/3 [00:00<00:00, 3.38it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 328/328 [00:06<00:00, 52.61it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "239it [00:01, 209.44it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "236it [00:00, 269.39it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 77/77 [00:01<00:00, 57.90it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 91/91 [00:00<00:00, 172.66it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.BpaNNCn48p/ipykernel_53064/3422303653.py:106: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/ouputgpkg/tile_r000020_c000016_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/csv_stats/tile_r000020_c000016_grain_stats.csv\n", + "done with segmentation + export\n", + "[494/720] tile_r000020_c000017\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.18it/s]\n", + "100%|██████████| 3/3 [00:00<00:00, 3.35it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 376/376 [00:06<00:00, 58.34it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "309it [00:00, 355.83it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "321it [00:00, 352.64it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 120/120 [00:01<00:00, 91.11it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 126/126 [00:00<00:00, 183.40it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.BpaNNCn48p/ipykernel_53064/3422303653.py:106: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/ouputgpkg/tile_r000020_c000017_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/csv_stats/tile_r000020_c000017_grain_stats.csv\n", + "done with segmentation + export\n", + "[495/720] tile_r000020_c000018\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.27it/s]\n", + "100%|██████████| 3/3 [00:00<00:00, 3.31it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 294/294 [00:05<00:00, 57.68it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "252it [00:01, 244.72it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "256it [00:01, 246.91it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 86/86 [00:01<00:00, 50.22it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 88/88 [00:00<00:00, 173.08it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.BpaNNCn48p/ipykernel_53064/3422303653.py:106: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/ouputgpkg/tile_r000020_c000018_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/csv_stats/tile_r000020_c000018_grain_stats.csv\n", + "done with segmentation + export\n", + "[496/720] tile_r000020_c000019\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.26it/s]\n", + "100%|██████████| 3/3 [00:00<00:00, 3.23it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 359/359 [00:06<00:00, 59.33it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "291it [00:00, 440.96it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "302it [00:00, 430.51it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 120/120 [00:01<00:00, 115.32it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 122/122 [00:00<00:00, 199.47it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.BpaNNCn48p/ipykernel_53064/3422303653.py:106: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/ouputgpkg/tile_r000020_c000019_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/csv_stats/tile_r000020_c000019_grain_stats.csv\n", + "done with segmentation + export\n", + "[497/720] tile_r000020_c000020\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.23it/s]\n", + "100%|██████████| 3/3 [00:00<00:00, 3.35it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 331/331 [00:06<00:00, 52.81it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "246it [00:00, 275.27it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "244it [00:00, 339.49it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 80/80 [00:01<00:00, 64.40it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 107/107 [00:00<00:00, 182.50it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.BpaNNCn48p/ipykernel_53064/3422303653.py:106: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/ouputgpkg/tile_r000020_c000020_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/csv_stats/tile_r000020_c000020_grain_stats.csv\n", + "done with segmentation + export\n", + "[498/720] tile_r000020_c000021\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.22it/s]\n", + "100%|██████████| 3/3 [00:00<00:00, 3.17it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 312/312 [00:06<00:00, 50.67it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "222it [00:01, 203.79it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "219it [00:00, 249.63it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 67/67 [00:01<00:00, 35.14it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 91/91 [00:00<00:00, 162.12it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n", + "Warning: len(gdf)=91 != len(grain_stats)=87 -> truncating to 87\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.BpaNNCn48p/ipykernel_53064/3422303653.py:106: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/ouputgpkg/tile_r000020_c000021_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/csv_stats/tile_r000020_c000021_grain_stats.csv\n", + "done with segmentation + export\n", + "[499/720] tile_r000020_c000022\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.17it/s]\n", + "100%|██████████| 3/3 [00:00<00:00, 3.42it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 368/368 [00:06<00:00, 56.33it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "287it [00:01, 229.20it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "299it [00:01, 228.25it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 110/110 [00:01<00:00, 61.27it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 114/114 [00:00<00:00, 178.35it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.BpaNNCn48p/ipykernel_53064/3422303653.py:106: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/ouputgpkg/tile_r000020_c000022_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/csv_stats/tile_r000020_c000022_grain_stats.csv\n", + "done with segmentation + export\n", + "[500/720] tile_r000020_c000023\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.30it/s]\n", + "100%|██████████| 3/3 [00:00<00:00, 3.33it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 273/273 [00:05<00:00, 49.54it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "209it [00:00, 310.59it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "219it [00:00, 316.90it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 75/75 [00:01<00:00, 59.11it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 85/85 [00:00<00:00, 161.71it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.BpaNNCn48p/ipykernel_53064/3422303653.py:106: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/ouputgpkg/tile_r000020_c000023_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/csv_stats/tile_r000020_c000023_grain_stats.csv\n", + "done with segmentation + export\n", + "[501/720] tile_r000020_c000024\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.23it/s]\n", + "100%|██████████| 3/3 [00:00<00:00, 3.30it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 322/322 [00:07<00:00, 41.10it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "189it [00:00, 250.87it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "212it [00:00, 255.51it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 81/81 [00:01<00:00, 65.81it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 87/87 [00:00<00:00, 195.75it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.BpaNNCn48p/ipykernel_53064/3422303653.py:106: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/ouputgpkg/tile_r000020_c000024_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/csv_stats/tile_r000020_c000024_grain_stats.csv\n", + "done with segmentation + export\n", + "[502/720] tile_r000020_c000025\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.17it/s]\n", + "100%|██████████| 3/3 [00:00<00:00, 3.22it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 249/249 [00:06<00:00, 39.70it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "138it [00:00, 163.78it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "137it [00:00, 206.52it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 36/36 [00:01<00:00, 28.72it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 57/57 [00:00<00:00, 133.17it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.BpaNNCn48p/ipykernel_53064/3422303653.py:106: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/ouputgpkg/tile_r000020_c000025_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/csv_stats/tile_r000020_c000025_grain_stats.csv\n", + "done with segmentation + export\n", + "[503/720] tile_r000020_c000026\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.19it/s]\n", + "100%|██████████| 3/3 [00:00<00:00, 3.21it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 211/211 [00:05<00:00, 37.91it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "126it [00:01, 103.67it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "117it [00:00, 291.90it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 31/31 [00:00<00:00, 31.64it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 57/57 [00:00<00:00, 111.42it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.BpaNNCn48p/ipykernel_53064/3422303653.py:106: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/ouputgpkg/tile_r000020_c000026_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/csv_stats/tile_r000020_c000026_grain_stats.csv\n", + "done with segmentation + export\n", + "[504/720] tile_r000020_c000027\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.38it/s]\n", + "100%|██████████| 3/3 [00:00<00:00, 3.35it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 296/296 [00:06<00:00, 42.46it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "169it [00:00, 209.44it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "181it [00:00, 205.77it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 48/48 [00:02<00:00, 20.01it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 63/63 [00:00<00:00, 136.82it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsshome1/0B/di54doz/.local/share/mamba/envs/segmenteverygrain/lib/python3.10/site-packages/segmenteverygrain/segmenteverygrain.py:2218: RuntimeWarning: divide by zero encountered in log2\n", + " phi_minor = -np.log2(minor_axis_length)\n", + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.BpaNNCn48p/ipykernel_53064/3422303653.py:106: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/ouputgpkg/tile_r000020_c000027_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/csv_stats/tile_r000020_c000027_grain_stats.csv\n", + "done with segmentation + export\n", + "[505/720] tile_r000020_c000028\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.24it/s]\n", + "100%|██████████| 3/3 [00:00<00:00, 3.26it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 109/109 [00:02<00:00, 40.31it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "65it [00:00, 243.66it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "64it [00:00, 303.52it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 21/21 [00:00<00:00, 46.39it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 28/28 [00:00<00:00, 160.16it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.BpaNNCn48p/ipykernel_53064/3422303653.py:106: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/ouputgpkg/tile_r000020_c000028_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/csv_stats/tile_r000020_c000028_grain_stats.csv\n", + "done with segmentation + export\n", + "[506/720] tile_r000020_c000029\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.31it/s]\n", + "100%|██████████| 3/3 [00:00<00:00, 3.35it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "0it [00:00, ?it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "0it [00:00, ?it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "0it [00:00, ?it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "0it [00:00, ?it/s]\n", + "0it [00:00, ?it/s]\n", + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.BpaNNCn48p/ipykernel_53064/3422303653.py:106: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n", + "No grains found -> skipping histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/ouputgpkg/tile_r000020_c000029_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/csv_stats/tile_r000020_c000029_grain_stats.csv\n", + "done with segmentation + export\n", + "[507/720] tile_r000020_c000030\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.25it/s]\n", + "100%|██████████| 3/3 [00:00<00:00, 3.31it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "0it [00:00, ?it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "0it [00:00, ?it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "0it [00:00, ?it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "0it [00:00, ?it/s]\n", + "0it [00:00, ?it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n", + "No grains found -> skipping histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/ouputgpkg/tile_r000020_c000030_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/csv_stats/tile_r000020_c000030_grain_stats.csv\n", + "done with segmentation + export\n", + "[508/720] tile_r000020_c000031\n", + " -> skipped (100% Nodata)\n", + "[509/720] tile_r000020_c000032\n", + " -> skipped (100% Nodata)\n", + "[510/720] tile_r000020_c000033\n", + " -> skipped (100% Nodata)\n", + "[511/720] tile_r000021_c000004\n", + " -> skipped (100% Nodata)\n", + "[512/720] tile_r000021_c000005\n", + " -> skipped (100% Nodata)\n", + "[513/720] tile_r000021_c000006\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.BpaNNCn48p/ipykernel_53064/3422303653.py:106: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " -> skipped (100% Nodata)\n", + "[514/720] tile_r000021_c000007\n", + " -> skipped (100% Nodata)\n", + "[515/720] tile_r000021_c000008\n", + " -> skipped (100% Nodata)\n", + "[516/720] tile_r000021_c000009\n", + " -> skipped (100% Nodata)\n", + "[517/720] tile_r000021_c000010\n", + " -> skipped (100% Nodata)\n", + "[518/720] tile_r000021_c000011\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.32it/s]\n", + "100%|██████████| 3/3 [00:00<00:00, 3.24it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 8/8 [00:00<00:00, 26.70it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "0it [00:00, ?it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "0it [00:00, ?it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "0it [00:00, ?it/s]\n", + "0it [00:00, ?it/s]\n", + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.BpaNNCn48p/ipykernel_53064/3422303653.py:106: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n", + "No grains found -> skipping histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/ouputgpkg/tile_r000021_c000011_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/csv_stats/tile_r000021_c000011_grain_stats.csv\n", + "done with segmentation + export\n", + "[519/720] tile_r000021_c000012\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.33it/s]\n", + "100%|██████████| 3/3 [00:00<00:00, 3.22it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 140/140 [00:02<00:00, 54.22it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "102it [00:00, 230.07it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "106it [00:00, 234.29it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 34/34 [00:00<00:00, 43.07it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 36/36 [00:00<00:00, 148.58it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.BpaNNCn48p/ipykernel_53064/3422303653.py:106: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/ouputgpkg/tile_r000021_c000012_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/csv_stats/tile_r000021_c000012_grain_stats.csv\n", + "done with segmentation + export\n", + "[520/720] tile_r000021_c000013\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.23it/s]\n", + "100%|██████████| 3/3 [00:00<00:00, 3.22it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 232/232 [00:04<00:00, 47.20it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "145it [00:00, 256.86it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "153it [00:00, 262.10it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 54/54 [00:00<00:00, 57.42it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 55/55 [00:00<00:00, 166.37it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.BpaNNCn48p/ipykernel_53064/3422303653.py:106: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/ouputgpkg/tile_r000021_c000013_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/csv_stats/tile_r000021_c000013_grain_stats.csv\n", + "done with segmentation + export\n", + "[521/720] tile_r000021_c000014\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.35it/s]\n", + "100%|██████████| 3/3 [00:00<00:00, 3.31it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 396/396 [00:06<00:00, 57.72it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "306it [00:01, 226.50it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "305it [00:01, 239.27it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 105/105 [00:01<00:00, 60.18it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 115/115 [00:04<00:00, 25.76it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.BpaNNCn48p/ipykernel_53064/3422303653.py:106: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/ouputgpkg/tile_r000021_c000014_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/csv_stats/tile_r000021_c000014_grain_stats.csv\n", + "done with segmentation + export\n", + "[522/720] tile_r000021_c000015\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 2.98it/s]\n", + "100%|██████████| 3/3 [00:00<00:00, 3.09it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 337/337 [00:06<00:00, 55.03it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "258it [00:00, 289.35it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "266it [00:00, 285.44it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 100/100 [00:01<00:00, 69.83it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 103/103 [00:00<00:00, 185.73it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.BpaNNCn48p/ipykernel_53064/3422303653.py:106: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/ouputgpkg/tile_r000021_c000015_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/csv_stats/tile_r000021_c000015_grain_stats.csv\n", + "done with segmentation + export\n", + "[523/720] tile_r000021_c000016\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.37it/s]\n", + "100%|██████████| 3/3 [00:00<00:00, 3.15it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 392/392 [00:06<00:00, 59.78it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "322it [00:01, 316.31it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "325it [00:01, 321.96it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 118/118 [00:01<00:00, 77.61it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 123/123 [00:00<00:00, 187.54it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.BpaNNCn48p/ipykernel_53064/3422303653.py:106: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/ouputgpkg/tile_r000021_c000016_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/csv_stats/tile_r000021_c000016_grain_stats.csv\n", + "done with segmentation + export\n", + "[524/720] tile_r000021_c000017\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.34it/s]\n", + "100%|██████████| 3/3 [00:00<00:00, 3.58it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 329/329 [00:05<00:00, 56.93it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "225it [00:00, 371.24it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "235it [00:00, 369.78it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 90/90 [00:01<00:00, 87.40it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 91/91 [00:00<00:00, 188.40it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.BpaNNCn48p/ipykernel_53064/3422303653.py:106: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/ouputgpkg/tile_r000021_c000017_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/csv_stats/tile_r000021_c000017_grain_stats.csv\n", + "done with segmentation + export\n", + "[525/720] tile_r000021_c000018\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.23it/s]\n", + "100%|██████████| 3/3 [00:00<00:00, 3.24it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 391/391 [00:06<00:00, 58.22it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "316it [00:00, 335.73it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "323it [00:00, 334.84it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 118/118 [00:01<00:00, 77.41it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 121/121 [00:00<00:00, 191.54it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.BpaNNCn48p/ipykernel_53064/3422303653.py:106: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/ouputgpkg/tile_r000021_c000018_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/csv_stats/tile_r000021_c000018_grain_stats.csv\n", + "done with segmentation + export\n", + "[526/720] tile_r000021_c000019\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.26it/s]\n", + "100%|██████████| 3/3 [00:00<00:00, 3.07it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 321/321 [00:05<00:00, 53.84it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "248it [00:01, 191.44it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "246it [00:01, 226.38it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 77/77 [00:02<00:00, 36.50it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 86/86 [00:00<00:00, 162.61it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.BpaNNCn48p/ipykernel_53064/3422303653.py:106: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/ouputgpkg/tile_r000021_c000019_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/csv_stats/tile_r000021_c000019_grain_stats.csv\n", + "done with segmentation + export\n", + "[527/720] tile_r000021_c000020\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.03it/s]\n", + "100%|██████████| 3/3 [00:00<00:00, 3.18it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 343/343 [00:06<00:00, 55.52it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "260it [00:01, 217.15it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "273it [00:01, 222.21it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 99/99 [00:01<00:00, 52.01it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 106/106 [00:00<00:00, 178.87it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.BpaNNCn48p/ipykernel_53064/3422303653.py:106: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/ouputgpkg/tile_r000021_c000020_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/csv_stats/tile_r000021_c000020_grain_stats.csv\n", + "done with segmentation + export\n", + "[528/720] tile_r000021_c000021\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.35it/s]\n", + "100%|██████████| 3/3 [00:00<00:00, 3.29it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 346/346 [00:06<00:00, 54.89it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "277it [00:00, 349.01it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "286it [00:00, 348.36it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 112/112 [00:01<00:00, 92.17it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 114/114 [00:00<00:00, 187.35it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.BpaNNCn48p/ipykernel_53064/3422303653.py:106: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/ouputgpkg/tile_r000021_c000021_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/csv_stats/tile_r000021_c000021_grain_stats.csv\n", + "done with segmentation + export\n", + "[529/720] tile_r000021_c000022\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.36it/s]\n", + "100%|██████████| 3/3 [00:00<00:00, 3.44it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 388/388 [00:07<00:00, 54.53it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "305it [00:01, 164.21it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "304it [00:01, 166.20it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 89/89 [00:03<00:00, 29.22it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 123/123 [00:00<00:00, 187.73it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.BpaNNCn48p/ipykernel_53064/3422303653.py:106: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/ouputgpkg/tile_r000021_c000022_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/csv_stats/tile_r000021_c000022_grain_stats.csv\n", + "done with segmentation + export\n", + "[530/720] tile_r000021_c000023\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.40it/s]\n", + "100%|██████████| 3/3 [00:00<00:00, 3.25it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 349/349 [00:06<00:00, 53.71it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "269it [00:00, 417.57it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "268it [00:00, 500.84it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 103/103 [00:00<00:00, 110.47it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 116/116 [00:00<00:00, 199.98it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.BpaNNCn48p/ipykernel_53064/3422303653.py:106: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/ouputgpkg/tile_r000021_c000023_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/csv_stats/tile_r000021_c000023_grain_stats.csv\n", + "done with segmentation + export\n", + "[531/720] tile_r000021_c000024\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.28it/s]\n", + "100%|██████████| 3/3 [00:00<00:00, 3.40it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 345/345 [00:07<00:00, 47.41it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "235it [00:02, 112.99it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "222it [00:00, 321.15it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 68/68 [00:01<00:00, 56.24it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 99/99 [00:00<00:00, 180.04it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.BpaNNCn48p/ipykernel_53064/3422303653.py:106: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/ouputgpkg/tile_r000021_c000024_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/csv_stats/tile_r000021_c000024_grain_stats.csv\n", + "done with segmentation + export\n", + "[532/720] tile_r000021_c000025\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.49it/s]\n", + "100%|██████████| 3/3 [00:00<00:00, 3.62it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 322/322 [00:06<00:00, 50.33it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "221it [00:00, 287.09it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "239it [00:00, 284.95it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 82/82 [00:01<00:00, 51.48it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 89/89 [00:00<00:00, 165.50it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.BpaNNCn48p/ipykernel_53064/3422303653.py:106: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/ouputgpkg/tile_r000021_c000025_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/csv_stats/tile_r000021_c000025_grain_stats.csv\n", + "done with segmentation + export\n", + "[533/720] tile_r000021_c000026\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.34it/s]\n", + "100%|██████████| 3/3 [00:00<00:00, 3.59it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 245/245 [00:07<00:00, 31.34it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "137it [00:02, 47.67it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "129it [00:00, 159.67it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 27/27 [00:02<00:00, 13.10it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 53/53 [00:00<00:00, 117.58it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.BpaNNCn48p/ipykernel_53064/3422303653.py:106: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/ouputgpkg/tile_r000021_c000026_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/csv_stats/tile_r000021_c000026_grain_stats.csv\n", + "done with segmentation + export\n", + "[534/720] tile_r000021_c000027\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.23it/s]\n", + "100%|██████████| 3/3 [00:00<00:00, 3.43it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 304/304 [00:08<00:00, 34.88it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "157it [00:01, 115.02it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "156it [00:01, 130.72it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 31/31 [00:04<00:00, 6.73it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 60/60 [00:00<00:00, 113.69it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.BpaNNCn48p/ipykernel_53064/3422303653.py:106: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/ouputgpkg/tile_r000021_c000027_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/csv_stats/tile_r000021_c000027_grain_stats.csv\n", + "done with segmentation + export\n", + "[535/720] tile_r000021_c000028\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.43it/s]\n", + "100%|██████████| 3/3 [00:00<00:00, 3.42it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 322/322 [00:06<00:00, 48.26it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "198it [00:01, 144.74it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "193it [00:01, 167.02it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 44/44 [00:02<00:00, 18.08it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 71/71 [00:00<00:00, 138.95it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.BpaNNCn48p/ipykernel_53064/3422303653.py:106: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/ouputgpkg/tile_r000021_c000028_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/csv_stats/tile_r000021_c000028_grain_stats.csv\n", + "done with segmentation + export\n", + "[536/720] tile_r000021_c000029\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.36it/s]\n", + "100%|██████████| 3/3 [00:00<00:00, 3.40it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 77/77 [00:01<00:00, 45.17it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "51it [00:00, 238.92it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "50it [00:00, 296.48it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 14/14 [00:00<00:00, 38.61it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 17/17 [00:00<00:00, 155.55it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.BpaNNCn48p/ipykernel_53064/3422303653.py:106: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/ouputgpkg/tile_r000021_c000029_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/csv_stats/tile_r000021_c000029_grain_stats.csv\n", + "done with segmentation + export\n", + "[537/720] tile_r000021_c000030\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.42it/s]\n", + "100%|██████████| 3/3 [00:00<00:00, 3.37it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 9/9 [00:00<00:00, 34.53it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "7it [00:00, 365.83it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "8it [00:00, 370.04it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 3/3 [00:00<00:00, 59.74it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:00<00:00, 119.11it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.BpaNNCn48p/ipykernel_53064/3422303653.py:106: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/ouputgpkg/tile_r000021_c000030_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/csv_stats/tile_r000021_c000030_grain_stats.csv\n", + "done with segmentation + export\n", + "[538/720] tile_r000021_c000031\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.34it/s]\n", + "100%|██████████| 3/3 [00:00<00:00, 3.41it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "0it [00:00, ?it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "0it [00:00, ?it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "0it [00:00, ?it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "0it [00:00, ?it/s]\n", + "0it [00:00, ?it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n", + "No grains found -> skipping histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/ouputgpkg/tile_r000021_c000031_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/csv_stats/tile_r000021_c000031_grain_stats.csv\n", + "done with segmentation + export\n", + "[539/720] tile_r000021_c000032\n", + " -> skipped (100% Nodata)\n", + "[540/720] tile_r000021_c000033\n", + " -> skipped (100% Nodata)\n", + "[541/720] tile_r000022_c000004\n", + " -> skipped (100% Nodata)\n", + "[542/720] tile_r000022_c000005\n", + " -> skipped (100% Nodata)\n", + "[543/720] tile_r000022_c000006\n", + " -> skipped (100% Nodata)\n", + "[544/720] tile_r000022_c000007\n", + " -> skipped (100% Nodata)\n", + "[545/720] tile_r000022_c000008\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.BpaNNCn48p/ipykernel_53064/3422303653.py:106: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " -> skipped (100% Nodata)\n", + "[546/720] tile_r000022_c000009\n", + " -> skipped (100% Nodata)\n", + "[547/720] tile_r000022_c000010\n", + " -> skipped (100% Nodata)\n", + "[548/720] tile_r000022_c000011\n", + " -> skipped (100% Nodata)\n", + "[549/720] tile_r000022_c000012\n", + " -> skipped (100% Nodata)\n", + "[550/720] tile_r000022_c000013\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.45it/s]\n", + "100%|██████████| 3/3 [00:00<00:00, 3.32it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 26/26 [00:00<00:00, 37.91it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2it [00:00, 531.90it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2it [00:00, 728.62it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 1/1 [00:00<00:00, 242.25it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 1/1 [00:00<00:00, 143.08it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.BpaNNCn48p/ipykernel_53064/3422303653.py:106: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/ouputgpkg/tile_r000022_c000013_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/csv_stats/tile_r000022_c000013_grain_stats.csv\n", + "done with segmentation + export\n", + "[551/720] tile_r000022_c000014\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.46it/s]\n", + "100%|██████████| 3/3 [00:00<00:00, 3.35it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 140/140 [00:02<00:00, 52.14it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "80it [00:00, 295.47it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "89it [00:00, 304.28it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 32/32 [00:00<00:00, 71.21it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 33/33 [00:00<00:00, 160.90it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.BpaNNCn48p/ipykernel_53064/3422303653.py:106: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/ouputgpkg/tile_r000022_c000014_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/csv_stats/tile_r000022_c000014_grain_stats.csv\n", + "done with segmentation + export\n", + "[552/720] tile_r000022_c000015\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.65it/s]\n", + "100%|██████████| 3/3 [00:00<00:00, 3.30it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 269/269 [00:05<00:00, 51.46it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "200it [00:00, 255.04it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "207it [00:00, 259.22it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 69/69 [00:01<00:00, 59.68it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 74/74 [00:00<00:00, 160.56it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.BpaNNCn48p/ipykernel_53064/3422303653.py:106: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/ouputgpkg/tile_r000022_c000015_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/csv_stats/tile_r000022_c000015_grain_stats.csv\n", + "done with segmentation + export\n", + "[553/720] tile_r000022_c000016\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.56it/s]\n", + "100%|██████████| 3/3 [00:00<00:00, 3.34it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 367/367 [00:06<00:00, 58.60it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "280it [00:00, 286.87it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "279it [00:00, 301.56it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 98/98 [00:01<00:00, 63.13it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 108/108 [00:00<00:00, 176.14it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.BpaNNCn48p/ipykernel_53064/3422303653.py:106: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/ouputgpkg/tile_r000022_c000016_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/csv_stats/tile_r000022_c000016_grain_stats.csv\n", + "done with segmentation + export\n", + "[554/720] tile_r000022_c000017\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.40it/s]\n", + "100%|██████████| 3/3 [00:00<00:00, 3.28it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 339/339 [00:05<00:00, 56.92it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "264it [00:00, 327.03it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "270it [00:00, 323.59it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 102/102 [00:01<00:00, 76.75it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 106/106 [00:00<00:00, 177.69it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.BpaNNCn48p/ipykernel_53064/3422303653.py:106: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/ouputgpkg/tile_r000022_c000017_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/csv_stats/tile_r000022_c000017_grain_stats.csv\n", + "done with segmentation + export\n", + "[555/720] tile_r000022_c000018\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.47it/s]\n", + "100%|██████████| 3/3 [00:00<00:00, 3.27it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 288/288 [00:05<00:00, 55.55it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "211it [00:00, 264.35it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "219it [00:00, 264.54it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 78/78 [00:01<00:00, 66.60it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 80/80 [00:00<00:00, 168.86it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.BpaNNCn48p/ipykernel_53064/3422303653.py:106: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/ouputgpkg/tile_r000022_c000018_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/csv_stats/tile_r000022_c000018_grain_stats.csv\n", + "done with segmentation + export\n", + "[556/720] tile_r000022_c000019\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.42it/s]\n", + "100%|██████████| 3/3 [00:00<00:00, 3.33it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 348/348 [00:06<00:00, 56.65it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "268it [00:00, 330.58it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "273it [00:00, 324.90it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 101/101 [00:01<00:00, 79.72it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 104/104 [00:00<00:00, 179.94it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.BpaNNCn48p/ipykernel_53064/3422303653.py:106: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/ouputgpkg/tile_r000022_c000019_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/csv_stats/tile_r000022_c000019_grain_stats.csv\n", + "done with segmentation + export\n", + "[557/720] tile_r000022_c000020\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.40it/s]\n", + "100%|██████████| 3/3 [00:00<00:00, 3.47it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 343/343 [00:05<00:00, 57.17it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "273it [00:00, 379.41it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "281it [00:00, 375.98it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 107/107 [00:01<00:00, 99.46it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 110/110 [00:00<00:00, 188.78it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.BpaNNCn48p/ipykernel_53064/3422303653.py:106: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/ouputgpkg/tile_r000022_c000020_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/csv_stats/tile_r000022_c000020_grain_stats.csv\n", + "done with segmentation + export\n", + "[558/720] tile_r000022_c000021\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.46it/s]\n", + "100%|██████████| 3/3 [00:00<00:00, 3.39it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 329/329 [00:05<00:00, 57.84it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "235it [00:00, 369.56it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "243it [00:00, 378.85it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 91/91 [00:01<00:00, 89.21it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 93/93 [00:00<00:00, 186.80it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.BpaNNCn48p/ipykernel_53064/3422303653.py:106: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/ouputgpkg/tile_r000022_c000021_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/csv_stats/tile_r000022_c000021_grain_stats.csv\n", + "done with segmentation + export\n", + "[559/720] tile_r000022_c000022\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.37it/s]\n", + "100%|██████████| 3/3 [00:00<00:00, 3.38it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 371/371 [00:06<00:00, 58.85it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "285it [00:00, 288.60it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "284it [00:00, 309.62it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 102/102 [00:01<00:00, 75.68it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 110/110 [00:00<00:00, 178.39it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.BpaNNCn48p/ipykernel_53064/3422303653.py:106: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/ouputgpkg/tile_r000022_c000022_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/csv_stats/tile_r000022_c000022_grain_stats.csv\n", + "done with segmentation + export\n", + "[560/720] tile_r000022_c000023\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.33it/s]\n", + "100%|██████████| 3/3 [00:00<00:00, 3.36it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 361/361 [00:06<00:00, 57.23it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "280it [00:01, 240.97it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "278it [00:00, 298.66it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 93/93 [00:02<00:00, 44.48it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 106/106 [00:00<00:00, 187.79it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.BpaNNCn48p/ipykernel_53064/3422303653.py:106: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/ouputgpkg/tile_r000022_c000023_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/csv_stats/tile_r000022_c000023_grain_stats.csv\n", + "done with segmentation + export\n", + "[561/720] tile_r000022_c000024\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.43it/s]\n", + "100%|██████████| 3/3 [00:00<00:00, 3.67it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 387/387 [00:07<00:00, 54.38it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "303it [00:00, 313.33it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "300it [00:00, 373.07it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 105/105 [00:01<00:00, 85.10it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 120/120 [00:00<00:00, 199.46it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.BpaNNCn48p/ipykernel_53064/3422303653.py:106: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/ouputgpkg/tile_r000022_c000024_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/csv_stats/tile_r000022_c000024_grain_stats.csv\n", + "done with segmentation + export\n", + "[562/720] tile_r000022_c000025\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.42it/s]\n", + "100%|██████████| 3/3 [00:00<00:00, 3.38it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 349/349 [00:06<00:00, 55.25it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "258it [00:00, 280.94it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "257it [00:00, 296.96it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 87/87 [00:01<00:00, 61.44it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 104/104 [00:00<00:00, 193.61it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.BpaNNCn48p/ipykernel_53064/3422303653.py:106: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/ouputgpkg/tile_r000022_c000025_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/csv_stats/tile_r000022_c000025_grain_stats.csv\n", + "done with segmentation + export\n", + "[563/720] tile_r000022_c000026\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.37it/s]\n", + "100%|██████████| 3/3 [00:00<00:00, 3.53it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 342/342 [00:06<00:00, 55.13it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "246it [00:00, 375.38it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "266it [00:00, 356.73it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 101/101 [00:01<00:00, 91.64it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 105/105 [00:00<00:00, 185.63it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.BpaNNCn48p/ipykernel_53064/3422303653.py:106: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/ouputgpkg/tile_r000022_c000026_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/csv_stats/tile_r000022_c000026_grain_stats.csv\n", + "done with segmentation + export\n", + "[564/720] tile_r000022_c000027\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.38it/s]\n", + "100%|██████████| 3/3 [00:00<00:00, 3.39it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 361/361 [00:06<00:00, 54.51it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "289it [00:02, 137.90it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "15it [00:00, 138.00it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 1/1 [00:00<00:00, 3.58it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 9/9 [00:00<00:00, 137.15it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.BpaNNCn48p/ipykernel_53064/3422303653.py:106: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/ouputgpkg/tile_r000022_c000027_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/csv_stats/tile_r000022_c000027_grain_stats.csv\n", + "done with segmentation + export\n", + "[565/720] tile_r000022_c000028\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.38it/s]\n", + "100%|██████████| 3/3 [00:00<00:00, 3.46it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 303/303 [00:06<00:00, 47.66it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "191it [00:01, 131.26it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "176it [00:00, 325.20it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 49/49 [00:01<00:00, 47.99it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 77/77 [00:00<00:00, 155.47it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.BpaNNCn48p/ipykernel_53064/3422303653.py:106: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/ouputgpkg/tile_r000022_c000028_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/csv_stats/tile_r000022_c000028_grain_stats.csv\n", + "done with segmentation + export\n", + "[566/720] tile_r000022_c000029\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.43it/s]\n", + "100%|██████████| 3/3 [00:00<00:00, 3.37it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 238/238 [00:05<00:00, 41.39it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "137it [00:01, 136.82it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "127it [00:00, 254.73it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 36/36 [00:00<00:00, 36.02it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 51/51 [00:00<00:00, 137.78it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.BpaNNCn48p/ipykernel_53064/3422303653.py:106: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/ouputgpkg/tile_r000022_c000029_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/csv_stats/tile_r000022_c000029_grain_stats.csv\n", + "done with segmentation + export\n", + "[567/720] tile_r000022_c000030\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.39it/s]\n", + "100%|██████████| 3/3 [00:00<00:00, 3.42it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 93/93 [00:01<00:00, 47.17it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "63it [00:00, 351.20it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "72it [00:00, 351.57it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 26/26 [00:00<00:00, 75.74it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 28/28 [00:00<00:00, 162.81it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.BpaNNCn48p/ipykernel_53064/3422303653.py:106: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/ouputgpkg/tile_r000022_c000030_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/csv_stats/tile_r000022_c000030_grain_stats.csv\n", + "done with segmentation + export\n", + "[568/720] tile_r000022_c000031\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.27it/s]\n", + "100%|██████████| 3/3 [00:00<00:00, 3.46it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 6/6 [00:00<00:00, 24.84it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "0it [00:00, ?it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "0it [00:00, ?it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "0it [00:00, ?it/s]\n", + "0it [00:00, ?it/s]\n", + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.BpaNNCn48p/ipykernel_53064/3422303653.py:106: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n", + "No grains found -> skipping histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/ouputgpkg/tile_r000022_c000031_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/csv_stats/tile_r000022_c000031_grain_stats.csv\n", + "done with segmentation + export\n", + "[569/720] tile_r000022_c000032\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.29it/s]\n", + "100%|██████████| 3/3 [00:00<00:00, 3.39it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "0it [00:00, ?it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "0it [00:00, ?it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "0it [00:00, ?it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "0it [00:00, ?it/s]\n", + "0it [00:00, ?it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n", + "No grains found -> skipping histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/ouputgpkg/tile_r000022_c000032_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/csv_stats/tile_r000022_c000032_grain_stats.csv\n", + "done with segmentation + export\n", + "[570/720] tile_r000022_c000033\n", + " -> skipped (100% Nodata)\n", + "[571/720] tile_r000023_c000004\n", + " -> skipped (100% Nodata)\n", + "[572/720] tile_r000023_c000005\n", + " -> skipped (100% Nodata)\n", + "[573/720] tile_r000023_c000006\n", + " -> skipped (100% Nodata)\n", + "[574/720] tile_r000023_c000007\n", + " -> skipped (100% Nodata)\n", + "[575/720] tile_r000023_c000008\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.BpaNNCn48p/ipykernel_53064/3422303653.py:106: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " -> skipped (100% Nodata)\n", + "[576/720] tile_r000023_c000009\n", + " -> skipped (100% Nodata)\n", + "[577/720] tile_r000023_c000010\n", + " -> skipped (100% Nodata)\n", + "[578/720] tile_r000023_c000011\n", + " -> skipped (100% Nodata)\n", + "[579/720] tile_r000023_c000012\n", + " -> skipped (100% Nodata)\n", + "[580/720] tile_r000023_c000013\n", + " -> skipped (100% Nodata)\n", + "[581/720] tile_r000023_c000014\n", + " -> skipped (100% Nodata)\n", + "[582/720] tile_r000023_c000015\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.38it/s]\n", + "100%|██████████| 3/3 [00:00<00:00, 3.42it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 6/6 [00:00<00:00, 24.76it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "0it [00:00, ?it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "0it [00:00, ?it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "0it [00:00, ?it/s]\n", + "0it [00:00, ?it/s]\n", + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.BpaNNCn48p/ipykernel_53064/3422303653.py:106: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n", + "No grains found -> skipping histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/ouputgpkg/tile_r000023_c000015_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/csv_stats/tile_r000023_c000015_grain_stats.csv\n", + "done with segmentation + export\n", + "[583/720] tile_r000023_c000016\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.45it/s]\n", + "100%|██████████| 3/3 [00:00<00:00, 3.40it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 58/58 [00:01<00:00, 48.57it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "32it [00:00, 413.48it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "39it [00:00, 368.03it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 15/15 [00:00<00:00, 98.50it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 15/15 [00:00<00:00, 158.61it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.BpaNNCn48p/ipykernel_53064/3422303653.py:106: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/ouputgpkg/tile_r000023_c000016_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/csv_stats/tile_r000023_c000016_grain_stats.csv\n", + "done with segmentation + export\n", + "[584/720] tile_r000023_c000017\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.34it/s]\n", + "100%|██████████| 3/3 [00:00<00:00, 3.41it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 124/124 [00:02<00:00, 52.76it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "68it [00:00, 302.62it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "72it [00:00, 304.42it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 23/23 [00:00<00:00, 58.77it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 23/23 [00:00<00:00, 151.33it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.BpaNNCn48p/ipykernel_53064/3422303653.py:106: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/ouputgpkg/tile_r000023_c000017_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/csv_stats/tile_r000023_c000017_grain_stats.csv\n", + "done with segmentation + export\n", + "[585/720] tile_r000023_c000018\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.39it/s]\n", + "100%|██████████| 3/3 [00:00<00:00, 3.46it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 297/297 [00:05<00:00, 57.23it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "220it [00:00, 295.58it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "218it [00:00, 340.57it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 73/73 [00:01<00:00, 56.48it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 85/85 [00:00<00:00, 175.90it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.BpaNNCn48p/ipykernel_53064/3422303653.py:106: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/ouputgpkg/tile_r000023_c000018_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/csv_stats/tile_r000023_c000018_grain_stats.csv\n", + "done with segmentation + export\n", + "[586/720] tile_r000023_c000019\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.37it/s]\n", + "100%|██████████| 3/3 [00:00<00:00, 3.38it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 325/325 [00:05<00:00, 58.96it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "244it [00:00, 264.07it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "242it [00:00, 323.47it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 87/87 [00:01<00:00, 81.93it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 96/96 [00:00<00:00, 181.13it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.BpaNNCn48p/ipykernel_53064/3422303653.py:106: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/ouputgpkg/tile_r000023_c000019_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/csv_stats/tile_r000023_c000019_grain_stats.csv\n", + "done with segmentation + export\n", + "[587/720] tile_r000023_c000020\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.52it/s]\n", + "100%|██████████| 3/3 [00:00<00:00, 3.35it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 330/330 [00:05<00:00, 58.69it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "271it [00:00, 329.79it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "280it [00:00, 330.55it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 103/103 [00:01<00:00, 86.68it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 106/106 [00:00<00:00, 173.36it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.BpaNNCn48p/ipykernel_53064/3422303653.py:106: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/ouputgpkg/tile_r000023_c000020_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/csv_stats/tile_r000023_c000020_grain_stats.csv\n", + "done with segmentation + export\n", + "[588/720] tile_r000023_c000021\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.40it/s]\n", + "100%|██████████| 3/3 [00:00<00:00, 3.53it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 361/361 [00:05<00:00, 61.72it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "305it [00:01, 280.15it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "313it [00:01, 286.36it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 110/110 [00:01<00:00, 62.26it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 112/112 [00:00<00:00, 165.55it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.BpaNNCn48p/ipykernel_53064/3422303653.py:106: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/ouputgpkg/tile_r000023_c000021_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/csv_stats/tile_r000023_c000021_grain_stats.csv\n", + "done with segmentation + export\n", + "[589/720] tile_r000023_c000022\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.49it/s]\n", + "100%|██████████| 3/3 [00:00<00:00, 3.05it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 419/419 [00:07<00:00, 56.61it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "330it [00:01, 283.82it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "341it [00:01, 287.10it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 125/125 [00:01<00:00, 64.67it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 127/127 [00:00<00:00, 185.96it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.BpaNNCn48p/ipykernel_53064/3422303653.py:106: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/ouputgpkg/tile_r000023_c000022_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/csv_stats/tile_r000023_c000022_grain_stats.csv\n", + "done with segmentation + export\n", + "[590/720] tile_r000023_c000023\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.17it/s]\n", + "100%|██████████| 3/3 [00:00<00:00, 3.17it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 338/338 [00:05<00:00, 61.81it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "231it [00:00, 287.40it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "230it [00:00, 296.77it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 77/77 [00:01<00:00, 64.04it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 89/89 [00:00<00:00, 163.84it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.BpaNNCn48p/ipykernel_53064/3422303653.py:106: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/ouputgpkg/tile_r000023_c000023_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/csv_stats/tile_r000023_c000023_grain_stats.csv\n", + "done with segmentation + export\n", + "[591/720] tile_r000023_c000024\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.58it/s]\n", + "100%|██████████| 3/3 [00:00<00:00, 3.69it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 368/368 [00:06<00:00, 56.92it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "288it [00:01, 205.77it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "298it [00:01, 207.28it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 100/100 [00:02<00:00, 47.10it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 104/104 [00:00<00:00, 173.67it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.BpaNNCn48p/ipykernel_53064/3422303653.py:106: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/ouputgpkg/tile_r000023_c000024_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/csv_stats/tile_r000023_c000024_grain_stats.csv\n", + "done with segmentation + export\n", + "[592/720] tile_r000023_c000025\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.32it/s]\n", + "100%|██████████| 3/3 [00:00<00:00, 3.38it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 393/393 [00:06<00:00, 59.09it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "324it [00:01, 304.68it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "333it [00:01, 304.16it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 118/118 [00:01<00:00, 72.48it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 119/119 [00:00<00:00, 187.83it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.BpaNNCn48p/ipykernel_53064/3422303653.py:106: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/ouputgpkg/tile_r000023_c000025_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/csv_stats/tile_r000023_c000025_grain_stats.csv\n", + "done with segmentation + export\n", + "[593/720] tile_r000023_c000026\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.28it/s]\n", + "100%|██████████| 3/3 [00:00<00:00, 3.35it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 328/328 [00:06<00:00, 48.67it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "238it [00:00, 335.24it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "237it [00:00, 359.19it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 77/77 [00:02<00:00, 38.38it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 94/94 [00:00<00:00, 169.10it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n", + "Warning: len(gdf)=94 != len(grain_stats)=93 -> truncating to 93\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.BpaNNCn48p/ipykernel_53064/3422303653.py:106: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/ouputgpkg/tile_r000023_c000026_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/csv_stats/tile_r000023_c000026_grain_stats.csv\n", + "done with segmentation + export\n", + "[594/720] tile_r000023_c000027\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.28it/s]\n", + "100%|██████████| 3/3 [00:00<00:00, 3.28it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 386/386 [00:06<00:00, 57.89it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "300it [00:01, 185.20it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "299it [00:01, 195.04it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 96/96 [00:02<00:00, 38.82it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 115/115 [00:00<00:00, 175.52it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.BpaNNCn48p/ipykernel_53064/3422303653.py:106: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/ouputgpkg/tile_r000023_c000027_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/csv_stats/tile_r000023_c000027_grain_stats.csv\n", + "done with segmentation + export\n", + "[595/720] tile_r000023_c000028\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.42it/s]\n", + "100%|██████████| 3/3 [00:00<00:00, 3.46it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 360/360 [00:06<00:00, 58.62it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "283it [00:00, 332.09it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "296it [00:00, 330.33it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 108/108 [00:01<00:00, 77.38it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 111/111 [00:00<00:00, 188.03it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.BpaNNCn48p/ipykernel_53064/3422303653.py:106: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/ouputgpkg/tile_r000023_c000028_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/csv_stats/tile_r000023_c000028_grain_stats.csv\n", + "done with segmentation + export\n", + "[596/720] tile_r000023_c000029\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.32it/s]\n", + "100%|██████████| 3/3 [00:00<00:00, 3.39it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 386/386 [00:07<00:00, 51.17it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "298it [00:01, 225.87it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "297it [00:01, 248.30it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 86/86 [00:02<00:00, 40.52it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 114/114 [00:00<00:00, 167.34it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n", + "Warning: len(gdf)=114 != len(grain_stats)=113 -> truncating to 113\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.BpaNNCn48p/ipykernel_53064/3422303653.py:106: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/ouputgpkg/tile_r000023_c000029_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/csv_stats/tile_r000023_c000029_grain_stats.csv\n", + "done with segmentation + export\n", + "[597/720] tile_r000023_c000030\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.31it/s]\n", + "100%|██████████| 3/3 [00:00<00:00, 3.35it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 165/165 [00:03<00:00, 45.68it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "76it [00:00, 312.70it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "85it [00:00, 322.16it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 29/29 [00:00<00:00, 43.32it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 34/34 [00:00<00:00, 141.52it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.BpaNNCn48p/ipykernel_53064/3422303653.py:106: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/ouputgpkg/tile_r000023_c000030_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/csv_stats/tile_r000023_c000030_grain_stats.csv\n", + "done with segmentation + export\n", + "[598/720] tile_r000023_c000031\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.42it/s]\n", + "100%|██████████| 3/3 [00:00<00:00, 3.46it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 123/123 [00:02<00:00, 44.86it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "57it [00:00, 364.74it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "61it [00:00, 372.83it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 22/22 [00:00<00:00, 76.00it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 22/22 [00:00<00:00, 146.15it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.BpaNNCn48p/ipykernel_53064/3422303653.py:106: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/ouputgpkg/tile_r000023_c000031_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/csv_stats/tile_r000023_c000031_grain_stats.csv\n", + "done with segmentation + export\n", + "[599/720] tile_r000023_c000032\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.53it/s]\n", + "100%|██████████| 3/3 [00:00<00:00, 3.56it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 8/8 [00:00<00:00, 29.68it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "3it [00:00, 1064.27it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "4it [00:00, 568.68it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 2/2 [00:00<00:00, 198.67it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 2/2 [00:00<00:00, 128.94it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n", + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/ouputgpkg/tile_r000023_c000032_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/csv_stats/tile_r000023_c000032_grain_stats.csv\n", + "done with segmentation + export\n", + "[600/720] tile_r000023_c000033\n", + " -> skipped (100% Nodata)\n", + "[601/720] tile_r000024_c000004\n", + " -> skipped (100% Nodata)\n", + "[602/720] tile_r000024_c000005\n", + " -> skipped (100% Nodata)\n", + "[603/720] tile_r000024_c000006\n", + " -> skipped (100% Nodata)\n", + "[604/720] tile_r000024_c000007\n", + " -> skipped (100% Nodata)\n", + "[605/720] tile_r000024_c000008\n", + " -> skipped (100% Nodata)\n", + "[606/720] tile_r000024_c000009\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.BpaNNCn48p/ipykernel_53064/3422303653.py:106: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " -> skipped (100% Nodata)\n", + "[607/720] tile_r000024_c000010\n", + " -> skipped (100% Nodata)\n", + "[608/720] tile_r000024_c000011\n", + " -> skipped (100% Nodata)\n", + "[609/720] tile_r000024_c000012\n", + " -> skipped (100% Nodata)\n", + "[610/720] tile_r000024_c000013\n", + " -> skipped (100% Nodata)\n", + "[611/720] tile_r000024_c000014\n", + " -> skipped (100% Nodata)\n", + "[612/720] tile_r000024_c000015\n", + " -> skipped (100% Nodata)\n", + "[613/720] tile_r000024_c000016\n", + " -> skipped (100% Nodata)\n", + "[614/720] tile_r000024_c000017\n", + " -> skipped (100% Nodata)\n", + "[615/720] tile_r000024_c000018\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.61it/s]\n", + "100%|██████████| 3/3 [00:00<00:00, 3.56it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 2/2 [00:00<00:00, 13.08it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "0it [00:00, ?it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "0it [00:00, ?it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "0it [00:00, ?it/s]\n", + "0it [00:00, ?it/s]\n", + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.BpaNNCn48p/ipykernel_53064/3422303653.py:106: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n", + "No grains found -> skipping histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/ouputgpkg/tile_r000024_c000018_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/csv_stats/tile_r000024_c000018_grain_stats.csv\n", + "done with segmentation + export\n", + "[616/720] tile_r000024_c000019\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.67it/s]\n", + "100%|██████████| 3/3 [00:00<00:00, 3.57it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 59/59 [00:01<00:00, 53.68it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "27it [00:00, 449.58it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "29it [00:00, 464.47it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 11/11 [00:00<00:00, 91.22it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\n", + "100%|██████████| 13/13 [00:00<00:00, 159.22it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.BpaNNCn48p/ipykernel_53064/3422303653.py:106: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/ouputgpkg/tile_r000024_c000019_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/csv_stats/tile_r000024_c000019_grain_stats.csv\n", + "done with segmentation + export\n", + "[617/720] tile_r000024_c000020\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.79it/s]\n", + "100%|██████████| 3/3 [00:00<00:00, 3.83it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 98/98 [00:01<00:00, 58.08it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "50it [00:00, 324.52it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "62it [00:00, 324.34it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 22/22 [00:00<00:00, 69.55it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 23/23 [00:00<00:00, 162.30it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.BpaNNCn48p/ipykernel_53064/3422303653.py:106: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/ouputgpkg/tile_r000024_c000020_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/csv_stats/tile_r000024_c000020_grain_stats.csv\n", + "done with segmentation + export\n", + "[618/720] tile_r000024_c000021\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.53it/s]\n", + "100%|██████████| 3/3 [00:00<00:00, 3.51it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 279/279 [00:05<00:00, 48.26it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "202it [00:00, 363.35it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "213it [00:00, 347.63it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 81/81 [00:01<00:00, 70.35it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 87/87 [00:00<00:00, 191.14it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.BpaNNCn48p/ipykernel_53064/3422303653.py:106: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/ouputgpkg/tile_r000024_c000021_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/csv_stats/tile_r000024_c000021_grain_stats.csv\n", + "done with segmentation + export\n", + "[619/720] tile_r000024_c000022\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.40it/s]\n", + "100%|██████████| 3/3 [00:00<00:00, 3.50it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 250/250 [00:04<00:00, 56.49it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "187it [00:00, 358.36it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "200it [00:00, 348.16it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 75/75 [00:00<00:00, 86.19it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 76/76 [00:00<00:00, 182.05it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.BpaNNCn48p/ipykernel_53064/3422303653.py:106: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/ouputgpkg/tile_r000024_c000022_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/csv_stats/tile_r000024_c000022_grain_stats.csv\n", + "done with segmentation + export\n", + "[620/720] tile_r000024_c000023\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.44it/s]\n", + "100%|██████████| 3/3 [00:00<00:00, 3.40it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 333/333 [00:05<00:00, 56.77it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "255it [00:01, 205.60it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "251it [00:00, 280.62it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 82/82 [00:01<00:00, 56.64it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 98/98 [00:00<00:00, 167.91it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.BpaNNCn48p/ipykernel_53064/3422303653.py:106: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/ouputgpkg/tile_r000024_c000023_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/csv_stats/tile_r000024_c000023_grain_stats.csv\n", + "done with segmentation + export\n", + "[621/720] tile_r000024_c000024\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.32it/s]\n", + "100%|██████████| 3/3 [00:00<00:00, 3.42it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 354/354 [00:06<00:00, 58.63it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "278it [00:00, 341.26it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "287it [00:00, 348.11it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 110/110 [00:01<00:00, 93.25it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 112/112 [00:00<00:00, 171.69it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.BpaNNCn48p/ipykernel_53064/3422303653.py:106: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/ouputgpkg/tile_r000024_c000024_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/csv_stats/tile_r000024_c000024_grain_stats.csv\n", + "done with segmentation + export\n", + "[622/720] tile_r000024_c000025\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.59it/s]\n", + "100%|██████████| 3/3 [00:00<00:00, 3.46it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 365/365 [00:06<00:00, 56.61it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "290it [00:01, 204.20it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "287it [00:01, 225.32it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 93/93 [00:01<00:00, 47.82it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 108/108 [00:00<00:00, 159.93it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.BpaNNCn48p/ipykernel_53064/3422303653.py:106: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/ouputgpkg/tile_r000024_c000025_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/csv_stats/tile_r000024_c000025_grain_stats.csv\n", + "done with segmentation + export\n", + "[623/720] tile_r000024_c000026\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.50it/s]\n", + "100%|██████████| 3/3 [00:00<00:00, 3.70it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 408/408 [00:07<00:00, 55.77it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "329it [00:00, 418.64it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "340it [00:00, 409.88it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 133/133 [00:01<00:00, 108.00it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 134/134 [00:00<00:00, 196.51it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.BpaNNCn48p/ipykernel_53064/3422303653.py:106: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/ouputgpkg/tile_r000024_c000026_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/csv_stats/tile_r000024_c000026_grain_stats.csv\n", + "done with segmentation + export\n", + "[624/720] tile_r000024_c000027\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.36it/s]\n", + "100%|██████████| 3/3 [00:00<00:00, 3.45it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 395/395 [00:08<00:00, 46.92it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "265it [00:00, 387.52it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "272it [00:00, 390.77it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 100/100 [00:01<00:00, 91.09it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 105/105 [00:00<00:00, 186.66it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.BpaNNCn48p/ipykernel_53064/3422303653.py:106: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/ouputgpkg/tile_r000024_c000027_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/csv_stats/tile_r000024_c000027_grain_stats.csv\n", + "done with segmentation + export\n", + "[625/720] tile_r000024_c000028\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.41it/s]\n", + "100%|██████████| 3/3 [00:00<00:00, 3.52it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 378/378 [00:06<00:00, 61.52it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "295it [00:00, 335.67it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "303it [00:00, 337.67it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 115/115 [00:01<00:00, 86.88it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 117/117 [00:00<00:00, 183.43it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.BpaNNCn48p/ipykernel_53064/3422303653.py:106: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/ouputgpkg/tile_r000024_c000028_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/csv_stats/tile_r000024_c000028_grain_stats.csv\n", + "done with segmentation + export\n", + "[626/720] tile_r000024_c000029\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.43it/s]\n", + "100%|██████████| 3/3 [00:00<00:00, 3.53it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 324/324 [00:05<00:00, 60.13it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "263it [00:00, 304.13it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "275it [00:00, 309.06it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 98/98 [00:01<00:00, 68.37it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 101/101 [00:00<00:00, 183.99it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.BpaNNCn48p/ipykernel_53064/3422303653.py:106: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/ouputgpkg/tile_r000024_c000029_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/csv_stats/tile_r000024_c000029_grain_stats.csv\n", + "done with segmentation + export\n", + "[627/720] tile_r000024_c000030\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.24it/s]\n", + "100%|██████████| 3/3 [00:00<00:00, 3.38it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 289/289 [00:07<00:00, 38.33it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "182it [00:02, 85.60it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "173it [00:00, 239.21it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 43/43 [00:01<00:00, 24.55it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 75/75 [00:00<00:00, 141.77it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.BpaNNCn48p/ipykernel_53064/3422303653.py:106: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/ouputgpkg/tile_r000024_c000030_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/csv_stats/tile_r000024_c000030_grain_stats.csv\n", + "done with segmentation + export\n", + "[628/720] tile_r000024_c000031\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.31it/s]\n", + "100%|██████████| 3/3 [00:00<00:00, 3.34it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 120/120 [00:02<00:00, 46.39it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "76it [00:00, 194.96it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "79it [00:00, 198.25it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 26/26 [00:00<00:00, 43.50it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 28/28 [00:00<00:00, 145.71it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.BpaNNCn48p/ipykernel_53064/3422303653.py:106: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/ouputgpkg/tile_r000024_c000031_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/csv_stats/tile_r000024_c000031_grain_stats.csv\n", + "done with segmentation + export\n", + "[629/720] tile_r000024_c000032\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.45it/s]\n", + "100%|██████████| 3/3 [00:00<00:00, 3.41it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 67/67 [00:01<00:00, 42.09it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "27it [00:00, 299.21it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "30it [00:00, 296.38it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 10/10 [00:00<00:00, 50.39it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\n", + "100%|██████████| 10/10 [00:00<00:00, 122.53it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.BpaNNCn48p/ipykernel_53064/3422303653.py:106: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/ouputgpkg/tile_r000024_c000032_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/csv_stats/tile_r000024_c000032_grain_stats.csv\n", + "done with segmentation + export\n", + "[630/720] tile_r000024_c000033\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.58it/s]\n", + "100%|██████████| 3/3 [00:00<00:00, 3.44it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "0it [00:00, ?it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "0it [00:00, ?it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "0it [00:00, ?it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "0it [00:00, ?it/s]\n", + "0it [00:00, ?it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n", + "No grains found -> skipping histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/ouputgpkg/tile_r000024_c000033_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/csv_stats/tile_r000024_c000033_grain_stats.csv\n", + "done with segmentation + export\n", + "[631/720] tile_r000025_c000004\n", + " -> skipped (100% Nodata)\n", + "[632/720] tile_r000025_c000005\n", + " -> skipped (100% Nodata)\n", + "[633/720] tile_r000025_c000006\n", + " -> skipped (100% Nodata)\n", + "[634/720] tile_r000025_c000007\n", + " -> skipped (100% Nodata)\n", + "[635/720] tile_r000025_c000008\n", + " -> skipped (100% Nodata)\n", + "[636/720] tile_r000025_c000009\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.BpaNNCn48p/ipykernel_53064/3422303653.py:106: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " -> skipped (100% Nodata)\n", + "[637/720] tile_r000025_c000010\n", + " -> skipped (100% Nodata)\n", + "[638/720] tile_r000025_c000011\n", + " -> skipped (100% Nodata)\n", + "[639/720] tile_r000025_c000012\n", + " -> skipped (100% Nodata)\n", + "[640/720] tile_r000025_c000013\n", + " -> skipped (100% Nodata)\n", + "[641/720] tile_r000025_c000014\n", + " -> skipped (100% Nodata)\n", + "[642/720] tile_r000025_c000015\n", + " -> skipped (100% Nodata)\n", + "[643/720] tile_r000025_c000016\n", + " -> skipped (100% Nodata)\n", + "[644/720] tile_r000025_c000017\n", + " -> skipped (100% Nodata)\n", + "[645/720] tile_r000025_c000018\n", + " -> skipped (100% Nodata)\n", + "[646/720] tile_r000025_c000019\n", + " -> skipped (100% Nodata)\n", + "[647/720] tile_r000025_c000020\n", + " -> skipped (100% Nodata)\n", + "[648/720] tile_r000025_c000021\n", + " -> skipped (100% Nodata)\n", + "[649/720] tile_r000025_c000022\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.45it/s]\n", + "100%|██████████| 3/3 [00:00<00:00, 3.36it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 28/28 [00:00<00:00, 38.19it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "1it [00:00, 1108.14it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2it [00:00, 640.35it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 1/1 [00:00<00:00, 388.33it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 1/1 [00:00<00:00, 183.74it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.BpaNNCn48p/ipykernel_53064/3422303653.py:106: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/ouputgpkg/tile_r000025_c000022_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/csv_stats/tile_r000025_c000022_grain_stats.csv\n", + "done with segmentation + export\n", + "[650/720] tile_r000025_c000023\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.43it/s]\n", + "100%|██████████| 3/3 [00:00<00:00, 3.43it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 84/84 [00:01<00:00, 50.65it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "52it [00:00, 340.00it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "56it [00:00, 339.38it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 19/19 [00:00<00:00, 56.60it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 23/23 [00:00<00:00, 146.98it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.BpaNNCn48p/ipykernel_53064/3422303653.py:106: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/ouputgpkg/tile_r000025_c000023_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/csv_stats/tile_r000025_c000023_grain_stats.csv\n", + "done with segmentation + export\n", + "[651/720] tile_r000025_c000024\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.42it/s]\n", + "100%|██████████| 3/3 [00:00<00:00, 3.38it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 303/303 [00:05<00:00, 56.11it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "221it [00:00, 285.20it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "219it [00:00, 313.37it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 72/72 [00:01<00:00, 60.43it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 88/88 [00:00<00:00, 177.55it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.BpaNNCn48p/ipykernel_53064/3422303653.py:106: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/ouputgpkg/tile_r000025_c000024_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/csv_stats/tile_r000025_c000024_grain_stats.csv\n", + "done with segmentation + export\n", + "[652/720] tile_r000025_c000025\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.46it/s]\n", + "100%|██████████| 3/3 [00:00<00:00, 3.36it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 321/321 [00:05<00:00, 55.66it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "225it [00:00, 296.84it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "235it [00:00, 291.22it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 81/81 [00:01<00:00, 45.93it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 85/85 [00:00<00:00, 172.89it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.BpaNNCn48p/ipykernel_53064/3422303653.py:106: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/ouputgpkg/tile_r000025_c000025_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/csv_stats/tile_r000025_c000025_grain_stats.csv\n", + "done with segmentation + export\n", + "[653/720] tile_r000025_c000026\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.43it/s]\n", + "100%|██████████| 3/3 [00:00<00:00, 3.48it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 320/320 [00:05<00:00, 54.69it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "246it [00:00, 267.34it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "254it [00:00, 274.79it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 88/88 [00:01<00:00, 62.94it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 91/91 [00:00<00:00, 174.78it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.BpaNNCn48p/ipykernel_53064/3422303653.py:106: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/ouputgpkg/tile_r000025_c000026_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/csv_stats/tile_r000025_c000026_grain_stats.csv\n", + "done with segmentation + export\n", + "[654/720] tile_r000025_c000027\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.46it/s]\n", + "100%|██████████| 3/3 [00:00<00:00, 3.41it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 360/360 [00:06<00:00, 57.00it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "278it [00:01, 173.58it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "272it [00:01, 191.13it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 85/85 [00:02<00:00, 33.96it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 93/93 [00:00<00:00, 162.64it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.BpaNNCn48p/ipykernel_53064/3422303653.py:106: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/ouputgpkg/tile_r000025_c000027_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/csv_stats/tile_r000025_c000027_grain_stats.csv\n", + "done with segmentation + export\n", + "[655/720] tile_r000025_c000028\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.44it/s]\n", + "100%|██████████| 3/3 [00:00<00:00, 3.48it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 268/268 [00:05<00:00, 52.78it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "210it [00:00, 265.29it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "217it [00:00, 275.33it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 80/80 [00:01<00:00, 60.05it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 84/84 [00:00<00:00, 180.51it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n", + "Warning: len(gdf)=84 != len(grain_stats)=83 -> truncating to 83\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.BpaNNCn48p/ipykernel_53064/3422303653.py:106: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/ouputgpkg/tile_r000025_c000028_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/csv_stats/tile_r000025_c000028_grain_stats.csv\n", + "done with segmentation + export\n", + "[656/720] tile_r000025_c000029\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.52it/s]\n", + "100%|██████████| 3/3 [00:00<00:00, 3.35it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 363/363 [00:06<00:00, 58.33it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "275it [00:00, 301.80it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "288it [00:00, 310.06it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 103/103 [00:01<00:00, 71.31it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 109/109 [00:00<00:00, 192.06it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.BpaNNCn48p/ipykernel_53064/3422303653.py:106: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/ouputgpkg/tile_r000025_c000029_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/csv_stats/tile_r000025_c000029_grain_stats.csv\n", + "done with segmentation + export\n", + "[657/720] tile_r000025_c000030\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.55it/s]\n", + "100%|██████████| 3/3 [00:00<00:00, 3.36it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 342/342 [00:06<00:00, 56.52it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "268it [00:00, 338.32it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "280it [00:00, 346.92it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 96/96 [00:01<00:00, 66.31it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 105/105 [00:00<00:00, 182.91it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.BpaNNCn48p/ipykernel_53064/3422303653.py:106: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/ouputgpkg/tile_r000025_c000030_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/csv_stats/tile_r000025_c000030_grain_stats.csv\n", + "done with segmentation + export\n", + "[658/720] tile_r000025_c000031\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.44it/s]\n", + "100%|██████████| 3/3 [00:00<00:00, 3.37it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 197/197 [00:03<00:00, 55.40it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "151it [00:00, 254.94it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "160it [00:00, 254.50it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 53/53 [00:00<00:00, 54.32it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 57/57 [00:00<00:00, 146.10it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.BpaNNCn48p/ipykernel_53064/3422303653.py:106: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/ouputgpkg/tile_r000025_c000031_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/csv_stats/tile_r000025_c000031_grain_stats.csv\n", + "done with segmentation + export\n", + "[659/720] tile_r000025_c000032\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.41it/s]\n", + "100%|██████████| 3/3 [00:00<00:00, 3.41it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 136/136 [00:02<00:00, 45.78it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "92it [00:00, 335.09it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "103it [00:00, 340.43it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 34/34 [00:00<00:00, 36.33it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 42/42 [00:00<00:00, 130.33it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsshome1/0B/di54doz/.local/share/mamba/envs/segmenteverygrain/lib/python3.10/site-packages/segmenteverygrain/segmenteverygrain.py:2217: RuntimeWarning: divide by zero encountered in log2\n", + " phi_major = -np.log2(major_axis_length) # major axis length in phi scale\n", + "/dss/dsshome1/0B/di54doz/.local/share/mamba/envs/segmenteverygrain/lib/python3.10/site-packages/segmenteverygrain/segmenteverygrain.py:2218: RuntimeWarning: divide by zero encountered in log2\n", + " phi_minor = -np.log2(minor_axis_length)\n", + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.BpaNNCn48p/ipykernel_53064/3422303653.py:106: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/ouputgpkg/tile_r000025_c000032_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/csv_stats/tile_r000025_c000032_grain_stats.csv\n", + "done with segmentation + export\n", + "[660/720] tile_r000025_c000033\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.42it/s]\n", + "100%|██████████| 3/3 [00:00<00:00, 3.44it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 13/13 [00:00<00:00, 38.88it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "8it [00:00, 494.84it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "8it [00:00, 502.78it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 3/3 [00:00<00:00, 85.32it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 3/3 [00:00<00:00, 132.21it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n", + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/ouputgpkg/tile_r000025_c000033_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/csv_stats/tile_r000025_c000033_grain_stats.csv\n", + "done with segmentation + export\n", + "[661/720] tile_r000026_c000004\n", + " -> skipped (100% Nodata)\n", + "[662/720] tile_r000026_c000005\n", + " -> skipped (100% Nodata)\n", + "[663/720] tile_r000026_c000006\n", + " -> skipped (100% Nodata)\n", + "[664/720] tile_r000026_c000007\n", + " -> skipped (100% Nodata)\n", + "[665/720] tile_r000026_c000008\n", + " -> skipped (100% Nodata)\n", + "[666/720] tile_r000026_c000009\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.BpaNNCn48p/ipykernel_53064/3422303653.py:106: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " -> skipped (100% Nodata)\n", + "[667/720] tile_r000026_c000010\n", + " -> skipped (100% Nodata)\n", + "[668/720] tile_r000026_c000011\n", + " -> skipped (100% Nodata)\n", + "[669/720] tile_r000026_c000012\n", + " -> skipped (100% Nodata)\n", + "[670/720] tile_r000026_c000013\n", + " -> skipped (100% Nodata)\n", + "[671/720] tile_r000026_c000014\n", + " -> skipped (100% Nodata)\n", + "[672/720] tile_r000026_c000015\n", + " -> skipped (100% Nodata)\n", + "[673/720] tile_r000026_c000016\n", + " -> skipped (100% Nodata)\n", + "[674/720] tile_r000026_c000017\n", + " -> skipped (100% Nodata)\n", + "[675/720] tile_r000026_c000018\n", + " -> skipped (100% Nodata)\n", + "[676/720] tile_r000026_c000019\n", + " -> skipped (100% Nodata)\n", + "[677/720] tile_r000026_c000020\n", + " -> skipped (100% Nodata)\n", + "[678/720] tile_r000026_c000021\n", + " -> skipped (100% Nodata)\n", + "[679/720] tile_r000026_c000022\n", + " -> skipped (100% Nodata)\n", + "[680/720] tile_r000026_c000023\n", + " -> skipped (100% Nodata)\n", + "[681/720] tile_r000026_c000024\n", + " -> skipped (100% Nodata)\n", + "[682/720] tile_r000026_c000025\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.36it/s]\n", + "100%|██████████| 3/3 [00:00<00:00, 3.30it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 65/65 [00:01<00:00, 50.56it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "26it [00:00, 365.68it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "28it [00:00, 357.54it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 10/10 [00:00<00:00, 74.40it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 11/11 [00:00<00:00, 175.50it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.BpaNNCn48p/ipykernel_53064/3422303653.py:106: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/ouputgpkg/tile_r000026_c000025_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/csv_stats/tile_r000026_c000025_grain_stats.csv\n", + "done with segmentation + export\n", + "[683/720] tile_r000026_c000026\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.51it/s]\n", + "100%|██████████| 3/3 [00:00<00:00, 3.40it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 115/115 [00:02<00:00, 50.38it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "82it [00:00, 242.83it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "86it [00:00, 254.34it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 28/28 [00:00<00:00, 49.02it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 32/32 [00:00<00:00, 158.78it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.BpaNNCn48p/ipykernel_53064/3422303653.py:106: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/ouputgpkg/tile_r000026_c000026_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/csv_stats/tile_r000026_c000026_grain_stats.csv\n", + "done with segmentation + export\n", + "[684/720] tile_r000026_c000027\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.48it/s]\n", + "100%|██████████| 3/3 [00:00<00:00, 3.47it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 171/171 [00:03<00:00, 47.47it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "102it [00:00, 106.99it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "96it [00:00, 263.46it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 27/27 [00:00<00:00, 32.56it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 33/33 [00:00<00:00, 123.11it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.BpaNNCn48p/ipykernel_53064/3422303653.py:106: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/ouputgpkg/tile_r000026_c000027_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/csv_stats/tile_r000026_c000027_grain_stats.csv\n", + "done with segmentation + export\n", + "[685/720] tile_r000026_c000028\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.46it/s]\n", + "100%|██████████| 3/3 [00:00<00:00, 3.43it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 200/200 [00:04<00:00, 44.68it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "124it [00:00, 251.66it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "130it [00:00, 256.25it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 44/44 [00:00<00:00, 49.82it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 48/48 [00:00<00:00, 171.03it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.BpaNNCn48p/ipykernel_53064/3422303653.py:106: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/ouputgpkg/tile_r000026_c000028_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/csv_stats/tile_r000026_c000028_grain_stats.csv\n", + "done with segmentation + export\n", + "[686/720] tile_r000026_c000029\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.53it/s]\n", + "100%|██████████| 3/3 [00:00<00:00, 3.39it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 204/204 [00:04<00:00, 48.84it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "145it [00:00, 225.52it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "144it [00:00, 289.66it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 37/37 [00:02<00:00, 12.90it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 64/64 [00:00<00:00, 155.08it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.BpaNNCn48p/ipykernel_53064/3422303653.py:106: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/ouputgpkg/tile_r000026_c000029_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/csv_stats/tile_r000026_c000029_grain_stats.csv\n", + "done with segmentation + export\n", + "[687/720] tile_r000026_c000030\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.37it/s]\n", + "100%|██████████| 3/3 [00:00<00:00, 3.46it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 231/231 [00:04<00:00, 56.73it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "165it [00:00, 416.51it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "163it [00:00, 454.78it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 58/58 [00:00<00:00, 90.75it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 70/70 [00:00<00:00, 165.41it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.BpaNNCn48p/ipykernel_53064/3422303653.py:106: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/ouputgpkg/tile_r000026_c000030_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/csv_stats/tile_r000026_c000030_grain_stats.csv\n", + "done with segmentation + export\n", + "[688/720] tile_r000026_c000031\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.38it/s]\n", + "100%|██████████| 3/3 [00:00<00:00, 3.29it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 199/199 [00:03<00:00, 53.00it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "149it [00:00, 296.70it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "165it [00:00, 300.44it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 53/53 [00:01<00:00, 42.85it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 58/58 [00:00<00:00, 154.89it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.BpaNNCn48p/ipykernel_53064/3422303653.py:106: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/ouputgpkg/tile_r000026_c000031_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/csv_stats/tile_r000026_c000031_grain_stats.csv\n", + "done with segmentation + export\n", + "[689/720] tile_r000026_c000032\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.38it/s]\n", + "100%|██████████| 3/3 [00:00<00:00, 3.40it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 140/140 [00:02<00:00, 52.85it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "97it [00:00, 353.06it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "103it [00:00, 350.29it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 41/41 [00:00<00:00, 93.19it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 41/41 [00:00<00:00, 171.38it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.BpaNNCn48p/ipykernel_53064/3422303653.py:106: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/ouputgpkg/tile_r000026_c000032_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/csv_stats/tile_r000026_c000032_grain_stats.csv\n", + "done with segmentation + export\n", + "[690/720] tile_r000026_c000033\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.44it/s]\n", + "100%|██████████| 3/3 [00:00<00:00, 3.49it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 172/172 [00:03<00:00, 43.11it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "92it [00:00, 313.42it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "101it [00:00, 336.96it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 35/35 [00:00<00:00, 53.84it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 38/38 [00:00<00:00, 161.95it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n", + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/ouputgpkg/tile_r000026_c000033_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/csv_stats/tile_r000026_c000033_grain_stats.csv\n", + "done with segmentation + export\n", + "[691/720] tile_r000027_c000004\n", + " -> skipped (100% Nodata)\n", + "[692/720] tile_r000027_c000005\n", + " -> skipped (100% Nodata)\n", + "[693/720] tile_r000027_c000006\n", + " -> skipped (100% Nodata)\n", + "[694/720] tile_r000027_c000007\n", + " -> skipped (100% Nodata)\n", + "[695/720] tile_r000027_c000008\n", + " -> skipped (100% Nodata)\n", + "[696/720] tile_r000027_c000009\n", + " -> skipped (100% Nodata)\n", + "[697/720] tile_r000027_c000010\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.BpaNNCn48p/ipykernel_53064/3422303653.py:106: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " -> skipped (100% Nodata)\n", + "[698/720] tile_r000027_c000011\n", + " -> skipped (100% Nodata)\n", + "[699/720] tile_r000027_c000012\n", + " -> skipped (100% Nodata)\n", + "[700/720] tile_r000027_c000013\n", + " -> skipped (100% Nodata)\n", + "[701/720] tile_r000027_c000014\n", + " -> skipped (100% Nodata)\n", + "[702/720] tile_r000027_c000015\n", + " -> skipped (100% Nodata)\n", + "[703/720] tile_r000027_c000016\n", + " -> skipped (100% Nodata)\n", + "[704/720] tile_r000027_c000017\n", + " -> skipped (100% Nodata)\n", + "[705/720] tile_r000027_c000018\n", + " -> skipped (100% Nodata)\n", + "[706/720] tile_r000027_c000019\n", + " -> skipped (100% Nodata)\n", + "[707/720] tile_r000027_c000020\n", + " -> skipped (100% Nodata)\n", + "[708/720] tile_r000027_c000021\n", + " -> skipped (100% Nodata)\n", + "[709/720] tile_r000027_c000022\n", + " -> skipped (100% Nodata)\n", + "[710/720] tile_r000027_c000023\n", + " -> skipped (100% Nodata)\n", + "[711/720] tile_r000027_c000024\n", + " -> skipped (100% Nodata)\n", + "[712/720] tile_r000027_c000025\n", + " -> skipped (100% Nodata)\n", + "[713/720] tile_r000027_c000026\n", + " -> skipped (100% Nodata)\n", + "[714/720] tile_r000027_c000027\n", + " -> skipped (100% Nodata)\n", + "[715/720] tile_r000027_c000028\n", + " -> skipped (100% Nodata)\n", + "[716/720] tile_r000027_c000029\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.32it/s]\n", + "100%|██████████| 3/3 [00:00<00:00, 3.45it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 19/19 [00:00<00:00, 36.56it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "4it [00:00, 1250.26it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "6it [00:00, 805.87it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 3/3 [00:00<00:00, 415.91it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 3/3 [00:00<00:00, 214.90it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.BpaNNCn48p/ipykernel_53064/3422303653.py:106: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/ouputgpkg/tile_r000027_c000029_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/csv_stats/tile_r000027_c000029_grain_stats.csv\n", + "done with segmentation + export\n", + "[717/720] tile_r000027_c000030\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.29it/s]\n", + "100%|██████████| 3/3 [00:00<00:00, 3.34it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 62/62 [00:01<00:00, 48.11it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "34it [00:00, 256.06it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "35it [00:00, 262.87it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 9/9 [00:00<00:00, 35.62it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 10/10 [00:00<00:00, 156.45it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.BpaNNCn48p/ipykernel_53064/3422303653.py:106: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/ouputgpkg/tile_r000027_c000030_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/csv_stats/tile_r000027_c000030_grain_stats.csv\n", + "done with segmentation + export\n", + "[718/720] tile_r000027_c000031\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.25it/s]\n", + "100%|██████████| 3/3 [00:00<00:00, 3.35it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 85/85 [00:01<00:00, 47.33it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "56it [00:00, 164.91it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "55it [00:00, 183.13it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 14/14 [00:00<00:00, 22.84it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 19/19 [00:00<00:00, 123.04it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.BpaNNCn48p/ipykernel_53064/3422303653.py:106: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/ouputgpkg/tile_r000027_c000031_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/csv_stats/tile_r000027_c000031_grain_stats.csv\n", + "done with segmentation + export\n", + "[719/720] tile_r000027_c000032\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.42it/s]\n", + "100%|██████████| 3/3 [00:00<00:00, 3.45it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 20/20 [00:00<00:00, 36.18it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "0it [00:00, ?it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "0it [00:00, ?it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "0it [00:00, ?it/s]\n", + "0it [00:00, ?it/s]\n", + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.BpaNNCn48p/ipykernel_53064/3422303653.py:106: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n", + "No grains found -> skipping histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/ouputgpkg/tile_r000027_c000032_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/csv_stats/tile_r000027_c000032_grain_stats.csv\n", + "done with segmentation + export\n", + "[720/720] tile_r000027_c000033\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 3.40it/s]\n", + "100%|██████████| 3/3 [00:00<00:00, 3.40it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 12/12 [00:00<00:00, 31.79it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "1it [00:00, 876.55it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2it [00:00, 591.08it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 1/1 [00:00<00:00, 314.77it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 1/1 [00:00<00:00, 161.74it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n", + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/ouputgpkg/tile_r000027_c000033_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/csv_stats/tile_r000027_c000033_grain_stats.csv\n", + "done with segmentation + export\n", + "Saved runtime metrics CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/runtime_metrics.csv\n", + " t_total_s t_unet_s t_label_s t_sam_s t_export_s \\\n", + "count 386.000000 386.000000 386.000000 386.000000 386.000000 \n", + "mean 14.259760 3.725836 0.358536 9.258405 0.874843 \n", + "std 5.556491 0.366294 0.107592 5.287168 0.410732 \n", + "min 3.825059 3.080257 0.013403 0.114704 0.288889 \n", + "25% 10.587949 3.524422 0.336180 5.562665 0.784285 \n", + "50% 15.596502 3.690534 0.394956 10.513601 0.911246 \n", + "75% 17.694350 3.846702 0.429321 12.375331 0.960549 \n", + "max 51.952966 7.919854 0.493560 46.972382 6.847601 \n", + "\n", + " n_prompts_used n_grains \n", + "count 386.000000 386.000000 \n", + "mean 235.898964 67.502591 \n", + "std 124.898120 40.352167 \n", + "min 0.000000 0.000000 \n", + "25% 140.000000 28.500000 \n", + "50% 282.000000 78.500000 \n", + "75% 336.750000 102.000000 \n", + "max 419.000000 134.000000 \n", + "\n", + "READY TABLE:\n", + "\n", + " metric value\n", + " n_tiles_total 720.000\n", + " n_tiles_ok 386.000\n", + " n_tiles_skipped_nodata 334.000\n", + " n_tiles_error 0.000\n", + "pixel_size_mm_per_px (median) 4.288\n", + " min_area_px (median) 17.085\n", + " total_runtime_min 91.738\n", + " tiles_per_min 4.208\n", + " total_s_per_tile (median) 15.597\n", + " unet_s (median) 3.691\n", + " label_s (median) 0.395\n", + " sam_s (median) 10.514\n", + " export_s (median) 0.911\n", + " prompts_used (median) 282.000\n", + " grains (median) 78.500\n", + "\n", + "Saved ready table CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F3/runtime_summary_ready_table.csv\n" + ] + } + ], + "source": [ + "import os\n", + "import glob\n", + "import time\n", + "import traceback\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "from tensorflow.keras.utils import load_img\n", + "import rasterio\n", + "\n", + "import pandas as pd\n", + "import torch\n", + "\n", + "# zusätzliche Imports für Nachbearbeitung (ohne deine Library-Zelle zu ändern)\n", + "import geopandas as gpd\n", + "from shapely.geometry import Polygon\n", + "from skimage.measure import regionprops_table\n", + "\n", + "# --- optional: prompt cap for SAM (None = no limit) ---\n", + "MAX_SAM_PROMPTS = 10000\n", + "PROMPT_SUBSAMPLE_MODE = \"random\" # \"random\" oder \"first\"\n", + "RNG_SEED = 42\n", + "rng = np.random.default_rng(RNG_SEED)\n", + "\n", + "# --- collect tiles ---\n", + "tiles = sorted(glob.glob(os.path.join(inputtiledir, \"*.tif\")))\n", + "print(f\"Found {len(tiles)} tiles\")\n", + "if len(tiles) == 0:\n", + " raise RuntimeError(\"No tiles found.\")\n", + "\n", + "# --- read pixel size once from first tile ---\n", + "with rasterio.open(tiles[0]) as src:\n", + " xres, yres = src.res # CRS units per pixel (usually meters)\n", + " crs = src.crs\n", + "\n", + "gsd_units = float((xres + yres) / 2.0)\n", + "gsd_mm = gsd_units * 1000.0 # assumes CRS units are meters (e.g., UTM)\n", + "\n", + "minor_mm = 20.0 # 2 cm\n", + "minor_px = minor_mm / gsd_mm\n", + "\n", + "# area threshold from minor-axis (circle assumption)\n", + "min_area_px = float(np.pi * (minor_px / 2.0) ** 2)\n", + "\n", + "print(\"CRS:\", crs)\n", + "print(f\"Pixel size: {gsd_units:.6f} units/px (~{gsd_mm:.3f} mm/px)\")\n", + "print(f\"2 cm => minor_px={minor_px:.2f} px -> min_area_px={min_area_px:.1f} px^2\")\n", + "\n", + "# ---- runtime metrics storage ----\n", + "rows = []\n", + "\n", + "def _gpu_free_gb():\n", + " if torch.cuda.is_available():\n", + " free, total = torch.cuda.mem_get_info()\n", + " return free / 1024**3\n", + " return None\n", + "\n", + "# --- loop ---\n", + "for i, fname in enumerate(tiles, start=1):\n", + " tile_id = os.path.splitext(os.path.basename(fname))[0]\n", + " print(f\"[{i}/{len(tiles)}] {tile_id}\")\n", + "\n", + " t0 = time.perf_counter()\n", + " gpu_free_before = _gpu_free_gb()\n", + "\n", + " # default metrics (filled progressively)\n", + " rec = {\n", + " \"tile_id\": tile_id,\n", + " \"fname\": fname,\n", + " \"status\": None,\n", + "\n", + " # fixed scene metadata\n", + " \"crs\": str(crs),\n", + " \"pixel_size_units_per_px\": gsd_units,\n", + " \"pixel_size_mm_per_px\": gsd_mm,\n", + " \"minor_mm_threshold\": minor_mm,\n", + " \"minor_px_threshold\": minor_px,\n", + " \"min_area_px\": min_area_px,\n", + "\n", + " # per-tile\n", + " \"H\": None,\n", + " \"W\": None,\n", + " \"n_prompts_before\": None,\n", + " \"n_prompts_used\": None,\n", + " \"prompt_cap_used\": None,\n", + " \"n_grains\": None,\n", + "\n", + " # timing\n", + " \"t_unet_s\": None,\n", + " \"t_label_s\": None,\n", + " \"t_sam_s\": None,\n", + " \"t_export_s\": None,\n", + " \"t_total_s\": None,\n", + "\n", + " # GPU mem\n", + " \"gpu_free_gb_before\": gpu_free_before,\n", + " \"gpu_free_gb_after\": None,\n", + "\n", + " # errors\n", + " \"error_msg\": None,\n", + " \"traceback_head\": None,\n", + " }\n", + "\n", + " try:\n", + " # ---- nodata check (kept as you have it) ----\n", + " with rasterio.open(fname) as src:\n", + " m = src.dataset_mask()\n", + " if not np.any(m):\n", + " print(\" -> skipped (100% Nodata)\")\n", + " rec[\"status\"] = \"skip_nodata\"\n", + " rec[\"t_total_s\"] = time.perf_counter() - t0\n", + " rec[\"gpu_free_gb_after\"] = _gpu_free_gb()\n", + " rows.append(rec)\n", + " continue\n", + "\n", + " # ---- load + predict (UNet) ----\n", + " t = time.perf_counter()\n", + " image = np.array(load_img(fname))\n", + " rec[\"H\"], rec[\"W\"] = int(image.shape[0]), int(image.shape[1])\n", + " image_pred = seg.predict_image(image, unet, I=256)\n", + " rec[\"t_unet_s\"] = time.perf_counter() - t\n", + "\n", + " # ---- prompts (Unet -> points) ----\n", + " t = time.perf_counter()\n", + " labels, coords = seg.label_grains(image, image_pred, dbs_max_dist=10.0)\n", + " rec[\"t_label_s\"] = time.perf_counter() - t\n", + "\n", + " coords = np.asarray(coords)\n", + " rec[\"n_prompts_before\"] = int(len(coords))\n", + " rec[\"prompt_cap_used\"] = False\n", + "\n", + " # ---- optional: cap number of prompts before SAM ----\n", + " if MAX_SAM_PROMPTS is not None and len(coords) > MAX_SAM_PROMPTS:\n", + " rec[\"prompt_cap_used\"] = True\n", + " n_before = len(coords)\n", + "\n", + " if PROMPT_SUBSAMPLE_MODE == \"first\":\n", + " keep_idx = np.arange(MAX_SAM_PROMPTS)\n", + " else: # random\n", + " keep_idx = np.sort(rng.choice(len(coords), size=MAX_SAM_PROMPTS, replace=False))\n", + "\n", + " coords = coords[keep_idx]\n", + "\n", + " # labels nur mitfiltern, wenn es ein 1D prompt-label array ist\n", + " try:\n", + " labels_arr = np.asarray(labels)\n", + " if labels_arr.ndim == 1 and len(labels_arr) == n_before:\n", + " labels = labels_arr[keep_idx]\n", + " except Exception:\n", + " pass\n", + "\n", + " print(f\"Prompt cap active: reduced prompts from {n_before} -> {len(coords)}\")\n", + "\n", + " rec[\"n_prompts_used\"] = int(len(coords))\n", + "\n", + " # ---- SAM segmentation ----\n", + " t = time.perf_counter()\n", + " all_grains, labels, mask_all, grain_data, fig, ax = seg.sam_segmentation(\n", + " sam,\n", + " image,\n", + " image_pred,\n", + " coords,\n", + " labels,\n", + " min_area=min_area_px,\n", + " plot_image=True,\n", + " remove_edge_grains=True,\n", + " remove_large_objects=True,\n", + " )\n", + " rec[\"t_sam_s\"] = time.perf_counter() - t\n", + " rec[\"n_grains\"] = int(len(all_grains))\n", + "\n", + " # ---- export/post ----\n", + " t = time.perf_counter()\n", + "\n", + " # 1) Segmentierungsplot speichern (robust fallback)\n", + " seg_plot_path = os.path.join(plotdirgravel, f\"{tile_id}.png\")\n", + " if fig is None:\n", + " fig, ax = plt.subplots(figsize=(8, 8))\n", + " ax.imshow(image)\n", + " for poly in all_grains:\n", + " x, y = poly.exterior.xy\n", + " ax.plot(x, y, linewidth=0.8)\n", + " ax.set_title(tile_id)\n", + " ax.axis(\"off\")\n", + "\n", + " fig.savefig(seg_plot_path, dpi=200, bbox_inches=\"tight\")\n", + " plt.close(fig)\n", + " print(\"Saved segmentation plot\")\n", + "\n", + " # 2) Polygone georeferenzieren (Pixel -> UTM / CRS des Tiles)\n", + " with rasterio.open(fname) as dataset:\n", + " projected_polys = []\n", + " for grain in all_grains:\n", + " px_x = np.asarray(grain.exterior.xy[0])\n", + " px_y = np.asarray(grain.exterior.xy[1])\n", + "\n", + " x_proj, y_proj = rasterio.transform.xy(\n", + " dataset.transform,\n", + " px_y, # rows\n", + " px_x # cols\n", + " )\n", + " poly = Polygon(np.vstack((x_proj, y_proj)).T)\n", + " projected_polys.append(poly)\n", + "\n", + " gdf = gpd.GeoDataFrame({\"geometry\": projected_polys}, geometry=\"geometry\", crs=dataset.crs)\n", + "\n", + " # 3) Eigenschaften / Statistiken aus labeled image\n", + " props = regionprops_table(\n", + " labels.astype(\"int\"),\n", + " properties=(\"label\", \"area\", \"centroid\", \"major_axis_length\", \"minor_axis_length\"),\n", + " )\n", + " grain_stats = pd.DataFrame(props)\n", + "\n", + " if len(grain_stats) != len(gdf):\n", + " nmin = min(len(grain_stats), len(gdf))\n", + " print(f\"Warning: len(gdf)={len(gdf)} != len(grain_stats)={len(grain_stats)} -> truncating to {nmin}\")\n", + " gdf = gdf.iloc[:nmin].copy()\n", + " grain_stats = grain_stats.iloc[:nmin].copy()\n", + "\n", + " centroid_x, centroid_y = rasterio.transform.xy(\n", + " dataset.transform,\n", + " grain_stats[\"centroid-0\"].values, # rows\n", + " grain_stats[\"centroid-1\"].values, # cols\n", + " )\n", + "\n", + " px_size_x = abs(dataset.transform.a)\n", + "\n", + " gdf[\"label\"] = grain_stats[\"label\"].values\n", + " gdf[\"area_px\"] = grain_stats[\"area\"].values\n", + " gdf[\"centroid_x\"] = centroid_x\n", + " gdf[\"centroid_y\"] = centroid_y\n", + " gdf[\"major_axis_px\"] = grain_stats[\"major_axis_length\"].values\n", + " gdf[\"minor_axis_px\"] = grain_stats[\"minor_axis_length\"].values\n", + " gdf[\"major_axis_length\"] = grain_stats[\"major_axis_length\"].values * px_size_x\n", + " gdf[\"minor_axis_length\"] = grain_stats[\"minor_axis_length\"].values * px_size_x\n", + " gdf[\"major_axis_mm\"] = gdf[\"major_axis_length\"] * 1000.0\n", + " gdf[\"minor_axis_mm\"] = gdf[\"minor_axis_length\"] * 1000.0\n", + "\n", + " # 4) Histogramm speichern\n", + " if len(gdf) > 0:\n", + " fig_hist, ax_hist = seg.plot_histogram_of_axis_lengths(\n", + " gdf[\"major_axis_mm\"],\n", + " gdf[\"minor_axis_mm\"],\n", + " binsize=0.25,\n", + " xlimits=[8, 2 * 256],\n", + " )\n", + " hist_plot_path = os.path.join(plotdirhist, f\"{tile_id}_hist.png\")\n", + " fig_hist.savefig(hist_plot_path, dpi=200, bbox_inches=\"tight\")\n", + " plt.close(fig_hist)\n", + " print(\"Saved histogram plot\")\n", + " else:\n", + " print(\"No grains found -> skipping histogram plot\")\n", + "\n", + " # 5) GPKG + CSV speichern\n", + " gpkg_path = os.path.join(ouputgpkg, f\"{tile_id}_grains.gpkg\")\n", + " csv_path = os.path.join(csvdir, f\"{tile_id}_grain_stats.csv\")\n", + "\n", + " gdf.to_file(gpkg_path, driver=\"GPKG\")\n", + " stats_df = gdf.drop(columns=\"geometry\").copy()\n", + " stats_df.to_csv(csv_path, index=False)\n", + "\n", + " print(f\"Saved GPKG: {gpkg_path}\")\n", + " print(f\"Saved stats CSV: {csv_path}\")\n", + "\n", + " rec[\"t_export_s\"] = time.perf_counter() - t\n", + "\n", + " # finalize\n", + " rec[\"status\"] = \"ok\"\n", + " rec[\"t_total_s\"] = time.perf_counter() - t0\n", + " rec[\"gpu_free_gb_after\"] = _gpu_free_gb()\n", + " rows.append(rec)\n", + "\n", + " print(\"done with segmentation + export\")\n", + "\n", + " except Exception as e:\n", + " rec[\"status\"] = \"error\"\n", + " rec[\"t_total_s\"] = time.perf_counter() - t0\n", + " rec[\"gpu_free_gb_after\"] = _gpu_free_gb()\n", + " rec[\"error_msg\"] = str(e)\n", + " rec[\"traceback_head\"] = traceback.format_exc(limit=8)\n", + " rows.append(rec)\n", + " print(\"ERROR on\", tile_id, \":\", e)\n", + "\n", + "# ---- save runtime metrics (Excel-friendly CSV) ----\n", + "df = pd.DataFrame(rows)\n", + "\n", + "metrics_csv = os.path.join(dir, \"runtime_metrics.csv\")\n", + "df.to_csv(metrics_csv, index=False)\n", + "print(\"Saved runtime metrics CSV:\", metrics_csv)\n", + "\n", + "# optional: quick summary table in notebook\n", + "summary = df[df[\"status\"] == \"ok\"][[\"t_total_s\",\"t_unet_s\",\"t_label_s\",\"t_sam_s\",\"t_export_s\",\"n_prompts_used\",\"n_grains\"]].describe()\n", + "print(summary)\n", + "\n", + "# ---- paper-ready summary table (median-focused) ----\n", + "ok = df[df[\"status\"] == \"ok\"].copy()\n", + "\n", + "n_ok = len(ok)\n", + "total_s = ok[\"t_total_s\"].sum()\n", + "tiles_per_min = (n_ok / (total_s / 60.0)) if total_s > 0 else np.nan\n", + "\n", + "ready = pd.DataFrame({\n", + " \"metric\": [\n", + " \"n_tiles_total\",\n", + " \"n_tiles_ok\",\n", + " \"n_tiles_skipped_nodata\",\n", + " \"n_tiles_error\",\n", + " \"pixel_size_mm_per_px (median)\",\n", + " \"min_area_px (median)\",\n", + " \"total_runtime_min\",\n", + " \"tiles_per_min\",\n", + " \"total_s_per_tile (median)\",\n", + " \"unet_s (median)\",\n", + " \"label_s (median)\",\n", + " \"sam_s (median)\",\n", + " \"export_s (median)\",\n", + " \"prompts_used (median)\",\n", + " \"grains (median)\",\n", + " ],\n", + " \"value\": [\n", + " int(len(df)),\n", + " int(n_ok),\n", + " int((df[\"status\"] == \"skip_nodata\").sum()),\n", + " int((df[\"status\"] == \"error\").sum()),\n", + " float(ok[\"pixel_size_mm_per_px\"].median()) if n_ok else np.nan,\n", + " float(ok[\"min_area_px\"].median()) if n_ok else np.nan,\n", + " float(total_s / 60.0) if n_ok else np.nan,\n", + " float(tiles_per_min) if n_ok else np.nan,\n", + " float(ok[\"t_total_s\"].median()) if n_ok else np.nan,\n", + " float(ok[\"t_unet_s\"].median()) if n_ok else np.nan,\n", + " float(ok[\"t_label_s\"].median()) if n_ok else np.nan,\n", + " float(ok[\"t_sam_s\"].median()) if n_ok else np.nan,\n", + " float(ok[\"t_export_s\"].median()) if n_ok else np.nan,\n", + " float(ok[\"n_prompts_used\"].median()) if n_ok else np.nan,\n", + " float(ok[\"n_grains\"].median()) if n_ok else np.nan,\n", + " ]\n", + "})\n", + "\n", + "# hübsch runden fürs Notebook\n", + "ready_disp = ready.copy()\n", + "ready_disp[\"value\"] = ready_disp[\"value\"].apply(lambda v: round(v, 3) if isinstance(v, (float, np.floating)) and pd.notnull(v) else v)\n", + "print(\"\\nREADY TABLE:\\n\")\n", + "print(ready_disp.to_string(index=False))\n", + "\n", + "# zusätzlich als CSV für Excel speichern\n", + "ready_csv = os.path.join(dir, \"runtime_summary_ready_table.csv\")\n", + "ready.to_csv(ready_csv, index=False)\n", + "print(\"\\nSaved ready table CSV:\", ready_csv)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "4f0a5a52", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.19" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/notebooks/01_SEG_leonie_loopingtiles_overfolders savememory.ipynb b/notebooks/01_SEG_leonie_loopingtiles_overfolders savememory.ipynb new file mode 100644 index 0000000..1323a67 --- /dev/null +++ b/notebooks/01_SEG_leonie_loopingtiles_overfolders savememory.ipynb @@ -0,0 +1,51979 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "623da314", + "metadata": {}, + "source": [ + "# Import packages" + ] + }, + { + "cell_type": "markdown", + "id": "2f3b877d", + "metadata": {}, + "source": [ + "Quick memory chceks:" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "f86592c6", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2026-03-12 15:38:21.150967: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.\n", + "2026-03-12 15:38:25.332229: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\n", + "To enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\n", + "2026-03-12 15:38:42.845561: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "TF GPUs: [PhysicalDevice(name='/physical_device:GPU:0', device_type='GPU')]\n" + ] + } + ], + "source": [ + "# to prevent blocking all memory\n", + "import os\n", + "os.environ[\"TF_FORCE_GPU_ALLOW_GROWTH\"] = \"true\"\n", + "\n", + "import tensorflow as tf\n", + "for gpu in tf.config.list_physical_devices(\"GPU\"):\n", + " tf.config.experimental.set_memory_growth(gpu, True)\n", + "\n", + "print(\"TF GPUs:\", tf.config.list_physical_devices(\"GPU\"))" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "ff94825b", + "metadata": {}, + "outputs": [], + "source": [ + "import cv2\n", + "from keras.utils import load_img\n", + "from keras.saving import load_model\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "import pandas as pd\n", + "from sam2.build_sam import build_sam2\n", + "from sam2.sam2_image_predictor import SAM2ImagePredictor\n", + "from skimage.measure import regionprops, regionprops_table\n", + "from tqdm import trange, tqdm\n", + "\n", + "import segmenteverygrain as seg\n", + "import segmenteverygrain.interactions as si\n", + "import os\n", + "import rasterio\n", + "import time, traceback\n", + "import pandas as pd\n", + "import torch\n", + "\n", + "# %matplotlib qt" + ] + }, + { + "cell_type": "markdown", + "id": "44e817f1", + "metadata": {}, + "source": [ + "## Load models" + ] + }, + { + "cell_type": "markdown", + "id": "442d52dc", + "metadata": {}, + "source": [ + "Maybe you need to clone first SAM2. Then add the yaml file in the bottom of the next chunk" + ] + }, + { + "cell_type": "markdown", + "id": "7fdb5f2a", + "metadata": {}, + "source": [ + "For terrabyte:" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "3965da5f", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", + "I0000 00:00:1773326375.478724 91170 gpu_device.cc:2020] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 79193 MB memory: -> device: 0, name: NVIDIA A100-SXM4-80GB, pci bus id: 0000:4b:00.0, compute capability: 8.0\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "UNet loaded\n", + "SAM2 loaded on cuda\n", + "torch reports free 77.7 GB / total 79.3 GB\n" + ] + } + ], + "source": [ + "import torch\n", + "\n", + "# 1) UNet laden (TF)\n", + "unet = load_model(\n", + " \"../models/seg_model.keras\",\n", + " custom_objects={\"weighted_crossentropy\": seg.weighted_crossentropy},\n", + ")\n", + "print(\"UNet loaded\")\n", + "\n", + "# 2) SAM2 laden (Torch)\n", + "device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n", + "cfg = \"configs/sam2.1/sam2.1_hiera_l.yaml\"\n", + "ckpt = \"/dss/dsshome1/0B/di54doz/segmenteverygrain/models/sam2.1_hiera_large.pt\"\n", + "sam = build_sam2(cfg, ckpt, device=device)\n", + "print(\"SAM2 loaded on\", device)\n", + "\n", + "# 3) kurzer Speicher-Check\n", + "free, total = torch.cuda.mem_get_info()\n", + "print(f\"torch reports free {free/1024**3:.1f} GB / total {total/1024**3:.1f} GB\")" + ] + }, + { + "cell_type": "markdown", + "id": "65b1894f", + "metadata": {}, + "source": [ + "For private Laptop:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "da8020af", + "metadata": {}, + "outputs": [], + "source": [ + "# UNET model\n", + "unet = load_model(\n", + " \"../models/seg_model.keras\",\n", + " custom_objects={\"weighted_crossentropy\": seg.weighted_crossentropy},\n", + ")\n", + "print(\"UNet loaded\")\n", + "\n", + "# Auto-detect device: CUDA for NVIDIA, MPS for Apple Silicon, CPU as fallback\n", + "import torch\n", + "if torch.cuda.is_available():\n", + " device = \"cuda\"\n", + "elif torch.backends.mps.is_available():\n", + " device = \"mps\"\n", + "else:\n", + " device = \"cpu\"\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "7b1dd05d", + "metadata": {}, + "outputs": [], + "source": [ + "# # SAM 2.1 checkpoints. Download from:\n", + "# # https://dl.fbaipublicfiles.com/segment_anything_2/092824/sam2.1_hiera_large.pt\n", + "\n", + "# sam = build_sam2(r\"C:\\Users\\leoni\\sam2\\sam2\\configs\\sam2.1\\sam2.1_hiera_l.yaml\", r\"C:\\Users\\leoni\\segmenteverygrain\\models\\sam2.1_hiera_large.pt\", device=device)\n" + ] + }, + { + "cell_type": "markdown", + "id": "659098c3", + "metadata": {}, + "source": [ + "## Funktion for processing one folder: " + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "1aba043f", + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "import glob\n", + "import time\n", + "import traceback\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "from tensorflow.keras.utils import load_img\n", + "import rasterio\n", + "\n", + "import pandas as pd\n", + "import torch\n", + "\n", + "import geopandas as gpd\n", + "from shapely.geometry import Polygon\n", + "from skimage.measure import regionprops_table\n", + "\n", + "# -----------------------------\n", + "# USER SETTINGS\n", + "# -----------------------------\n", + "OUT_ROOT = \"/dss/tbyscratch/0B/di54doz/seg\" # alles hier rein\n", + "os.makedirs(OUT_ROOT, exist_ok=True)\n", + "\n", + "MAX_SAM_PROMPTS = 3000\n", + "PROMPT_SUBSAMPLE_MODE = \"random\" # \"random\" oder \"first\"\n", + "RNG_SEED = 42\n", + "rng = np.random.default_rng(RNG_SEED)\n", + "\n", + "# Save label mask TIFF always\n", + "SAVE_LABEL_TIF = True\n", + "\n", + "# Keep plots\n", + "SAVE_PEBBLE_PNG = True\n", + "\n", + "# Histogram only for sample (saves a lot of time)\n", + "SAVE_HIST_SAMPLE = True\n", + "N_HIST_PER_FOLDER = 100 # random tiles per folder for histogram PNGs\n", + "\n", + "# Save GPKG only for sample\n", + "SAVE_GPKG_SAMPLE = True\n", + "N_GPKG_PER_FOLDER = 100 # random tiles per folder\n", + "\n", + "# SEG plotting: avoid drawing ellipse axes from segmenteverygrain\n", + "SEG_PLOT_IMAGE = False # <- important: prevents a/b axes overlays\n", + "\n", + "def _gpu_free_gb():\n", + " if torch.cuda.is_available():\n", + " free, total = torch.cuda.mem_get_info()\n", + " return free / 1024**3\n", + " return None\n", + "\n", + "def setup_out_folder(in_folder: str, out_root: str):\n", + " folder_name = os.path.basename(os.path.normpath(in_folder))\n", + " out_folder = os.path.join(out_root, folder_name)\n", + "\n", + " out_gpkg = os.path.join(out_folder, \"ouputgpkg\")\n", + " out_csv = os.path.join(out_folder, \"csv_stats\")\n", + " out_plot = os.path.join(out_folder, \"pebbleplots\")\n", + " out_hist = os.path.join(out_folder, \"histplots\")\n", + " out_masks = os.path.join(out_folder, \"masktifs\")\n", + "\n", + " for p in [out_gpkg, out_csv, out_plot, out_hist, out_masks]:\n", + " os.makedirs(p, exist_ok=True)\n", + "\n", + " return out_folder, out_gpkg, out_csv, out_plot, out_hist, out_masks\n", + "\n", + "def collect_tiles(folder: str):\n", + " inputtiledir = os.path.join(folder, \"inputtiles\")\n", + " tiles_in_input = sorted(glob.glob(os.path.join(inputtiledir, \"*.tif\")))\n", + " tiles_in_root = sorted(glob.glob(os.path.join(folder, \"*.tif\")))\n", + " tiles = tiles_in_input if len(tiles_in_input) > 0 else tiles_in_root\n", + "\n", + " source = \"inputtiles\" if len(tiles_in_input) > 0 else \"folder root\"\n", + " print(f\"Found {len(tiles)} tiles in {folder} ({source})\")\n", + "\n", + " if len(tiles) == 0:\n", + " raise RuntimeError(f\"No tiles found in {inputtiledir} or in {folder}\")\n", + "\n", + " return tiles\n", + "\n", + "def process_one_folder(folder: str, out_root: str = OUT_ROOT):\n", + " tiles = collect_tiles(folder)\n", + "\n", + " out_folder, out_gpkg, out_csv, out_plot, out_hist, out_masks = setup_out_folder(folder, out_root)\n", + " print(\"OUT_FOLDER:\", out_folder)\n", + "\n", + " tile_ids_all = [os.path.splitext(os.path.basename(p))[0] for p in tiles]\n", + "\n", + " # sample for GPKG export\n", + " if SAVE_GPKG_SAMPLE:\n", + " if len(tile_ids_all) <= N_GPKG_PER_FOLDER:\n", + " gpkg_tile_ids = set(tile_ids_all)\n", + " else:\n", + " gpkg_tile_ids = set(rng.choice(tile_ids_all, size=N_GPKG_PER_FOLDER, replace=False))\n", + " print(f\"GPKG sample: {len(gpkg_tile_ids)} / {len(tile_ids_all)} tiles\")\n", + " else:\n", + " gpkg_tile_ids = set()\n", + "\n", + " # sample for histogram plots\n", + " if SAVE_HIST_SAMPLE:\n", + " if len(tile_ids_all) <= N_HIST_PER_FOLDER:\n", + " hist_tile_ids = set(tile_ids_all)\n", + " else:\n", + " hist_tile_ids = set(rng.choice(tile_ids_all, size=N_HIST_PER_FOLDER, replace=False))\n", + " print(f\"HIST sample: {len(hist_tile_ids)} / {len(tile_ids_all)} tiles\")\n", + " else:\n", + " hist_tile_ids = set()\n", + "\n", + " # read pixel size once from first tile\n", + " with rasterio.open(tiles[0]) as src:\n", + " xres, yres = src.res\n", + " crs = src.crs\n", + "\n", + " gsd_units = float((xres + yres) / 2.0)\n", + " gsd_mm = gsd_units * 1000.0\n", + "\n", + " minor_mm = 20.0\n", + " minor_px = minor_mm / gsd_mm\n", + " min_area_px = float(np.pi * (minor_px / 2.0) ** 2)\n", + "\n", + " print(\"CRS:\", crs)\n", + " print(f\"Pixel size: {gsd_units:.6f} units/px (~{gsd_mm:.3f} mm/px)\")\n", + " print(f\"2 cm => minor_px={minor_px:.2f} px -> min_area_px={min_area_px:.1f} px^2\")\n", + "\n", + " rows = []\n", + " metrics_csv = os.path.join(out_folder, \"runtime_metrics.csv\")\n", + " pd.DataFrame([]).to_csv(metrics_csv, index=False) # creates empty file early\n", + "\n", + " for i, fname in enumerate(tiles, start=1):\n", + " tile_id = os.path.splitext(os.path.basename(fname))[0]\n", + " print(f\"[{i}/{len(tiles)}] {tile_id}\")\n", + " with open(os.path.join(out_folder, \"_progress.txt\"), \"w\") as f:\n", + " f.write(f\"{i}/{len(tiles)} {tile_id}\\n\")\n", + "\n", + " t0 = time.perf_counter()\n", + " gpu_free_before = _gpu_free_gb()\n", + "\n", + " rec = {\n", + " \"folder\": os.path.basename(folder),\n", + " \"tile_id\": tile_id,\n", + " \"fname\": fname,\n", + " \"status\": None,\n", + "\n", + " \"crs\": str(crs),\n", + " \"pixel_size_units_per_px\": gsd_units,\n", + " \"pixel_size_mm_per_px\": gsd_mm,\n", + " \"minor_mm_threshold\": minor_mm,\n", + " \"minor_px_threshold\": minor_px,\n", + " \"min_area_px\": min_area_px,\n", + "\n", + " \"H\": None,\n", + " \"W\": None,\n", + " \"n_prompts_before\": None,\n", + " \"n_prompts_used\": None,\n", + " \"prompt_cap_used\": None,\n", + " \"n_grains\": None,\n", + "\n", + " \"t_unet_s\": None,\n", + " \"t_label_s\": None,\n", + " \"t_sam_s\": None,\n", + " \"t_export_s\": None,\n", + " \"t_total_s\": None,\n", + "\n", + " \"gpu_free_gb_before\": gpu_free_before,\n", + " \"gpu_free_gb_after\": None,\n", + "\n", + " \"error_msg\": None,\n", + " \"traceback_head\": None,\n", + " }\n", + "\n", + " try:\n", + " # nodata check\n", + " with rasterio.open(fname) as src:\n", + " m = src.dataset_mask()\n", + " if not np.any(m):\n", + " print(\" -> skipped (100% Nodata)\")\n", + " rec[\"status\"] = \"skip_nodata\"\n", + " rec[\"t_total_s\"] = time.perf_counter() - t0\n", + " rec[\"gpu_free_gb_after\"] = _gpu_free_gb()\n", + " rows.append(rec)\n", + " continue\n", + "\n", + " # load + predict (UNet)\n", + " t = time.perf_counter()\n", + " image = np.array(load_img(fname))\n", + " rec[\"H\"], rec[\"W\"] = int(image.shape[0]), int(image.shape[1])\n", + " image_pred = seg.predict_image(image, unet, I=256)\n", + " rec[\"t_unet_s\"] = time.perf_counter() - t\n", + "\n", + " # prompts\n", + " t = time.perf_counter()\n", + " labels_pts, coords = seg.label_grains(image, image_pred, dbs_max_dist=10.0)\n", + " rec[\"t_label_s\"] = time.perf_counter() - t\n", + "\n", + " coords = np.asarray(coords)\n", + " rec[\"n_prompts_before\"] = int(len(coords))\n", + " rec[\"prompt_cap_used\"] = False\n", + "\n", + " if MAX_SAM_PROMPTS is not None and len(coords) > MAX_SAM_PROMPTS:\n", + " rec[\"prompt_cap_used\"] = True\n", + " n_before = len(coords)\n", + "\n", + " if PROMPT_SUBSAMPLE_MODE == \"first\":\n", + " keep_idx = np.arange(MAX_SAM_PROMPTS)\n", + " else:\n", + " keep_idx = np.sort(rng.choice(len(coords), size=MAX_SAM_PROMPTS, replace=False))\n", + "\n", + " coords = coords[keep_idx]\n", + "\n", + " try:\n", + " labels_arr = np.asarray(labels_pts)\n", + " if labels_arr.ndim == 1 and len(labels_arr) == n_before:\n", + " labels_pts = labels_arr[keep_idx]\n", + " except Exception:\n", + " pass\n", + "\n", + " print(f\"Prompt cap active: reduced prompts from {n_before} -> {len(coords)}\")\n", + "\n", + " rec[\"n_prompts_used\"] = int(len(coords))\n", + "\n", + " # SAM segmentation\n", + " t = time.perf_counter()\n", + " all_grains, labels, mask_all, grain_data, fig, ax = seg.sam_segmentation(\n", + " sam,\n", + " image,\n", + " image_pred,\n", + " coords,\n", + " labels_pts,\n", + " min_area=min_area_px,\n", + " plot_image=SEG_PLOT_IMAGE, # <- no built-in axis overlays\n", + " remove_edge_grains=True,\n", + " remove_large_objects=True,\n", + " )\n", + " rec[\"t_sam_s\"] = time.perf_counter() - t\n", + " rec[\"n_grains\"] = int(len(all_grains))\n", + "\n", + " # export/post\n", + " t = time.perf_counter()\n", + "\n", + " # 1) pebble PNG (fallback plot, no axes, no a/b axis overlays)\n", + " if SAVE_PEBBLE_PNG:\n", + " seg_plot_path = os.path.join(out_plot, f\"{tile_id}.png\")\n", + "\n", + " if fig is None:\n", + " fig, ax = plt.subplots(figsize=(8, 8))\n", + " ax.imshow(image)\n", + " for poly in all_grains:\n", + " x, y = poly.exterior.xy\n", + " ax.plot(x, y, linewidth=0.8)\n", + " ax.axis(\"off\")\n", + " else:\n", + " try:\n", + " for a in fig.axes:\n", + " a.axis(\"off\")\n", + " except Exception:\n", + " pass\n", + "\n", + " fig.savefig(seg_plot_path, dpi=200, bbox_inches=\"tight\")\n", + " plt.close(fig)\n", + "\n", + " # 2) label mask GeoTIFF (0..N), georeferenced, filtered result from SEG\n", + " if SAVE_LABEL_TIF:\n", + " with rasterio.open(fname) as ds:\n", + " prof = ds.profile.copy()\n", + " prof.update(driver=\"GTiff\", count=1, dtype=\"uint32\", compress=\"lzw\")\n", + " out_mask = os.path.join(out_masks, f\"{tile_id}_labels.tif\")\n", + " with rasterio.open(out_mask, \"w\", **prof) as dst:\n", + " dst.write(labels.astype(\"uint32\"), 1)\n", + "\n", + " # 3) stats from labels (fast)\n", + " with rasterio.open(fname) as dataset:\n", + " px_size_x = abs(dataset.transform.a)\n", + "\n", + " props = regionprops_table(\n", + " labels.astype(\"int\"),\n", + " properties=(\"label\", \"area\", \"centroid\", \"major_axis_length\", \"minor_axis_length\"),\n", + " )\n", + " grain_stats = pd.DataFrame(props)\n", + "\n", + " if len(grain_stats) > 0:\n", + " centroid_x, centroid_y = rasterio.transform.xy(\n", + " dataset.transform,\n", + " grain_stats[\"centroid-0\"].values,\n", + " grain_stats[\"centroid-1\"].values,\n", + " )\n", + "\n", + " grain_stats[\"centroid_x\"] = centroid_x\n", + " grain_stats[\"centroid_y\"] = centroid_y\n", + " grain_stats.rename(columns={\"area\": \"area_px\"}, inplace=True)\n", + " grain_stats[\"major_axis_px\"] = grain_stats[\"major_axis_length\"]\n", + " grain_stats[\"minor_axis_px\"] = grain_stats[\"minor_axis_length\"]\n", + " grain_stats[\"major_axis_length\"] = grain_stats[\"major_axis_length\"] * px_size_x\n", + " grain_stats[\"minor_axis_length\"] = grain_stats[\"minor_axis_length\"] * px_size_x\n", + " grain_stats[\"major_axis_mm\"] = grain_stats[\"major_axis_length\"] * 1000.0\n", + " grain_stats[\"minor_axis_mm\"] = grain_stats[\"minor_axis_length\"] * 1000.0\n", + "\n", + " out_cols = [\n", + " \"label\", \"area_px\", \"centroid_x\", \"centroid_y\",\n", + " \"major_axis_px\", \"minor_axis_px\",\n", + " \"major_axis_length\", \"minor_axis_length\",\n", + " \"major_axis_mm\", \"minor_axis_mm\",\n", + " ]\n", + " grain_stats_out = grain_stats[out_cols].copy()\n", + " else:\n", + " grain_stats_out = pd.DataFrame(columns=[\n", + " \"label\",\"area_px\",\"centroid_x\",\"centroid_y\",\n", + " \"major_axis_px\",\"minor_axis_px\",\n", + " \"major_axis_length\",\"minor_axis_length\",\n", + " \"major_axis_mm\",\"minor_axis_mm\"\n", + " ])\n", + "\n", + " csv_path = os.path.join(out_csv, f\"{tile_id}_grain_stats.csv\")\n", + " grain_stats_out.to_csv(csv_path, index=False)\n", + "\n", + " # 4) histogram PNG only for sampled tiles\n", + " if SAVE_HIST_SAMPLE and (tile_id in hist_tile_ids) and len(grain_stats_out) > 0:\n", + " fig_hist, ax_hist = seg.plot_histogram_of_axis_lengths(\n", + " grain_stats_out[\"major_axis_mm\"],\n", + " grain_stats_out[\"minor_axis_mm\"],\n", + " binsize=0.25,\n", + " xlimits=[8, 2 * 256],\n", + " )\n", + " hist_plot_path = os.path.join(out_hist, f\"{tile_id}_hist.png\")\n", + " fig_hist.savefig(hist_plot_path, dpi=200, bbox_inches=\"tight\")\n", + " plt.close(fig_hist)\n", + "\n", + " # 5) GPKG only for sampled tiles\n", + " if SAVE_GPKG_SAMPLE and (tile_id in gpkg_tile_ids):\n", + " with rasterio.open(fname) as dataset:\n", + " projected_polys = []\n", + " for grain in all_grains:\n", + " px_x = np.asarray(grain.exterior.xy[0])\n", + " px_y = np.asarray(grain.exterior.xy[1])\n", + " x_proj, y_proj = rasterio.transform.xy(dataset.transform, px_y, px_x)\n", + " poly = Polygon(np.vstack((x_proj, y_proj)).T)\n", + " projected_polys.append(poly)\n", + "\n", + " gdf = gpd.GeoDataFrame({\"geometry\": projected_polys}, geometry=\"geometry\", crs=dataset.crs)\n", + "\n", + " gpkg_path = os.path.join(out_gpkg, f\"{tile_id}_grains.gpkg\")\n", + " gdf.to_file(gpkg_path, driver=\"GPKG\")\n", + "\n", + " rec[\"t_export_s\"] = time.perf_counter() - t\n", + " rec[\"status\"] = \"ok\"\n", + " rec[\"t_total_s\"] = time.perf_counter() - t0\n", + " rec[\"gpu_free_gb_after\"] = _gpu_free_gb()\n", + " rows.append(rec)\n", + " pd.DataFrame(rows).to_csv(metrics_csv, index=False)\n", + "\n", + " except Exception as e:\n", + " rec[\"status\"] = \"error\"\n", + " rec[\"t_total_s\"] = time.perf_counter() - t0\n", + " rec[\"gpu_free_gb_after\"] = _gpu_free_gb()\n", + " rec[\"error_msg\"] = str(e)\n", + " rec[\"traceback_head\"] = traceback.format_exc(limit=8)\n", + " rows.append(rec)\n", + " pd.DataFrame(rows).to_csv(metrics_csv, index=False)\n", + " print(\"ERROR on\", tile_id, \":\", e)\n", + "\n", + " # runtime metrics for this folder\n", + " df = pd.DataFrame(rows)\n", + " metrics_csv = os.path.join(out_folder, \"runtime_metrics.csv\")\n", + " df.to_csv(metrics_csv, index=False)\n", + " print(\"Saved runtime metrics CSV:\", metrics_csv)\n", + "\n", + " # ready table\n", + " ok = df[df[\"status\"] == \"ok\"].copy()\n", + " n_ok = len(ok)\n", + " total_s = ok[\"t_total_s\"].sum()\n", + " tiles_per_min = (n_ok / (total_s / 60.0)) if total_s > 0 else np.nan\n", + "\n", + " ready = pd.DataFrame({\n", + " \"metric\": [\n", + " \"n_tiles_total\",\n", + " \"n_tiles_ok\",\n", + " \"n_tiles_skipped_nodata\",\n", + " \"n_tiles_error\",\n", + " \"total_runtime_min\",\n", + " \"tiles_per_min\",\n", + " \"total_s_per_tile (median)\",\n", + " \"unet_s (median)\",\n", + " \"label_s (median)\",\n", + " \"sam_s (median)\",\n", + " \"export_s (median)\",\n", + " \"prompts_used (median)\",\n", + " \"grains (median)\",\n", + " ],\n", + " \"value\": [\n", + " int(len(df)),\n", + " int(n_ok),\n", + " int((df[\"status\"] == \"skip_nodata\").sum()),\n", + " int((df[\"status\"] == \"error\").sum()),\n", + " float(total_s / 60.0) if n_ok else np.nan,\n", + " float(tiles_per_min) if n_ok else np.nan,\n", + " float(ok[\"t_total_s\"].median()) if n_ok else np.nan,\n", + " float(ok[\"t_unet_s\"].median()) if n_ok else np.nan,\n", + " float(ok[\"t_label_s\"].median()) if n_ok else np.nan,\n", + " float(ok[\"t_sam_s\"].median()) if n_ok else np.nan,\n", + " float(ok[\"t_export_s\"].median()) if n_ok else np.nan,\n", + " float(ok[\"n_prompts_used\"].median()) if n_ok else np.nan,\n", + " float(ok[\"n_grains\"].median()) if n_ok else np.nan,\n", + " ]\n", + " })\n", + "\n", + " ready_csv = os.path.join(out_folder, \"runtime_summary_ready_table.csv\")\n", + " ready.to_csv(ready_csv, index=False)\n", + " print(\"Saved ready table CSV:\", ready_csv)\n", + "\n", + " return df, ready" + ] + }, + { + "cell_type": "markdown", + "id": "7615fed0", + "metadata": {}, + "source": [ + "# Set folder strucure" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "277720e8", + "metadata": {}, + "outputs": [], + "source": [ + "# import os, glob\n", + "\n", + "# BASE = \"/dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain\"\n", + "# subdirs = [\"inputtiles\", \"ouputgpkg\", \"pebbleplots\", \"histplots\", \"csv_stats\"]\n", + "\n", + "# folders = sorted(glob.glob(os.path.join(BASE, \"F*\")))\n", + "# print(\"Found F folders:\", [os.path.basename(f) for f in folders])\n", + "\n", + "# for folder in folders:\n", + "# made_any = False\n", + "# for sd in subdirs:\n", + "# path = os.path.join(folder, sd)\n", + "# if not os.path.isdir(path):\n", + "# os.makedirs(path, exist_ok=True)\n", + "# made_any = True\n", + "# if made_any:\n", + "# print(\"Initialized structure:\", os.path.basename(folder))\n", + "# else:\n", + "# print(\"OK already structured:\", os.path.basename(folder))" + ] + }, + { + "cell_type": "markdown", + "id": "f9193000", + "metadata": {}, + "source": [ + "# Run segmentation" + ] + }, + { + "cell_type": "markdown", + "id": "df758347", + "metadata": {}, + "source": [ + "## Not including Masks and saving all statistiks, plots and images and gpkgs" + ] + }, + { + "cell_type": "markdown", + "id": "d6f27358", + "metadata": {}, + "source": [ + "For only one folder:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c6b8888c", + "metadata": {}, + "outputs": [], + "source": [ + "# process_one_folder(\"/dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F9\")" + ] + }, + { + "cell_type": "markdown", + "id": "b1d0a4c9", + "metadata": {}, + "source": [ + "For processing over several folders (F-folders):" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "76cc409f", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "PROCESS: F23 tiles: 997\n", + "Found 997 tiles in /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F23 (folder root)\n", + "OUT_FOLDER: /dss/tbyscratch/0B/di54doz/seg/F23\n", + "GPKG sample: 100 / 997 tiles\n", + "HIST sample: 100 / 997 tiles\n", + "CRS: EPSG:32643\n", + "Pixel size: 0.002877 units/px (~2.877 mm/px)\n", + "2 cm => minor_px=6.95 px -> min_area_px=38.0 px^2\n", + "[1/997] tile_r000004_c000007\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + " 0%| | 0/5 [00:00 30\u001b[0m \u001b[43mprocess_one_folder\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfolder\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 31\u001b[0m processed \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m \u001b[38;5;241m1\u001b[39m\n", + "Cell \u001b[0;32mIn[4], line 222\u001b[0m, in \u001b[0;36mprocess_one_folder\u001b[0;34m(folder, out_root)\u001b[0m\n\u001b[1;32m 220\u001b[0m \u001b[38;5;66;03m# SAM segmentation\u001b[39;00m\n\u001b[1;32m 221\u001b[0m t \u001b[38;5;241m=\u001b[39m time\u001b[38;5;241m.\u001b[39mperf_counter()\n\u001b[0;32m--> 222\u001b[0m all_grains, labels, mask_all, grain_data, fig, ax \u001b[38;5;241m=\u001b[39m \u001b[43mseg\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msam_segmentation\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 223\u001b[0m \u001b[43m \u001b[49m\u001b[43msam\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 224\u001b[0m \u001b[43m \u001b[49m\u001b[43mimage\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 225\u001b[0m \u001b[43m \u001b[49m\u001b[43mimage_pred\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 226\u001b[0m \u001b[43m \u001b[49m\u001b[43mcoords\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 227\u001b[0m \u001b[43m \u001b[49m\u001b[43mlabels_pts\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 228\u001b[0m \u001b[43m \u001b[49m\u001b[43mmin_area\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmin_area_px\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 229\u001b[0m \u001b[43m \u001b[49m\u001b[43mplot_image\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mSEG_PLOT_IMAGE\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;66;43;03m# <- no built-in axis overlays\u001b[39;49;00m\n\u001b[1;32m 230\u001b[0m \u001b[43m \u001b[49m\u001b[43mremove_edge_grains\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[1;32m 231\u001b[0m \u001b[43m \u001b[49m\u001b[43mremove_large_objects\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[1;32m 232\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 233\u001b[0m rec[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mt_sam_s\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m time\u001b[38;5;241m.\u001b[39mperf_counter() \u001b[38;5;241m-\u001b[39m t\n\u001b[1;32m 234\u001b[0m rec[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mn_grains\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mint\u001b[39m(\u001b[38;5;28mlen\u001b[39m(all_grains))\n", + "File \u001b[0;32m~/.local/share/mamba/envs/segmenteverygrain/lib/python3.10/site-packages/segmenteverygrain/segmenteverygrain.py:725\u001b[0m, in \u001b[0;36msam_segmentation\u001b[0;34m(sam, image, image_pred, coords, labels, min_area, plot_image, remove_edge_grains, remove_large_objects, keep_edges)\u001b[0m\n\u001b[1;32m 723\u001b[0m x \u001b[38;5;241m=\u001b[39m coords[i, \u001b[38;5;241m0\u001b[39m]\n\u001b[1;32m 724\u001b[0m y \u001b[38;5;241m=\u001b[39m coords[i, \u001b[38;5;241m1\u001b[39m]\n\u001b[0;32m--> 725\u001b[0m sx, sy, mask \u001b[38;5;241m=\u001b[39m \u001b[43mone_point_prompt\u001b[49m\u001b[43m(\u001b[49m\u001b[43mx\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43my\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mimage\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mpredictor\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 726\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m np\u001b[38;5;241m.\u001b[39mmax(mask) \u001b[38;5;241m>\u001b[39m \u001b[38;5;241m0\u001b[39m:\n\u001b[1;32m 727\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m remove_edge_grains:\n\u001b[1;32m 728\u001b[0m \u001b[38;5;66;03m# Determine which edges to check based on keep_edges\u001b[39;00m\n", + "File \u001b[0;32m~/.local/share/mamba/envs/segmenteverygrain/lib/python3.10/site-packages/segmenteverygrain/segmenteverygrain.py:354\u001b[0m, in \u001b[0;36mone_point_prompt\u001b[0;34m(x, y, image, predictor, ax)\u001b[0m\n\u001b[1;32m 352\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 353\u001b[0m mask \u001b[38;5;241m=\u001b[39m masks[ind]\n\u001b[0;32m--> 354\u001b[0m contours \u001b[38;5;241m=\u001b[39m \u001b[43mmeasure\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfind_contours\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmask\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m0.5\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m 355\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(contours) \u001b[38;5;241m>\u001b[39m \u001b[38;5;241m0\u001b[39m:\n\u001b[1;32m 356\u001b[0m sx \u001b[38;5;241m=\u001b[39m contours[\u001b[38;5;241m0\u001b[39m][:, \u001b[38;5;241m1\u001b[39m]\n", + "File \u001b[0;32m~/.local/share/mamba/envs/segmenteverygrain/lib/python3.10/site-packages/skimage/measure/_find_contours.py:146\u001b[0m, in \u001b[0;36mfind_contours\u001b[0;34m(image, level, fully_connected, positive_orientation, mask)\u001b[0m\n\u001b[1;32m 143\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m level \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m 144\u001b[0m level \u001b[38;5;241m=\u001b[39m (np\u001b[38;5;241m.\u001b[39mnanmin(image) \u001b[38;5;241m+\u001b[39m np\u001b[38;5;241m.\u001b[39mnanmax(image)) \u001b[38;5;241m/\u001b[39m \u001b[38;5;241m2.0\u001b[39m\n\u001b[0;32m--> 146\u001b[0m segments \u001b[38;5;241m=\u001b[39m \u001b[43m_get_contour_segments\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 147\u001b[0m \u001b[43m \u001b[49m\u001b[43mimage\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mastype\u001b[49m\u001b[43m(\u001b[49m\u001b[43mnp\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfloat64\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mfloat\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mlevel\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mfully_connected\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m==\u001b[39;49m\u001b[43m \u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mhigh\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmask\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmask\u001b[49m\n\u001b[1;32m 148\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 149\u001b[0m contours \u001b[38;5;241m=\u001b[39m _assemble_contours(segments)\n\u001b[1;32m 150\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m positive_orientation \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mhigh\u001b[39m\u001b[38;5;124m'\u001b[39m:\n", + "\u001b[0;31mKeyboardInterrupt\u001b[0m: " + ] + } + ], + "source": [ + "import os, glob\n", + "\n", + "BASE = \"/dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain\"\n", + "folders = sorted(glob.glob(os.path.join(BASE, \"F*\")))\n", + "\n", + "MAX_TO_PROCESS = 20\n", + "processed = 0\n", + "\n", + "for folder in folders:\n", + " if processed >= MAX_TO_PROCESS:\n", + " print(\"Reached MAX_TO_PROCESS, stopping.\")\n", + " break\n", + "\n", + " # 1) skip wenn schon Ergebnisse (GPKG) vorhanden\n", + " gpkg_files = glob.glob(os.path.join(folder, \"ouputgpkg\", \"*.gpkg\"))\n", + " if len(gpkg_files) > 0:\n", + " print(\"SKIP already has GPKG:\", os.path.basename(folder), \"n_gpkg:\", len(gpkg_files))\n", + " continue\n", + "\n", + " # 2) skip wenn noch keine tiles drin sind (inputtiles/ ODER folder-root)\n", + " tiles = glob.glob(os.path.join(folder, \"inputtiles\", \"*.tif\"))\n", + " if len(tiles) == 0:\n", + " tiles = glob.glob(os.path.join(folder, \"*.tif\"))\n", + "\n", + " if len(tiles) == 0:\n", + " print(\"SKIP no tiles yet:\", os.path.basename(folder))\n", + " continue\n", + "\n", + " print(\"PROCESS:\", os.path.basename(folder), \"tiles:\", len(tiles))\n", + " process_one_folder(folder)\n", + " processed += 1" + ] + }, + { + "cell_type": "markdown", + "id": "4f88b769", + "metadata": {}, + "source": [ + "# Plotting raster like in IG2" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "4f0a5a52", + "metadata": {}, + "outputs": [], + "source": [ + "# Pretty overlays for SEG label masks\n", + "# Makes a random sample of PNGs per F-folder, using your palette + black outline.\n", + "# Reads:\n", + "# SEG outputs: OUT_ROOT//masktifs/*_labels.tif\n", + "# Original tiles: SEG_BASE//inputtiles/*.tif (fallback: SEG_BASE//*.tif)\n", + "# Writes:\n", + "# OUT_ROOT//pebbleplots_pretty/__pretty.png\n", + "\n", + "import os, glob\n", + "import numpy as np\n", + "import rasterio\n", + "import matplotlib.pyplot as plt\n", + "\n", + "from skimage.segmentation import find_boundaries\n", + "from matplotlib.colors import ListedColormap\n", + "\n", + "# -----------------------------\n", + "# SETTINGS\n", + "# -----------------------------\n", + "SEG_BASE = \"/dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain\"\n", + "OUT_ROOT = \"/dss/tbyscratch/0B/di54doz/seg\" # your SEG OUT_ROOT from segmentation runs\n", + "\n", + "MAX_FOLDERS = None # None = all\n", + "SAMPLE_PER_FOLDER = 25 # how many overlays per folder\n", + "RNG_SEED = 42\n", + "\n", + "OVERLAY_ALPHA = 0.45\n", + "OUTLINE_ALPHA = 0.95\n", + "DPI = 200\n", + "\n", + "PALETTE_HEX = [\"#065143\", \"#77CBB9\", \"#5B2E48\", \"#F96900\", \"#F4D35E\"]\n", + "\n", + "# -----------------------------\n", + "# HELPERS\n", + "# -----------------------------\n", + "def hex_to_rgba(h, a=1.0):\n", + " h = h.lstrip(\"#\")\n", + " r = int(h[0:2], 16) / 255.0\n", + " g = int(h[2:4], 16) / 255.0\n", + " b = int(h[4:6], 16) / 255.0\n", + " return (r, g, b, a)\n", + "\n", + "PALETTE_RGBA = [hex_to_rgba(h, 1.0) for h in PALETTE_HEX]\n", + "\n", + "def make_palette_cmap(n_labels, rng, alpha=1.0):\n", + " colors = [(0, 0, 0, 0)] # transparent background\n", + " for _ in range(n_labels):\n", + " c = list(PALETTE_RGBA[int(rng.integers(0, len(PALETTE_RGBA)))])\n", + " c[3] = alpha\n", + " colors.append(tuple(c))\n", + " return ListedColormap(colors)\n", + "\n", + "def find_tile_dir(seg_folder: str) -> str:\n", + " cand = os.path.join(seg_folder, \"inputtiles\")\n", + " if os.path.isdir(cand) and len(glob.glob(os.path.join(cand, \"*.tif\"))) > 0:\n", + " return cand\n", + " return seg_folder\n", + "\n", + "# -----------------------------\n", + "# RUN\n", + "# -----------------------------\n", + "folders = sorted(glob.glob(os.path.join(SEG_BASE, \"F*\")))\n", + "rng = np.random.default_rng(RNG_SEED)\n", + "\n", + "done_folders = 0\n", + "done_pngs_total = 0\n", + "\n", + "for folder in folders:\n", + " if MAX_FOLDERS is not None and done_folders >= MAX_FOLDERS:\n", + " print(\"Reached MAX_FOLDERS, stopping.\")\n", + " break\n", + "\n", + " folder_name = os.path.basename(os.path.normpath(folder))\n", + " out_dir = os.path.join(OUT_ROOT, folder_name)\n", + " mask_dir = os.path.join(out_dir, \"masktifs\")\n", + "\n", + " if not os.path.isdir(mask_dir):\n", + " print(\"SKIP no masktifs:\", folder_name)\n", + " continue\n", + "\n", + " mask_files = sorted(glob.glob(os.path.join(mask_dir, \"*_labels.tif\")))\n", + " if len(mask_files) == 0:\n", + " print(\"SKIP no label masks:\", folder_name)\n", + " continue\n", + "\n", + " tile_dir = find_tile_dir(folder)\n", + "\n", + " png_dir = os.path.join(out_dir, \"pebbleplots_pretty\")\n", + " os.makedirs(png_dir, exist_ok=True)\n", + "\n", + " n = min(SAMPLE_PER_FOLDER, len(mask_files))\n", + " pick = list(rng.choice(mask_files, size=n, replace=False))\n", + "\n", + " print(\"\\nFOLDER:\", folder_name, \"masks:\", len(mask_files), \"sample:\", n)\n", + "\n", + " made = 0\n", + " for mf in pick:\n", + " tile_id = os.path.basename(mf).replace(\"_labels.tif\", \"\")\n", + " src_tile = os.path.join(tile_dir, f\"{tile_id}.tif\")\n", + " if not os.path.exists(src_tile):\n", + " continue\n", + "\n", + " out_png = os.path.join(png_dir, f\"{folder_name}_{tile_id}_pretty.png\")\n", + " if os.path.exists(out_png):\n", + " continue\n", + "\n", + " with rasterio.open(src_tile) as src:\n", + " img = src.read([1, 2, 3]).transpose(1, 2, 0)\n", + "\n", + " with rasterio.open(mf) as ms:\n", + " labels = ms.read(1)\n", + "\n", + " nlab = int(labels.max())\n", + " if nlab == 0:\n", + " continue\n", + "\n", + " cmap = make_palette_cmap(nlab, rng, alpha=1.0)\n", + " boundaries = find_boundaries(labels, mode=\"outer\")\n", + "\n", + " boundary_rgba = np.zeros((labels.shape[0], labels.shape[1], 4), dtype=float)\n", + " boundary_rgba[..., 3] = 0.0\n", + " boundary_rgba[boundaries] = (0.0, 0.0, 0.0, OUTLINE_ALPHA)\n", + "\n", + " plt.figure(figsize=(7, 7))\n", + " plt.imshow(img)\n", + " plt.imshow(labels, cmap=cmap, alpha=OVERLAY_ALPHA, interpolation=\"nearest\")\n", + " plt.imshow(boundary_rgba, interpolation=\"nearest\")\n", + " plt.axis(\"off\")\n", + " plt.title(f\"{folder_name} | {tile_id}\")\n", + " plt.savefig(out_png, dpi=DPI, bbox_inches=\"tight\")\n", + " plt.close()\n", + "\n", + " made += 1\n", + " done_pngs_total += 1\n", + "\n", + " print(\" made overlays:\", made, \"->\", png_dir)\n", + " done_folders += 1\n", + "\n", + "print(\"\\nDONE. folders:\", done_folders, \"total overlays:\", done_pngs_total)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.19" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/notebooks/01_SEG_leonie_loopingtiles_overfolders.ipynb b/notebooks/01_SEG_leonie_loopingtiles_overfolders.ipynb new file mode 100644 index 0000000..c220bef --- /dev/null +++ b/notebooks/01_SEG_leonie_loopingtiles_overfolders.ipynb @@ -0,0 +1,128778 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "623da314", + "metadata": {}, + "source": [ + "# Import packages" + ] + }, + { + "cell_type": "markdown", + "id": "2f3b877d", + "metadata": {}, + "source": [ + "Quick memory chceks:" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "f86592c6", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2026-03-10 07:53:44.260650: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.\n", + "2026-03-10 07:53:48.547612: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\n", + "To enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\n", + "2026-03-10 07:54:06.376794: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "TF GPUs: [PhysicalDevice(name='/physical_device:GPU:0', device_type='GPU')]\n" + ] + } + ], + "source": [ + "# to prevent blocking all memory\n", + "import os\n", + "os.environ[\"TF_FORCE_GPU_ALLOW_GROWTH\"] = \"true\"\n", + "\n", + "import tensorflow as tf\n", + "for gpu in tf.config.list_physical_devices(\"GPU\"):\n", + " tf.config.experimental.set_memory_growth(gpu, True)\n", + "\n", + "print(\"TF GPUs:\", tf.config.list_physical_devices(\"GPU\"))" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "ff94825b", + "metadata": {}, + "outputs": [], + "source": [ + "import cv2\n", + "from keras.utils import load_img\n", + "from keras.saving import load_model\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "import pandas as pd\n", + "from sam2.build_sam import build_sam2\n", + "from sam2.sam2_image_predictor import SAM2ImagePredictor\n", + "from skimage.measure import regionprops, regionprops_table\n", + "from tqdm import trange, tqdm\n", + "\n", + "import segmenteverygrain as seg\n", + "import segmenteverygrain.interactions as si\n", + "import os\n", + "import rasterio\n", + "import time, traceback\n", + "import pandas as pd\n", + "import torch\n", + "\n", + "# %matplotlib qt" + ] + }, + { + "cell_type": "markdown", + "id": "44e817f1", + "metadata": {}, + "source": [ + "## Load models" + ] + }, + { + "cell_type": "markdown", + "id": "442d52dc", + "metadata": {}, + "source": [ + "Maybe you need to clone first SAM2. Then add the yaml file in the bottom of the next chunk" + ] + }, + { + "cell_type": "markdown", + "id": "7fdb5f2a", + "metadata": {}, + "source": [ + "For terrabyte:" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "3965da5f", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", + "I0000 00:00:1773125700.791880 69166 gpu_device.cc:2020] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 79193 MB memory: -> device: 0, name: NVIDIA A100-SXM4-80GB, pci bus id: 0000:31:00.0, compute capability: 8.0\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "UNet loaded\n", + "SAM2 loaded on cuda\n", + "torch reports free 77.7 GB / total 79.3 GB\n" + ] + } + ], + "source": [ + "import torch\n", + "\n", + "# 1) UNet laden (TF)\n", + "unet = load_model(\n", + " \"../models/seg_model.keras\",\n", + " custom_objects={\"weighted_crossentropy\": seg.weighted_crossentropy},\n", + ")\n", + "print(\"UNet loaded\")\n", + "\n", + "# 2) SAM2 laden (Torch)\n", + "device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n", + "cfg = \"configs/sam2.1/sam2.1_hiera_l.yaml\"\n", + "ckpt = \"/dss/dsshome1/0B/di54doz/segmenteverygrain/models/sam2.1_hiera_large.pt\"\n", + "sam = build_sam2(cfg, ckpt, device=device)\n", + "print(\"SAM2 loaded on\", device)\n", + "\n", + "# 3) kurzer Speicher-Check\n", + "free, total = torch.cuda.mem_get_info()\n", + "print(f\"torch reports free {free/1024**3:.1f} GB / total {total/1024**3:.1f} GB\")" + ] + }, + { + "cell_type": "markdown", + "id": "65b1894f", + "metadata": {}, + "source": [ + "For private Laptop:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "da8020af", + "metadata": {}, + "outputs": [], + "source": [ + "# UNET model\n", + "unet = load_model(\n", + " \"../models/seg_model.keras\",\n", + " custom_objects={\"weighted_crossentropy\": seg.weighted_crossentropy},\n", + ")\n", + "print(\"UNet loaded\")\n", + "\n", + "# Auto-detect device: CUDA for NVIDIA, MPS for Apple Silicon, CPU as fallback\n", + "import torch\n", + "if torch.cuda.is_available():\n", + " device = \"cuda\"\n", + "elif torch.backends.mps.is_available():\n", + " device = \"mps\"\n", + "else:\n", + " device = \"cpu\"\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "7b1dd05d", + "metadata": {}, + "outputs": [], + "source": [ + "# # SAM 2.1 checkpoints. Download from:\n", + "# # https://dl.fbaipublicfiles.com/segment_anything_2/092824/sam2.1_hiera_large.pt\n", + "\n", + "# sam = build_sam2(r\"C:\\Users\\leoni\\sam2\\sam2\\configs\\sam2.1\\sam2.1_hiera_l.yaml\", r\"C:\\Users\\leoni\\segmenteverygrain\\models\\sam2.1_hiera_large.pt\", device=device)\n" + ] + }, + { + "cell_type": "markdown", + "id": "659098c3", + "metadata": {}, + "source": [ + "## Funktion for processing one folder: " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "1aba043f", + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "import glob\n", + "import time\n", + "import traceback\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "from tensorflow.keras.utils import load_img\n", + "import rasterio\n", + "\n", + "import pandas as pd\n", + "import torch\n", + "\n", + "import geopandas as gpd\n", + "from shapely.geometry import Polygon\n", + "from skimage.measure import regionprops_table\n", + "\n", + "# --- optional: prompt cap for SAM (None = no limit) ---\n", + "MAX_SAM_PROMPTS = 3000\n", + "PROMPT_SUBSAMPLE_MODE = \"random\" # \"random\" oder \"first\"\n", + "RNG_SEED = 42\n", + "rng = np.random.default_rng(RNG_SEED)\n", + "\n", + "def setup_folder(folder_path: str):\n", + " os.makedirs(os.path.join(folder_path, \"ouputgpkg\"), exist_ok=True)\n", + " os.makedirs(os.path.join(folder_path, \"inputtiles\"), exist_ok=True)\n", + " os.makedirs(os.path.join(folder_path, \"pebbleplots\"), exist_ok=True)\n", + " os.makedirs(os.path.join(folder_path, \"histplots\"), exist_ok=True)\n", + " os.makedirs(os.path.join(folder_path, \"csv_stats\"), exist_ok=True)\n", + "\n", + " inputtiledir = os.path.join(folder_path, \"inputtiles\")\n", + " ouputgpkg = os.path.join(folder_path, \"ouputgpkg\")\n", + " csvdir = os.path.join(folder_path, \"csv_stats\")\n", + " plotdirgravel = os.path.join(folder_path, \"pebbleplots\")\n", + " plotdirhist = os.path.join(folder_path, \"histplots\")\n", + "\n", + " return inputtiledir, ouputgpkg, csvdir, plotdirgravel, plotdirhist\n", + "\n", + "def _gpu_free_gb():\n", + " if torch.cuda.is_available():\n", + " free, total = torch.cuda.mem_get_info()\n", + " return free / 1024**3\n", + " return None\n", + "\n", + "def process_one_folder(folder: str):\n", + " inputtiledir, ouputgpkg, csvdir, plotdirgravel, plotdirhist = setup_folder(folder)\n", + "\n", + " # --- collect tiles ---\n", + " tiles_in_input = sorted(glob.glob(os.path.join(inputtiledir, \"*.tif\")))\n", + " tiles_in_root = sorted(glob.glob(os.path.join(folder, \"*.tif\")))\n", + " \n", + " tiles = tiles_in_input if len(tiles_in_input) > 0 else tiles_in_root\n", + " \n", + " print(f\"Found {len(tiles)} tiles in {folder} \"\n", + " f\"({'inputtiles' if len(tiles_in_input) > 0 else 'folder root'})\")\n", + " \n", + " if len(tiles) == 0:\n", + " raise RuntimeError(f\"No tiles found in {inputtiledir} or in {folder}\")\n", + "\n", + " \n", + " # --- read pixel size once from first tile ---\n", + " with rasterio.open(tiles[0]) as src:\n", + " xres, yres = src.res\n", + " crs = src.crs\n", + "\n", + " gsd_units = float((xres + yres) / 2.0)\n", + " gsd_mm = gsd_units * 1000.0\n", + "\n", + " minor_mm = 20.0\n", + " minor_px = minor_mm / gsd_mm\n", + " min_area_px = float(np.pi * (minor_px / 2.0) ** 2)\n", + "\n", + " print(\"CRS:\", crs)\n", + " print(f\"Pixel size: {gsd_units:.6f} units/px (~{gsd_mm:.3f} mm/px)\")\n", + " print(f\"2 cm => minor_px={minor_px:.2f} px -> min_area_px={min_area_px:.1f} px^2\")\n", + "\n", + " rows = []\n", + "\n", + " # --- loop ---\n", + " for i, fname in enumerate(tiles, start=1):\n", + " tile_id = os.path.splitext(os.path.basename(fname))[0]\n", + " print(f\"[{i}/{len(tiles)}] {tile_id}\")\n", + "\n", + " t0 = time.perf_counter()\n", + " gpu_free_before = _gpu_free_gb()\n", + "\n", + " rec = {\n", + " \"folder\": os.path.basename(folder),\n", + " \"tile_id\": tile_id,\n", + " \"fname\": fname,\n", + " \"status\": None,\n", + "\n", + " \"crs\": str(crs),\n", + " \"pixel_size_units_per_px\": gsd_units,\n", + " \"pixel_size_mm_per_px\": gsd_mm,\n", + " \"minor_mm_threshold\": minor_mm,\n", + " \"minor_px_threshold\": minor_px,\n", + " \"min_area_px\": min_area_px,\n", + "\n", + " \"H\": None,\n", + " \"W\": None,\n", + " \"n_prompts_before\": None,\n", + " \"n_prompts_used\": None,\n", + " \"prompt_cap_used\": None,\n", + " \"n_grains\": None,\n", + "\n", + " \"t_unet_s\": None,\n", + " \"t_label_s\": None,\n", + " \"t_sam_s\": None,\n", + " \"t_export_s\": None,\n", + " \"t_total_s\": None,\n", + "\n", + " \"gpu_free_gb_before\": gpu_free_before,\n", + " \"gpu_free_gb_after\": None,\n", + "\n", + " \"error_msg\": None,\n", + " \"traceback_head\": None,\n", + " }\n", + "\n", + " try:\n", + " # ---- nodata check ----\n", + " with rasterio.open(fname) as src:\n", + " m = src.dataset_mask()\n", + " if not np.any(m):\n", + " print(\" -> skipped (100% Nodata)\")\n", + " rec[\"status\"] = \"skip_nodata\"\n", + " rec[\"t_total_s\"] = time.perf_counter() - t0\n", + " rec[\"gpu_free_gb_after\"] = _gpu_free_gb()\n", + " rows.append(rec)\n", + " continue\n", + "\n", + " # ---- load + predict (UNet) ----\n", + " t = time.perf_counter()\n", + " image = np.array(load_img(fname))\n", + " rec[\"H\"], rec[\"W\"] = int(image.shape[0]), int(image.shape[1])\n", + " image_pred = seg.predict_image(image, unet, I=256)\n", + " rec[\"t_unet_s\"] = time.perf_counter() - t\n", + "\n", + " # ---- prompts ----\n", + " t = time.perf_counter()\n", + " labels, coords = seg.label_grains(image, image_pred, dbs_max_dist=10.0)\n", + " rec[\"t_label_s\"] = time.perf_counter() - t\n", + "\n", + " coords = np.asarray(coords)\n", + " rec[\"n_prompts_before\"] = int(len(coords))\n", + " rec[\"prompt_cap_used\"] = False\n", + "\n", + " if MAX_SAM_PROMPTS is not None and len(coords) > MAX_SAM_PROMPTS:\n", + " rec[\"prompt_cap_used\"] = True\n", + " n_before = len(coords)\n", + "\n", + " if PROMPT_SUBSAMPLE_MODE == \"first\":\n", + " keep_idx = np.arange(MAX_SAM_PROMPTS)\n", + " else:\n", + " keep_idx = np.sort(rng.choice(len(coords), size=MAX_SAM_PROMPTS, replace=False))\n", + "\n", + " coords = coords[keep_idx]\n", + "\n", + " try:\n", + " labels_arr = np.asarray(labels)\n", + " if labels_arr.ndim == 1 and len(labels_arr) == n_before:\n", + " labels = labels_arr[keep_idx]\n", + " except Exception:\n", + " pass\n", + "\n", + " print(f\"Prompt cap active: reduced prompts from {n_before} -> {len(coords)}\")\n", + "\n", + " rec[\"n_prompts_used\"] = int(len(coords))\n", + "\n", + " # ---- SAM segmentation ----\n", + " t = time.perf_counter()\n", + " all_grains, labels, mask_all, grain_data, fig, ax = seg.sam_segmentation(\n", + " sam,\n", + " image,\n", + " image_pred,\n", + " coords,\n", + " labels,\n", + " min_area=min_area_px,\n", + " plot_image=True,\n", + " remove_edge_grains=True,\n", + " remove_large_objects=True,\n", + " )\n", + " rec[\"t_sam_s\"] = time.perf_counter() - t\n", + " rec[\"n_grains\"] = int(len(all_grains))\n", + "\n", + " # ---- export/post ----\n", + " t = time.perf_counter()\n", + "\n", + " # 1) segmentation plot (fallback if fig None)\n", + " seg_plot_path = os.path.join(plotdirgravel, f\"{tile_id}.png\")\n", + " if fig is None:\n", + " fig, ax = plt.subplots(figsize=(8, 8))\n", + " ax.imshow(image)\n", + " for poly in all_grains:\n", + " x, y = poly.exterior.xy\n", + " ax.plot(x, y, linewidth=0.8)\n", + " ax.set_title(tile_id)\n", + " ax.axis(\"off\")\n", + "\n", + " fig.savefig(seg_plot_path, dpi=200, bbox_inches=\"tight\")\n", + " plt.close(fig)\n", + " print(\"Saved segmentation plot\")\n", + "\n", + " # 2) georef polygons + stats\n", + " with rasterio.open(fname) as dataset:\n", + " projected_polys = []\n", + " for grain in all_grains:\n", + " px_x = np.asarray(grain.exterior.xy[0])\n", + " px_y = np.asarray(grain.exterior.xy[1])\n", + "\n", + " x_proj, y_proj = rasterio.transform.xy(dataset.transform, px_y, px_x)\n", + " poly = Polygon(np.vstack((x_proj, y_proj)).T)\n", + " projected_polys.append(poly)\n", + "\n", + " gdf = gpd.GeoDataFrame({\"geometry\": projected_polys}, geometry=\"geometry\", crs=dataset.crs)\n", + "\n", + " props = regionprops_table(\n", + " labels.astype(\"int\"),\n", + " properties=(\"label\", \"area\", \"centroid\", \"major_axis_length\", \"minor_axis_length\"),\n", + " )\n", + " grain_stats = pd.DataFrame(props)\n", + "\n", + " if len(grain_stats) != len(gdf):\n", + " nmin = min(len(grain_stats), len(gdf))\n", + " print(f\"Warning: len(gdf)={len(gdf)} != len(grain_stats)={len(grain_stats)} -> truncating to {nmin}\")\n", + " gdf = gdf.iloc[:nmin].copy()\n", + " grain_stats = grain_stats.iloc[:nmin].copy()\n", + "\n", + " centroid_x, centroid_y = rasterio.transform.xy(\n", + " dataset.transform,\n", + " grain_stats[\"centroid-0\"].values,\n", + " grain_stats[\"centroid-1\"].values,\n", + " )\n", + "\n", + " px_size_x = abs(dataset.transform.a)\n", + "\n", + " gdf[\"label\"] = grain_stats[\"label\"].values\n", + " gdf[\"area_px\"] = grain_stats[\"area\"].values\n", + " gdf[\"centroid_x\"] = centroid_x\n", + " gdf[\"centroid_y\"] = centroid_y\n", + " gdf[\"major_axis_px\"] = grain_stats[\"major_axis_length\"].values\n", + " gdf[\"minor_axis_px\"] = grain_stats[\"minor_axis_length\"].values\n", + " gdf[\"major_axis_length\"] = grain_stats[\"major_axis_length\"].values * px_size_x\n", + " gdf[\"minor_axis_length\"] = grain_stats[\"minor_axis_length\"].values * px_size_x\n", + " gdf[\"major_axis_mm\"] = gdf[\"major_axis_length\"] * 1000.0\n", + " gdf[\"minor_axis_mm\"] = gdf[\"minor_axis_length\"] * 1000.0\n", + "\n", + " # 3) histogram plot\n", + " if len(gdf) > 0:\n", + " fig_hist, ax_hist = seg.plot_histogram_of_axis_lengths(\n", + " gdf[\"major_axis_mm\"],\n", + " gdf[\"minor_axis_mm\"],\n", + " binsize=0.25,\n", + " xlimits=[8, 2 * 256],\n", + " )\n", + " hist_plot_path = os.path.join(plotdirhist, f\"{tile_id}_hist.png\")\n", + " fig_hist.savefig(hist_plot_path, dpi=200, bbox_inches=\"tight\")\n", + " plt.close(fig_hist)\n", + " print(\"Saved histogram plot\")\n", + " else:\n", + " print(\"No grains found -> skipping histogram plot\")\n", + "\n", + " # 4) write gpkg + csv\n", + " gpkg_path = os.path.join(ouputgpkg, f\"{tile_id}_grains.gpkg\")\n", + " csv_path = os.path.join(csvdir, f\"{tile_id}_grain_stats.csv\")\n", + "\n", + " gdf.to_file(gpkg_path, driver=\"GPKG\")\n", + " gdf.drop(columns=\"geometry\").to_csv(csv_path, index=False)\n", + "\n", + " print(f\"Saved GPKG: {gpkg_path}\")\n", + " print(f\"Saved stats CSV: {csv_path}\")\n", + "\n", + " rec[\"t_export_s\"] = time.perf_counter() - t\n", + "\n", + " rec[\"status\"] = \"ok\"\n", + " rec[\"t_total_s\"] = time.perf_counter() - t0\n", + " rec[\"gpu_free_gb_after\"] = _gpu_free_gb()\n", + " rows.append(rec)\n", + "\n", + " print(\"done with segmentation + export\")\n", + "\n", + " except Exception as e:\n", + " rec[\"status\"] = \"error\"\n", + " rec[\"t_total_s\"] = time.perf_counter() - t0\n", + " rec[\"gpu_free_gb_after\"] = _gpu_free_gb()\n", + " rec[\"error_msg\"] = str(e)\n", + " rec[\"traceback_head\"] = traceback.format_exc(limit=8)\n", + " rows.append(rec)\n", + " print(\"ERROR on\", tile_id, \":\", e)\n", + "\n", + " # ---- save runtime metrics (Excel-friendly CSV) ----\n", + " df = pd.DataFrame(rows)\n", + "\n", + " metrics_csv = os.path.join(folder, \"runtime_metrics.csv\")\n", + " df.to_csv(metrics_csv, index=False)\n", + " print(\"Saved runtime metrics CSV:\", metrics_csv)\n", + "\n", + " # optional: quick describe\n", + " summary = df[df[\"status\"] == \"ok\"][[\"t_total_s\",\"t_unet_s\",\"t_label_s\",\"t_sam_s\",\"t_export_s\",\"n_prompts_used\",\"n_grains\"]].describe()\n", + " print(summary)\n", + "\n", + " # ---- paper-ready summary table (median-focused) ----\n", + " ok = df[df[\"status\"] == \"ok\"].copy()\n", + "\n", + " n_ok = len(ok)\n", + " total_s = ok[\"t_total_s\"].sum()\n", + " tiles_per_min = (n_ok / (total_s / 60.0)) if total_s > 0 else np.nan\n", + "\n", + " ready = pd.DataFrame({\n", + " \"metric\": [\n", + " \"n_tiles_total\",\n", + " \"n_tiles_ok\",\n", + " \"n_tiles_skipped_nodata\",\n", + " \"n_tiles_error\",\n", + " \"pixel_size_mm_per_px (median)\",\n", + " \"min_area_px (median)\",\n", + " \"total_runtime_min\",\n", + " \"tiles_per_min\",\n", + " \"total_s_per_tile (median)\",\n", + " \"unet_s (median)\",\n", + " \"label_s (median)\",\n", + " \"sam_s (median)\",\n", + " \"export_s (median)\",\n", + " \"prompts_used (median)\",\n", + " \"grains (median)\",\n", + " ],\n", + " \"value\": [\n", + " int(len(df)),\n", + " int(n_ok),\n", + " int((df[\"status\"] == \"skip_nodata\").sum()),\n", + " int((df[\"status\"] == \"error\").sum()),\n", + " float(ok[\"pixel_size_mm_per_px\"].median()) if n_ok else np.nan,\n", + " float(ok[\"min_area_px\"].median()) if n_ok else np.nan,\n", + " float(total_s / 60.0) if n_ok else np.nan,\n", + " float(tiles_per_min) if n_ok else np.nan,\n", + " float(ok[\"t_total_s\"].median()) if n_ok else np.nan,\n", + " float(ok[\"t_unet_s\"].median()) if n_ok else np.nan,\n", + " float(ok[\"t_label_s\"].median()) if n_ok else np.nan,\n", + " float(ok[\"t_sam_s\"].median()) if n_ok else np.nan,\n", + " float(ok[\"t_export_s\"].median()) if n_ok else np.nan,\n", + " float(ok[\"n_prompts_used\"].median()) if n_ok else np.nan,\n", + " float(ok[\"n_grains\"].median()) if n_ok else np.nan,\n", + " ]\n", + " })\n", + "\n", + " ready_disp = ready.copy()\n", + " ready_disp[\"value\"] = ready_disp[\"value\"].apply(\n", + " lambda v: round(v, 3) if isinstance(v, (float, np.floating)) and pd.notnull(v) else v\n", + " )\n", + " print(\"\\nREADY TABLE:\\n\")\n", + " print(ready_disp.to_string(index=False))\n", + "\n", + " ready_csv = os.path.join(folder, \"runtime_summary_ready_table.csv\")\n", + " ready.to_csv(ready_csv, index=False)\n", + " print(\"\\nSaved ready table CSV:\", ready_csv)\n", + "\n", + " return df, ready" + ] + }, + { + "cell_type": "markdown", + "id": "7615fed0", + "metadata": {}, + "source": [ + "# Set folder strucure" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "277720e8", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Found F folders: ['F18', 'F19', 'F19_2', 'F20', 'F22_1', 'F22_2', 'F23', 'F23_2', 'F24', 'F26', 'F27', 'F27_2', 'F28', 'F29', 'F30', 'F33', 'F34_1', 'F34_2', 'F38', 'F38_2', 'F39', 'F40', 'F41', 'F41_2', 'F42', 'F42_2']\n", + "OK already structured: F18\n", + "OK already structured: F19\n", + "OK already structured: F19_2\n", + "OK already structured: F20\n", + "OK already structured: F22_1\n", + "OK already structured: F22_2\n", + "OK already structured: F23\n", + "OK already structured: F23_2\n", + "OK already structured: F24\n", + "OK already structured: F26\n", + "OK already structured: F27\n", + "OK already structured: F27_2\n", + "OK already structured: F28\n", + "OK already structured: F29\n", + "OK already structured: F30\n", + "OK already structured: F33\n", + "OK already structured: F34_1\n", + "OK already structured: F34_2\n", + "OK already structured: F38\n", + "OK already structured: F38_2\n", + "OK already structured: F39\n", + "OK already structured: F40\n", + "OK already structured: F41\n", + "OK already structured: F41_2\n", + "OK already structured: F42\n", + "OK already structured: F42_2\n" + ] + } + ], + "source": [ + "import os, glob\n", + "\n", + "BASE = \"/dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain\"\n", + "subdirs = [\"inputtiles\", \"ouputgpkg\", \"pebbleplots\", \"histplots\", \"csv_stats\"]\n", + "\n", + "folders = sorted(glob.glob(os.path.join(BASE, \"F*\")))\n", + "print(\"Found F folders:\", [os.path.basename(f) for f in folders])\n", + "\n", + "for folder in folders:\n", + " made_any = False\n", + " for sd in subdirs:\n", + " path = os.path.join(folder, sd)\n", + " if not os.path.isdir(path):\n", + " os.makedirs(path, exist_ok=True)\n", + " made_any = True\n", + " if made_any:\n", + " print(\"Initialized structure:\", os.path.basename(folder))\n", + " else:\n", + " print(\"OK already structured:\", os.path.basename(folder))" + ] + }, + { + "cell_type": "markdown", + "id": "f9193000", + "metadata": {}, + "source": [ + "# Run segmentation" + ] + }, + { + "cell_type": "markdown", + "id": "df758347", + "metadata": {}, + "source": [ + "## Not including Masks and saving all statistiks, plots and images and gpkgs" + ] + }, + { + "cell_type": "markdown", + "id": "d6f27358", + "metadata": {}, + "source": [ + "For only one folder:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c6b8888c", + "metadata": {}, + "outputs": [], + "source": [ + "process_one_folder(\"/dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F9\")" + ] + }, + { + "cell_type": "markdown", + "id": "b1d0a4c9", + "metadata": {}, + "source": [ + "For processing over several folders (F-folders):" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "76cc409f", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "SKIP already has GPKG: F18 n_gpkg: 824\n", + "SKIP already has GPKG: F19 n_gpkg: 1000\n", + "SKIP already has GPKG: F19_2 n_gpkg: 105\n", + "SKIP already has GPKG: F20 n_gpkg: 715\n", + "PROCESS: F22_1 tiles: 1053\n", + "Found 1053 tiles in /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1 (folder root)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "CRS: EPSG:32643\n", + "Pixel size: 0.002806 units/px (~2.806 mm/px)\n", + "2 cm => minor_px=7.13 px -> min_area_px=39.9 px^2\n", + "[1/1053] tile_r000004_c000042\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + " 0%| | 0/5 [00:00 skipping histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000004_c000044_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000004_c000044_grain_stats.csv\n", + "done with segmentation + export\n", + "[4/1053] tile_r000005_c000041\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.42it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.37it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 199/199 [00:05<00:00, 37.26it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "156it [00:00, 401.70it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "162it [00:00, 396.37it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 64/64 [00:00<00:00, 107.49it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 66/66 [00:00<00:00, 202.84it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000005_c000041_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000005_c000041_grain_stats.csv\n", + "done with segmentation + export\n", + "[5/1053] tile_r000005_c000042\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.44it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.43it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 801/801 [00:20<00:00, 38.84it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "727it [00:02, 303.30it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "745it [00:02, 304.71it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 268/268 [00:03<00:00, 79.76it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 279/279 [00:01<00:00, 198.91it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000005_c000042_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000005_c000042_grain_stats.csv\n", + "done with segmentation + export\n", + "[6/1053] tile_r000005_c000043\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.39it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.45it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 916/916 [00:22<00:00, 39.89it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "789it [00:02, 330.21it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "811it [00:02, 332.54it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 301/301 [00:03<00:00, 82.34it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 318/318 [00:01<00:00, 204.14it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n", + "Warning: len(gdf)=318 != len(grain_stats)=316 -> truncating to 316\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000005_c000043_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000005_c000043_grain_stats.csv\n", + "done with segmentation + export\n", + "[7/1053] tile_r000005_c000044\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.47it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.47it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 723/723 [00:21<00:00, 34.09it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "615it [00:01, 539.55it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "638it [00:01, 534.27it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 268/268 [00:01<00:00, 163.41it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 271/271 [00:01<00:00, 254.89it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000005_c000044_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000005_c000044_grain_stats.csv\n", + "done with segmentation + export\n", + "[8/1053] tile_r000005_c000045\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.44it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.47it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 17/17 [00:00<00:00, 24.65it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2it [00:00, 737.20it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2it [00:00, 956.95it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 1/1 [00:00<00:00, 341.92it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 1/1 [00:00<00:00, 183.29it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000005_c000045_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000005_c000045_grain_stats.csv\n", + "done with segmentation + export\n", + "[9/1053] tile_r000006_c000039\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.43it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.42it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 84/84 [00:02<00:00, 30.45it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "34it [00:00, 522.92it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "35it [00:00, 560.46it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 14/14 [00:00<00:00, 161.61it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 14/14 [00:00<00:00, 213.33it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000006_c000039_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000006_c000039_grain_stats.csv\n", + "done with segmentation + export\n", + "[10/1053] tile_r000006_c000040\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.45it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.43it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 978/978 [00:27<00:00, 36.07it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "886it [00:01, 459.03it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "916it [00:01, 462.27it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 378/378 [00:02<00:00, 163.59it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 387/387 [00:01<00:00, 258.79it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000006_c000040_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000006_c000040_grain_stats.csv\n", + "done with segmentation + export\n", + "[11/1053] tile_r000006_c000041\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.48it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.49it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 1237/1237 [00:31<00:00, 39.14it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "1126it [00:02, 515.63it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "1159it [00:02, 505.06it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 494/494 [00:02<00:00, 178.16it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 499/499 [00:02<00:00, 248.19it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000006_c000041_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000006_c000041_grain_stats.csv\n", + "done with segmentation + export\n", + "[12/1053] tile_r000006_c000042\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.47it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.47it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 1347/1347 [00:34<00:00, 39.11it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "1227it [00:02, 492.26it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "1225it [00:02, 518.98it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 487/487 [00:02<00:00, 162.99it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 536/536 [00:02<00:00, 263.00it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000006_c000042_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000006_c000042_grain_stats.csv\n", + "done with segmentation + export\n", + "[13/1053] tile_r000006_c000043\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.43it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.44it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 1494/1494 [00:37<00:00, 39.32it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "1358it [00:01, 696.63it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "1396it [00:02, 686.42it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 609/609 [00:02<00:00, 237.87it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 619/619 [00:02<00:00, 278.97it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000006_c000043_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000006_c000043_grain_stats.csv\n", + "done with segmentation + export\n", + "[14/1053] tile_r000006_c000044\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.39it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.39it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 1609/1609 [00:41<00:00, 38.50it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "1453it [00:02, 614.63it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "1449it [00:02, 649.88it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 606/606 [00:03<00:00, 191.30it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 662/662 [00:02<00:00, 272.75it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n", + "Warning: len(gdf)=662 != len(grain_stats)=660 -> truncating to 660\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000006_c000044_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000006_c000044_grain_stats.csv\n", + "done with segmentation + export\n", + "[15/1053] tile_r000006_c000045\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.36it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.42it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 954/954 [00:27<00:00, 34.35it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "822it [00:01, 561.69it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "820it [00:01, 694.72it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 330/330 [00:01<00:00, 169.34it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 371/371 [00:01<00:00, 189.97it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000006_c000045_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000006_c000045_grain_stats.csv\n", + "done with segmentation + export\n", + "[16/1053] tile_r000006_c000046\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.38it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.33it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 62/62 [00:01<00:00, 31.67it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "32it [00:00, 389.30it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "33it [00:00, 398.68it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 12/12 [00:00<00:00, 117.96it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\n", + "100%|██████████| 12/12 [00:00<00:00, 224.61it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000006_c000046_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000006_c000046_grain_stats.csv\n", + "done with segmentation + export\n", + "[17/1053] tile_r000007_c000037\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.34it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.40it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:00<00:00, 16.85it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "0it [00:00, ?it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "0it [00:00, ?it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "0it [00:00, ?it/s]\n", + "0it [00:00, ?it/s]\n", + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n", + "No grains found -> skipping histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000007_c000037_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000007_c000037_grain_stats.csv\n", + "done with segmentation + export\n", + "[18/1053] tile_r000007_c000038\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.38it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.40it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 233/233 [00:06<00:00, 34.97it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "177it [00:00, 382.48it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "189it [00:00, 382.26it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 66/66 [00:00<00:00, 82.35it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 69/69 [00:00<00:00, 201.56it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n", + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000007_c000038_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000007_c000038_grain_stats.csv\n", + "done with segmentation + export\n", + "[19/1053] tile_r000007_c000039\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.42it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.38it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 747/747 [00:19<00:00, 37.36it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "635it [00:01, 483.17it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "666it [00:01, 476.62it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 253/253 [00:02<00:00, 110.55it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 276/276 [00:01<00:00, 236.32it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n", + "Warning: len(gdf)=276 != len(grain_stats)=275 -> truncating to 275\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000007_c000039_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000007_c000039_grain_stats.csv\n", + "done with segmentation + export\n", + "[20/1053] tile_r000007_c000040\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.37it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.40it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 935/935 [00:24<00:00, 37.90it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "810it [00:01, 527.89it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "859it [00:01, 523.33it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 341/341 [00:02<00:00, 133.72it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 356/356 [00:01<00:00, 241.35it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000007_c000040_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000007_c000040_grain_stats.csv\n", + "done with segmentation + export\n", + "[21/1053] tile_r000007_c000041\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 1.84it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.41it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 1152/1152 [00:30<00:00, 38.00it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "1014it [00:01, 513.04it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "1083it [00:02, 505.28it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 431/431 [00:02<00:00, 157.68it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 442/442 [00:01<00:00, 252.13it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n", + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000007_c000041_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000007_c000041_grain_stats.csv\n", + "done with segmentation + export\n", + "[22/1053] tile_r000007_c000042\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.37it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.40it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 1276/1276 [00:33<00:00, 38.36it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "1149it [00:02, 472.07it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "1147it [00:02, 485.04it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 432/432 [00:02<00:00, 148.95it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 522/522 [00:02<00:00, 255.57it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000007_c000042_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000007_c000042_grain_stats.csv\n", + "done with segmentation + export\n", + "[23/1053] tile_r000007_c000043\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.37it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.44it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 1335/1335 [00:34<00:00, 38.40it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "1188it [00:02, 501.84it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "1261it [00:02, 495.55it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 510/510 [00:03<00:00, 156.31it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 522/522 [00:02<00:00, 250.56it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000007_c000043_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000007_c000043_grain_stats.csv\n", + "done with segmentation + export\n", + "[24/1053] tile_r000007_c000044\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.35it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.39it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 1639/1639 [00:43<00:00, 37.98it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "1486it [00:03, 444.11it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "1544it [00:03, 434.68it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 621/621 [00:04<00:00, 143.10it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 645/645 [00:02<00:00, 250.47it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000007_c000044_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000007_c000044_grain_stats.csv\n", + "done with segmentation + export\n", + "[25/1053] tile_r000007_c000045\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.41it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.45it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 1453/1453 [00:36<00:00, 39.38it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "1333it [00:03, 417.33it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "1330it [00:03, 439.30it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 504/504 [00:03<00:00, 127.28it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 584/584 [00:02<00:00, 242.82it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsshome1/0B/di54doz/.local/share/mamba/envs/segmenteverygrain/lib/python3.10/site-packages/segmenteverygrain/segmenteverygrain.py:2217: RuntimeWarning: divide by zero encountered in log2\n", + " phi_major = -np.log2(major_axis_length) # major axis length in phi scale\n", + "/dss/dsshome1/0B/di54doz/.local/share/mamba/envs/segmenteverygrain/lib/python3.10/site-packages/segmenteverygrain/segmenteverygrain.py:2218: RuntimeWarning: divide by zero encountered in log2\n", + " phi_minor = -np.log2(minor_axis_length)\n", + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000007_c000045_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000007_c000045_grain_stats.csv\n", + "done with segmentation + export\n", + "[26/1053] tile_r000007_c000046\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.31it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.33it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 1361/1361 [00:36<00:00, 37.45it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "1223it [00:02, 446.14it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "1274it [00:02, 439.35it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 522/522 [00:03<00:00, 164.97it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 533/533 [00:03<00:00, 172.32it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000007_c000046_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000007_c000046_grain_stats.csv\n", + "done with segmentation + export\n", + "[27/1053] tile_r000007_c000047\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.40it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.40it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 163/163 [00:04<00:00, 34.50it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "107it [00:00, 417.04it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "110it [00:00, 415.34it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 44/44 [00:00<00:00, 142.97it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 45/45 [00:00<00:00, 169.45it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000007_c000047_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000007_c000047_grain_stats.csv\n", + "done with segmentation + export\n", + "[28/1053] tile_r000008_c000036\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.35it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.36it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 179/179 [00:05<00:00, 34.86it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "127it [00:00, 327.63it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "129it [00:00, 336.86it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 42/42 [00:00<00:00, 63.35it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 45/45 [00:00<00:00, 190.79it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000008_c000036_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000008_c000036_grain_stats.csv\n", + "done with segmentation + export\n", + "[29/1053] tile_r000008_c000037\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.35it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.40it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 610/610 [00:18<00:00, 33.39it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "487it [00:01, 316.01it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "518it [00:01, 313.90it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 182/182 [00:02<00:00, 77.15it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 189/189 [00:00<00:00, 199.46it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000008_c000037_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000008_c000037_grain_stats.csv\n", + "done with segmentation + export\n", + "[30/1053] tile_r000008_c000038\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.36it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.44it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 764/764 [00:19<00:00, 38.80it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "655it [00:01, 379.47it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "706it [00:01, 380.41it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 259/259 [00:02<00:00, 96.06it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 265/265 [00:01<00:00, 210.40it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000008_c000038_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000008_c000038_grain_stats.csv\n", + "done with segmentation + export\n", + "[31/1053] tile_r000008_c000039\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.42it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.47it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 900/900 [00:23<00:00, 38.73it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "795it [00:02, 369.65it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "850it [00:02, 364.02it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 319/319 [00:03<00:00, 104.93it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 334/334 [00:01<00:00, 213.66it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000008_c000039_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000008_c000039_grain_stats.csv\n", + "done with segmentation + export\n", + "[32/1053] tile_r000008_c000040\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.45it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.47it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 1075/1075 [00:27<00:00, 39.09it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "941it [00:03, 311.76it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "997it [00:03, 308.02it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 354/354 [00:04<00:00, 84.83it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 373/373 [00:01<00:00, 214.43it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000008_c000040_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000008_c000040_grain_stats.csv\n", + "done with segmentation + export\n", + "[33/1053] tile_r000008_c000041\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.47it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.50it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 1281/1281 [00:32<00:00, 39.53it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "1156it [00:03, 346.15it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "1154it [00:03, 349.71it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 391/391 [00:04<00:00, 96.04it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 469/469 [00:03<00:00, 147.22it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000008_c000041_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000008_c000041_grain_stats.csv\n", + "done with segmentation + export\n", + "[34/1053] tile_r000008_c000042\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.47it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.40it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 1315/1315 [00:34<00:00, 38.65it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "1128it [00:03, 304.74it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "1127it [00:03, 310.29it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 362/362 [00:04<00:00, 75.11it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 450/450 [00:01<00:00, 225.38it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000008_c000042_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000008_c000042_grain_stats.csv\n", + "done with segmentation + export\n", + "[35/1053] tile_r000008_c000043\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.43it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.46it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 1143/1143 [00:29<00:00, 39.35it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "994it [00:02, 361.29it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "1055it [00:02, 353.80it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 380/380 [00:03<00:00, 100.01it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 399/399 [00:01<00:00, 223.39it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000008_c000043_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000008_c000043_grain_stats.csv\n", + "done with segmentation + export\n", + "[36/1053] tile_r000008_c000044\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.50it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.42it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 1437/1437 [00:36<00:00, 39.66it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "1268it [00:03, 352.01it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "1265it [00:03, 368.58it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 458/458 [00:03<00:00, 115.06it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 531/531 [00:02<00:00, 229.97it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000008_c000044_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000008_c000044_grain_stats.csv\n", + "done with segmentation + export\n", + "[37/1053] tile_r000008_c000045\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.47it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.54it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 1283/1283 [00:32<00:00, 39.71it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "1147it [00:04, 246.63it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "1135it [00:03, 295.34it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 374/374 [00:05<00:00, 73.99it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 445/445 [00:01<00:00, 225.56it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000008_c000045_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000008_c000045_grain_stats.csv\n", + "done with segmentation + export\n", + "[38/1053] tile_r000008_c000046\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.48it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.46it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 1429/1429 [00:35<00:00, 40.22it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "1280it [00:03, 332.28it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "1275it [00:03, 346.93it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 452/452 [00:04<00:00, 94.87it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 530/530 [00:02<00:00, 235.06it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000008_c000046_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000008_c000046_grain_stats.csv\n", + "done with segmentation + export\n", + "[39/1053] tile_r000008_c000047\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.46it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.47it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 1379/1379 [00:36<00:00, 37.58it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "1230it [00:03, 311.74it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "1225it [00:03, 339.79it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 446/446 [00:04<00:00, 102.73it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 509/509 [00:02<00:00, 232.60it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000008_c000047_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000008_c000047_grain_stats.csv\n", + "done with segmentation + export\n", + "[40/1053] tile_r000008_c000048\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.47it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.47it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 213/213 [00:05<00:00, 36.00it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "159it [00:00, 446.06it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "171it [00:00, 439.41it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 71/71 [00:00<00:00, 156.18it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 72/72 [00:00<00:00, 233.99it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000008_c000048_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000008_c000048_grain_stats.csv\n", + "done with segmentation + export\n", + "[41/1053] tile_r000009_c000034\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.46it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.50it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 56/56 [00:01<00:00, 32.91it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "32it [00:00, 191.12it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "35it [00:00, 195.63it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 11/11 [00:00<00:00, 46.52it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 14/14 [00:00<00:00, 181.22it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000009_c000034_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000009_c000034_grain_stats.csv\n", + "done with segmentation + export\n", + "[42/1053] tile_r000009_c000035\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.48it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.51it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 525/525 [00:15<00:00, 34.49it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "428it [00:02, 208.75it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "424it [00:01, 236.68it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 134/134 [00:02<00:00, 61.91it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 159/159 [00:00<00:00, 181.06it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000009_c000035_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000009_c000035_grain_stats.csv\n", + "done with segmentation + export\n", + "[43/1053] tile_r000009_c000036\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:01<00:00, 2.51it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.54it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 564/564 [00:14<00:00, 39.34it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "466it [00:01, 316.65it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "493it [00:01, 313.53it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 168/168 [00:02<00:00, 68.55it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 174/174 [00:00<00:00, 191.30it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000009_c000036_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000009_c000036_grain_stats.csv\n", + "done with segmentation + export\n", + "[44/1053] tile_r000009_c000037\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:01<00:00, 2.53it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.47it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 692/692 [00:17<00:00, 38.75it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "598it [00:02, 270.15it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "629it [00:02, 271.47it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 204/204 [00:03<00:00, 62.21it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 216/216 [00:01<00:00, 189.42it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000009_c000037_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000009_c000037_grain_stats.csv\n", + "done with segmentation + export\n", + "[45/1053] tile_r000009_c000038\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:01<00:00, 2.54it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.52it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 648/648 [00:16<00:00, 39.65it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "542it [00:01, 306.32it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "563it [00:01, 303.45it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 192/192 [00:02<00:00, 68.45it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 203/203 [00:01<00:00, 189.43it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000009_c000038_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000009_c000038_grain_stats.csv\n", + "done with segmentation + export\n", + "[46/1053] tile_r000009_c000039\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:01<00:00, 2.50it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.48it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 942/942 [00:23<00:00, 39.91it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "779it [00:02, 269.79it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "815it [00:03, 268.67it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 281/281 [00:04<00:00, 69.32it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 294/294 [00:01<00:00, 205.59it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000009_c000039_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000009_c000039_grain_stats.csv\n", + "done with segmentation + export\n", + "[47/1053] tile_r000009_c000040\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:01<00:00, 2.53it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.52it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 918/918 [00:23<00:00, 39.58it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "796it [00:03, 235.33it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "832it [00:03, 237.63it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 264/264 [00:04<00:00, 54.23it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 282/282 [00:01<00:00, 194.44it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000009_c000040_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000009_c000040_grain_stats.csv\n", + "done with segmentation + export\n", + "[48/1053] tile_r000009_c000041\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:01<00:00, 2.51it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.56it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 1016/1016 [00:25<00:00, 39.26it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "859it [00:03, 274.54it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "858it [00:03, 278.93it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 271/271 [00:04<00:00, 65.97it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 324/324 [00:03<00:00, 106.42it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000009_c000041_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000009_c000041_grain_stats.csv\n", + "done with segmentation + export\n", + "[49/1053] tile_r000009_c000042\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.47it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.51it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 1202/1202 [00:30<00:00, 39.04it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "1053it [00:03, 315.26it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "1115it [00:03, 312.82it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 402/402 [00:04<00:00, 92.00it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 414/414 [00:01<00:00, 217.33it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000009_c000042_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000009_c000042_grain_stats.csv\n", + "done with segmentation + export\n", + "[50/1053] tile_r000009_c000043\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.50it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.50it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 1075/1075 [00:27<00:00, 39.60it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "935it [00:03, 242.18it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "934it [00:03, 247.76it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 298/298 [00:04<00:00, 64.12it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 355/355 [00:01<00:00, 202.42it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000009_c000043_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000009_c000043_grain_stats.csv\n", + "done with segmentation + export\n", + "[51/1053] tile_r000009_c000044\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.48it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.49it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 1433/1433 [00:35<00:00, 40.51it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "1276it [00:03, 329.99it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "1274it [00:03, 337.96it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 449/449 [00:04<00:00, 105.99it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 516/516 [00:02<00:00, 224.77it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000009_c000044_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000009_c000044_grain_stats.csv\n", + "done with segmentation + export\n", + "[52/1053] tile_r000009_c000045\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.44it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.43it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 1216/1216 [00:30<00:00, 39.35it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "1094it [00:02, 368.01it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "1148it [00:03, 362.36it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 445/445 [00:03<00:00, 121.48it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 458/458 [00:02<00:00, 222.90it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000009_c000045_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000009_c000045_grain_stats.csv\n", + "done with segmentation + export\n", + "[53/1053] tile_r000009_c000046\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.46it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.55it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 1157/1157 [00:29<00:00, 38.67it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "1040it [00:03, 320.41it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "1036it [00:03, 338.34it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 353/353 [00:03<00:00, 90.54it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 431/431 [00:01<00:00, 220.16it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000009_c000046_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000009_c000046_grain_stats.csv\n", + "done with segmentation + export\n", + "[54/1053] tile_r000009_c000047\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.49it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.50it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 1311/1311 [00:32<00:00, 40.33it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "1176it [00:03, 298.04it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "1175it [00:03, 306.51it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 408/408 [00:04<00:00, 88.16it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 476/476 [00:02<00:00, 227.01it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000009_c000047_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000009_c000047_grain_stats.csv\n", + "done with segmentation + export\n", + "[55/1053] tile_r000009_c000048\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.47it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.51it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 1430/1430 [00:35<00:00, 40.35it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "1300it [00:03, 389.20it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "1336it [00:03, 388.80it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 538/538 [00:03<00:00, 146.12it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 548/548 [00:02<00:00, 242.04it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000009_c000048_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000009_c000048_grain_stats.csv\n", + "done with segmentation + export\n", + "[56/1053] tile_r000009_c000049\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.45it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.49it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 313/313 [00:08<00:00, 37.43it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "253it [00:00, 387.42it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "261it [00:00, 387.66it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 107/107 [00:00<00:00, 148.88it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 107/107 [00:00<00:00, 216.02it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000009_c000049_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000009_c000049_grain_stats.csv\n", + "done with segmentation + export\n", + "[57/1053] tile_r000010_c000032\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.45it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.50it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 8/8 [00:00<00:00, 20.24it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "0it [00:00, ?it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "0it [00:00, ?it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "0it [00:00, ?it/s]\n", + "0it [00:00, ?it/s]\n", + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n", + "No grains found -> skipping histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000010_c000032_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000010_c000032_grain_stats.csv\n", + "done with segmentation + export\n", + "[58/1053] tile_r000010_c000033\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.47it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.44it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 421/421 [00:11<00:00, 37.63it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "336it [00:01, 220.91it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "358it [00:01, 222.84it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 128/128 [00:02<00:00, 63.64it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 130/130 [00:00<00:00, 187.68it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000010_c000033_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000010_c000033_grain_stats.csv\n", + "done with segmentation + export\n", + "[59/1053] tile_r000010_c000034\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.48it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.48it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 891/891 [00:23<00:00, 37.99it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "779it [00:02, 297.96it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "822it [00:02, 294.74it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 290/290 [00:03<00:00, 76.88it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 308/308 [00:01<00:00, 197.06it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000010_c000034_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000010_c000034_grain_stats.csv\n", + "done with segmentation + export\n", + "[60/1053] tile_r000010_c000035\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.48it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.52it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 792/792 [00:19<00:00, 40.28it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "690it [00:02, 280.53it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "728it [00:02, 282.16it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 247/247 [00:03<00:00, 70.43it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 260/260 [00:01<00:00, 190.60it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n", + "Warning: len(gdf)=260 != len(grain_stats)=259 -> truncating to 259\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000010_c000035_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000010_c000035_grain_stats.csv\n", + "done with segmentation + export\n", + "[61/1053] tile_r000010_c000036\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.49it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.48it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 515/515 [00:13<00:00, 38.70it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "426it [00:01, 300.42it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "449it [00:01, 298.09it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 150/150 [00:02<00:00, 60.44it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 158/158 [00:00<00:00, 180.10it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000010_c000036_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000010_c000036_grain_stats.csv\n", + "done with segmentation + export\n", + "[62/1053] tile_r000010_c000037\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.49it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.48it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 650/650 [00:16<00:00, 38.75it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "529it [00:01, 269.68it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "549it [00:02, 269.05it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 181/181 [00:02<00:00, 64.15it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 188/188 [00:00<00:00, 190.23it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000010_c000037_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000010_c000037_grain_stats.csv\n", + "done with segmentation + export\n", + "[63/1053] tile_r000010_c000038\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:01<00:00, 2.51it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.53it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 714/714 [00:18<00:00, 39.64it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "583it [00:01, 316.37it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "612it [00:01, 309.55it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 210/210 [00:02<00:00, 75.53it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 218/218 [00:01<00:00, 193.48it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000010_c000038_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000010_c000038_grain_stats.csv\n", + "done with segmentation + export\n", + "[64/1053] tile_r000010_c000039\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.46it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.19it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 693/693 [00:18<00:00, 38.43it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "578it [00:02, 275.83it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "606it [00:02, 276.03it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 209/209 [00:03<00:00, 69.19it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 218/218 [00:01<00:00, 193.38it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000010_c000039_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000010_c000039_grain_stats.csv\n", + "done with segmentation + export\n", + "[65/1053] tile_r000010_c000040\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.49it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.54it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 794/794 [00:20<00:00, 38.11it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "680it [00:02, 289.10it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "679it [00:02, 290.27it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 211/211 [00:03<00:00, 67.42it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 267/267 [00:01<00:00, 195.40it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000010_c000040_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000010_c000040_grain_stats.csv\n", + "done with segmentation + export\n", + "[66/1053] tile_r000010_c000041\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.29it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.44it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 988/988 [00:24<00:00, 39.86it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "870it [00:02, 292.47it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "869it [00:02, 297.24it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 275/275 [00:03<00:00, 71.02it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 338/338 [00:01<00:00, 201.62it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000010_c000041_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000010_c000041_grain_stats.csv\n", + "done with segmentation + export\n", + "[67/1053] tile_r000010_c000042\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:01<00:00, 2.50it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.49it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 1018/1018 [00:25<00:00, 39.93it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "878it [00:03, 272.91it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "877it [00:03, 279.32it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 284/284 [00:04<00:00, 70.14it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 332/332 [00:01<00:00, 197.76it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000010_c000042_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000010_c000042_grain_stats.csv\n", + "done with segmentation + export\n", + "[68/1053] tile_r000010_c000043\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:01<00:00, 2.50it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.48it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 1024/1024 [00:26<00:00, 39.13it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "875it [00:02, 315.06it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "920it [00:02, 311.94it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 341/341 [00:03<00:00, 92.63it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 350/350 [00:01<00:00, 204.78it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000010_c000043_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000010_c000043_grain_stats.csv\n", + "done with segmentation + export\n", + "[69/1053] tile_r000010_c000044\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.48it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.50it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 1223/1223 [00:30<00:00, 40.06it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "1106it [00:03, 287.09it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "1148it [00:04, 286.07it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 412/412 [00:04<00:00, 86.61it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 425/425 [00:02<00:00, 210.58it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000010_c000044_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000010_c000044_grain_stats.csv\n", + "done with segmentation + export\n", + "[70/1053] tile_r000010_c000045\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.45it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.51it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 1088/1088 [00:27<00:00, 40.29it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "957it [00:03, 274.97it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "953it [00:03, 286.84it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 306/306 [00:04<00:00, 73.49it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 380/380 [00:01<00:00, 201.25it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsshome1/0B/di54doz/.local/share/mamba/envs/segmenteverygrain/lib/python3.10/site-packages/segmenteverygrain/segmenteverygrain.py:2218: RuntimeWarning: divide by zero encountered in log2\n", + " phi_minor = -np.log2(minor_axis_length)\n", + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000010_c000045_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000010_c000045_grain_stats.csv\n", + "done with segmentation + export\n", + "[71/1053] tile_r000010_c000046\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.44it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.47it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 1167/1167 [00:29<00:00, 39.78it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "1031it [00:03, 277.76it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "1028it [00:03, 282.85it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 347/347 [00:04<00:00, 75.42it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 411/411 [00:01<00:00, 214.44it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000010_c000046_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000010_c000046_grain_stats.csv\n", + "done with segmentation + export\n", + "[72/1053] tile_r000010_c000047\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.50it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.46it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 1256/1256 [00:32<00:00, 38.20it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "1083it [00:03, 320.30it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "1139it [00:03, 315.16it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 423/423 [00:04<00:00, 99.15it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 438/438 [00:02<00:00, 218.82it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000010_c000047_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000010_c000047_grain_stats.csv\n", + "done with segmentation + export\n", + "[73/1053] tile_r000010_c000048\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:01<00:00, 2.54it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.55it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 1277/1277 [00:31<00:00, 40.61it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "1118it [00:03, 312.79it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "1116it [00:03, 318.74it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 378/378 [00:04<00:00, 84.70it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 449/449 [00:02<00:00, 219.67it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000010_c000048_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000010_c000048_grain_stats.csv\n", + "done with segmentation + export\n", + "[74/1053] tile_r000010_c000049\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:01<00:00, 2.53it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.49it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 1456/1456 [00:35<00:00, 40.65it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "1305it [00:03, 350.40it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "1337it [00:03, 350.20it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 518/518 [00:04<00:00, 121.49it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 532/532 [00:02<00:00, 230.90it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000010_c000049_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000010_c000049_grain_stats.csv\n", + "done with segmentation + export\n", + "[75/1053] tile_r000010_c000050\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:01<00:00, 2.52it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.51it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 481/481 [00:12<00:00, 39.58it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "410it [00:01, 359.42it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "424it [00:01, 361.80it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 172/172 [00:01<00:00, 140.88it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 178/178 [00:00<00:00, 225.85it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000010_c000050_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000010_c000050_grain_stats.csv\n", + "done with segmentation + export\n", + "[76/1053] tile_r000011_c000031\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.46it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.49it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 259/259 [00:06<00:00, 38.41it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "201it [00:00, 342.64it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "209it [00:00, 341.48it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 77/77 [00:00<00:00, 108.22it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 81/81 [00:00<00:00, 198.52it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000011_c000031_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000011_c000031_grain_stats.csv\n", + "done with segmentation + export\n", + "[77/1053] tile_r000011_c000032\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:01<00:00, 2.50it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.50it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 818/818 [00:20<00:00, 39.45it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "717it [00:02, 247.98it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "743it [00:03, 247.60it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 253/253 [00:03<00:00, 72.00it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 267/267 [00:01<00:00, 190.58it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000011_c000032_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000011_c000032_grain_stats.csv\n", + "done with segmentation + export\n", + "[78/1053] tile_r000011_c000033\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:01<00:00, 2.53it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.55it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 1085/1085 [00:26<00:00, 40.42it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "954it [00:03, 301.02it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "953it [00:03, 304.07it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 317/317 [00:03<00:00, 84.59it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 369/369 [00:01<00:00, 205.63it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000011_c000033_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000011_c000033_grain_stats.csv\n", + "done with segmentation + export\n", + "[79/1053] tile_r000011_c000034\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:01<00:00, 2.51it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.52it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 876/876 [00:21<00:00, 40.25it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "740it [00:02, 307.04it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "768it [00:02, 303.03it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 266/266 [00:03<00:00, 79.11it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 282/282 [00:01<00:00, 201.98it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000011_c000034_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000011_c000034_grain_stats.csv\n", + "done with segmentation + export\n", + "[80/1053] tile_r000011_c000035\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:01<00:00, 2.52it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.50it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 741/741 [00:18<00:00, 39.80it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "628it [00:02, 286.44it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "657it [00:02, 282.25it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 227/227 [00:03<00:00, 71.97it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 241/241 [00:01<00:00, 184.32it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n", + "Warning: len(gdf)=241 != len(grain_stats)=240 -> truncating to 240\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000011_c000035_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000011_c000035_grain_stats.csv\n", + "done with segmentation + export\n", + "[81/1053] tile_r000011_c000036\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.48it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.56it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 618/618 [00:15<00:00, 39.49it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "516it [00:01, 288.61it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "547it [00:01, 289.45it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 189/189 [00:02<00:00, 73.29it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 194/194 [00:01<00:00, 188.88it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000011_c000036_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000011_c000036_grain_stats.csv\n", + "done with segmentation + export\n", + "[82/1053] tile_r000011_c000037\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.48it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.50it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 685/685 [00:17<00:00, 38.75it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "543it [00:02, 263.54it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "572it [00:02, 259.52it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 195/195 [00:02<00:00, 67.90it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 207/207 [00:01<00:00, 192.12it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000011_c000037_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000011_c000037_grain_stats.csv\n", + "done with segmentation + export\n", + "[83/1053] tile_r000011_c000038\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:01<00:00, 2.51it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.51it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 719/719 [00:18<00:00, 39.29it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "612it [00:02, 239.54it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "637it [00:02, 239.71it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 214/214 [00:03<00:00, 63.03it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 222/222 [00:01<00:00, 193.82it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000011_c000038_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000011_c000038_grain_stats.csv\n", + "done with segmentation + export\n", + "[84/1053] tile_r000011_c000039\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.47it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.46it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 846/846 [00:23<00:00, 36.23it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "715it [00:02, 309.61it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "761it [00:02, 308.47it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 270/270 [00:03<00:00, 87.10it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 276/276 [00:01<00:00, 215.22it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000011_c000039_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000011_c000039_grain_stats.csv\n", + "done with segmentation + export\n", + "[85/1053] tile_r000011_c000040\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.49it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.51it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 776/776 [00:20<00:00, 38.08it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "606it [00:02, 292.78it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "605it [00:01, 307.20it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 192/192 [00:02<00:00, 71.56it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 233/233 [00:01<00:00, 197.59it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000011_c000040_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000011_c000040_grain_stats.csv\n", + "done with segmentation + export\n", + "[86/1053] tile_r000011_c000041\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.44it/s]\n", + "100%|██████████| 4/4 [00:04<00:00, 1.03s/it]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 955/955 [00:24<00:00, 38.55it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "819it [00:03, 263.42it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "865it [00:03, 262.53it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 298/298 [00:04<00:00, 68.37it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 309/309 [00:01<00:00, 199.72it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000011_c000041_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000011_c000041_grain_stats.csv\n", + "done with segmentation + export\n", + "[87/1053] tile_r000011_c000042\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.44it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.46it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 1208/1208 [00:31<00:00, 38.31it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "1019it [00:03, 282.85it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "1072it [00:03, 282.46it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 374/374 [00:04<00:00, 77.98it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 399/399 [00:01<00:00, 208.74it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n", + "Warning: len(gdf)=399 != len(grain_stats)=398 -> truncating to 398\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000011_c000042_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000011_c000042_grain_stats.csv\n", + "done with segmentation + export\n", + "[88/1053] tile_r000011_c000043\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.47it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.52it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 1097/1097 [00:26<00:00, 40.64it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "966it [00:03, 282.44it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "1013it [00:03, 282.65it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 357/357 [00:04<00:00, 85.30it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 371/371 [00:01<00:00, 205.08it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000011_c000043_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000011_c000043_grain_stats.csv\n", + "done with segmentation + export\n", + "[89/1053] tile_r000011_c000044\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:01<00:00, 2.51it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.50it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 1100/1100 [00:28<00:00, 38.83it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "946it [00:03, 311.02it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "987it [00:03, 307.76it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 350/350 [00:04<00:00, 81.54it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 372/372 [00:01<00:00, 208.10it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000011_c000044_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000011_c000044_grain_stats.csv\n", + "done with segmentation + export\n", + "[90/1053] tile_r000011_c000045\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:01<00:00, 2.52it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.51it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 1268/1268 [00:31<00:00, 40.23it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "1120it [00:03, 290.62it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "1167it [00:04, 291.14it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 425/425 [00:04<00:00, 91.72it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 441/441 [00:02<00:00, 209.03it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000011_c000045_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000011_c000045_grain_stats.csv\n", + "done with segmentation + export\n", + "[91/1053] tile_r000011_c000046\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:01<00:00, 2.52it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.49it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 1301/1301 [00:33<00:00, 39.08it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "1142it [00:04, 283.85it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "1189it [00:04, 282.50it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 428/428 [00:04<00:00, 85.92it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 448/448 [00:02<00:00, 214.90it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000011_c000046_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000011_c000046_grain_stats.csv\n", + "done with segmentation + export\n", + "[92/1053] tile_r000011_c000047\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.47it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.55it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 1408/1408 [00:35<00:00, 39.50it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "1254it [00:04, 307.83it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "1298it [00:04, 306.90it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 463/463 [00:05<00:00, 92.19it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 492/492 [00:02<00:00, 218.79it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n", + "Warning: len(gdf)=492 != len(grain_stats)=491 -> truncating to 491\n", + "Saved histogram plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000011_c000047_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000011_c000047_grain_stats.csv\n", + "done with segmentation + export\n", + "[93/1053] tile_r000011_c000048\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:01<00:00, 2.52it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.52it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 1209/1209 [00:30<00:00, 39.42it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "1067it [00:03, 300.59it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "1065it [00:03, 309.93it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 355/355 [00:04<00:00, 83.23it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 421/421 [00:01<00:00, 213.52it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000011_c000048_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000011_c000048_grain_stats.csv\n", + "done with segmentation + export\n", + "[94/1053] tile_r000011_c000049\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.48it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.50it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 1336/1336 [00:33<00:00, 39.53it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "1194it [00:04, 286.35it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "1191it [00:04, 296.51it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 419/419 [00:04<00:00, 95.08it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 483/483 [00:02<00:00, 218.82it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000011_c000049_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000011_c000049_grain_stats.csv\n", + "done with segmentation + export\n", + "[95/1053] tile_r000011_c000050\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.46it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.46it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 1372/1372 [00:33<00:00, 40.59it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "1232it [00:03, 315.15it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "1268it [00:04, 314.87it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 483/483 [00:04<00:00, 113.12it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 488/488 [00:02<00:00, 222.70it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000011_c000050_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000011_c000050_grain_stats.csv\n", + "done with segmentation + export\n", + "[96/1053] tile_r000011_c000051\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:01<00:00, 2.51it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.50it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 628/628 [00:16<00:00, 38.94it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "550it [00:02, 256.88it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "564it [00:02, 256.56it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 210/210 [00:02<00:00, 87.08it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 214/214 [00:00<00:00, 220.64it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000011_c000051_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000011_c000051_grain_stats.csv\n", + "done with segmentation + export\n", + "[97/1053] tile_r000011_c000052\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.46it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.46it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "0it [00:00, ?it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "0it [00:00, ?it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "0it [00:00, ?it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "0it [00:00, ?it/s]\n", + "0it [00:00, ?it/s]\n", + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n", + "No grains found -> skipping histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000011_c000052_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000011_c000052_grain_stats.csv\n", + "done with segmentation + export\n", + "[98/1053] tile_r000012_c000029\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.49it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.53it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 80/80 [00:02<00:00, 33.04it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "49it [00:00, 350.13it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "52it [00:00, 358.29it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 19/19 [00:00<00:00, 92.80it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 19/19 [00:00<00:00, 194.82it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000012_c000029_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000012_c000029_grain_stats.csv\n", + "done with segmentation + export\n", + "[99/1053] tile_r000012_c000030\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:01<00:00, 2.51it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.47it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 839/839 [00:23<00:00, 36.16it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "723it [00:02, 352.61it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "722it [00:02, 358.97it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 258/258 [00:02<00:00, 101.97it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 304/304 [00:01<00:00, 222.49it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000012_c000030_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000012_c000030_grain_stats.csv\n", + "done with segmentation + export\n", + "[100/1053] tile_r000012_c000031\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:01<00:00, 2.53it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.49it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 930/930 [00:23<00:00, 39.60it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "774it [00:02, 270.95it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "812it [00:03, 269.34it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 290/290 [00:03<00:00, 76.22it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 301/301 [00:01<00:00, 195.63it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsshome1/0B/di54doz/.local/share/mamba/envs/segmenteverygrain/lib/python3.10/site-packages/segmenteverygrain/segmenteverygrain.py:2218: RuntimeWarning: divide by zero encountered in log2\n", + " phi_minor = -np.log2(minor_axis_length)\n", + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000012_c000031_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000012_c000031_grain_stats.csv\n", + "done with segmentation + export\n", + "[101/1053] tile_r000012_c000032\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.49it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.49it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 786/786 [00:19<00:00, 39.70it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "668it [00:02, 294.05it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "705it [00:02, 291.70it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 247/247 [00:03<00:00, 73.21it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 261/261 [00:01<00:00, 184.89it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000012_c000032_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000012_c000032_grain_stats.csv\n", + "done with segmentation + export\n", + "[102/1053] tile_r000012_c000033\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.44it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.44it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 821/821 [00:20<00:00, 39.75it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "699it [00:02, 288.39it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "731it [00:02, 287.58it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 262/262 [00:03<00:00, 82.07it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 274/274 [00:01<00:00, 189.83it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000012_c000033_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000012_c000033_grain_stats.csv\n", + "done with segmentation + export\n", + "[103/1053] tile_r000012_c000034\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.46it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.50it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 822/822 [00:20<00:00, 39.86it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "705it [00:02, 278.41it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "738it [00:02, 273.73it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 252/252 [00:03<00:00, 73.57it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 269/269 [00:01<00:00, 192.79it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000012_c000034_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000012_c000034_grain_stats.csv\n", + "done with segmentation + export\n", + "[104/1053] tile_r000012_c000035\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.42it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.47it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 741/741 [00:19<00:00, 37.82it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "609it [00:02, 228.79it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "638it [00:02, 223.34it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 206/206 [00:04<00:00, 50.59it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 215/215 [00:01<00:00, 180.78it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000012_c000035_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000012_c000035_grain_stats.csv\n", + "done with segmentation + export\n", + "[105/1053] tile_r000012_c000036\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:01<00:00, 2.52it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.51it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 537/537 [00:13<00:00, 39.77it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "445it [00:01, 280.93it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "478it [00:01, 280.37it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 167/167 [00:02<00:00, 72.26it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 175/175 [00:00<00:00, 185.28it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000012_c000036_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000012_c000036_grain_stats.csv\n", + "done with segmentation + export\n", + "[106/1053] tile_r000012_c000037\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.49it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.45it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 761/761 [00:19<00:00, 39.90it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "636it [00:02, 265.34it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "634it [00:02, 279.87it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 197/197 [00:03<00:00, 61.20it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 239/239 [00:01<00:00, 196.00it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000012_c000037_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000012_c000037_grain_stats.csv\n", + "done with segmentation + export\n", + "[107/1053] tile_r000012_c000038\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:01<00:00, 2.55it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.50it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 917/917 [00:22<00:00, 40.24it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "830it [00:02, 280.58it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "827it [00:02, 292.53it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 272/272 [00:03<00:00, 76.16it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 333/333 [00:01<00:00, 209.54it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000012_c000038_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000012_c000038_grain_stats.csv\n", + "done with segmentation + export\n", + "[108/1053] tile_r000012_c000039\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:01<00:00, 2.52it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.44it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 696/696 [00:17<00:00, 40.11it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "595it [00:01, 326.66it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "629it [00:01, 327.75it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 239/239 [00:02<00:00, 101.39it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 243/243 [00:01<00:00, 210.29it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000012_c000039_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000012_c000039_grain_stats.csv\n", + "done with segmentation + export\n", + "[109/1053] tile_r000012_c000040\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:01<00:00, 2.53it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.53it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 910/910 [00:22<00:00, 40.03it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "741it [00:02, 299.55it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "789it [00:02, 298.80it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 292/292 [00:03<00:00, 90.82it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 304/304 [00:01<00:00, 202.52it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000012_c000040_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000012_c000040_grain_stats.csv\n", + "done with segmentation + export\n", + "[110/1053] tile_r000012_c000041\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:01<00:00, 2.52it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.47it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 889/889 [00:22<00:00, 39.60it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "783it [00:02, 290.18it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "820it [00:02, 287.40it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 294/294 [00:03<00:00, 82.47it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 305/305 [00:01<00:00, 200.26it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000012_c000041_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000012_c000041_grain_stats.csv\n", + "done with segmentation + export\n", + "[111/1053] tile_r000012_c000042\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.49it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.52it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 918/918 [00:23<00:00, 39.38it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "771it [00:02, 267.37it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "770it [00:02, 268.97it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 245/245 [00:03<00:00, 66.22it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 296/296 [00:01<00:00, 196.36it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000012_c000042_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000012_c000042_grain_stats.csv\n", + "done with segmentation + export\n", + "[112/1053] tile_r000012_c000043\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.49it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.53it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 1072/1072 [00:26<00:00, 40.47it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "958it [00:03, 312.27it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "1005it [00:03, 306.94it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 380/380 [00:03<00:00, 99.56it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 386/386 [00:01<00:00, 208.84it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000012_c000043_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000012_c000043_grain_stats.csv\n", + "done with segmentation + export\n", + "[113/1053] tile_r000012_c000044\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.46it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.48it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 922/922 [00:23<00:00, 38.99it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "778it [00:02, 271.57it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "820it [00:03, 269.88it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 288/288 [00:03<00:00, 75.07it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 298/298 [00:01<00:00, 192.44it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000012_c000044_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000012_c000044_grain_stats.csv\n", + "done with segmentation + export\n", + "[114/1053] tile_r000012_c000045\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.48it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.46it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 1004/1004 [00:25<00:00, 40.00it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "860it [00:03, 258.64it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "914it [00:03, 258.30it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 328/328 [00:04<00:00, 76.87it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 346/346 [00:01<00:00, 200.24it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000012_c000045_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000012_c000045_grain_stats.csv\n", + "done with segmentation + export\n", + "[115/1053] tile_r000012_c000046\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:01<00:00, 2.51it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.51it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 1195/1195 [00:29<00:00, 40.10it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "1051it [00:03, 318.43it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "1096it [00:03, 313.59it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 405/405 [00:04<00:00, 91.34it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 420/420 [00:01<00:00, 211.23it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000012_c000046_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000012_c000046_grain_stats.csv\n", + "done with segmentation + export\n", + "[116/1053] tile_r000012_c000047\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:01<00:00, 2.51it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.49it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 1121/1121 [00:28<00:00, 39.60it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "962it [00:03, 313.09it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "1014it [00:03, 308.45it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 387/387 [00:03<00:00, 99.60it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 403/403 [00:01<00:00, 216.41it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000012_c000047_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000012_c000047_grain_stats.csv\n", + "done with segmentation + export\n", + "[117/1053] tile_r000012_c000048\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.47it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.46it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 995/995 [00:24<00:00, 40.14it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "870it [00:03, 278.96it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "869it [00:03, 282.44it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 278/278 [00:03<00:00, 70.98it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 336/336 [00:01<00:00, 204.03it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000012_c000048_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000012_c000048_grain_stats.csv\n", + "done with segmentation + export\n", + "[118/1053] tile_r000012_c000049\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.49it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.52it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 1106/1106 [00:27<00:00, 39.63it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "953it [00:03, 303.26it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "950it [00:03, 312.39it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 320/320 [00:03<00:00, 81.08it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 371/371 [00:01<00:00, 214.24it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000012_c000049_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000012_c000049_grain_stats.csv\n", + "done with segmentation + export\n", + "[119/1053] tile_r000012_c000050\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:01<00:00, 2.51it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.50it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 1127/1127 [00:27<00:00, 40.41it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "997it [00:03, 315.79it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "1043it [00:03, 312.08it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 391/391 [00:03<00:00, 98.67it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 405/405 [00:01<00:00, 213.89it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000012_c000050_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000012_c000050_grain_stats.csv\n", + "done with segmentation + export\n", + "[120/1053] tile_r000012_c000051\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:01<00:00, 2.51it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.49it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 1277/1277 [00:31<00:00, 40.76it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "1133it [00:03, 305.12it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "1177it [00:03, 305.11it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 447/447 [00:04<00:00, 96.04it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 463/463 [00:02<00:00, 215.82it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000012_c000051_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000012_c000051_grain_stats.csv\n", + "done with segmentation + export\n", + "[121/1053] tile_r000012_c000052\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.46it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.43it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 483/483 [00:12<00:00, 39.83it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "422it [00:01, 294.45it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "435it [00:01, 294.05it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 165/165 [00:01<00:00, 107.26it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 170/170 [00:00<00:00, 213.76it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000012_c000052_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000012_c000052_grain_stats.csv\n", + "done with segmentation + export\n", + "[122/1053] tile_r000013_c000027\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:01<00:00, 2.52it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.52it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 11/11 [00:00<00:00, 24.31it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2it [00:00, 912.90it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2it [00:00, 958.70it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 1/1 [00:00<00:00, 290.46it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 1/1 [00:00<00:00, 207.94it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000013_c000027_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000013_c000027_grain_stats.csv\n", + "done with segmentation + export\n", + "[123/1053] tile_r000013_c000028\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.44it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.49it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 590/590 [00:15<00:00, 37.09it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "477it [00:01, 347.64it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "495it [00:01, 348.32it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 185/185 [00:01<00:00, 108.29it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 200/200 [00:00<00:00, 222.38it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000013_c000028_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000013_c000028_grain_stats.csv\n", + "done with segmentation + export\n", + "[124/1053] tile_r000013_c000029\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.48it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.50it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 1034/1034 [00:26<00:00, 39.48it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "908it [00:02, 310.20it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "947it [00:03, 309.57it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 338/338 [00:03<00:00, 86.46it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 355/355 [00:01<00:00, 207.29it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000013_c000029_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000013_c000029_grain_stats.csv\n", + "done with segmentation + export\n", + "[125/1053] tile_r000013_c000030\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.45it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.39it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 748/748 [00:19<00:00, 39.12it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "603it [00:02, 300.44it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "637it [00:02, 301.41it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 223/223 [00:02<00:00, 79.68it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 232/232 [00:01<00:00, 198.00it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000013_c000030_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000013_c000030_grain_stats.csv\n", + "done with segmentation + export\n", + "[126/1053] tile_r000013_c000031\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.43it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.48it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 517/517 [00:13<00:00, 37.55it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "386it [00:01, 270.07it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "405it [00:01, 274.65it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 130/130 [00:02<00:00, 58.81it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 136/136 [00:00<00:00, 177.58it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000013_c000031_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000013_c000031_grain_stats.csv\n", + "done with segmentation + export\n", + "[127/1053] tile_r000013_c000032\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.46it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.13it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 793/793 [00:20<00:00, 38.43it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "649it [00:02, 282.16it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "648it [00:02, 290.63it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 197/197 [00:03<00:00, 61.48it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 246/246 [00:01<00:00, 190.59it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000013_c000032_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000013_c000032_grain_stats.csv\n", + "done with segmentation + export\n", + "[128/1053] tile_r000013_c000033\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.44it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.46it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 731/731 [00:18<00:00, 38.71it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "588it [00:02, 227.89it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "608it [00:02, 221.24it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 188/188 [00:03<00:00, 49.85it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 201/201 [00:01<00:00, 179.25it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000013_c000033_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000013_c000033_grain_stats.csv\n", + "done with segmentation + export\n", + "[129/1053] tile_r000013_c000034\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:01<00:00, 2.51it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.46it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 792/792 [00:20<00:00, 39.34it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "660it [00:02, 240.83it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "659it [00:02, 245.47it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 194/194 [00:03<00:00, 50.22it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 235/235 [00:01<00:00, 173.82it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000013_c000034_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000013_c000034_grain_stats.csv\n", + "done with segmentation + export\n", + "[130/1053] tile_r000013_c000035\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:01<00:00, 2.52it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.48it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 627/627 [00:16<00:00, 38.06it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "507it [00:02, 221.03it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "506it [00:02, 219.00it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 137/137 [00:03<00:00, 41.27it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 168/168 [00:00<00:00, 176.68it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000013_c000035_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000013_c000035_grain_stats.csv\n", + "done with segmentation + export\n", + "[131/1053] tile_r000013_c000036\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:01<00:00, 2.54it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.53it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 509/509 [00:13<00:00, 38.39it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "419it [00:01, 251.99it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "418it [00:01, 261.64it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 125/125 [00:02<00:00, 49.57it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 152/152 [00:00<00:00, 177.50it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000013_c000036_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000013_c000036_grain_stats.csv\n", + "done with segmentation + export\n", + "[132/1053] tile_r000013_c000037\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.47it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.51it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 589/589 [00:14<00:00, 39.87it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "488it [00:02, 234.41it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "515it [00:02, 234.91it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 163/163 [00:03<00:00, 54.13it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 169/169 [00:00<00:00, 172.19it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000013_c000037_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000013_c000037_grain_stats.csv\n", + "done with segmentation + export\n", + "[133/1053] tile_r000013_c000038\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:01<00:00, 2.53it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.47it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 805/805 [00:20<00:00, 39.51it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "694it [00:02, 251.64it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "736it [00:02, 250.34it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 264/264 [00:03<00:00, 77.88it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 269/269 [00:01<00:00, 193.72it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000013_c000038_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000013_c000038_grain_stats.csv\n", + "done with segmentation + export\n", + "[134/1053] tile_r000013_c000039\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:01<00:00, 2.52it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.51it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 884/884 [00:22<00:00, 39.40it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "772it [00:02, 299.40it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "809it [00:02, 297.35it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 291/291 [00:03<00:00, 86.73it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 306/306 [00:01<00:00, 210.79it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000013_c000039_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000013_c000039_grain_stats.csv\n", + "done with segmentation + export\n", + "[135/1053] tile_r000013_c000040\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:01<00:00, 2.50it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.53it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 641/641 [00:16<00:00, 38.46it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "515it [00:01, 259.67it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "550it [00:02, 264.87it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 187/187 [00:02<00:00, 66.00it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 194/194 [00:00<00:00, 194.37it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000013_c000040_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000013_c000040_grain_stats.csv\n", + "done with segmentation + export\n", + "[136/1053] tile_r000013_c000041\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.50it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.49it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 783/783 [00:20<00:00, 39.15it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "671it [00:02, 273.48it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "704it [00:02, 272.68it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 240/240 [00:03<00:00, 63.74it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 251/251 [00:01<00:00, 201.30it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000013_c000041_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000013_c000041_grain_stats.csv\n", + "done with segmentation + export\n", + "[137/1053] tile_r000013_c000042\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:01<00:00, 2.51it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.50it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 1099/1099 [00:28<00:00, 38.61it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "913it [00:03, 239.86it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "40it [00:00, 957.95it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 1/1 [00:00<00:00, 11.26it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\n", + "100%|██████████| 38/38 [00:00<00:00, 213.93it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000013_c000042_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000013_c000042_grain_stats.csv\n", + "done with segmentation + export\n", + "[138/1053] tile_r000013_c000043\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:01<00:00, 2.52it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.50it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 896/896 [00:22<00:00, 39.70it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "744it [00:02, 271.47it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "743it [00:02, 271.06it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 237/237 [00:03<00:00, 66.52it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 288/288 [00:01<00:00, 196.52it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000013_c000043_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000013_c000043_grain_stats.csv\n", + "done with segmentation + export\n", + "[139/1053] tile_r000013_c000044\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.45it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.53it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 1055/1055 [00:27<00:00, 38.25it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "884it [00:03, 245.89it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "883it [00:03, 246.54it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 278/278 [00:04<00:00, 60.12it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 334/334 [00:01<00:00, 197.87it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000013_c000044_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000013_c000044_grain_stats.csv\n", + "done with segmentation + export\n", + "[140/1053] tile_r000013_c000045\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.49it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.48it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 1099/1099 [00:27<00:00, 39.39it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "936it [00:04, 225.42it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "934it [00:04, 231.79it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 301/301 [00:05<00:00, 57.62it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 343/343 [00:01<00:00, 192.05it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000013_c000045_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000013_c000045_grain_stats.csv\n", + "done with segmentation + export\n", + "[141/1053] tile_r000013_c000046\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.47it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.53it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 1115/1115 [00:28<00:00, 39.68it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "964it [00:03, 280.87it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "1009it [00:03, 281.25it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 366/366 [00:04<00:00, 83.48it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 383/383 [00:01<00:00, 209.34it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n", + "Warning: len(gdf)=383 != len(grain_stats)=382 -> truncating to 382\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsshome1/0B/di54doz/.local/share/mamba/envs/segmenteverygrain/lib/python3.10/site-packages/segmenteverygrain/segmenteverygrain.py:2218: RuntimeWarning: divide by zero encountered in log2\n", + " phi_minor = -np.log2(minor_axis_length)\n", + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000013_c000046_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000013_c000046_grain_stats.csv\n", + "done with segmentation + export\n", + "[142/1053] tile_r000013_c000047\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.47it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.49it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 1325/1325 [00:33<00:00, 39.93it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "1187it [00:04, 260.31it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "1244it [00:04, 260.51it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 443/443 [00:05<00:00, 78.71it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 470/470 [00:02<00:00, 222.02it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000013_c000047_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000013_c000047_grain_stats.csv\n", + "done with segmentation + export\n", + "[143/1053] tile_r000013_c000048\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.48it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.48it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 1121/1121 [00:28<00:00, 39.26it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "966it [00:03, 304.50it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "1017it [00:03, 301.98it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 361/361 [00:04<00:00, 85.42it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 376/376 [00:01<00:00, 212.08it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000013_c000048_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000013_c000048_grain_stats.csv\n", + "done with segmentation + export\n", + "[144/1053] tile_r000013_c000049\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:01<00:00, 2.53it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.48it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 1070/1070 [00:27<00:00, 39.32it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "892it [00:03, 291.26it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "933it [00:03, 286.84it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 320/320 [00:04<00:00, 74.80it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 333/333 [00:01<00:00, 203.50it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000013_c000049_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000013_c000049_grain_stats.csv\n", + "done with segmentation + export\n", + "[145/1053] tile_r000013_c000050\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.48it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.47it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 989/989 [00:24<00:00, 40.43it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "898it [00:02, 300.04it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "957it [00:03, 296.37it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 361/361 [00:03<00:00, 98.62it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 364/364 [00:01<00:00, 206.12it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000013_c000050_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000013_c000050_grain_stats.csv\n", + "done with segmentation + export\n", + "[146/1053] tile_r000013_c000051\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.44it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.49it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 1137/1137 [00:28<00:00, 40.20it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "1005it [00:03, 309.38it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "1053it [00:03, 306.15it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 400/400 [00:03<00:00, 104.41it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 413/413 [00:01<00:00, 211.72it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000013_c000051_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000013_c000051_grain_stats.csv\n", + "done with segmentation + export\n", + "[147/1053] tile_r000013_c000052\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:01<00:00, 2.54it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.55it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 1210/1210 [00:30<00:00, 39.33it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "1058it [00:04, 241.32it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "1057it [00:04, 246.14it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 356/356 [00:05<00:00, 70.66it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 392/392 [00:01<00:00, 202.52it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000013_c000052_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000013_c000052_grain_stats.csv\n", + "done with segmentation + export\n", + "[148/1053] tile_r000013_c000053\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:01<00:00, 2.52it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.50it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 285/285 [00:07<00:00, 38.30it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "240it [00:00, 303.87it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "239it [00:00, 309.31it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 90/90 [00:00<00:00, 109.25it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 96/96 [00:00<00:00, 217.84it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000013_c000053_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000013_c000053_grain_stats.csv\n", + "done with segmentation + export\n", + "[149/1053] tile_r000014_c000026\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.49it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.53it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 283/283 [00:07<00:00, 35.59it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "211it [00:00, 423.81it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "227it [00:00, 418.92it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 93/93 [00:00<00:00, 148.72it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 95/95 [00:00<00:00, 233.39it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000014_c000026_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000014_c000026_grain_stats.csv\n", + "done with segmentation + export\n", + "[150/1053] tile_r000014_c000027\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:01<00:00, 2.51it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.42it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 1014/1014 [00:25<00:00, 39.07it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "913it [00:02, 407.43it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "964it [00:02, 397.53it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 381/381 [00:02<00:00, 135.35it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 391/391 [00:01<00:00, 236.15it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000014_c000027_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000014_c000027_grain_stats.csv\n", + "done with segmentation + export\n", + "[151/1053] tile_r000014_c000028\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.48it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.49it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 1115/1115 [00:27<00:00, 40.02it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "957it [00:03, 310.08it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "990it [00:03, 309.40it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 357/357 [00:03<00:00, 91.90it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 374/374 [00:01<00:00, 210.86it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000014_c000028_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000014_c000028_grain_stats.csv\n", + "done with segmentation + export\n", + "[152/1053] tile_r000014_c000029\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.47it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.53it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 879/879 [00:21<00:00, 40.33it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "772it [00:02, 362.07it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "821it [00:02, 357.34it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 297/297 [00:03<00:00, 95.52it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 309/309 [00:01<00:00, 214.77it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000014_c000029_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000014_c000029_grain_stats.csv\n", + "done with segmentation + export\n", + "[153/1053] tile_r000014_c000030\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.39it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.38it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 432/432 [00:11<00:00, 38.17it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "359it [00:01, 268.28it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "377it [00:01, 269.67it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 135/135 [00:02<00:00, 67.41it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 137/137 [00:00<00:00, 181.65it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000014_c000030_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000014_c000030_grain_stats.csv\n", + "done with segmentation + export\n", + "[154/1053] tile_r000014_c000031\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.41it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.44it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 423/423 [00:10<00:00, 38.71it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "360it [00:01, 266.41it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "374it [00:01, 269.11it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 122/122 [00:02<00:00, 53.40it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 127/127 [00:00<00:00, 175.71it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000014_c000031_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000014_c000031_grain_stats.csv\n", + "done with segmentation + export\n", + "[155/1053] tile_r000014_c000032\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.42it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.50it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 462/462 [00:11<00:00, 38.86it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "369it [00:01, 289.22it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "385it [00:01, 288.34it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 130/130 [00:02<00:00, 61.46it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 135/135 [00:00<00:00, 182.02it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000014_c000032_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000014_c000032_grain_stats.csv\n", + "done with segmentation + export\n", + "[156/1053] tile_r000014_c000033\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.40it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.48it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 453/453 [00:11<00:00, 38.50it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "361it [00:01, 261.37it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "355it [00:01, 275.97it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 101/101 [00:02<00:00, 50.40it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 123/123 [00:00<00:00, 169.57it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000014_c000033_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000014_c000033_grain_stats.csv\n", + "done with segmentation + export\n", + "[157/1053] tile_r000014_c000034\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.48it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.47it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 538/538 [00:13<00:00, 38.51it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "420it [00:01, 268.16it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "437it [00:01, 270.33it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 140/140 [00:02<00:00, 55.78it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 147/147 [00:00<00:00, 165.81it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000014_c000034_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000014_c000034_grain_stats.csv\n", + "done with segmentation + export\n", + "[158/1053] tile_r000014_c000035\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.45it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.50it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 568/568 [00:14<00:00, 39.10it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "475it [00:02, 211.53it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "499it [00:02, 214.20it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 158/158 [00:03<00:00, 47.08it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 166/166 [00:01<00:00, 165.46it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000014_c000035_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000014_c000035_grain_stats.csv\n", + "done with segmentation + export\n", + "[159/1053] tile_r000014_c000036\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.46it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.46it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 539/539 [00:13<00:00, 39.56it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "443it [00:01, 275.83it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "465it [00:01, 275.61it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 154/154 [00:02<00:00, 61.93it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 160/160 [00:00<00:00, 177.72it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000014_c000036_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000014_c000036_grain_stats.csv\n", + "done with segmentation + export\n", + "[160/1053] tile_r000014_c000037\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.48it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.48it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 660/660 [00:16<00:00, 39.52it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "548it [00:02, 254.09it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "573it [00:02, 255.70it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 198/198 [00:02<00:00, 69.32it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 205/205 [00:01<00:00, 192.79it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000014_c000037_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000014_c000037_grain_stats.csv\n", + "done with segmentation + export\n", + "[161/1053] tile_r000014_c000038\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:01<00:00, 2.50it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.55it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 673/673 [00:16<00:00, 40.05it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "566it [00:02, 247.48it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "598it [00:02, 248.71it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 196/196 [00:03<00:00, 61.21it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 203/203 [00:01<00:00, 188.46it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000014_c000038_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000014_c000038_grain_stats.csv\n", + "done with segmentation + export\n", + "[162/1053] tile_r000014_c000039\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.46it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.52it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 784/784 [00:19<00:00, 40.42it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "676it [00:01, 344.43it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "724it [00:02, 336.03it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 276/276 [00:02<00:00, 102.64it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 288/288 [00:01<00:00, 209.46it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000014_c000039_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000014_c000039_grain_stats.csv\n", + "done with segmentation + export\n", + "[163/1053] tile_r000014_c000040\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:01<00:00, 2.52it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.48it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 697/697 [00:17<00:00, 39.78it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "600it [00:02, 292.93it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "621it [00:02, 295.20it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 222/222 [00:02<00:00, 81.02it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 230/230 [00:01<00:00, 209.47it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000014_c000040_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000014_c000040_grain_stats.csv\n", + "done with segmentation + export\n", + "[164/1053] tile_r000014_c000041\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.48it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.49it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 693/693 [00:17<00:00, 38.91it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "564it [00:01, 282.54it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "562it [00:01, 297.28it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 176/176 [00:02<00:00, 68.56it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 222/222 [00:01<00:00, 193.38it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000014_c000041_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000014_c000041_grain_stats.csv\n", + "done with segmentation + export\n", + "[165/1053] tile_r000014_c000042\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.45it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.52it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 719/719 [00:19<00:00, 37.59it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "607it [00:02, 269.90it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "606it [00:02, 276.19it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 190/190 [00:03<00:00, 61.77it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 239/239 [00:01<00:00, 191.88it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n", + "Warning: len(gdf)=239 != len(grain_stats)=238 -> truncating to 238\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000014_c000042_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000014_c000042_grain_stats.csv\n", + "done with segmentation + export\n", + "[166/1053] tile_r000014_c000043\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.49it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.51it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 979/979 [00:25<00:00, 38.65it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "823it [00:03, 212.01it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "821it [00:03, 216.00it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 236/236 [00:05<00:00, 43.74it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 294/294 [00:01<00:00, 192.84it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsshome1/0B/di54doz/.local/share/mamba/envs/segmenteverygrain/lib/python3.10/site-packages/segmenteverygrain/segmenteverygrain.py:2217: RuntimeWarning: divide by zero encountered in log2\n", + " phi_major = -np.log2(major_axis_length) # major axis length in phi scale\n", + "/dss/dsshome1/0B/di54doz/.local/share/mamba/envs/segmenteverygrain/lib/python3.10/site-packages/segmenteverygrain/segmenteverygrain.py:2218: RuntimeWarning: divide by zero encountered in log2\n", + " phi_minor = -np.log2(minor_axis_length)\n", + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000014_c000043_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000014_c000043_grain_stats.csv\n", + "done with segmentation + export\n", + "[167/1053] tile_r000014_c000044\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:01<00:00, 2.51it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.50it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 858/858 [00:21<00:00, 39.13it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "725it [00:03, 224.28it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "759it [00:03, 224.35it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 249/249 [00:04<00:00, 59.64it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 260/260 [00:01<00:00, 186.68it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000014_c000044_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000014_c000044_grain_stats.csv\n", + "done with segmentation + export\n", + "[168/1053] tile_r000014_c000045\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:01<00:00, 2.51it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.53it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 793/793 [00:20<00:00, 39.36it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "693it [00:03, 218.33it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "742it [00:03, 218.46it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 254/254 [00:04<00:00, 54.48it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 264/264 [00:01<00:00, 186.94it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000014_c000045_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000014_c000045_grain_stats.csv\n", + "done with segmentation + export\n", + "[169/1053] tile_r000014_c000046\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:01<00:00, 2.51it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.44it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 1024/1024 [00:26<00:00, 39.12it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "904it [00:03, 230.92it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "898it [00:03, 244.34it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 279/279 [00:05<00:00, 54.73it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 345/345 [00:01<00:00, 198.08it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000014_c000046_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000014_c000046_grain_stats.csv\n", + "done with segmentation + export\n", + "[170/1053] tile_r000014_c000047\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.47it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.48it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 1048/1048 [00:26<00:00, 39.65it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "917it [00:03, 304.17it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "916it [00:02, 311.16it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 314/314 [00:03<00:00, 80.56it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 377/377 [00:01<00:00, 216.31it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000014_c000047_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000014_c000047_grain_stats.csv\n", + "done with segmentation + export\n", + "[171/1053] tile_r000014_c000048\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.48it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.52it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 997/997 [00:25<00:00, 39.65it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "852it [00:03, 279.71it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "907it [00:03, 279.79it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 319/319 [00:04<00:00, 72.25it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 332/332 [00:01<00:00, 210.30it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000014_c000048_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000014_c000048_grain_stats.csv\n", + "done with segmentation + export\n", + "[172/1053] tile_r000014_c000049\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.48it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.49it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 764/764 [00:19<00:00, 38.70it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "638it [00:02, 297.27it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "673it [00:02, 295.38it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 236/236 [00:02<00:00, 82.92it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 244/244 [00:01<00:00, 195.65it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000014_c000049_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000014_c000049_grain_stats.csv\n", + "done with segmentation + export\n", + "[173/1053] tile_r000014_c000050\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.49it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.51it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 532/532 [00:13<00:00, 38.58it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "440it [00:01, 313.45it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "468it [00:01, 309.09it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 168/168 [00:02<00:00, 77.29it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 176/176 [00:00<00:00, 180.13it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000014_c000050_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000014_c000050_grain_stats.csv\n", + "done with segmentation + export\n", + "[174/1053] tile_r000014_c000051\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.48it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.49it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 946/946 [00:23<00:00, 39.81it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "829it [00:03, 261.07it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "827it [00:03, 265.28it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 267/267 [00:04<00:00, 65.84it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 323/323 [00:01<00:00, 204.97it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000014_c000051_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000014_c000051_grain_stats.csv\n", + "done with segmentation + export\n", + "[175/1053] tile_r000014_c000052\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.41it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.50it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 972/972 [00:24<00:00, 40.00it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "857it [00:03, 243.67it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "903it [00:03, 244.25it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 321/321 [00:04<00:00, 66.26it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 336/336 [00:01<00:00, 200.47it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000014_c000052_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000014_c000052_grain_stats.csv\n", + "done with segmentation + export\n", + "[176/1053] tile_r000014_c000053\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.42it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.52it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 1125/1125 [00:27<00:00, 40.46it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "1016it [00:04, 249.25it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "1042it [00:04, 252.19it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 394/394 [00:04<00:00, 88.88it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 400/400 [00:01<00:00, 209.40it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000014_c000053_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000014_c000053_grain_stats.csv\n", + "done with segmentation + export\n", + "[177/1053] tile_r000014_c000054\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:01<00:00, 2.50it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.52it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 98/98 [00:03<00:00, 32.35it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "51it [00:00, 215.31it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "53it [00:00, 225.87it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 16/16 [00:00<00:00, 48.40it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 16/16 [00:00<00:00, 172.00it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000014_c000054_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000014_c000054_grain_stats.csv\n", + "done with segmentation + export\n", + "[178/1053] tile_r000015_c000024\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.46it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.49it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 33/33 [00:01<00:00, 28.79it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "16it [00:00, 554.24it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "18it [00:00, 541.34it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 8/8 [00:00<00:00, 157.21it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 8/8 [00:00<00:00, 194.10it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000015_c000024_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000015_c000024_grain_stats.csv\n", + "done with segmentation + export\n", + "[179/1053] tile_r000015_c000025\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.49it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.47it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 547/547 [00:15<00:00, 35.57it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "475it [00:00, 535.31it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "507it [00:00, 517.60it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 207/207 [00:01<00:00, 159.50it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 222/222 [00:00<00:00, 255.15it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000015_c000025_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000015_c000025_grain_stats.csv\n", + "done with segmentation + export\n", + "[180/1053] tile_r000015_c000026\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.45it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.47it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 854/854 [00:22<00:00, 38.04it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "723it [00:03, 182.94it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "718it [00:03, 226.88it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 258/258 [00:04<00:00, 61.04it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 312/312 [00:01<00:00, 226.65it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000015_c000026_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000015_c000026_grain_stats.csv\n", + "done with segmentation + export\n", + "[181/1053] tile_r000015_c000027\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:01<00:00, 2.55it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.51it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 928/928 [00:23<00:00, 39.28it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "829it [00:01, 430.88it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "828it [00:01, 452.55it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 286/286 [00:02<00:00, 105.83it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 355/355 [00:01<00:00, 216.05it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000015_c000027_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000015_c000027_grain_stats.csv\n", + "done with segmentation + export\n", + "[182/1053] tile_r000015_c000028\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:01<00:00, 2.54it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.49it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 696/696 [00:17<00:00, 39.15it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "596it [00:01, 337.92it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "630it [00:01, 341.30it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 225/225 [00:02<00:00, 89.91it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 235/235 [00:01<00:00, 214.97it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000015_c000028_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000015_c000028_grain_stats.csv\n", + "done with segmentation + export\n", + "[183/1053] tile_r000015_c000029\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.46it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.48it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 474/474 [00:12<00:00, 39.19it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "391it [00:01, 369.68it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "415it [00:01, 377.72it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 153/153 [00:01<00:00, 94.17it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 160/160 [00:00<00:00, 203.41it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000015_c000029_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000015_c000029_grain_stats.csv\n", + "done with segmentation + export\n", + "[184/1053] tile_r000015_c000030\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.45it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.56it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 283/283 [00:07<00:00, 38.74it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "214it [00:00, 358.69it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "229it [00:00, 364.17it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 82/82 [00:01<00:00, 78.68it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 85/85 [00:00<00:00, 183.60it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000015_c000030_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000015_c000030_grain_stats.csv\n", + "done with segmentation + export\n", + "[185/1053] tile_r000015_c000031\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.49it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.50it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 333/333 [00:08<00:00, 37.90it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "240it [00:00, 310.11it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "259it [00:00, 304.45it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 90/90 [00:01<00:00, 69.06it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 94/94 [00:00<00:00, 177.74it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000015_c000031_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000015_c000031_grain_stats.csv\n", + "done with segmentation + export\n", + "[186/1053] tile_r000015_c000032\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.47it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.53it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 272/272 [00:07<00:00, 35.82it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "190it [00:00, 289.31it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "205it [00:00, 293.55it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 69/69 [00:01<00:00, 63.48it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 72/72 [00:00<00:00, 181.34it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000015_c000032_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000015_c000032_grain_stats.csv\n", + "done with segmentation + export\n", + "[187/1053] tile_r000015_c000033\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:01<00:00, 2.51it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.52it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 368/368 [00:09<00:00, 38.13it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "282it [00:01, 263.91it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "281it [00:00, 283.82it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 80/80 [00:01<00:00, 48.08it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 95/95 [00:00<00:00, 173.69it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000015_c000033_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000015_c000033_grain_stats.csv\n", + "done with segmentation + export\n", + "[188/1053] tile_r000015_c000034\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.49it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.48it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 358/358 [00:09<00:00, 37.80it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "290it [00:01, 224.58it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "289it [00:01, 241.92it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 86/86 [00:01<00:00, 46.20it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 98/98 [00:00<00:00, 168.06it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000015_c000034_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000015_c000034_grain_stats.csv\n", + "done with segmentation + export\n", + "[189/1053] tile_r000015_c000035\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:01<00:00, 2.52it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.51it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 565/565 [00:14<00:00, 39.30it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "452it [00:01, 252.83it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "477it [00:01, 250.22it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 164/164 [00:02<00:00, 65.79it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 174/174 [00:00<00:00, 182.93it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000015_c000035_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000015_c000035_grain_stats.csv\n", + "done with segmentation + export\n", + "[190/1053] tile_r000015_c000036\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:01<00:00, 2.51it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.49it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 679/679 [00:17<00:00, 39.63it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "599it [00:02, 260.00it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "629it [00:02, 260.53it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 213/213 [00:03<00:00, 63.57it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 223/223 [00:01<00:00, 189.44it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000015_c000036_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000015_c000036_grain_stats.csv\n", + "done with segmentation + export\n", + "[191/1053] tile_r000015_c000037\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.45it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.47it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 566/566 [00:14<00:00, 39.16it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "459it [00:01, 243.35it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "479it [00:01, 244.64it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 159/159 [00:02<00:00, 59.09it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 166/166 [00:00<00:00, 172.01it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000015_c000037_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000015_c000037_grain_stats.csv\n", + "done with segmentation + export\n", + "[192/1053] tile_r000015_c000038\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.49it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.52it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 533/533 [00:13<00:00, 38.17it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "442it [00:01, 285.14it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "473it [00:01, 286.12it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 165/165 [00:02<00:00, 70.38it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 170/170 [00:00<00:00, 182.33it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000015_c000038_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000015_c000038_grain_stats.csv\n", + "done with segmentation + export\n", + "[193/1053] tile_r000015_c000039\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:01<00:00, 2.51it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.48it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 789/789 [00:20<00:00, 39.08it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "679it [00:02, 286.79it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "714it [00:02, 285.58it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 253/253 [00:03<00:00, 79.04it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 262/262 [00:01<00:00, 201.07it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000015_c000039_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000015_c000039_grain_stats.csv\n", + "done with segmentation + export\n", + "[194/1053] tile_r000015_c000040\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.50it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.53it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 824/824 [00:20<00:00, 39.75it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "720it [00:02, 260.54it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "768it [00:02, 258.52it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 263/263 [00:03<00:00, 67.20it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 274/274 [00:01<00:00, 204.14it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000015_c000040_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000015_c000040_grain_stats.csv\n", + "done with segmentation + export\n", + "[195/1053] tile_r000015_c000041\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:01<00:00, 2.50it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.47it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 742/742 [00:18<00:00, 39.39it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "633it [00:02, 294.31it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "666it [00:02, 291.51it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 231/231 [00:03<00:00, 76.96it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 245/245 [00:01<00:00, 203.66it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000015_c000041_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000015_c000041_grain_stats.csv\n", + "done with segmentation + export\n", + "[196/1053] tile_r000015_c000042\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.34it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.33it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 608/608 [00:15<00:00, 39.05it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "522it [00:02, 234.30it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "557it [00:02, 235.33it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 181/181 [00:03<00:00, 53.42it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 195/195 [00:01<00:00, 182.68it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000015_c000042_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000015_c000042_grain_stats.csv\n", + "done with segmentation + export\n", + "[197/1053] tile_r000015_c000043\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.36it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.38it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 662/662 [00:17<00:00, 38.60it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "550it [00:02, 238.92it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "549it [00:02, 246.29it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 171/171 [00:03<00:00, 54.95it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 213/213 [00:01<00:00, 188.30it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000015_c000043_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000015_c000043_grain_stats.csv\n", + "done with segmentation + export\n", + "[198/1053] tile_r000015_c000044\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.37it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.41it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 761/761 [00:20<00:00, 37.59it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "593it [00:01, 316.98it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "637it [00:02, 318.42it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 235/235 [00:02<00:00, 94.83it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 247/247 [00:01<00:00, 197.69it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000015_c000044_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000015_c000044_grain_stats.csv\n", + "done with segmentation + export\n", + "[199/1053] tile_r000015_c000045\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.44it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.43it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 817/817 [00:21<00:00, 38.33it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "668it [00:02, 260.31it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "667it [00:02, 264.36it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 206/206 [00:03<00:00, 55.19it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 260/260 [00:01<00:00, 183.03it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000015_c000045_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000015_c000045_grain_stats.csv\n", + "done with segmentation + export\n", + "[200/1053] tile_r000015_c000046\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.46it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.40it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 883/883 [00:22<00:00, 39.64it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "772it [00:02, 272.39it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "824it [00:03, 273.44it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 292/292 [00:03<00:00, 76.52it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 303/303 [00:01<00:00, 193.42it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n", + "Warning: len(gdf)=303 != len(grain_stats)=302 -> truncating to 302\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000015_c000046_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000015_c000046_grain_stats.csv\n", + "done with segmentation + export\n", + "[201/1053] tile_r000015_c000047\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.49it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.43it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 947/947 [00:24<00:00, 38.80it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "808it [00:03, 237.62it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "805it [00:03, 256.49it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 264/264 [00:04<00:00, 63.07it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 313/313 [00:01<00:00, 200.10it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000015_c000047_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000015_c000047_grain_stats.csv\n", + "done with segmentation + export\n", + "[202/1053] tile_r000015_c000048\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.50it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.44it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 783/783 [00:20<00:00, 39.05it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "639it [00:01, 353.99it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "685it [00:01, 348.67it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 257/257 [00:02<00:00, 96.16it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 268/268 [00:01<00:00, 205.79it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000015_c000048_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000015_c000048_grain_stats.csv\n", + "done with segmentation + export\n", + "[203/1053] tile_r000015_c000049\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.48it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.48it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 956/956 [00:24<00:00, 39.37it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "857it [00:02, 293.82it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "897it [00:03, 295.49it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 311/311 [00:03<00:00, 81.05it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 329/329 [00:01<00:00, 211.35it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000015_c000049_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000015_c000049_grain_stats.csv\n", + "done with segmentation + export\n", + "[204/1053] tile_r000015_c000050\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:01<00:00, 2.51it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.48it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 911/911 [00:22<00:00, 40.44it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "814it [00:02, 288.68it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "812it [00:02, 297.38it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 274/274 [00:03<00:00, 81.69it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 320/320 [00:01<00:00, 215.02it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000015_c000050_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000015_c000050_grain_stats.csv\n", + "done with segmentation + export\n", + "[205/1053] tile_r000015_c000051\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.50it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.50it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 1062/1062 [00:26<00:00, 40.20it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "940it [00:03, 277.22it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "939it [00:03, 285.20it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 300/300 [00:04<00:00, 71.75it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 359/359 [00:01<00:00, 208.44it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000015_c000051_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000015_c000051_grain_stats.csv\n", + "done with segmentation + export\n", + "[206/1053] tile_r000015_c000052\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:01<00:00, 2.52it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.49it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 1044/1044 [00:25<00:00, 40.23it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "914it [00:03, 278.84it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "957it [00:03, 275.19it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 351/351 [00:04<00:00, 86.44it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 359/359 [00:01<00:00, 203.50it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000015_c000052_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000015_c000052_grain_stats.csv\n", + "done with segmentation + export\n", + "[207/1053] tile_r000015_c000053\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.48it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.45it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 1061/1061 [00:26<00:00, 39.32it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "932it [00:05, 183.12it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "930it [00:05, 185.98it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 297/297 [00:07<00:00, 41.99it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 338/338 [00:01<00:00, 189.71it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000015_c000053_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000015_c000053_grain_stats.csv\n", + "done with segmentation + export\n", + "[208/1053] tile_r000015_c000054\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.47it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.40it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 714/714 [00:18<00:00, 38.44it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "633it [00:03, 187.09it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "632it [00:03, 193.75it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 209/209 [00:03<00:00, 54.41it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 234/234 [00:01<00:00, 182.43it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000015_c000054_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000015_c000054_grain_stats.csv\n", + "done with segmentation + export\n", + "[209/1053] tile_r000015_c000055\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.44it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.44it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 14/14 [00:00<00:00, 24.69it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2it [00:00, 772.15it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2it [00:00, 963.99it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 1/1 [00:00<00:00, 308.47it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 1/1 [00:00<00:00, 211.23it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n", + "Saved histogram plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000015_c000055_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000015_c000055_grain_stats.csv\n", + "done with segmentation + export\n", + "[210/1053] tile_r000016_c000022\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:01<00:00, 2.50it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.48it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 11/11 [00:00<00:00, 24.52it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "4it [00:00, 635.12it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "5it [00:00, 648.51it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 2/2 [00:00<00:00, 148.57it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 2/2 [00:00<00:00, 190.81it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000016_c000022_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000016_c000022_grain_stats.csv\n", + "done with segmentation + export\n", + "[211/1053] tile_r000016_c000023\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.46it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.51it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 166/166 [00:04<00:00, 34.60it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "120it [00:00, 513.95it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "128it [00:00, 485.23it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 49/49 [00:00<00:00, 137.88it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 50/50 [00:00<00:00, 237.27it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000016_c000023_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000016_c000023_grain_stats.csv\n", + "done with segmentation + export\n", + "[212/1053] tile_r000016_c000024\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.49it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.53it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 322/322 [00:08<00:00, 37.87it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "284it [00:00, 604.49it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "308it [00:00, 590.94it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 123/123 [00:00<00:00, 128.01it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 128/128 [00:00<00:00, 234.90it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n", + "Warning: len(gdf)=128 != len(grain_stats)=126 -> truncating to 126\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000016_c000024_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000016_c000024_grain_stats.csv\n", + "done with segmentation + export\n", + "[213/1053] tile_r000016_c000025\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.44it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.50it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 379/379 [00:10<00:00, 37.83it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "310it [00:00, 702.49it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "337it [00:00, 648.15it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 143/143 [00:00<00:00, 204.16it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 147/147 [00:00<00:00, 258.75it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000016_c000025_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000016_c000025_grain_stats.csv\n", + "done with segmentation + export\n", + "[214/1053] tile_r000016_c000026\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.49it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.54it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 206/206 [00:06<00:00, 29.89it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "109it [00:00, 247.38it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "112it [00:00, 259.00it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 34/34 [00:01<00:00, 31.29it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 38/38 [00:00<00:00, 161.57it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000016_c000026_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000016_c000026_grain_stats.csv\n", + "done with segmentation + export\n", + "[215/1053] tile_r000016_c000027\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.43it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.53it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 527/527 [00:14<00:00, 36.76it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "435it [00:01, 319.42it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "432it [00:01, 361.98it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 130/130 [00:02<00:00, 63.63it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 163/163 [00:00<00:00, 199.49it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000016_c000027_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000016_c000027_grain_stats.csv\n", + "done with segmentation + export\n", + "[216/1053] tile_r000016_c000028\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.46it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.52it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 512/512 [00:13<00:00, 37.02it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "429it [00:01, 363.52it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "451it [00:01, 367.99it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 157/157 [00:01<00:00, 80.51it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 165/165 [00:00<00:00, 206.18it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000016_c000028_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000016_c000028_grain_stats.csv\n", + "done with segmentation + export\n", + "[217/1053] tile_r000016_c000029\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.50it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.46it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 306/306 [00:07<00:00, 38.26it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "244it [00:00, 311.50it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "243it [00:00, 309.53it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 72/72 [00:01<00:00, 52.54it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 87/87 [00:00<00:00, 189.92it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsshome1/0B/di54doz/.local/share/mamba/envs/segmenteverygrain/lib/python3.10/site-packages/segmenteverygrain/segmenteverygrain.py:2217: RuntimeWarning: divide by zero encountered in log2\n", + " phi_major = -np.log2(major_axis_length) # major axis length in phi scale\n", + "/dss/dsshome1/0B/di54doz/.local/share/mamba/envs/segmenteverygrain/lib/python3.10/site-packages/segmenteverygrain/segmenteverygrain.py:2218: RuntimeWarning: divide by zero encountered in log2\n", + " phi_minor = -np.log2(minor_axis_length)\n", + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000016_c000029_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000016_c000029_grain_stats.csv\n", + "done with segmentation + export\n", + "[218/1053] tile_r000016_c000030\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.50it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.51it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 397/397 [00:10<00:00, 39.38it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "333it [00:01, 313.47it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "347it [00:01, 318.13it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 119/119 [00:01<00:00, 62.99it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 123/123 [00:00<00:00, 190.30it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000016_c000030_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000016_c000030_grain_stats.csv\n", + "done with segmentation + export\n", + "[219/1053] tile_r000016_c000031\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:01<00:00, 2.53it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.51it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 268/268 [00:07<00:00, 38.08it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "194it [00:00, 361.81it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "201it [00:00, 358.74it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 70/70 [00:00<00:00, 74.78it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 74/74 [00:00<00:00, 185.71it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000016_c000031_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000016_c000031_grain_stats.csv\n", + "done with segmentation + export\n", + "[220/1053] tile_r000016_c000032\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.41it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.51it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 315/315 [00:08<00:00, 37.87it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "239it [00:00, 239.47it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "249it [00:01, 244.36it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 78/78 [00:01<00:00, 45.35it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 83/83 [00:00<00:00, 174.84it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000016_c000032_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000016_c000032_grain_stats.csv\n", + "done with segmentation + export\n", + "[221/1053] tile_r000016_c000033\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.49it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.46it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 415/415 [00:10<00:00, 39.04it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "342it [00:01, 259.75it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "352it [00:01, 263.82it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 116/116 [00:01<00:00, 61.16it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 118/118 [00:00<00:00, 183.84it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000016_c000033_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000016_c000033_grain_stats.csv\n", + "done with segmentation + export\n", + "[222/1053] tile_r000016_c000034\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.48it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.56it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 594/594 [00:15<00:00, 38.81it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "491it [00:02, 245.27it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "517it [00:02, 243.47it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 163/163 [00:03<00:00, 52.27it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 174/174 [00:00<00:00, 178.14it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000016_c000034_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000016_c000034_grain_stats.csv\n", + "done with segmentation + export\n", + "[223/1053] tile_r000016_c000035\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.46it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.52it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 686/686 [00:17<00:00, 39.04it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "568it [00:02, 234.39it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "588it [00:02, 233.46it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 185/185 [00:03<00:00, 50.73it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 198/198 [00:01<00:00, 185.05it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsshome1/0B/di54doz/.local/share/mamba/envs/segmenteverygrain/lib/python3.10/site-packages/segmenteverygrain/segmenteverygrain.py:2217: RuntimeWarning: divide by zero encountered in log2\n", + " phi_major = -np.log2(major_axis_length) # major axis length in phi scale\n", + "/dss/dsshome1/0B/di54doz/.local/share/mamba/envs/segmenteverygrain/lib/python3.10/site-packages/segmenteverygrain/segmenteverygrain.py:2218: RuntimeWarning: divide by zero encountered in log2\n", + " phi_minor = -np.log2(minor_axis_length)\n", + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000016_c000035_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000016_c000035_grain_stats.csv\n", + "done with segmentation + export\n", + "[224/1053] tile_r000016_c000036\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.48it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.47it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 470/470 [00:11<00:00, 40.06it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "394it [00:01, 283.47it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "415it [00:01, 285.73it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 141/141 [00:02<00:00, 67.57it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 144/144 [00:00<00:00, 181.03it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000016_c000036_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000016_c000036_grain_stats.csv\n", + "done with segmentation + export\n", + "[225/1053] tile_r000016_c000037\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.46it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.46it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 713/713 [00:18<00:00, 38.57it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "582it [00:02, 258.76it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "580it [00:02, 264.92it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 177/177 [00:03<00:00, 58.61it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 217/217 [00:01<00:00, 187.10it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000016_c000037_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000016_c000037_grain_stats.csv\n", + "done with segmentation + export\n", + "[226/1053] tile_r000016_c000038\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.46it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.55it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 607/607 [00:16<00:00, 37.11it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "490it [00:01, 253.25it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "523it [00:02, 253.38it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 175/175 [00:02<00:00, 60.48it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 184/184 [00:01<00:00, 179.52it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000016_c000038_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000016_c000038_grain_stats.csv\n", + "done with segmentation + export\n", + "[227/1053] tile_r000016_c000039\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:01<00:00, 2.56it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.53it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 519/519 [00:13<00:00, 39.25it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "446it [00:01, 291.06it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "475it [00:01, 292.20it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 171/171 [00:02<00:00, 83.17it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 174/174 [00:00<00:00, 192.50it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000016_c000039_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000016_c000039_grain_stats.csv\n", + "done with segmentation + export\n", + "[228/1053] tile_r000016_c000040\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.46it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.50it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 863/863 [00:21<00:00, 39.59it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "776it [00:02, 293.95it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "818it [00:02, 292.90it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 285/285 [00:03<00:00, 79.41it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 302/302 [00:01<00:00, 207.42it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000016_c000040_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000016_c000040_grain_stats.csv\n", + "done with segmentation + export\n", + "[229/1053] tile_r000016_c000041\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.46it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.50it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 639/639 [00:16<00:00, 39.28it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "550it [00:02, 257.03it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "546it [00:02, 270.33it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 173/173 [00:02<00:00, 58.07it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 207/207 [00:01<00:00, 197.05it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000016_c000041_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000016_c000041_grain_stats.csv\n", + "done with segmentation + export\n", + "[230/1053] tile_r000016_c000042\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.45it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.46it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 740/740 [00:18<00:00, 39.12it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "614it [00:02, 282.92it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "613it [00:02, 288.88it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 188/188 [00:02<00:00, 64.74it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 239/239 [00:01<00:00, 194.37it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000016_c000042_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000016_c000042_grain_stats.csv\n", + "done with segmentation + export\n", + "[231/1053] tile_r000016_c000043\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.41it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.54it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 808/808 [00:20<00:00, 40.20it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "721it [00:02, 294.50it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "760it [00:02, 294.18it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 272/272 [00:03<00:00, 82.17it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 284/284 [00:01<00:00, 203.47it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000016_c000043_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000016_c000043_grain_stats.csv\n", + "done with segmentation + export\n", + "[232/1053] tile_r000016_c000044\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.48it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.52it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 703/703 [00:18<00:00, 38.72it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "562it [00:02, 245.06it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "561it [00:02, 247.11it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 171/171 [00:02<00:00, 61.03it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 209/209 [00:01<00:00, 184.73it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000016_c000044_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000016_c000044_grain_stats.csv\n", + "done with segmentation + export\n", + "[233/1053] tile_r000016_c000045\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.49it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.50it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 756/756 [00:19<00:00, 39.28it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "618it [00:02, 253.65it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "647it [00:02, 251.35it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 221/221 [00:03<00:00, 63.11it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 230/230 [00:01<00:00, 185.49it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000016_c000045_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000016_c000045_grain_stats.csv\n", + "done with segmentation + export\n", + "[234/1053] tile_r000016_c000046\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.50it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.50it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 707/707 [00:18<00:00, 37.88it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "582it [00:02, 238.76it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "609it [00:02, 237.12it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 201/201 [00:03<00:00, 51.50it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 217/217 [00:01<00:00, 178.19it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000016_c000046_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000016_c000046_grain_stats.csv\n", + "done with segmentation + export\n", + "[235/1053] tile_r000016_c000047\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.48it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.53it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 836/836 [00:21<00:00, 38.95it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "716it [00:02, 256.19it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "715it [00:02, 259.17it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 214/214 [00:03<00:00, 54.71it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 264/264 [00:01<00:00, 188.78it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000016_c000047_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000016_c000047_grain_stats.csv\n", + "done with segmentation + export\n", + "[236/1053] tile_r000016_c000048\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.46it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.55it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 745/745 [00:18<00:00, 39.75it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "642it [00:02, 281.46it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "676it [00:02, 279.85it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 237/237 [00:03<00:00, 74.45it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 246/246 [00:01<00:00, 202.36it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000016_c000048_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000016_c000048_grain_stats.csv\n", + "done with segmentation + export\n", + "[237/1053] tile_r000016_c000049\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.46it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.45it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 905/905 [00:23<00:00, 38.60it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "766it [00:02, 295.95it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "765it [00:02, 297.71it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 243/243 [00:03<00:00, 66.28it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 297/297 [00:01<00:00, 213.37it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000016_c000049_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000016_c000049_grain_stats.csv\n", + "done with segmentation + export\n", + "[238/1053] tile_r000016_c000050\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:01<00:00, 2.52it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.48it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 778/778 [00:19<00:00, 39.34it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "637it [00:01, 323.35it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "682it [00:02, 319.39it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 251/251 [00:02<00:00, 89.15it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 261/261 [00:01<00:00, 202.96it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000016_c000050_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000016_c000050_grain_stats.csv\n", + "done with segmentation + export\n", + "[239/1053] tile_r000016_c000051\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.49it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.48it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 845/845 [00:21<00:00, 39.99it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "749it [00:02, 289.31it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "748it [00:02, 294.75it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 236/236 [00:03<00:00, 68.12it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 299/299 [00:01<00:00, 202.76it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000016_c000051_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000016_c000051_grain_stats.csv\n", + "done with segmentation + export\n", + "[240/1053] tile_r000016_c000052\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.47it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.49it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 659/659 [00:16<00:00, 40.25it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "557it [00:02, 242.53it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "587it [00:02, 246.63it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 195/195 [00:03<00:00, 59.44it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 202/202 [00:01<00:00, 188.84it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000016_c000052_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000016_c000052_grain_stats.csv\n", + "done with segmentation + export\n", + "[241/1053] tile_r000016_c000053\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.49it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.48it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 848/848 [00:21<00:00, 39.87it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "709it [00:02, 242.21it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "707it [00:02, 254.96it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 231/231 [00:03<00:00, 65.27it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 273/273 [00:01<00:00, 195.92it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000016_c000053_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000016_c000053_grain_stats.csv\n", + "done with segmentation + export\n", + "[242/1053] tile_r000016_c000054\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:01<00:00, 2.53it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.46it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 830/830 [00:21<00:00, 38.38it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "697it [00:03, 178.26it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "694it [00:03, 182.15it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 191/191 [00:05<00:00, 34.30it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 234/234 [00:01<00:00, 169.16it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000016_c000054_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000016_c000054_grain_stats.csv\n", + "done with segmentation + export\n", + "[243/1053] tile_r000016_c000055\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.46it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.55it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 335/335 [00:09<00:00, 37.07it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "272it [00:01, 221.49it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "270it [00:01, 233.06it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 77/77 [00:01<00:00, 47.27it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 94/94 [00:00<00:00, 179.09it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000016_c000055_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000016_c000055_grain_stats.csv\n", + "done with segmentation + export\n", + "[244/1053] tile_r000017_c000021\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:01<00:00, 2.50it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.51it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 86/86 [00:02<00:00, 35.96it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "63it [00:00, 863.94it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "76it [00:00, 798.03it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 33/33 [00:00<00:00, 243.79it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 33/33 [00:00<00:00, 266.44it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000017_c000021_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000017_c000021_grain_stats.csv\n", + "done with segmentation + export\n", + "[245/1053] tile_r000017_c000022\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.48it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.49it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 259/259 [00:07<00:00, 33.53it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "193it [00:00, 681.86it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "214it [00:00, 670.20it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 94/94 [00:00<00:00, 231.80it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 94/94 [00:00<00:00, 264.95it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000017_c000022_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000017_c000022_grain_stats.csv\n", + "done with segmentation + export\n", + "[246/1053] tile_r000017_c000023\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.47it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.49it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 78/78 [00:02<00:00, 29.29it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "26it [00:00, 589.42it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "30it [00:00, 521.94it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 14/14 [00:00<00:00, 309.27it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 14/14 [00:00<00:00, 239.45it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000017_c000023_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000017_c000023_grain_stats.csv\n", + "done with segmentation + export\n", + "[247/1053] tile_r000017_c000024\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.44it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.46it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 103/103 [00:04<00:00, 21.16it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "19it [00:00, 1024.11it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "26it [00:00, 651.49it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 12/12 [00:00<00:00, 205.53it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 13/13 [00:00<00:00, 85.29it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000017_c000024_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000017_c000024_grain_stats.csv\n", + "done with segmentation + export\n", + "[248/1053] tile_r000017_c000025\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.49it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.49it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 193/193 [00:07<00:00, 25.29it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "49it [00:00, 264.96it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "61it [00:00, 289.60it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 23/23 [00:00<00:00, 55.73it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 26/26 [00:00<00:00, 144.58it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000017_c000025_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000017_c000025_grain_stats.csv\n", + "done with segmentation + export\n", + "[249/1053] tile_r000017_c000026\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.25it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.22it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 149/149 [00:07<00:00, 19.63it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "47it [00:00, 437.51it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "57it [00:00, 388.66it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 21/21 [00:01<00:00, 20.76it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 25/25 [00:00<00:00, 169.02it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000017_c000026_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000017_c000026_grain_stats.csv\n", + "done with segmentation + export\n", + "[250/1053] tile_r000017_c000027\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.14it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.28it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 234/234 [00:07<00:00, 32.80it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "159it [00:00, 357.99it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "180it [00:00, 334.45it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 63/63 [00:00<00:00, 70.78it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 69/69 [00:00<00:00, 150.86it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000017_c000027_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000017_c000027_grain_stats.csv\n", + "done with segmentation + export\n", + "[251/1053] tile_r000017_c000028\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.31it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.28it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 171/171 [00:04<00:00, 35.73it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "132it [00:00, 478.57it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "154it [00:00, 463.81it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 61/61 [00:00<00:00, 103.28it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 63/63 [00:00<00:00, 194.72it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000017_c000028_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000017_c000028_grain_stats.csv\n", + "done with segmentation + export\n", + "[252/1053] tile_r000017_c000029\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.36it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.38it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 182/182 [00:04<00:00, 37.45it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "139it [00:00, 401.32it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "149it [00:00, 418.39it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 53/53 [00:00<00:00, 76.24it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 55/55 [00:00<00:00, 193.81it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000017_c000029_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000017_c000029_grain_stats.csv\n", + "done with segmentation + export\n", + "[253/1053] tile_r000017_c000030\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.39it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.39it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 167/167 [00:05<00:00, 30.92it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "110it [00:00, 346.84it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "121it [00:00, 339.79it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 47/47 [00:00<00:00, 84.99it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 48/48 [00:00<00:00, 189.03it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000017_c000030_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000017_c000030_grain_stats.csv\n", + "done with segmentation + export\n", + "[254/1053] tile_r000017_c000031\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.36it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.41it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 174/174 [00:04<00:00, 39.44it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "142it [00:00, 479.25it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "154it [00:00, 471.18it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 62/62 [00:00<00:00, 107.11it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 63/63 [00:00<00:00, 199.16it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000017_c000031_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000017_c000031_grain_stats.csv\n", + "done with segmentation + export\n", + "[255/1053] tile_r000017_c000032\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.42it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.44it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 231/231 [00:05<00:00, 39.12it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "204it [00:00, 375.93it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "216it [00:00, 370.06it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 77/77 [00:00<00:00, 83.92it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 79/79 [00:00<00:00, 173.44it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000017_c000032_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000017_c000032_grain_stats.csv\n", + "done with segmentation + export\n", + "[256/1053] tile_r000017_c000033\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.38it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.43it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 344/344 [00:08<00:00, 38.58it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "282it [00:01, 273.74it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "300it [00:01, 274.26it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 98/98 [00:01<00:00, 57.77it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 101/101 [00:00<00:00, 178.13it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000017_c000033_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000017_c000033_grain_stats.csv\n", + "done with segmentation + export\n", + "[257/1053] tile_r000017_c000034\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.43it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.42it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 360/360 [00:09<00:00, 39.16it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "283it [00:01, 269.58it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "303it [00:01, 272.85it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 103/103 [00:01<00:00, 68.47it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 107/107 [00:00<00:00, 185.94it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000017_c000034_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000017_c000034_grain_stats.csv\n", + "done with segmentation + export\n", + "[258/1053] tile_r000017_c000035\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.42it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.47it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 478/478 [00:12<00:00, 37.87it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "377it [00:01, 241.20it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "376it [00:01, 247.96it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 110/110 [00:02<00:00, 45.64it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 133/133 [00:00<00:00, 175.09it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000017_c000035_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000017_c000035_grain_stats.csv\n", + "done with segmentation + export\n", + "[259/1053] tile_r000017_c000036\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.41it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.45it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 444/444 [00:11<00:00, 37.71it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "337it [00:01, 293.58it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "351it [00:01, 290.89it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 115/115 [00:01<00:00, 59.37it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 121/121 [00:00<00:00, 177.43it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000017_c000036_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000017_c000036_grain_stats.csv\n", + "done with segmentation + export\n", + "[260/1053] tile_r000017_c000037\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.49it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.41it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 853/853 [00:21<00:00, 39.89it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "750it [00:03, 211.43it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "781it [00:03, 213.43it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 263/263 [00:04<00:00, 60.25it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 275/275 [00:01<00:00, 186.09it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000017_c000037_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000017_c000037_grain_stats.csv\n", + "done with segmentation + export\n", + "[261/1053] tile_r000017_c000038\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.48it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.46it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 761/761 [00:19<00:00, 39.59it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "644it [00:02, 292.12it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "674it [00:02, 284.95it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 229/229 [00:03<00:00, 73.72it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 239/239 [00:01<00:00, 195.11it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000017_c000038_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000017_c000038_grain_stats.csv\n", + "done with segmentation + export\n", + "[262/1053] tile_r000017_c000039\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.49it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.48it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 713/713 [00:18<00:00, 39.44it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "591it [00:02, 229.61it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "623it [00:02, 227.49it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 209/209 [00:03<00:00, 56.59it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 218/218 [00:01<00:00, 192.05it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000017_c000039_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000017_c000039_grain_stats.csv\n", + "done with segmentation + export\n", + "[263/1053] tile_r000017_c000040\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.42it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.46it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 524/524 [00:13<00:00, 38.71it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "412it [00:01, 282.58it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "435it [00:01, 282.33it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 146/146 [00:02<00:00, 64.27it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 151/151 [00:00<00:00, 183.45it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000017_c000040_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000017_c000040_grain_stats.csv\n", + "done with segmentation + export\n", + "[264/1053] tile_r000017_c000041\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.46it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.44it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 518/518 [00:13<00:00, 39.09it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "432it [00:01, 298.70it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "449it [00:01, 292.46it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 159/159 [00:02<00:00, 75.02it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 167/167 [00:00<00:00, 193.23it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000017_c000041_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000017_c000041_grain_stats.csv\n", + "done with segmentation + export\n", + "[265/1053] tile_r000017_c000042\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.46it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.43it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 601/601 [00:15<00:00, 39.22it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "455it [00:01, 309.35it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "473it [00:01, 308.36it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 168/168 [00:02<00:00, 81.94it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 172/172 [00:00<00:00, 195.97it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000017_c000042_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000017_c000042_grain_stats.csv\n", + "done with segmentation + export\n", + "[266/1053] tile_r000017_c000043\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.49it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.45it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 452/452 [00:11<00:00, 38.32it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "361it [00:01, 282.65it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "386it [00:01, 284.76it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 138/138 [00:02<00:00, 66.89it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 139/139 [00:00<00:00, 179.83it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000017_c000043_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000017_c000043_grain_stats.csv\n", + "done with segmentation + export\n", + "[267/1053] tile_r000017_c000044\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.47it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.47it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 693/693 [00:17<00:00, 39.14it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "580it [00:02, 255.75it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "577it [00:02, 272.13it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 167/167 [00:03<00:00, 53.88it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 208/208 [00:01<00:00, 188.74it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000017_c000044_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000017_c000044_grain_stats.csv\n", + "done with segmentation + export\n", + "[268/1053] tile_r000017_c000045\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.46it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.48it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 509/509 [00:13<00:00, 37.20it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "374it [00:01, 271.09it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "392it [00:01, 267.35it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 133/133 [00:02<00:00, 65.00it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 140/140 [00:00<00:00, 177.83it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000017_c000045_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000017_c000045_grain_stats.csv\n", + "done with segmentation + export\n", + "[269/1053] tile_r000017_c000046\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.29it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.48it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 487/487 [00:12<00:00, 38.37it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "391it [00:01, 231.80it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "390it [00:01, 245.78it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 118/118 [00:02<00:00, 50.25it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 148/148 [00:00<00:00, 177.10it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000017_c000046_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000017_c000046_grain_stats.csv\n", + "done with segmentation + export\n", + "[270/1053] tile_r000017_c000047\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.48it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.51it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 451/451 [00:11<00:00, 38.06it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "345it [00:01, 245.70it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "366it [00:01, 243.86it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 123/123 [00:02<00:00, 57.45it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 128/128 [00:00<00:00, 177.38it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000017_c000047_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000017_c000047_grain_stats.csv\n", + "done with segmentation + export\n", + "[271/1053] tile_r000017_c000048\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:01<00:00, 2.57it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.46it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 387/387 [00:10<00:00, 37.98it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "304it [00:01, 258.03it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "326it [00:01, 258.32it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 109/109 [00:01<00:00, 57.40it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 113/113 [00:00<00:00, 172.21it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000017_c000048_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000017_c000048_grain_stats.csv\n", + "done with segmentation + export\n", + "[272/1053] tile_r000017_c000049\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.47it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.55it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 589/589 [00:16<00:00, 36.14it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "476it [00:01, 331.33it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "514it [00:01, 334.16it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 190/190 [00:02<00:00, 89.41it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 197/197 [00:01<00:00, 195.83it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n", + "Warning: len(gdf)=197 != len(grain_stats)=196 -> truncating to 196\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000017_c000049_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000017_c000049_grain_stats.csv\n", + "done with segmentation + export\n", + "[273/1053] tile_r000017_c000050\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:01<00:00, 2.52it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.52it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 575/575 [00:14<00:00, 39.30it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "472it [00:01, 340.04it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "503it [00:01, 338.07it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 180/180 [00:02<00:00, 84.64it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 187/187 [00:00<00:00, 198.23it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000017_c000050_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000017_c000050_grain_stats.csv\n", + "done with segmentation + export\n", + "[274/1053] tile_r000017_c000051\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:01<00:00, 2.52it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.51it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 422/422 [00:10<00:00, 38.83it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "317it [00:01, 285.87it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "340it [00:01, 286.54it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 121/121 [00:01<00:00, 65.23it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 123/123 [00:00<00:00, 181.27it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000017_c000051_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000017_c000051_grain_stats.csv\n", + "done with segmentation + export\n", + "[275/1053] tile_r000017_c000052\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:01<00:00, 2.51it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.53it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 698/698 [00:17<00:00, 38.95it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "571it [00:02, 284.12it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "596it [00:02, 282.05it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 207/207 [00:02<00:00, 70.39it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 214/214 [00:01<00:00, 197.56it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n", + "Warning: len(gdf)=214 != len(grain_stats)=213 -> truncating to 213\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000017_c000052_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000017_c000052_grain_stats.csv\n", + "done with segmentation + export\n", + "[276/1053] tile_r000017_c000053\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.48it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.49it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 497/497 [00:13<00:00, 37.78it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "386it [00:01, 240.40it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "385it [00:01, 255.31it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 116/116 [00:02<00:00, 52.54it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 150/150 [00:00<00:00, 176.94it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000017_c000053_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000017_c000053_grain_stats.csv\n", + "done with segmentation + export\n", + "[277/1053] tile_r000017_c000054\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.46it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.52it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 737/737 [00:19<00:00, 37.61it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "577it [00:02, 240.78it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "576it [00:02, 242.50it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 180/180 [00:03<00:00, 57.00it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 224/224 [00:01<00:00, 182.08it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000017_c000054_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000017_c000054_grain_stats.csv\n", + "done with segmentation + export\n", + "[278/1053] tile_r000017_c000055\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:01<00:00, 2.50it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.48it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 702/702 [00:18<00:00, 37.57it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "584it [00:02, 239.89it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "583it [00:02, 245.43it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 175/175 [00:03<00:00, 53.25it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 218/218 [00:01<00:00, 176.01it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n", + "Warning: len(gdf)=218 != len(grain_stats)=217 -> truncating to 217\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsshome1/0B/di54doz/.local/share/mamba/envs/segmenteverygrain/lib/python3.10/site-packages/segmenteverygrain/segmenteverygrain.py:2217: RuntimeWarning: divide by zero encountered in log2\n", + " phi_major = -np.log2(major_axis_length) # major axis length in phi scale\n", + "/dss/dsshome1/0B/di54doz/.local/share/mamba/envs/segmenteverygrain/lib/python3.10/site-packages/segmenteverygrain/segmenteverygrain.py:2218: RuntimeWarning: divide by zero encountered in log2\n", + " phi_minor = -np.log2(minor_axis_length)\n", + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000017_c000055_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000017_c000055_grain_stats.csv\n", + "done with segmentation + export\n", + "[279/1053] tile_r000017_c000056\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:01<00:00, 2.52it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.52it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 289/289 [00:08<00:00, 35.66it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "218it [00:00, 262.49it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "227it [00:00, 261.96it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 84/84 [00:01<00:00, 81.42it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 84/84 [00:00<00:00, 189.75it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000017_c000056_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000017_c000056_grain_stats.csv\n", + "done with segmentation + export\n", + "[280/1053] tile_r000018_c000019\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:01<00:00, 2.51it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.53it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 20/20 [00:00<00:00, 24.79it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "3it [00:00, 439.52it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "3it [00:00, 478.53it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 1/1 [00:00<00:00, 63.40it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 1/1 [00:00<00:00, 168.42it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n", + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000018_c000019_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000018_c000019_grain_stats.csv\n", + "done with segmentation + export\n", + "[281/1053] tile_r000018_c000020\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.49it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.46it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 237/237 [00:07<00:00, 33.39it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "199it [00:00, 368.59it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "214it [00:00, 358.23it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 79/79 [00:00<00:00, 85.12it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 83/83 [00:00<00:00, 179.53it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000018_c000020_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000018_c000020_grain_stats.csv\n", + "done with segmentation + export\n", + "[282/1053] tile_r000018_c000021\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:01<00:00, 2.58it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.52it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 98/98 [00:04<00:00, 22.51it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "33it [00:00, 336.33it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "37it [00:00, 332.44it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 15/15 [00:00<00:00, 114.07it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 15/15 [00:00<00:00, 120.36it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000018_c000021_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000018_c000021_grain_stats.csv\n", + "done with segmentation + export\n", + "[283/1053] tile_r000018_c000022\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:01<00:00, 2.51it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.51it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 78/78 [00:04<00:00, 18.69it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "0it [00:00, ?it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "0it [00:00, ?it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "0it [00:00, ?it/s]\n", + "0it [00:00, ?it/s]\n", + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n", + "No grains found -> skipping histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000018_c000022_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000018_c000022_grain_stats.csv\n", + "done with segmentation + export\n", + "[284/1053] tile_r000018_c000023\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.44it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.50it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 291/291 [00:12<00:00, 22.48it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "195it [00:15, 12.57it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "193it [00:14, 13.23it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 49/49 [00:36<00:00, 1.35it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 71/71 [00:00<00:00, 154.45it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000018_c000023_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000018_c000023_grain_stats.csv\n", + "done with segmentation + export\n", + "[285/1053] tile_r000018_c000024\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:01<00:00, 2.51it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.48it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 143/143 [00:06<00:00, 21.29it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "23it [00:00, 33.00it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "20it [00:00, 47.87it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 1/1 [00:01<00:00, 1.02s/it]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 12/12 [00:00<00:00, 77.77it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000018_c000024_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000018_c000024_grain_stats.csv\n", + "done with segmentation + export\n", + "[286/1053] tile_r000018_c000025\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.49it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.52it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 66/66 [00:02<00:00, 25.68it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "45it [00:00, 75.83it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "43it [00:00, 423.85it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 12/12 [00:00<00:00, 35.05it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 24/24 [00:00<00:00, 68.63it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000018_c000025_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000018_c000025_grain_stats.csv\n", + "done with segmentation + export\n", + "[287/1053] tile_r000018_c000026\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:01<00:00, 2.53it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.57it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 115/115 [00:04<00:00, 26.61it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "68it [00:00, 156.41it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "90it [00:00, 156.51it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 31/31 [00:03<00:00, 8.88it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 40/40 [00:00<00:00, 92.05it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000018_c000026_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000018_c000026_grain_stats.csv\n", + "done with segmentation + export\n", + "[288/1053] tile_r000018_c000027\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.45it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.48it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 226/226 [00:11<00:00, 18.90it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "95it [00:15, 6.02it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "83it [00:10, 8.08it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 7/7 [00:24<00:00, 3.56s/it]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 29/29 [00:00<00:00, 132.89it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000018_c000027_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000018_c000027_grain_stats.csv\n", + "done with segmentation + export\n", + "[289/1053] tile_r000018_c000028\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.50it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.48it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 176/176 [00:07<00:00, 22.36it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "84it [00:02, 39.90it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "73it [00:00, 80.39it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 15/15 [00:02<00:00, 6.38it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 37/37 [00:00<00:00, 89.65it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000018_c000028_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000018_c000028_grain_stats.csv\n", + "done with segmentation + export\n", + "[290/1053] tile_r000018_c000029\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.49it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.48it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 321/321 [00:12<00:00, 26.55it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "199it [00:04, 41.28it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "180it [00:02, 64.46it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 40/40 [00:06<00:00, 6.49it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 89/89 [00:00<00:00, 174.24it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000018_c000029_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000018_c000029_grain_stats.csv\n", + "done with segmentation + export\n", + "[291/1053] tile_r000018_c000030\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:01<00:00, 2.53it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.55it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 430/430 [00:13<00:00, 32.42it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "331it [00:01, 188.98it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "319it [00:01, 301.01it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 95/95 [00:01<00:00, 57.56it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 146/146 [00:00<00:00, 176.81it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000018_c000030_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000018_c000030_grain_stats.csv\n", + "done with segmentation + export\n", + "[292/1053] tile_r000018_c000031\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:01<00:00, 2.53it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.54it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 358/358 [00:10<00:00, 34.13it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "251it [00:01, 176.80it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "247it [00:00, 275.01it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 70/70 [00:01<00:00, 40.34it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 97/97 [00:00<00:00, 166.62it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000018_c000031_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000018_c000031_grain_stats.csv\n", + "done with segmentation + export\n", + "[293/1053] tile_r000018_c000032\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.49it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.52it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 439/439 [00:11<00:00, 37.17it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "317it [00:00, 342.23it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "316it [00:00, 366.21it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 96/96 [00:01<00:00, 68.28it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 127/127 [00:00<00:00, 188.28it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000018_c000032_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000018_c000032_grain_stats.csv\n", + "done with segmentation + export\n", + "[294/1053] tile_r000018_c000033\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.48it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.46it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 296/296 [00:08<00:00, 34.22it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "220it [00:00, 415.88it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "238it [00:00, 406.32it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 84/84 [00:01<00:00, 78.24it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 87/87 [00:00<00:00, 190.45it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000018_c000033_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000018_c000033_grain_stats.csv\n", + "done with segmentation + export\n", + "[295/1053] tile_r000018_c000034\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:01<00:00, 2.55it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.47it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 399/399 [00:11<00:00, 34.27it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "286it [00:01, 244.15it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "284it [00:01, 258.26it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 78/78 [00:01<00:00, 42.99it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 103/103 [00:00<00:00, 163.79it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000018_c000034_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000018_c000034_grain_stats.csv\n", + "done with segmentation + export\n", + "[296/1053] tile_r000018_c000035\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.46it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.48it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 566/566 [00:14<00:00, 39.54it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "458it [00:02, 202.70it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "456it [00:02, 209.66it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 126/126 [00:03<00:00, 39.72it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 161/161 [00:00<00:00, 174.62it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000018_c000035_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000018_c000035_grain_stats.csv\n", + "done with segmentation + export\n", + "[297/1053] tile_r000018_c000036\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:01<00:00, 2.52it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.39it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 556/556 [00:14<00:00, 38.50it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "435it [00:03, 144.45it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "451it [00:03, 144.70it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 146/146 [00:03<00:00, 40.97it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 155/155 [00:00<00:00, 172.75it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000018_c000036_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000018_c000036_grain_stats.csv\n", + "done with segmentation + export\n", + "[298/1053] tile_r000018_c000037\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.48it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.52it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 707/707 [00:17<00:00, 39.43it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "615it [00:02, 236.25it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "642it [00:02, 234.50it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 212/212 [00:03<00:00, 60.16it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 219/219 [00:01<00:00, 181.60it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000018_c000037_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000018_c000037_grain_stats.csv\n", + "done with segmentation + export\n", + "[299/1053] tile_r000018_c000038\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.49it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.55it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 710/710 [00:18<00:00, 39.42it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "591it [00:02, 251.51it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "616it [00:02, 250.18it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 212/212 [00:03<00:00, 70.20it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 219/219 [00:01<00:00, 190.29it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000018_c000038_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000018_c000038_grain_stats.csv\n", + "done with segmentation + export\n", + "[300/1053] tile_r000018_c000039\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.49it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.46it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 851/851 [00:21<00:00, 40.13it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "743it [00:02, 254.67it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "783it [00:03, 253.52it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 266/266 [00:03<00:00, 69.96it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 278/278 [00:01<00:00, 208.57it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000018_c000039_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000018_c000039_grain_stats.csv\n", + "done with segmentation + export\n", + "[301/1053] tile_r000018_c000040\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:01<00:00, 2.52it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.47it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 572/572 [00:15<00:00, 37.71it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "454it [00:01, 248.72it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "486it [00:01, 257.29it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 167/167 [00:02<00:00, 60.08it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 169/169 [00:00<00:00, 193.42it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000018_c000040_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000018_c000040_grain_stats.csv\n", + "done with segmentation + export\n", + "[302/1053] tile_r000018_c000041\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.47it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.49it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 556/556 [00:13<00:00, 39.80it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "445it [00:01, 305.02it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "470it [00:01, 301.84it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 163/163 [00:02<00:00, 73.16it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 164/164 [00:00<00:00, 182.05it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000018_c000041_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000018_c000041_grain_stats.csv\n", + "done with segmentation + export\n", + "[303/1053] tile_r000018_c000042\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.49it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.53it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 575/575 [00:14<00:00, 39.58it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "495it [00:02, 217.74it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "523it [00:02, 217.23it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 167/167 [00:03<00:00, 47.64it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 176/176 [00:00<00:00, 183.06it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000018_c000042_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000018_c000042_grain_stats.csv\n", + "done with segmentation + export\n", + "[304/1053] tile_r000018_c000043\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.49it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.50it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 581/581 [00:14<00:00, 39.85it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "482it [00:01, 243.42it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "481it [00:01, 248.10it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 142/142 [00:02<00:00, 51.55it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 175/175 [00:00<00:00, 183.33it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000018_c000043_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000018_c000043_grain_stats.csv\n", + "done with segmentation + export\n", + "[305/1053] tile_r000018_c000044\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.50it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.56it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 575/575 [00:14<00:00, 39.46it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "483it [00:01, 246.89it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "482it [00:01, 266.38it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 150/150 [00:02<00:00, 60.90it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 183/183 [00:00<00:00, 186.23it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000018_c000044_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000018_c000044_grain_stats.csv\n", + "done with segmentation + export\n", + "[306/1053] tile_r000018_c000045\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.48it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.49it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 416/416 [00:10<00:00, 38.66it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "311it [00:01, 267.80it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "333it [00:01, 266.57it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 110/110 [00:01<00:00, 56.26it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 115/115 [00:00<00:00, 177.48it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000018_c000045_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000018_c000045_grain_stats.csv\n", + "done with segmentation + export\n", + "[307/1053] tile_r000018_c000046\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:01<00:00, 2.51it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.51it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 230/230 [00:06<00:00, 38.01it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "170it [00:00, 266.13it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "179it [00:00, 267.05it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 60/60 [00:00<00:00, 61.96it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 62/62 [00:00<00:00, 168.31it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000018_c000046_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000018_c000046_grain_stats.csv\n", + "done with segmentation + export\n", + "[308/1053] tile_r000018_c000047\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:01<00:00, 2.55it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.50it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 282/282 [00:07<00:00, 37.54it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "220it [00:00, 301.47it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "236it [00:00, 299.46it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 80/80 [00:01<00:00, 67.00it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 84/84 [00:00<00:00, 174.64it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000018_c000047_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000018_c000047_grain_stats.csv\n", + "done with segmentation + export\n", + "[309/1053] tile_r000018_c000048\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:01<00:00, 2.51it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.51it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 356/356 [00:09<00:00, 38.13it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "243it [00:00, 254.58it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "261it [00:01, 251.18it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 84/84 [00:01<00:00, 50.59it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 88/88 [00:00<00:00, 155.31it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000018_c000048_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000018_c000048_grain_stats.csv\n", + "done with segmentation + export\n", + "[310/1053] tile_r000018_c000049\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:01<00:00, 2.50it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.55it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 429/429 [00:10<00:00, 39.45it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "363it [00:01, 267.33it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "380it [00:01, 264.14it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 133/133 [00:01<00:00, 70.72it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 140/140 [00:00<00:00, 189.11it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000018_c000049_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000018_c000049_grain_stats.csv\n", + "done with segmentation + export\n", + "[311/1053] tile_r000018_c000050\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.50it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.53it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 262/262 [00:06<00:00, 38.00it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "193it [00:00, 225.42it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "204it [00:00, 229.36it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 62/62 [00:01<00:00, 41.30it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 64/64 [00:00<00:00, 184.05it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000018_c000050_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000018_c000050_grain_stats.csv\n", + "done with segmentation + export\n", + "[312/1053] tile_r000018_c000051\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:01<00:00, 2.51it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.47it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 477/477 [00:12<00:00, 38.70it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "379it [00:01, 278.58it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "378it [00:01, 283.92it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 115/115 [00:02<00:00, 55.26it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 142/142 [00:00<00:00, 197.60it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000018_c000051_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000018_c000051_grain_stats.csv\n", + "done with segmentation + export\n", + "[313/1053] tile_r000018_c000052\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.50it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.52it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 346/346 [00:08<00:00, 39.11it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "267it [00:01, 216.05it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "276it [00:01, 232.31it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 83/83 [00:02<00:00, 41.01it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 90/90 [00:00<00:00, 179.17it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000018_c000052_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000018_c000052_grain_stats.csv\n", + "done with segmentation + export\n", + "[314/1053] tile_r000018_c000053\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.47it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.55it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 317/317 [00:08<00:00, 38.71it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "243it [00:01, 241.07it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "256it [00:01, 243.95it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 76/76 [00:01<00:00, 41.68it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 82/82 [00:00<00:00, 153.72it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000018_c000053_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000018_c000053_grain_stats.csv\n", + "done with segmentation + export\n", + "[315/1053] tile_r000018_c000054\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:01<00:00, 2.51it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.57it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 466/466 [00:13<00:00, 33.94it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "338it [00:01, 243.08it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "334it [00:01, 278.46it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 98/98 [00:02<00:00, 45.63it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 123/123 [00:00<00:00, 173.70it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n", + "Warning: len(gdf)=123 != len(grain_stats)=122 -> truncating to 122\n", + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000018_c000054_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000018_c000054_grain_stats.csv\n", + "done with segmentation + export\n", + "[316/1053] tile_r000018_c000055\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.47it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.34it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 1060/1060 [00:26<00:00, 39.55it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "940it [00:04, 213.75it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "939it [00:04, 217.82it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 292/292 [00:05<00:00, 56.67it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 341/341 [00:01<00:00, 187.04it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000018_c000055_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000018_c000055_grain_stats.csv\n", + "done with segmentation + export\n", + "[317/1053] tile_r000018_c000056\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:01<00:00, 2.53it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.45it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 1058/1058 [00:26<00:00, 39.88it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "955it [00:03, 246.08it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "984it [00:04, 243.48it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 350/350 [00:04<00:00, 76.04it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 362/362 [00:01<00:00, 193.29it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000018_c000056_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000018_c000056_grain_stats.csv\n", + "done with segmentation + export\n", + "[318/1053] tile_r000018_c000057\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.49it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.50it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 177/177 [00:04<00:00, 35.50it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "125it [00:00, 295.07it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "134it [00:00, 295.61it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 48/48 [00:00<00:00, 81.05it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 53/53 [00:00<00:00, 194.80it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000018_c000057_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000018_c000057_grain_stats.csv\n", + "done with segmentation + export\n", + "[319/1053] tile_r000019_c000017\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.43it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.51it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "0it [00:00, ?it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "0it [00:00, ?it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "0it [00:00, ?it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "0it [00:00, ?it/s]\n", + "0it [00:00, ?it/s]\n", + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n", + "No grains found -> skipping histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000019_c000017_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000019_c000017_grain_stats.csv\n", + "done with segmentation + export\n", + "[320/1053] tile_r000019_c000018\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.48it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.51it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 55/55 [00:02<00:00, 23.61it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "4it [00:00, 341.61it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "4it [00:00, 380.78it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 1/1 [00:00<00:00, 37.09it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 2/2 [00:00<00:00, 109.28it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000019_c000018_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000019_c000018_grain_stats.csv\n", + "done with segmentation + export\n", + "[321/1053] tile_r000019_c000019\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.46it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.53it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 345/345 [00:08<00:00, 38.42it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "294it [00:00, 428.78it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "312it [00:00, 423.35it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 127/127 [00:00<00:00, 139.81it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 128/128 [00:00<00:00, 217.55it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000019_c000019_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000019_c000019_grain_stats.csv\n", + "done with segmentation + export\n", + "[322/1053] tile_r000019_c000020\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:01<00:00, 2.51it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.47it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 112/112 [00:04<00:00, 24.96it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "24it [00:00, 122.01it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "27it [00:00, 119.40it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 9/9 [00:00<00:00, 15.46it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 11/11 [00:00<00:00, 75.69it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000019_c000020_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000019_c000020_grain_stats.csv\n", + "done with segmentation + export\n", + "[323/1053] tile_r000019_c000021\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.48it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.52it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 548/548 [00:17<00:00, 31.58it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "355it [00:00, 578.48it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "374it [00:00, 564.79it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 156/156 [00:00<00:00, 210.53it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 158/158 [00:00<00:00, 259.37it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000019_c000021_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000019_c000021_grain_stats.csv\n", + "done with segmentation + export\n", + "[324/1053] tile_r000019_c000022\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.46it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.52it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 703/703 [00:18<00:00, 38.63it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "601it [00:01, 578.46it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "600it [00:00, 647.26it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 227/227 [00:01<00:00, 192.15it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 300/300 [00:01<00:00, 273.41it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000019_c000022_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000019_c000022_grain_stats.csv\n", + "done with segmentation + export\n", + "[325/1053] tile_r000019_c000023\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:01<00:00, 2.54it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.50it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 434/434 [00:12<00:00, 35.18it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "312it [00:00, 582.25it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "334it [00:00, 562.53it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 138/138 [00:00<00:00, 150.39it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 146/146 [00:00<00:00, 222.01it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000019_c000023_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000019_c000023_grain_stats.csv\n", + "done with segmentation + export\n", + "[326/1053] tile_r000019_c000024\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.21it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.19it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 161/161 [00:07<00:00, 21.42it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "43it [00:00, 43.77it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "38it [00:00, 107.26it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 6/6 [00:01<00:00, 4.89it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 17/17 [00:00<00:00, 82.33it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000019_c000024_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000019_c000024_grain_stats.csv\n", + "done with segmentation + export\n", + "[327/1053] tile_r000019_c000025\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.17it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.26it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 80/80 [00:03<00:00, 22.39it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "35it [00:00, 397.81it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "48it [00:00, 341.85it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 21/21 [00:00<00:00, 34.21it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 26/26 [00:00<00:00, 107.01it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000019_c000025_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000019_c000025_grain_stats.csv\n", + "done with segmentation + export\n", + "[328/1053] tile_r000019_c000026\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.27it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.27it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 129/129 [00:05<00:00, 22.30it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "23it [00:00, 956.53it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "36it [00:00, 689.41it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 17/17 [00:00<00:00, 173.96it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 17/17 [00:00<00:00, 159.82it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000019_c000026_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000019_c000026_grain_stats.csv\n", + "done with segmentation + export\n", + "[329/1053] tile_r000019_c000027\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.24it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.28it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 148/148 [00:05<00:00, 25.14it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "56it [00:00, 121.30it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "55it [00:00, 220.75it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 9/9 [00:03<00:00, 2.71it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 33/33 [00:00<00:00, 129.12it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000019_c000027_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000019_c000027_grain_stats.csv\n", + "done with segmentation + export\n", + "[330/1053] tile_r000019_c000028\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.30it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.30it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 219/219 [00:09<00:00, 22.31it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "122it [00:10, 11.48it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "105it [00:08, 12.87it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 17/17 [00:17<00:00, 1.01s/it]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 42/42 [00:00<00:00, 100.69it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n", + "Warning: len(gdf)=42 != len(grain_stats)=41 -> truncating to 41\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000019_c000028_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000019_c000028_grain_stats.csv\n", + "done with segmentation + export\n", + "[331/1053] tile_r000019_c000029\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.28it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.36it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 248/248 [00:09<00:00, 26.12it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "98it [00:00, 143.19it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "97it [00:00, 145.46it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 21/21 [00:01<00:00, 14.79it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 49/49 [00:00<00:00, 92.62it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000019_c000029_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000019_c000029_grain_stats.csv\n", + "done with segmentation + export\n", + "[332/1053] tile_r000019_c000030\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.36it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.40it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 199/199 [00:07<00:00, 25.64it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "111it [00:01, 56.06it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "99it [00:00, 183.42it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 17/17 [00:01<00:00, 14.77it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 59/59 [00:00<00:00, 121.33it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000019_c000030_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000019_c000030_grain_stats.csv\n", + "done with segmentation + export\n", + "[333/1053] tile_r000019_c000031\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.38it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.39it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 159/159 [00:05<00:00, 27.34it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "63it [00:00, 134.30it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "80it [00:00, 139.83it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 28/28 [00:01<00:00, 14.53it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 32/32 [00:00<00:00, 103.51it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000019_c000031_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000019_c000031_grain_stats.csv\n", + "done with segmentation + export\n", + "[334/1053] tile_r000019_c000032\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.34it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.37it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 174/174 [00:06<00:00, 27.48it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "101it [00:00, 206.64it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "134it [00:00, 198.59it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 55/55 [00:02<00:00, 25.43it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 62/62 [00:00<00:00, 114.27it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000019_c000032_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000019_c000032_grain_stats.csv\n", + "done with segmentation + export\n", + "[335/1053] tile_r000019_c000033\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.40it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.47it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 569/569 [00:15<00:00, 35.76it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "414it [00:01, 233.65it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "403it [00:01, 384.74it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 136/136 [00:01<00:00, 77.39it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 188/188 [00:01<00:00, 182.70it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000019_c000033_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000019_c000033_grain_stats.csv\n", + "done with segmentation + export\n", + "[336/1053] tile_r000019_c000034\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.44it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.46it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 867/867 [00:22<00:00, 39.39it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "780it [00:02, 286.17it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "779it [00:02, 291.48it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 255/255 [00:03<00:00, 69.72it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 301/301 [00:01<00:00, 199.84it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n", + "Warning: len(gdf)=301 != len(grain_stats)=300 -> truncating to 300\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000019_c000034_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000019_c000034_grain_stats.csv\n", + "done with segmentation + export\n", + "[337/1053] tile_r000019_c000035\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.47it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.47it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 806/806 [00:20<00:00, 39.15it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "677it [00:02, 236.05it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "714it [00:03, 235.30it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 245/245 [00:04<00:00, 54.51it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 261/261 [00:01<00:00, 190.11it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000019_c000035_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000019_c000035_grain_stats.csv\n", + "done with segmentation + export\n", + "[338/1053] tile_r000019_c000036\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.45it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.50it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 663/663 [00:17<00:00, 38.83it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "540it [00:02, 250.51it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "569it [00:02, 250.92it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 186/186 [00:03<00:00, 57.28it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 204/204 [00:01<00:00, 176.36it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000019_c000036_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000019_c000036_grain_stats.csv\n", + "done with segmentation + export\n", + "[339/1053] tile_r000019_c000037\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.43it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.43it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 512/512 [00:13<00:00, 38.94it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "427it [00:01, 228.37it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "426it [00:01, 231.56it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 128/128 [00:02<00:00, 51.00it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 149/149 [00:00<00:00, 184.44it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000019_c000037_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000019_c000037_grain_stats.csv\n", + "done with segmentation + export\n", + "[340/1053] tile_r000019_c000038\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.41it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.49it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 672/672 [00:17<00:00, 39.39it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "566it [00:02, 211.30it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "582it [00:02, 219.78it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 187/187 [00:03<00:00, 55.09it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 199/199 [00:01<00:00, 185.17it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n", + "Warning: len(gdf)=199 != len(grain_stats)=198 -> truncating to 198\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsshome1/0B/di54doz/.local/share/mamba/envs/segmenteverygrain/lib/python3.10/site-packages/segmenteverygrain/segmenteverygrain.py:2218: RuntimeWarning: divide by zero encountered in log2\n", + " phi_minor = -np.log2(minor_axis_length)\n", + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000019_c000038_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000019_c000038_grain_stats.csv\n", + "done with segmentation + export\n", + "[341/1053] tile_r000019_c000039\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.47it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.50it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 734/734 [00:18<00:00, 40.14it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "626it [00:02, 293.89it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "657it [00:02, 291.07it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 232/232 [00:02<00:00, 81.80it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 246/246 [00:01<00:00, 199.46it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000019_c000039_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000019_c000039_grain_stats.csv\n", + "done with segmentation + export\n", + "[342/1053] tile_r000019_c000040\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.44it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.48it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 728/728 [00:18<00:00, 39.49it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "617it [00:02, 245.86it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "650it [00:02, 243.56it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 226/226 [00:03<00:00, 64.34it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 233/233 [00:01<00:00, 189.86it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000019_c000040_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000019_c000040_grain_stats.csv\n", + "done with segmentation + export\n", + "[343/1053] tile_r000019_c000041\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:01<00:00, 2.50it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.54it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 576/576 [00:14<00:00, 38.89it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "453it [00:01, 285.31it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "473it [00:01, 285.98it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 160/160 [00:02<00:00, 68.16it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 167/167 [00:00<00:00, 186.16it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000019_c000041_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000019_c000041_grain_stats.csv\n", + "done with segmentation + export\n", + "[344/1053] tile_r000019_c000042\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.48it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.52it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 429/429 [00:11<00:00, 38.31it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "351it [00:01, 241.97it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "367it [00:01, 242.58it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 118/118 [00:02<00:00, 50.69it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 124/124 [00:00<00:00, 168.35it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000019_c000042_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000019_c000042_grain_stats.csv\n", + "done with segmentation + export\n", + "[345/1053] tile_r000019_c000043\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.48it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.52it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 300/300 [00:07<00:00, 38.96it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "224it [00:00, 258.90it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "237it [00:00, 261.16it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 83/83 [00:01<00:00, 63.66it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 84/84 [00:00<00:00, 161.60it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000019_c000043_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000019_c000043_grain_stats.csv\n", + "done with segmentation + export\n", + "[346/1053] tile_r000019_c000044\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.43it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.50it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 486/486 [00:12<00:00, 39.96it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "420it [00:02, 198.33it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "419it [00:02, 200.50it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 114/114 [00:02<00:00, 38.01it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 146/146 [00:00<00:00, 168.17it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000019_c000044_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000019_c000044_grain_stats.csv\n", + "done with segmentation + export\n", + "[347/1053] tile_r000019_c000045\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.48it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.54it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 467/467 [00:11<00:00, 40.02it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "402it [00:01, 282.31it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "426it [00:01, 282.31it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 146/146 [00:02<00:00, 65.19it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 150/150 [00:00<00:00, 178.85it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000019_c000045_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000019_c000045_grain_stats.csv\n", + "done with segmentation + export\n", + "[348/1053] tile_r000019_c000046\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.40it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.52it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 540/540 [00:13<00:00, 39.75it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "466it [00:01, 257.74it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "494it [00:01, 251.83it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 173/173 [00:02<00:00, 66.37it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 176/176 [00:00<00:00, 178.34it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000019_c000046_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000019_c000046_grain_stats.csv\n", + "done with segmentation + export\n", + "[349/1053] tile_r000019_c000047\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:01<00:00, 2.52it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.48it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 463/463 [00:11<00:00, 39.06it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "363it [00:01, 218.24it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "383it [00:01, 216.36it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 124/124 [00:02<00:00, 47.17it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 126/126 [00:00<00:00, 172.16it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000019_c000047_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000019_c000047_grain_stats.csv\n", + "done with segmentation + export\n", + "[350/1053] tile_r000019_c000048\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.34it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.53it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 289/289 [00:07<00:00, 37.09it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "209it [00:00, 242.71it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "221it [00:00, 243.54it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 70/70 [00:01<00:00, 48.12it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 74/74 [00:00<00:00, 157.25it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000019_c000048_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000019_c000048_grain_stats.csv\n", + "done with segmentation + export\n", + "[351/1053] tile_r000019_c000049\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:01<00:00, 2.54it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.49it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 281/281 [00:08<00:00, 34.64it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "197it [00:00, 235.79it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "203it [00:00, 241.01it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 63/63 [00:01<00:00, 43.08it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 63/63 [00:00<00:00, 160.62it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000019_c000049_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000019_c000049_grain_stats.csv\n", + "done with segmentation + export\n", + "[352/1053] tile_r000019_c000050\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:01<00:00, 2.52it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.50it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 202/202 [00:05<00:00, 37.80it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "152it [00:00, 314.22it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "166it [00:00, 313.47it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 59/59 [00:00<00:00, 71.35it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 61/61 [00:00<00:00, 169.47it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000019_c000050_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000019_c000050_grain_stats.csv\n", + "done with segmentation + export\n", + "[353/1053] tile_r000019_c000051\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.50it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.50it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 333/333 [00:08<00:00, 37.28it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "248it [00:00, 315.25it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "265it [00:00, 335.76it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 102/102 [00:01<00:00, 87.36it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 105/105 [00:00<00:00, 198.94it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000019_c000051_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000019_c000051_grain_stats.csv\n", + "done with segmentation + export\n", + "[354/1053] tile_r000019_c000052\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:01<00:00, 2.53it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.49it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 152/152 [00:03<00:00, 38.08it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "116it [00:00, 295.55it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "127it [00:00, 297.20it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 42/42 [00:00<00:00, 54.47it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 43/43 [00:00<00:00, 165.10it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000019_c000052_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000019_c000052_grain_stats.csv\n", + "done with segmentation + export\n", + "[355/1053] tile_r000019_c000053\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.42it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.53it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 297/297 [00:07<00:00, 39.07it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "236it [00:00, 402.72it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "250it [00:00, 394.82it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 94/94 [00:01<00:00, 93.85it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 100/100 [00:00<00:00, 199.11it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000019_c000053_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000019_c000053_grain_stats.csv\n", + "done with segmentation + export\n", + "[356/1053] tile_r000019_c000054\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.45it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.56it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 489/489 [00:14<00:00, 33.82it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "388it [00:01, 305.10it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "424it [00:01, 304.93it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 148/148 [00:01<00:00, 79.74it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 157/157 [00:00<00:00, 190.55it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000019_c000054_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000019_c000054_grain_stats.csv\n", + "done with segmentation + export\n", + "[357/1053] tile_r000019_c000055\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:01<00:00, 2.51it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.50it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 777/777 [00:19<00:00, 38.88it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "655it [00:03, 210.97it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "651it [00:02, 223.88it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 200/200 [00:04<00:00, 49.78it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 242/242 [00:01<00:00, 181.00it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000019_c000055_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000019_c000055_grain_stats.csv\n", + "done with segmentation + export\n", + "[358/1053] tile_r000019_c000056\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.32it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.53it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 928/928 [00:23<00:00, 38.81it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "749it [00:03, 244.44it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "771it [00:03, 244.48it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 259/259 [00:04<00:00, 62.59it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 269/269 [00:01<00:00, 189.12it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000019_c000056_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000019_c000056_grain_stats.csv\n", + "done with segmentation + export\n", + "[359/1053] tile_r000019_c000057\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.48it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.50it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 964/964 [00:23<00:00, 40.63it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "870it [00:03, 240.70it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "886it [00:03, 239.69it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 314/314 [00:04<00:00, 75.49it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 322/322 [00:01<00:00, 195.28it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000019_c000057_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000019_c000057_grain_stats.csv\n", + "done with segmentation + export\n", + "[360/1053] tile_r000019_c000058\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.48it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.54it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 48/48 [00:01<00:00, 33.57it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "29it [00:00, 392.81it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "33it [00:00, 389.44it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 13/13 [00:00<00:00, 144.22it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\n", + "100%|██████████| 13/13 [00:00<00:00, 223.11it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000019_c000058_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000019_c000058_grain_stats.csv\n", + "done with segmentation + export\n", + "[361/1053] tile_r000020_c000016\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.46it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.48it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 23/23 [00:01<00:00, 20.43it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "0it [00:00, ?it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "0it [00:00, ?it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "0it [00:00, ?it/s]\n", + "0it [00:00, ?it/s]\n", + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n", + "No grains found -> skipping histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000020_c000016_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000020_c000016_grain_stats.csv\n", + "done with segmentation + export\n", + "[362/1053] tile_r000020_c000017\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.45it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.50it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 127/127 [00:04<00:00, 31.17it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "80it [00:00, 327.53it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "84it [00:00, 279.34it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 31/31 [00:00<00:00, 68.55it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 34/34 [00:00<00:00, 176.08it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000020_c000017_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000020_c000017_grain_stats.csv\n", + "done with segmentation + export\n", + "[363/1053] tile_r000020_c000018\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.49it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.52it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 142/142 [00:05<00:00, 26.19it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "49it [00:00, 664.08it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "55it [00:00, 496.81it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 24/24 [00:00<00:00, 135.91it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 24/24 [00:00<00:00, 152.48it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000020_c000018_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000020_c000018_grain_stats.csv\n", + "done with segmentation + export\n", + "[364/1053] tile_r000020_c000019\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:01<00:00, 2.52it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.52it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 1319/1319 [00:31<00:00, 41.64it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "1223it [00:02, 537.55it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "1222it [00:02, 552.80it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 508/508 [00:02<00:00, 186.95it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 549/549 [00:02<00:00, 259.79it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000020_c000019_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000020_c000019_grain_stats.csv\n", + "done with segmentation + export\n", + "[365/1053] tile_r000020_c000020\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:01<00:00, 2.57it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.49it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 1411/1411 [00:36<00:00, 38.31it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "1249it [00:02, 543.00it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "1248it [00:02, 558.39it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 492/492 [00:02<00:00, 183.97it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 555/555 [00:01<00:00, 281.89it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000020_c000020_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000020_c000020_grain_stats.csv\n", + "done with segmentation + export\n", + "[366/1053] tile_r000020_c000021\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:01<00:00, 2.55it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.50it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 1679/1679 [00:41<00:00, 40.47it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "1555it [00:02, 623.07it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "1606it [00:02, 599.90it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 685/685 [00:03<00:00, 222.98it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 698/698 [00:02<00:00, 283.15it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000020_c000021_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000020_c000021_grain_stats.csv\n", + "done with segmentation + export\n", + "[367/1053] tile_r000020_c000022\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:01<00:00, 2.51it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.52it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 798/798 [00:20<00:00, 39.45it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "724it [00:01, 620.59it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "788it [00:01, 603.36it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 336/336 [00:01<00:00, 213.27it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 341/341 [00:01<00:00, 260.42it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000020_c000022_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000020_c000022_grain_stats.csv\n", + "done with segmentation + export\n", + "[368/1053] tile_r000020_c000023\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.48it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.48it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 803/803 [00:22<00:00, 36.12it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "667it [00:01, 371.00it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "665it [00:01, 421.62it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 237/237 [00:02<00:00, 95.05it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 304/304 [00:01<00:00, 234.91it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000020_c000023_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000020_c000023_grain_stats.csv\n", + "done with segmentation + export\n", + "[369/1053] tile_r000020_c000024\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.49it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.51it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 702/702 [00:19<00:00, 36.07it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "613it [00:06, 97.15it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "61it [00:00, 71.18it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 1/1 [00:01<00:00, 1.68s/it]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 49/49 [00:00<00:00, 195.94it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000020_c000024_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000020_c000024_grain_stats.csv\n", + "done with segmentation + export\n", + "[370/1053] tile_r000020_c000025\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.47it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.49it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 472/472 [00:13<00:00, 35.41it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "343it [00:01, 291.49it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "341it [00:01, 301.76it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 102/102 [00:02<00:00, 48.62it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 150/150 [00:00<00:00, 185.11it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000020_c000025_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000020_c000025_grain_stats.csv\n", + "done with segmentation + export\n", + "[371/1053] tile_r000020_c000026\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:01<00:00, 2.54it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.49it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 254/254 [00:08<00:00, 31.21it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "149it [00:01, 120.20it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "143it [00:00, 210.52it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 28/28 [00:02<00:00, 11.82it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 65/65 [00:00<00:00, 118.07it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000020_c000026_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000020_c000026_grain_stats.csv\n", + "done with segmentation + export\n", + "[372/1053] tile_r000020_c000027\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:01<00:00, 2.51it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.45it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 322/322 [00:10<00:00, 31.79it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "209it [00:00, 268.48it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "208it [00:00, 307.81it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 52/52 [00:01<00:00, 28.98it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 107/107 [00:00<00:00, 130.40it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000020_c000027_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000020_c000027_grain_stats.csv\n", + "done with segmentation + export\n", + "[373/1053] tile_r000020_c000028\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:01<00:00, 2.55it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.51it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 283/283 [00:09<00:00, 30.55it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "188it [00:01, 102.25it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "179it [00:01, 152.95it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 35/35 [00:07<00:00, 4.62it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 81/81 [00:00<00:00, 108.78it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000020_c000028_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000020_c000028_grain_stats.csv\n", + "done with segmentation + export\n", + "[374/1053] tile_r000020_c000029\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.49it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.48it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 313/313 [00:12<00:00, 25.47it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "176it [00:01, 103.11it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "168it [00:01, 161.21it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 36/36 [00:02<00:00, 15.33it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 71/71 [00:00<00:00, 114.14it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000020_c000029_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000020_c000029_grain_stats.csv\n", + "done with segmentation + export\n", + "[375/1053] tile_r000020_c000030\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.48it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.48it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 417/417 [00:11<00:00, 35.43it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "339it [00:01, 178.44it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "333it [00:01, 205.26it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 83/83 [00:04<00:00, 19.04it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 138/138 [00:00<00:00, 153.38it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000020_c000030_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000020_c000030_grain_stats.csv\n", + "done with segmentation + export\n", + "[376/1053] tile_r000020_c000031\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:01<00:00, 2.54it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.51it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 426/426 [00:12<00:00, 34.45it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "324it [00:01, 255.75it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "323it [00:01, 258.51it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 83/83 [00:02<00:00, 28.57it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 154/154 [00:00<00:00, 166.72it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000020_c000031_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000020_c000031_grain_stats.csv\n", + "done with segmentation + export\n", + "[377/1053] tile_r000020_c000032\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:01<00:00, 2.51it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.50it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 502/502 [00:16<00:00, 30.59it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "359it [00:00, 477.20it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "357it [00:00, 568.87it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 119/119 [00:01<00:00, 102.98it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 183/183 [00:00<00:00, 205.60it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000020_c000032_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000020_c000032_grain_stats.csv\n", + "done with segmentation + export\n", + "[378/1053] tile_r000020_c000033\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:01<00:00, 2.52it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.49it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 560/560 [00:16<00:00, 34.43it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "406it [00:01, 267.90it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "404it [00:01, 289.73it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 127/127 [00:02<00:00, 43.76it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 187/187 [00:00<00:00, 188.44it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000020_c000033_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000020_c000033_grain_stats.csv\n", + "done with segmentation + export\n", + "[379/1053] tile_r000020_c000034\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:01<00:00, 2.50it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.51it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 1001/1001 [00:24<00:00, 40.24it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "867it [00:02, 323.69it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "907it [00:02, 327.45it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 342/342 [00:03<00:00, 97.22it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 353/353 [00:01<00:00, 204.10it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000020_c000034_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000020_c000034_grain_stats.csv\n", + "done with segmentation + export\n", + "[380/1053] tile_r000020_c000035\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:01<00:00, 2.53it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.52it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 1030/1030 [00:26<00:00, 39.49it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "864it [00:03, 262.03it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "907it [00:03, 260.76it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 315/315 [00:04<00:00, 68.49it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 333/333 [00:01<00:00, 205.34it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000020_c000035_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000020_c000035_grain_stats.csv\n", + "done with segmentation + export\n", + "[381/1053] tile_r000020_c000036\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:01<00:00, 2.52it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.48it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 901/901 [00:22<00:00, 39.55it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "766it [00:02, 260.54it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "762it [00:02, 277.72it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 231/231 [00:04<00:00, 57.24it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 284/284 [00:01<00:00, 190.34it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000020_c000036_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000020_c000036_grain_stats.csv\n", + "done with segmentation + export\n", + "[382/1053] tile_r000020_c000037\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:01<00:00, 2.51it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.48it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 733/733 [00:18<00:00, 39.39it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "603it [00:02, 245.62it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "629it [00:02, 244.38it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 205/205 [00:03<00:00, 59.58it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 218/218 [00:01<00:00, 181.97it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000020_c000037_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000020_c000037_grain_stats.csv\n", + "done with segmentation + export\n", + "[383/1053] tile_r000020_c000038\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.48it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.47it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 690/690 [00:17<00:00, 39.48it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "579it [00:02, 237.66it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "611it [00:02, 236.69it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 205/205 [00:03<00:00, 60.98it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 217/217 [00:01<00:00, 180.19it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000020_c000038_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000020_c000038_grain_stats.csv\n", + "done with segmentation + export\n", + "[384/1053] tile_r000020_c000039\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:01<00:00, 2.51it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.45it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 884/884 [00:22<00:00, 39.87it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "763it [00:02, 269.71it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "762it [00:02, 274.72it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 243/243 [00:03<00:00, 69.02it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 281/281 [00:01<00:00, 206.16it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000020_c000039_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000020_c000039_grain_stats.csv\n", + "done with segmentation + export\n", + "[385/1053] tile_r000020_c000040\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.49it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.48it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 825/825 [00:20<00:00, 39.88it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "710it [00:02, 292.04it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "736it [00:02, 291.18it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 272/272 [00:03<00:00, 86.70it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 285/285 [00:01<00:00, 206.89it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000020_c000040_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000020_c000040_grain_stats.csv\n", + "done with segmentation + export\n", + "[386/1053] tile_r000020_c000041\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:01<00:00, 2.51it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.53it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 648/648 [00:16<00:00, 40.11it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "538it [00:01, 290.21it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "565it [00:01, 294.28it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 198/198 [00:02<00:00, 79.71it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 205/205 [00:01<00:00, 195.64it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000020_c000041_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000020_c000041_grain_stats.csv\n", + "done with segmentation + export\n", + "[387/1053] tile_r000020_c000042\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:01<00:00, 2.58it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.53it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 811/811 [00:20<00:00, 40.12it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "698it [00:02, 279.15it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "735it [00:02, 278.43it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 253/253 [00:03<00:00, 74.55it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 268/268 [00:01<00:00, 202.22it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000020_c000042_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000020_c000042_grain_stats.csv\n", + "done with segmentation + export\n", + "[388/1053] tile_r000020_c000043\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:01<00:00, 2.51it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.51it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 413/413 [00:10<00:00, 38.22it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "308it [00:00, 323.92it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "326it [00:01, 324.59it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 118/118 [00:01<00:00, 84.10it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 120/120 [00:00<00:00, 185.25it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000020_c000043_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000020_c000043_grain_stats.csv\n", + "done with segmentation + export\n", + "[389/1053] tile_r000020_c000044\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.44it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.27it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 513/513 [00:13<00:00, 38.90it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "410it [00:01, 238.86it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "409it [00:01, 255.05it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 121/121 [00:02<00:00, 52.06it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 151/151 [00:00<00:00, 175.33it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000020_c000044_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000020_c000044_grain_stats.csv\n", + "done with segmentation + export\n", + "[390/1053] tile_r000020_c000045\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.43it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.49it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 449/449 [00:12<00:00, 36.85it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "344it [00:01, 233.65it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "366it [00:01, 227.96it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 121/121 [00:02<00:00, 51.03it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 131/131 [00:00<00:00, 153.68it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000020_c000045_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000020_c000045_grain_stats.csv\n", + "done with segmentation + export\n", + "[391/1053] tile_r000020_c000046\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.48it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.51it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 290/290 [00:07<00:00, 37.89it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "219it [00:01, 186.71it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "230it [00:01, 189.15it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 68/68 [00:01<00:00, 36.35it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 70/70 [00:00<00:00, 161.32it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000020_c000046_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000020_c000046_grain_stats.csv\n", + "done with segmentation + export\n", + "[392/1053] tile_r000020_c000047\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:01<00:00, 2.50it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.52it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 238/238 [00:07<00:00, 33.54it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "154it [00:00, 171.69it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "160it [00:00, 173.04it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 46/46 [00:01<00:00, 33.99it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 46/46 [00:00<00:00, 132.61it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000020_c000047_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000020_c000047_grain_stats.csv\n", + "done with segmentation + export\n", + "[393/1053] tile_r000020_c000048\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.48it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.50it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 293/293 [00:07<00:00, 37.32it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "234it [00:01, 184.66it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "242it [00:01, 183.32it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 71/71 [00:02<00:00, 34.33it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 73/73 [00:00<00:00, 146.49it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000020_c000048_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000020_c000048_grain_stats.csv\n", + "done with segmentation + export\n", + "[394/1053] tile_r000020_c000049\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:01<00:00, 2.55it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.36it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 217/217 [00:06<00:00, 34.46it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "155it [00:00, 242.76it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "164it [00:00, 247.27it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 50/50 [00:01<00:00, 47.91it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 53/53 [00:00<00:00, 157.37it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000020_c000049_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000020_c000049_grain_stats.csv\n", + "done with segmentation + export\n", + "[395/1053] tile_r000020_c000050\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:01<00:00, 2.50it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.52it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 327/327 [00:08<00:00, 39.16it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "262it [00:01, 255.06it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "278it [00:01, 256.13it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 84/84 [00:01<00:00, 43.83it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 91/91 [00:00<00:00, 154.46it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000020_c000050_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000020_c000050_grain_stats.csv\n", + "done with segmentation + export\n", + "[396/1053] tile_r000020_c000051\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.47it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.54it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 594/594 [00:14<00:00, 40.50it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "508it [00:01, 314.22it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "544it [00:01, 299.57it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 201/201 [00:02<00:00, 80.40it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 207/207 [00:00<00:00, 208.43it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000020_c000051_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000020_c000051_grain_stats.csv\n", + "done with segmentation + export\n", + "[397/1053] tile_r000020_c000052\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:01<00:00, 2.52it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.48it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 726/726 [00:18<00:00, 39.82it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "632it [00:02, 302.34it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "668it [00:02, 302.67it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 236/236 [00:02<00:00, 82.65it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 246/246 [00:01<00:00, 213.81it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000020_c000052_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000020_c000052_grain_stats.csv\n", + "done with segmentation + export\n", + "[398/1053] tile_r000020_c000053\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.49it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.53it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 665/665 [00:16<00:00, 39.64it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "564it [00:01, 305.94it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "600it [00:01, 307.72it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 225/225 [00:02<00:00, 89.96it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 231/231 [00:01<00:00, 208.32it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n", + "Warning: len(gdf)=231 != len(grain_stats)=230 -> truncating to 230\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000020_c000053_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000020_c000053_grain_stats.csv\n", + "done with segmentation + export\n", + "[399/1053] tile_r000020_c000054\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:01<00:00, 2.50it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.53it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 1281/1281 [00:31<00:00, 40.48it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "1155it [00:05, 216.81it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "1152it [00:04, 233.54it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 402/402 [00:05<00:00, 74.03it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 442/442 [00:10<00:00, 42.32it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000020_c000054_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000020_c000054_grain_stats.csv\n", + "done with segmentation + export\n", + "[400/1053] tile_r000020_c000055\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.23it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.26it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 1119/1119 [00:28<00:00, 39.43it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "954it [00:04, 234.00it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "977it [00:04, 235.46it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 350/350 [00:05<00:00, 65.47it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 362/362 [00:01<00:00, 194.05it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000020_c000055_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000020_c000055_grain_stats.csv\n", + "done with segmentation + export\n", + "[401/1053] tile_r000020_c000056\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.29it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.36it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 1146/1146 [00:28<00:00, 40.72it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "1017it [00:04, 208.62it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "1046it [00:04, 212.21it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 382/382 [00:05<00:00, 72.63it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 384/384 [00:01<00:00, 195.45it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000020_c000056_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000020_c000056_grain_stats.csv\n", + "done with segmentation + export\n", + "[402/1053] tile_r000020_c000057\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.33it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.39it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 1056/1056 [00:26<00:00, 40.43it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "933it [00:04, 225.59it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "952it [00:04, 225.28it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 338/338 [00:04<00:00, 72.74it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 339/339 [00:01<00:00, 189.32it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000020_c000057_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000020_c000057_grain_stats.csv\n", + "done with segmentation + export\n", + "[403/1053] tile_r000020_c000058\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.39it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.45it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 100/100 [00:02<00:00, 35.44it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "65it [00:00, 219.13it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "65it [00:00, 221.39it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 23/23 [00:00<00:00, 58.34it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 23/23 [00:00<00:00, 175.13it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000020_c000058_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000020_c000058_grain_stats.csv\n", + "done with segmentation + export\n", + "[404/1053] tile_r000021_c000014\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.42it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.45it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 82/82 [00:02<00:00, 33.89it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "55it [00:00, 217.10it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "58it [00:00, 222.18it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 20/20 [00:00<00:00, 53.03it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 20/20 [00:00<00:00, 165.04it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000021_c000014_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000021_c000014_grain_stats.csv\n", + "done with segmentation + export\n", + "[405/1053] tile_r000021_c000015\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.43it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.51it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 343/343 [00:08<00:00, 39.85it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "297it [00:01, 264.17it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "311it [00:01, 262.31it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 114/114 [00:01<00:00, 67.56it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 119/119 [00:00<00:00, 183.23it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000021_c000015_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000021_c000015_grain_stats.csv\n", + "done with segmentation + export\n", + "[406/1053] tile_r000021_c000016\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.29it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.41it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 487/487 [00:13<00:00, 36.12it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "373it [00:01, 261.20it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "371it [00:01, 272.75it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 115/115 [00:02<00:00, 57.48it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 141/141 [00:00<00:00, 186.85it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000021_c000016_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000021_c000016_grain_stats.csv\n", + "done with segmentation + export\n", + "[407/1053] tile_r000021_c000017\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.45it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.45it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 834/834 [00:20<00:00, 40.48it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "749it [00:02, 320.15it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "793it [00:02, 309.82it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 297/297 [00:03<00:00, 83.22it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 306/306 [00:01<00:00, 216.70it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000021_c000017_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000021_c000017_grain_stats.csv\n", + "done with segmentation + export\n", + "[408/1053] tile_r000021_c000018\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.45it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.42it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 827/827 [00:21<00:00, 39.05it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "699it [00:01, 384.39it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "735it [00:01, 378.55it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 277/277 [00:02<00:00, 97.91it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 283/283 [00:01<00:00, 215.26it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000021_c000018_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000021_c000018_grain_stats.csv\n", + "done with segmentation + export\n", + "[409/1053] tile_r000021_c000019\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.48it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.46it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 1468/1468 [00:35<00:00, 40.93it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "1352it [00:02, 548.98it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "1394it [00:02, 534.07it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 611/611 [00:02<00:00, 213.56it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 621/621 [00:02<00:00, 255.49it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000021_c000019_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000021_c000019_grain_stats.csv\n", + "done with segmentation + export\n", + "[410/1053] tile_r000021_c000020\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.47it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.47it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 1615/1615 [00:41<00:00, 38.72it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "1471it [00:02, 576.20it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "1470it [00:02, 587.15it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 597/597 [00:02<00:00, 206.70it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 679/679 [00:02<00:00, 278.37it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000021_c000020_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000021_c000020_grain_stats.csv\n", + "done with segmentation + export\n", + "[411/1053] tile_r000021_c000021\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.50it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.49it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 1406/1406 [00:35<00:00, 39.64it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "1299it [00:01, 656.84it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "1297it [00:01, 728.46it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 546/546 [00:02<00:00, 247.77it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 606/606 [00:02<00:00, 289.92it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000021_c000021_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000021_c000021_grain_stats.csv\n", + "done with segmentation + export\n", + "[412/1053] tile_r000021_c000022\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.45it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.46it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 1100/1100 [00:27<00:00, 39.42it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "966it [00:03, 306.58it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "81it [00:00, 170.91it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 2/2 [00:01<00:00, 1.32it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 66/66 [00:00<00:00, 200.75it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000021_c000022_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000021_c000022_grain_stats.csv\n", + "done with segmentation + export\n", + "[413/1053] tile_r000021_c000023\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.46it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.54it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 1324/1324 [00:34<00:00, 37.85it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "1195it [00:02, 573.14it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "1261it [00:02, 552.21it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 540/540 [00:02<00:00, 208.02it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 552/552 [00:02<00:00, 268.20it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000021_c000023_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000021_c000023_grain_stats.csv\n", + "done with segmentation + export\n", + "[414/1053] tile_r000021_c000024\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.49it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.47it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 756/756 [00:19<00:00, 38.72it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "666it [00:01, 361.42it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "661it [00:01, 432.51it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 226/226 [00:02<00:00, 91.89it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 310/310 [00:01<00:00, 229.87it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000021_c000024_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000021_c000024_grain_stats.csv\n", + "done with segmentation + export\n", + "[415/1053] tile_r000021_c000025\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.44it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.46it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 997/997 [00:26<00:00, 37.60it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "831it [00:02, 297.64it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "824it [00:02, 360.24it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 287/287 [00:03<00:00, 75.26it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 375/375 [00:01<00:00, 225.30it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n", + "Warning: len(gdf)=375 != len(grain_stats)=371 -> truncating to 371\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000021_c000025_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000021_c000025_grain_stats.csv\n", + "done with segmentation + export\n", + "[416/1053] tile_r000021_c000026\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:01<00:00, 2.53it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.52it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 957/957 [00:25<00:00, 37.88it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "803it [00:01, 506.93it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "878it [00:01, 490.30it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 356/356 [00:02<00:00, 137.65it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 366/366 [00:01<00:00, 239.51it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000021_c000026_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000021_c000026_grain_stats.csv\n", + "done with segmentation + export\n", + "[417/1053] tile_r000021_c000027\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:01<00:00, 2.51it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.53it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 811/811 [00:21<00:00, 37.93it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "683it [00:01, 501.46it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "739it [00:01, 499.13it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 304/304 [00:02<00:00, 120.20it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 323/323 [00:01<00:00, 229.26it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000021_c000027_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000021_c000027_grain_stats.csv\n", + "done with segmentation + export\n", + "[418/1053] tile_r000021_c000028\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.43it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.49it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 724/724 [00:19<00:00, 38.04it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "593it [00:01, 330.14it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "590it [00:01, 352.35it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 185/185 [00:03<00:00, 51.62it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 252/252 [00:01<00:00, 207.90it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000021_c000028_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000021_c000028_grain_stats.csv\n", + "done with segmentation + export\n", + "[419/1053] tile_r000021_c000029\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.49it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.47it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 573/573 [00:15<00:00, 36.17it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "451it [00:01, 265.51it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "450it [00:01, 277.05it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 129/129 [00:04<00:00, 30.20it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 203/203 [00:01<00:00, 169.58it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n", + "Warning: len(gdf)=203 != len(grain_stats)=202 -> truncating to 202\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000021_c000029_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000021_c000029_grain_stats.csv\n", + "done with segmentation + export\n", + "[420/1053] tile_r000021_c000030\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:01<00:00, 2.51it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.53it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 664/664 [00:17<00:00, 37.73it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "557it [00:02, 235.29it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "553it [00:02, 268.50it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 148/148 [00:03<00:00, 37.73it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 231/231 [00:01<00:00, 181.52it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000021_c000030_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000021_c000030_grain_stats.csv\n", + "done with segmentation + export\n", + "[421/1053] tile_r000021_c000031\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:01<00:00, 2.53it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.55it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 639/639 [00:16<00:00, 37.88it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "509it [00:01, 272.85it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "563it [00:01, 284.62it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 202/202 [00:03<00:00, 52.69it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 224/224 [00:01<00:00, 196.84it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n", + "Warning: len(gdf)=224 != len(grain_stats)=223 -> truncating to 223\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000021_c000031_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000021_c000031_grain_stats.csv\n", + "done with segmentation + export\n", + "[422/1053] tile_r000021_c000032\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:01<00:00, 2.51it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.44it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 354/354 [00:10<00:00, 32.50it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "231it [00:00, 495.24it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "259it [00:00, 487.57it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 96/96 [00:00<00:00, 100.05it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 106/106 [00:00<00:00, 186.22it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000021_c000032_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000021_c000032_grain_stats.csv\n", + "done with segmentation + export\n", + "[423/1053] tile_r000021_c000033\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.46it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.53it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 508/508 [00:14<00:00, 35.12it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "371it [00:01, 228.62it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "366it [00:01, 261.21it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 120/120 [00:02<00:00, 50.35it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 157/157 [00:00<00:00, 204.52it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000021_c000033_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000021_c000033_grain_stats.csv\n", + "done with segmentation + export\n", + "[424/1053] tile_r000021_c000034\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:01<00:00, 2.52it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.48it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 1001/1001 [00:25<00:00, 39.65it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "868it [00:02, 392.10it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "920it [00:02, 398.23it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 361/361 [00:02<00:00, 127.08it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 375/375 [00:01<00:00, 222.28it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000021_c000034_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000021_c000034_grain_stats.csv\n", + "done with segmentation + export\n", + "[425/1053] tile_r000021_c000035\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:01<00:00, 2.53it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.53it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 1056/1056 [00:26<00:00, 39.61it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "902it [00:02, 307.82it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "941it [00:03, 307.79it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 343/343 [00:03<00:00, 93.94it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 360/360 [00:01<00:00, 193.30it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000021_c000035_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000021_c000035_grain_stats.csv\n", + "done with segmentation + export\n", + "[426/1053] tile_r000021_c000036\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.48it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.50it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 714/714 [00:18<00:00, 39.08it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "599it [00:03, 184.18it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "584it [00:01, 299.82it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 184/184 [00:02<00:00, 67.58it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 240/240 [00:01<00:00, 188.45it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000021_c000036_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000021_c000036_grain_stats.csv\n", + "done with segmentation + export\n", + "[427/1053] tile_r000021_c000037\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:01<00:00, 2.51it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.54it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 595/595 [00:15<00:00, 37.95it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "487it [00:01, 290.52it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "485it [00:01, 300.95it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 148/148 [00:02<00:00, 61.82it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 191/191 [00:01<00:00, 177.92it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000021_c000037_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000021_c000037_grain_stats.csv\n", + "done with segmentation + export\n", + "[428/1053] tile_r000021_c000038\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:01<00:00, 2.52it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.52it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 724/724 [00:18<00:00, 39.42it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "587it [00:02, 239.15it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "617it [00:02, 247.59it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 204/204 [00:03<00:00, 60.01it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 214/214 [00:01<00:00, 183.19it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000021_c000038_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000021_c000038_grain_stats.csv\n", + "done with segmentation + export\n", + "[429/1053] tile_r000021_c000039\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:01<00:00, 2.50it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.49it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 824/824 [00:20<00:00, 39.27it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "702it [00:02, 254.13it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "727it [00:02, 253.12it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 255/255 [00:03<00:00, 74.09it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 264/264 [00:01<00:00, 195.87it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000021_c000039_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000021_c000039_grain_stats.csv\n", + "done with segmentation + export\n", + "[430/1053] tile_r000021_c000040\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.48it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.52it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 666/666 [00:17<00:00, 38.78it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "533it [00:02, 258.28it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "564it [00:02, 268.60it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 205/205 [00:02<00:00, 79.99it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 213/213 [00:01<00:00, 202.48it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000021_c000040_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000021_c000040_grain_stats.csv\n", + "done with segmentation + export\n", + "[431/1053] tile_r000021_c000041\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.48it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.46it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 778/778 [00:19<00:00, 40.15it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "687it [00:02, 307.74it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "723it [00:02, 304.94it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 266/266 [00:03<00:00, 88.59it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 271/271 [00:01<00:00, 203.87it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000021_c000041_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000021_c000041_grain_stats.csv\n", + "done with segmentation + export\n", + "[432/1053] tile_r000021_c000042\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.49it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.47it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 788/788 [00:20<00:00, 38.79it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "682it [00:02, 269.41it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "717it [00:02, 267.07it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 244/244 [00:03<00:00, 70.09it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 259/259 [00:01<00:00, 192.02it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000021_c000042_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000021_c000042_grain_stats.csv\n", + "done with segmentation + export\n", + "[433/1053] tile_r000021_c000043\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:01<00:00, 2.51it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.47it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 656/656 [00:16<00:00, 38.99it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "534it [00:01, 281.08it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "561it [00:01, 281.42it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 190/190 [00:02<00:00, 67.89it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 203/203 [00:01<00:00, 188.20it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000021_c000043_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000021_c000043_grain_stats.csv\n", + "done with segmentation + export\n", + "[434/1053] tile_r000021_c000044\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.44it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.54it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 682/682 [00:22<00:00, 30.92it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "439it [00:02, 200.89it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "463it [00:02, 201.41it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 141/141 [00:03<00:00, 41.24it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 157/157 [00:00<00:00, 176.66it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000021_c000044_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000021_c000044_grain_stats.csv\n", + "done with segmentation + export\n", + "[435/1053] tile_r000021_c000045\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:01<00:00, 2.52it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.56it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 299/299 [00:09<00:00, 31.65it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "195it [00:02, 65.21it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "187it [00:02, 81.64it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 43/43 [00:03<00:00, 13.37it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 58/58 [00:00<00:00, 121.69it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000021_c000045_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000021_c000045_grain_stats.csv\n", + "done with segmentation + export\n", + "[436/1053] tile_r000021_c000046\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.47it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.45it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 149/149 [00:03<00:00, 38.13it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "130it [00:00, 195.33it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "135it [00:00, 197.11it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 39/39 [00:01<00:00, 32.72it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 39/39 [00:00<00:00, 146.24it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000021_c000046_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000021_c000046_grain_stats.csv\n", + "done with segmentation + export\n", + "[437/1053] tile_r000021_c000047\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.45it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.49it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 382/382 [00:10<00:00, 36.96it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "299it [00:01, 220.61it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "304it [00:01, 223.02it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 93/93 [00:01<00:00, 48.85it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 94/94 [00:00<00:00, 170.01it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000021_c000047_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000021_c000047_grain_stats.csv\n", + "done with segmentation + export\n", + "[438/1053] tile_r000021_c000048\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.49it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.48it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 318/318 [00:08<00:00, 37.83it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "285it [00:01, 182.11it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "296it [00:01, 182.79it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 85/85 [00:02<00:00, 34.70it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 88/88 [00:00<00:00, 153.84it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000021_c000048_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000021_c000048_grain_stats.csv\n", + "done with segmentation + export\n", + "[439/1053] tile_r000021_c000049\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.46it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.51it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 289/289 [00:07<00:00, 38.62it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "230it [00:00, 311.95it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "252it [00:00, 317.45it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 87/87 [00:01<00:00, 66.21it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 88/88 [00:00<00:00, 161.45it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000021_c000049_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000021_c000049_grain_stats.csv\n", + "done with segmentation + export\n", + "[440/1053] tile_r000021_c000050\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.48it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.45it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 313/313 [00:07<00:00, 39.39it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "265it [00:01, 229.14it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "278it [00:01, 231.65it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 89/89 [00:01<00:00, 51.91it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 94/94 [00:00<00:00, 167.38it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000021_c000050_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000021_c000050_grain_stats.csv\n", + "done with segmentation + export\n", + "[441/1053] tile_r000021_c000051\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:01<00:00, 2.55it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.39it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 605/605 [00:16<00:00, 37.77it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "488it [00:01, 249.64it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "514it [00:02, 249.42it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 177/177 [00:02<00:00, 64.19it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 181/181 [00:00<00:00, 187.52it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000021_c000051_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000021_c000051_grain_stats.csv\n", + "done with segmentation + export\n", + "[442/1053] tile_r000021_c000052\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.48it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.52it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 824/824 [00:20<00:00, 40.02it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "697it [00:02, 248.09it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "696it [00:02, 262.02it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 221/221 [00:03<00:00, 65.02it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 265/265 [00:01<00:00, 194.80it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000021_c000052_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000021_c000052_grain_stats.csv\n", + "done with segmentation + export\n", + "[443/1053] tile_r000021_c000053\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.50it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.48it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 951/951 [00:23<00:00, 40.67it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "813it [00:03, 267.03it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "843it [00:03, 268.61it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 313/313 [00:03<00:00, 84.63it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 329/329 [00:01<00:00, 210.35it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsshome1/0B/di54doz/.local/share/mamba/envs/segmenteverygrain/lib/python3.10/site-packages/segmenteverygrain/segmenteverygrain.py:2217: RuntimeWarning: divide by zero encountered in log2\n", + " phi_major = -np.log2(major_axis_length) # major axis length in phi scale\n", + "/dss/dsshome1/0B/di54doz/.local/share/mamba/envs/segmenteverygrain/lib/python3.10/site-packages/segmenteverygrain/segmenteverygrain.py:2218: RuntimeWarning: divide by zero encountered in log2\n", + " phi_minor = -np.log2(minor_axis_length)\n", + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000021_c000053_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000021_c000053_grain_stats.csv\n", + "done with segmentation + export\n", + "[444/1053] tile_r000021_c000054\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.36it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.49it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 1227/1227 [00:30<00:00, 39.91it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "1053it [00:04, 234.66it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "1050it [00:04, 242.61it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 357/357 [00:04<00:00, 74.64it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 396/396 [00:01<00:00, 202.50it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000021_c000054_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000021_c000054_grain_stats.csv\n", + "done with segmentation + export\n", + "[445/1053] tile_r000021_c000055\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.47it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.43it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 1187/1187 [00:29<00:00, 39.82it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "1018it [00:04, 223.01it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "1039it [00:04, 225.43it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 368/368 [00:05<00:00, 73.48it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 376/376 [00:01<00:00, 197.77it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000021_c000055_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000021_c000055_grain_stats.csv\n", + "done with segmentation + export\n", + "[446/1053] tile_r000021_c000056\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.48it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.48it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 1086/1086 [00:27<00:00, 40.18it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "945it [00:04, 198.76it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "966it [00:04, 199.83it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 339/339 [00:05<00:00, 64.39it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 346/346 [00:01<00:00, 188.70it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000021_c000056_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000021_c000056_grain_stats.csv\n", + "done with segmentation + export\n", + "[447/1053] tile_r000021_c000057\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.48it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.44it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 1095/1095 [00:27<00:00, 39.91it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "954it [00:04, 209.51it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "973it [00:04, 209.96it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 348/348 [00:05<00:00, 69.33it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 354/354 [00:01<00:00, 193.30it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000021_c000057_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000021_c000057_grain_stats.csv\n", + "done with segmentation + export\n", + "[448/1053] tile_r000021_c000058\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.49it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.53it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 88/88 [00:02<00:00, 33.43it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "49it [00:00, 279.50it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "49it [00:00, 281.47it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 15/15 [00:00<00:00, 51.72it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 17/17 [00:00<00:00, 182.68it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000021_c000058_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000021_c000058_grain_stats.csv\n", + "done with segmentation + export\n", + "[449/1053] tile_r000022_c000013\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.47it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.38it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 266/266 [00:07<00:00, 36.88it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "192it [00:00, 216.32it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "202it [00:00, 217.36it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 66/66 [00:01<00:00, 51.70it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 69/69 [00:00<00:00, 161.87it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000022_c000013_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000022_c000013_grain_stats.csv\n", + "done with segmentation + export\n", + "[450/1053] tile_r000022_c000014\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:01<00:00, 2.50it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.47it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 727/727 [00:18<00:00, 39.05it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "625it [00:02, 216.33it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "624it [00:02, 225.00it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 196/196 [00:03<00:00, 52.70it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 236/236 [00:01<00:00, 167.37it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000022_c000014_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000022_c000014_grain_stats.csv\n", + "done with segmentation + export\n", + "[451/1053] tile_r000022_c000015\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.50it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.46it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 776/776 [00:19<00:00, 39.71it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "661it [00:02, 227.17it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "692it [00:03, 226.99it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 240/240 [00:03<00:00, 60.85it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 247/247 [00:01<00:00, 175.56it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000022_c000015_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000022_c000015_grain_stats.csv\n", + "done with segmentation + export\n", + "[452/1053] tile_r000022_c000016\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.49it/s]\n", + "100%|██████████| 4/4 [00:02<00:00, 1.79it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 881/881 [00:22<00:00, 38.81it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "771it [00:03, 235.70it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "810it [00:03, 236.46it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 269/269 [00:04<00:00, 57.10it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 282/282 [00:01<00:00, 182.85it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000022_c000016_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000022_c000016_grain_stats.csv\n", + "done with segmentation + export\n", + "[453/1053] tile_r000022_c000017\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.43it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.48it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 750/750 [00:18<00:00, 39.52it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "643it [00:02, 302.66it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "677it [00:02, 298.46it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 239/239 [00:03<00:00, 78.29it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 250/250 [00:01<00:00, 179.79it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000022_c000017_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000022_c000017_grain_stats.csv\n", + "done with segmentation + export\n", + "[454/1053] tile_r000022_c000018\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.43it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.51it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 823/823 [00:20<00:00, 39.73it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "707it [00:02, 301.04it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "743it [00:02, 299.28it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 263/263 [00:03<00:00, 78.33it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 271/271 [00:01<00:00, 199.48it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000022_c000018_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000022_c000018_grain_stats.csv\n", + "done with segmentation + export\n", + "[455/1053] tile_r000022_c000019\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.47it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.51it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 1208/1208 [00:29<00:00, 40.82it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "1075it [00:02, 398.61it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "1113it [00:02, 393.69it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 445/445 [00:03<00:00, 136.42it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 454/454 [00:01<00:00, 227.55it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000022_c000019_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000022_c000019_grain_stats.csv\n", + "done with segmentation + export\n", + "[456/1053] tile_r000022_c000020\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.48it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.45it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 1558/1558 [00:38<00:00, 40.55it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "1422it [00:02, 539.18it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "1421it [00:02, 553.22it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 584/584 [00:03<00:00, 188.48it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 651/651 [00:02<00:00, 268.52it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000022_c000020_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000022_c000020_grain_stats.csv\n", + "done with segmentation + export\n", + "[457/1053] tile_r000022_c000021\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.48it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.50it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 1371/1371 [00:34<00:00, 39.89it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "1243it [00:01, 682.50it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "1304it [00:01, 667.73it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 576/576 [00:02<00:00, 269.72it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 583/583 [00:02<00:00, 286.58it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000022_c000021_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000022_c000021_grain_stats.csv\n", + "done with segmentation + export\n", + "[458/1053] tile_r000022_c000022\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.44it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.51it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 915/915 [00:25<00:00, 36.14it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "787it [00:01, 611.25it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "786it [00:01, 688.64it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 282/282 [00:01<00:00, 188.09it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 397/397 [00:01<00:00, 264.64it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000022_c000022_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000022_c000022_grain_stats.csv\n", + "done with segmentation + export\n", + "[459/1053] tile_r000022_c000023\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.48it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.48it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 901/901 [00:26<00:00, 34.38it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "789it [00:07, 103.95it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "780it [00:07, 108.51it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 265/265 [00:14<00:00, 18.90it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 370/370 [00:01<00:00, 251.59it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000022_c000023_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000022_c000023_grain_stats.csv\n", + "done with segmentation + export\n", + "[460/1053] tile_r000022_c000024\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.48it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.48it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 1080/1080 [00:26<00:00, 40.90it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "971it [00:01, 656.82it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "1036it [00:01, 647.21it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 440/440 [00:02<00:00, 187.80it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 449/449 [00:01<00:00, 265.20it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000022_c000024_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000022_c000024_grain_stats.csv\n", + "done with segmentation + export\n", + "[461/1053] tile_r000022_c000025\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.48it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.50it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 1044/1044 [00:26<00:00, 39.02it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "912it [00:01, 486.59it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "910it [00:01, 513.14it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 340/340 [00:02<00:00, 134.45it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 397/397 [00:01<00:00, 248.46it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000022_c000025_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000022_c000025_grain_stats.csv\n", + "done with segmentation + export\n", + "[462/1053] tile_r000022_c000026\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.23it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.31it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 970/970 [00:24<00:00, 39.41it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "835it [00:01, 511.14it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "834it [00:01, 545.01it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 308/308 [00:02<00:00, 136.65it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 373/373 [00:01<00:00, 242.79it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000022_c000026_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000022_c000026_grain_stats.csv\n", + "done with segmentation + export\n", + "[463/1053] tile_r000022_c000027\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.34it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.35it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 853/853 [00:22<00:00, 38.27it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "715it [00:01, 452.76it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "711it [00:01, 525.77it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 239/239 [00:02<00:00, 104.89it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 348/348 [00:01<00:00, 226.38it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000022_c000027_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000022_c000027_grain_stats.csv\n", + "done with segmentation + export\n", + "[464/1053] tile_r000022_c000028\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.37it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.44it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 679/679 [00:18<00:00, 37.66it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "554it [00:01, 403.78it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "551it [00:01, 438.96it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 163/163 [00:02<00:00, 59.22it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 261/261 [00:01<00:00, 202.49it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n", + "Warning: len(gdf)=261 != len(grain_stats)=258 -> truncating to 258\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000022_c000028_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000022_c000028_grain_stats.csv\n", + "done with segmentation + export\n", + "[465/1053] tile_r000022_c000029\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.39it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.40it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 589/589 [00:15<00:00, 36.84it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "470it [00:01, 260.76it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "464it [00:01, 302.95it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 116/116 [00:03<00:00, 31.42it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 201/201 [00:01<00:00, 166.88it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n", + "Warning: len(gdf)=201 != len(grain_stats)=200 -> truncating to 200\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000022_c000029_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000022_c000029_grain_stats.csv\n", + "done with segmentation + export\n", + "[466/1053] tile_r000022_c000030\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.47it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.46it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 542/542 [00:14<00:00, 37.08it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "437it [00:01, 255.43it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "431it [00:01, 414.83it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 113/113 [00:02<00:00, 45.13it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 218/218 [00:01<00:00, 178.18it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n", + "Warning: len(gdf)=218 != len(grain_stats)=217 -> truncating to 217\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000022_c000030_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000022_c000030_grain_stats.csv\n", + "done with segmentation + export\n", + "[467/1053] tile_r000022_c000031\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.46it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.48it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 524/524 [00:14<00:00, 35.56it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "438it [00:01, 347.79it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "434it [00:00, 493.76it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 128/128 [00:01<00:00, 67.95it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 215/215 [00:01<00:00, 212.58it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000022_c000031_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000022_c000031_grain_stats.csv\n", + "done with segmentation + export\n", + "[468/1053] tile_r000022_c000032\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.43it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.46it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 554/554 [00:14<00:00, 37.05it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "464it [00:01, 443.10it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "463it [00:00, 498.34it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 141/141 [00:01<00:00, 85.78it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 229/229 [00:01<00:00, 199.39it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsshome1/0B/di54doz/.local/share/mamba/envs/segmenteverygrain/lib/python3.10/site-packages/segmenteverygrain/segmenteverygrain.py:2218: RuntimeWarning: divide by zero encountered in log2\n", + " phi_minor = -np.log2(minor_axis_length)\n", + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000022_c000032_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000022_c000032_grain_stats.csv\n", + "done with segmentation + export\n", + "[469/1053] tile_r000022_c000033\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.41it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.52it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 525/525 [00:15<00:00, 33.00it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "371it [00:00, 511.27it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "422it [00:00, 492.34it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 170/170 [00:01<00:00, 119.46it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 182/182 [00:00<00:00, 209.72it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000022_c000033_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000022_c000033_grain_stats.csv\n", + "done with segmentation + export\n", + "[470/1053] tile_r000022_c000034\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.44it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.45it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 825/825 [00:21<00:00, 38.20it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "708it [00:03, 211.54it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "704it [00:02, 242.38it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 226/226 [00:04<00:00, 47.48it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 295/295 [00:01<00:00, 214.49it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000022_c000034_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000022_c000034_grain_stats.csv\n", + "done with segmentation + export\n", + "[471/1053] tile_r000022_c000035\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:01<00:00, 2.51it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.49it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 692/692 [00:17<00:00, 39.32it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "609it [00:01, 322.19it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "608it [00:01, 337.96it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 194/194 [00:02<00:00, 72.90it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 248/248 [00:01<00:00, 201.27it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n", + "Warning: len(gdf)=248 != len(grain_stats)=247 -> truncating to 247\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000022_c000035_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000022_c000035_grain_stats.csv\n", + "done with segmentation + export\n", + "[472/1053] tile_r000022_c000036\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.47it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.47it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 532/532 [00:13<00:00, 38.14it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "438it [00:01, 266.06it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "466it [00:01, 270.53it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 155/155 [00:02<00:00, 57.78it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 168/168 [00:00<00:00, 183.78it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000022_c000036_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000022_c000036_grain_stats.csv\n", + "done with segmentation + export\n", + "[473/1053] tile_r000022_c000037\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.44it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.45it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 578/578 [00:15<00:00, 37.63it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "477it [00:01, 294.48it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "501it [00:01, 296.17it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 174/174 [00:02<00:00, 72.08it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 185/185 [00:01<00:00, 183.51it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000022_c000037_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000022_c000037_grain_stats.csv\n", + "done with segmentation + export\n", + "[474/1053] tile_r000022_c000038\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.49it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.40it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 510/510 [00:13<00:00, 38.76it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "424it [00:01, 230.34it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "438it [00:01, 234.70it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 139/139 [00:02<00:00, 51.42it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 146/146 [00:00<00:00, 171.15it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000022_c000038_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000022_c000038_grain_stats.csv\n", + "done with segmentation + export\n", + "[475/1053] tile_r000022_c000039\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.48it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.49it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 423/423 [00:10<00:00, 38.93it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "346it [00:01, 307.61it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "368it [00:01, 301.45it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 131/131 [00:01<00:00, 81.25it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 135/135 [00:00<00:00, 180.07it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000022_c000039_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000022_c000039_grain_stats.csv\n", + "done with segmentation + export\n", + "[476/1053] tile_r000022_c000040\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.46it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.49it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 444/444 [00:11<00:00, 38.52it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "344it [00:01, 316.94it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "362it [00:01, 320.65it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 130/130 [00:01<00:00, 76.56it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 133/133 [00:00<00:00, 186.18it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000022_c000040_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000022_c000040_grain_stats.csv\n", + "done with segmentation + export\n", + "[477/1053] tile_r000022_c000041\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:01<00:00, 2.54it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.49it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 528/528 [00:13<00:00, 39.94it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "463it [00:01, 310.22it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "498it [00:01, 310.44it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 179/179 [00:02<00:00, 84.36it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 185/185 [00:00<00:00, 204.12it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000022_c000041_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000022_c000041_grain_stats.csv\n", + "done with segmentation + export\n", + "[478/1053] tile_r000022_c000042\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.47it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.51it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 406/406 [00:10<00:00, 37.13it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "309it [00:01, 307.32it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "330it [00:01, 308.32it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 117/117 [00:01<00:00, 69.43it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 121/121 [00:00<00:00, 183.09it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000022_c000042_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000022_c000042_grain_stats.csv\n", + "done with segmentation + export\n", + "[479/1053] tile_r000022_c000043\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:01<00:00, 2.50it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.50it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 411/411 [00:10<00:00, 38.67it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "330it [00:01, 305.75it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "357it [00:01, 298.42it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 124/124 [00:01<00:00, 75.33it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 129/129 [00:00<00:00, 188.95it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n", + "Warning: len(gdf)=129 != len(grain_stats)=128 -> truncating to 128\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000022_c000043_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000022_c000043_grain_stats.csv\n", + "done with segmentation + export\n", + "[480/1053] tile_r000022_c000044\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.45it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.49it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 631/631 [00:18<00:00, 33.52it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "440it [00:02, 191.48it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "439it [00:02, 212.86it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 118/118 [00:03<00:00, 32.98it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 158/158 [00:00<00:00, 167.86it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000022_c000044_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000022_c000044_grain_stats.csv\n", + "done with segmentation + export\n", + "[481/1053] tile_r000022_c000045\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.47it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.54it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 498/498 [00:13<00:00, 36.40it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "399it [00:01, 210.51it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "418it [00:01, 209.96it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 132/132 [00:02<00:00, 44.39it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 141/141 [00:00<00:00, 166.72it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000022_c000045_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000022_c000045_grain_stats.csv\n", + "done with segmentation + export\n", + "[482/1053] tile_r000022_c000046\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.47it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.54it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 416/416 [00:10<00:00, 39.58it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "351it [00:01, 246.84it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "368it [00:01, 259.02it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 123/123 [00:01<00:00, 67.35it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 127/127 [00:00<00:00, 174.76it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000022_c000046_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000022_c000046_grain_stats.csv\n", + "done with segmentation + export\n", + "[483/1053] tile_r000022_c000047\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.49it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.46it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 573/573 [00:14<00:00, 39.30it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "486it [00:02, 226.83it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "510it [00:02, 235.71it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 171/171 [00:02<00:00, 60.58it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 178/178 [00:01<00:00, 173.83it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000022_c000047_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000022_c000047_grain_stats.csv\n", + "done with segmentation + export\n", + "[484/1053] tile_r000022_c000048\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:01<00:00, 2.53it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.48it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 523/523 [00:13<00:00, 39.33it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "429it [00:02, 185.25it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "428it [00:02, 193.46it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 122/122 [00:03<00:00, 40.26it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 151/151 [00:00<00:00, 163.94it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000022_c000048_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000022_c000048_grain_stats.csv\n", + "done with segmentation + export\n", + "[485/1053] tile_r000022_c000049\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.47it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.48it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 547/547 [00:13<00:00, 39.44it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "464it [00:01, 244.84it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "492it [00:01, 247.68it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 166/166 [00:02<00:00, 62.64it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 176/176 [00:00<00:00, 178.81it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000022_c000049_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000022_c000049_grain_stats.csv\n", + "done with segmentation + export\n", + "[486/1053] tile_r000022_c000050\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.46it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.46it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 520/520 [00:13<00:00, 39.28it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "442it [00:02, 218.96it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "461it [00:02, 219.30it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 150/150 [00:02<00:00, 53.27it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 153/153 [00:00<00:00, 171.28it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000022_c000050_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000022_c000050_grain_stats.csv\n", + "done with segmentation + export\n", + "[487/1053] tile_r000022_c000051\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.45it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.47it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 505/505 [00:13<00:00, 38.52it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "383it [00:01, 272.06it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "400it [00:01, 271.34it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 137/137 [00:02<00:00, 64.00it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 145/145 [00:00<00:00, 174.76it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000022_c000051_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000022_c000051_grain_stats.csv\n", + "done with segmentation + export\n", + "[488/1053] tile_r000022_c000052\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.48it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.53it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 689/689 [00:17<00:00, 39.81it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "592it [00:02, 234.29it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "640it [00:02, 234.93it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 217/217 [00:03<00:00, 55.03it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 228/228 [00:01<00:00, 198.79it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000022_c000052_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000022_c000052_grain_stats.csv\n", + "done with segmentation + export\n", + "[489/1053] tile_r000022_c000053\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:01<00:00, 2.52it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.48it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 879/879 [00:21<00:00, 39.97it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "750it [00:03, 222.27it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "776it [00:03, 221.92it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 270/270 [00:04<00:00, 56.79it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 284/284 [00:01<00:00, 195.59it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000022_c000053_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000022_c000053_grain_stats.csv\n", + "done with segmentation + export\n", + "[490/1053] tile_r000022_c000054\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:01<00:00, 2.54it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.51it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 1103/1103 [00:27<00:00, 40.41it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "977it [00:03, 253.34it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "995it [00:03, 251.15it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 372/372 [00:04<00:00, 84.01it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 373/373 [00:01<00:00, 199.27it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000022_c000054_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000022_c000054_grain_stats.csv\n", + "done with segmentation + export\n", + "[491/1053] tile_r000022_c000055\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.50it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.47it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 1131/1131 [00:28<00:00, 39.99it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "961it [00:04, 236.26it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "977it [00:04, 235.73it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 354/354 [00:04<00:00, 77.67it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 357/357 [00:01<00:00, 196.49it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000022_c000055_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000022_c000055_grain_stats.csv\n", + "done with segmentation + export\n", + "[492/1053] tile_r000022_c000056\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.43it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.43it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 989/989 [00:25<00:00, 39.50it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "828it [00:04, 204.74it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "842it [00:04, 202.55it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 302/302 [00:04<00:00, 65.53it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 309/309 [00:01<00:00, 183.00it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000022_c000056_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000022_c000056_grain_stats.csv\n", + "done with segmentation + export\n", + "[493/1053] tile_r000022_c000057\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.44it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.52it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 735/735 [00:18<00:00, 39.15it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "621it [00:03, 186.62it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "630it [00:03, 187.70it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 221/221 [00:03<00:00, 63.40it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 221/221 [00:01<00:00, 166.84it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000022_c000057_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000022_c000057_grain_stats.csv\n", + "done with segmentation + export\n", + "[494/1053] tile_r000022_c000058\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.43it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.53it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 49/49 [00:01<00:00, 31.77it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "25it [00:00, 226.69it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "26it [00:00, 229.02it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 7/7 [00:00<00:00, 33.89it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 7/7 [00:00<00:00, 185.87it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000022_c000058_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000022_c000058_grain_stats.csv\n", + "done with segmentation + export\n", + "[495/1053] tile_r000023_c000011\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.45it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.50it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 12/12 [00:00<00:00, 22.03it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "1it [00:00, 1006.55it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2it [00:00, 451.24it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 1/1 [00:00<00:00, 182.30it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 1/1 [00:00<00:00, 113.15it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n", + "Saved histogram plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000023_c000011_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000023_c000011_grain_stats.csv\n", + "done with segmentation + export\n", + "[496/1053] tile_r000023_c000012\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.44it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.45it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 345/345 [00:09<00:00, 37.19it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "271it [00:01, 165.97it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "283it [00:01, 169.09it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 81/81 [00:02<00:00, 37.15it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 91/91 [00:00<00:00, 151.42it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000023_c000012_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000023_c000012_grain_stats.csv\n", + "done with segmentation + export\n", + "[497/1053] tile_r000023_c000013\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.41it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.50it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 795/795 [00:20<00:00, 39.54it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "691it [00:03, 200.09it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "712it [00:03, 196.32it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 223/223 [00:04<00:00, 46.90it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 236/236 [00:01<00:00, 162.07it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000023_c000013_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000023_c000013_grain_stats.csv\n", + "done with segmentation + export\n", + "[498/1053] tile_r000023_c000014\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.49it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.51it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 774/774 [00:19<00:00, 39.08it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "627it [00:02, 216.24it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "638it [00:02, 216.05it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 218/218 [00:03<00:00, 60.47it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 231/231 [00:01<00:00, 164.44it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000023_c000014_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000023_c000014_grain_stats.csv\n", + "done with segmentation + export\n", + "[499/1053] tile_r000023_c000015\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.49it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.46it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 866/866 [00:21<00:00, 39.69it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "743it [00:03, 205.35it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "742it [00:03, 208.01it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 231/231 [00:04<00:00, 52.61it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 255/255 [00:01<00:00, 174.26it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000023_c000015_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000023_c000015_grain_stats.csv\n", + "done with segmentation + export\n", + "[500/1053] tile_r000023_c000016\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:01<00:00, 2.50it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.55it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 724/724 [00:18<00:00, 39.16it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "627it [00:03, 182.61it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "626it [00:03, 182.99it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 181/181 [00:04<00:00, 41.02it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 214/214 [00:01<00:00, 168.04it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000023_c000016_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000023_c000016_grain_stats.csv\n", + "done with segmentation + export\n", + "[501/1053] tile_r000023_c000017\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:01<00:00, 2.54it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.52it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 702/702 [00:17<00:00, 39.00it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "562it [00:02, 200.39it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "561it [00:02, 205.82it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 165/165 [00:03<00:00, 46.02it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 199/199 [00:01<00:00, 164.92it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000023_c000017_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000023_c000017_grain_stats.csv\n", + "done with segmentation + export\n", + "[502/1053] tile_r000023_c000018\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.46it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.50it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 893/893 [00:23<00:00, 38.69it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "757it [00:03, 235.26it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "781it [00:03, 228.34it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 270/270 [00:04<00:00, 66.36it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 283/283 [00:01<00:00, 186.54it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000023_c000018_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000023_c000018_grain_stats.csv\n", + "done with segmentation + export\n", + "[503/1053] tile_r000023_c000019\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:01<00:00, 2.53it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.45it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 1017/1017 [00:25<00:00, 40.07it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "882it [00:03, 287.34it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "918it [00:03, 301.09it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 325/325 [00:03<00:00, 82.26it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 337/337 [00:01<00:00, 204.32it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000023_c000019_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000023_c000019_grain_stats.csv\n", + "done with segmentation + export\n", + "[504/1053] tile_r000023_c000020\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:01<00:00, 2.55it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.51it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 1315/1315 [00:33<00:00, 38.86it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "1136it [00:03, 372.02it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "1135it [00:03, 368.71it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 400/400 [00:03<00:00, 107.80it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 477/477 [00:02<00:00, 236.47it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000023_c000020_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000023_c000020_grain_stats.csv\n", + "done with segmentation + export\n", + "[505/1053] tile_r000023_c000021\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.46it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.48it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 1617/1617 [00:39<00:00, 40.65it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "1498it [00:03, 487.62it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "1557it [00:03, 495.63it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 661/661 [00:03<00:00, 195.57it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 680/680 [00:02<00:00, 267.28it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000023_c000021_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000023_c000021_grain_stats.csv\n", + "done with segmentation + export\n", + "[506/1053] tile_r000023_c000022\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.50it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.46it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 1029/1029 [00:26<00:00, 39.42it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "937it [00:01, 823.84it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "1008it [00:01, 792.56it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 452/452 [00:01<00:00, 282.45it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 457/457 [00:01<00:00, 289.59it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000023_c000022_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000023_c000022_grain_stats.csv\n", + "done with segmentation + export\n", + "[507/1053] tile_r000023_c000023\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.45it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.48it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 1069/1069 [00:28<00:00, 37.46it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "940it [00:01, 507.06it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "936it [00:01, 630.78it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 353/353 [00:02<00:00, 158.09it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 431/431 [00:01<00:00, 266.49it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000023_c000023_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000023_c000023_grain_stats.csv\n", + "done with segmentation + export\n", + "[508/1053] tile_r000023_c000024\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.46it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.53it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 889/889 [00:23<00:00, 38.25it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "737it [00:01, 395.84it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "731it [00:01, 562.88it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 276/276 [00:02<00:00, 137.68it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 340/340 [00:01<00:00, 259.07it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000023_c000024_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000023_c000024_grain_stats.csv\n", + "done with segmentation + export\n", + "[509/1053] tile_r000023_c000025\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:01<00:00, 2.54it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.48it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 729/729 [00:19<00:00, 38.24it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "588it [00:01, 460.09it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "584it [00:00, 611.29it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 212/212 [00:01<00:00, 142.44it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 261/261 [00:01<00:00, 229.74it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000023_c000025_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000023_c000025_grain_stats.csv\n", + "done with segmentation + export\n", + "[510/1053] tile_r000023_c000026\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.46it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.50it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 760/760 [00:19<00:00, 38.68it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "639it [00:01, 371.57it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "637it [00:01, 406.10it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 221/221 [00:02<00:00, 93.61it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 278/278 [00:01<00:00, 226.74it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n", + "Warning: len(gdf)=278 != len(grain_stats)=276 -> truncating to 276\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000023_c000026_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000023_c000026_grain_stats.csv\n", + "done with segmentation + export\n", + "[511/1053] tile_r000023_c000027\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.48it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.51it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 378/378 [00:10<00:00, 35.67it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "291it [00:01, 219.29it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "288it [00:01, 261.50it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 90/90 [00:03<00:00, 28.51it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 126/126 [00:00<00:00, 184.26it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n", + "Warning: len(gdf)=126 != len(grain_stats)=125 -> truncating to 125\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000023_c000027_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000023_c000027_grain_stats.csv\n", + "done with segmentation + export\n", + "[512/1053] tile_r000023_c000028\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:01<00:00, 2.54it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.48it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 229/229 [00:07<00:00, 28.64it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "154it [00:01, 88.45it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "145it [00:01, 124.30it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 23/23 [00:05<00:00, 3.97it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 66/66 [00:00<00:00, 118.42it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000023_c000028_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000023_c000028_grain_stats.csv\n", + "done with segmentation + export\n", + "[513/1053] tile_r000023_c000029\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.46it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.51it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 356/356 [00:10<00:00, 33.23it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "240it [00:03, 78.97it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "221it [00:01, 216.39it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 53/53 [00:02<00:00, 23.73it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 115/115 [00:00<00:00, 138.54it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000023_c000029_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000023_c000029_grain_stats.csv\n", + "done with segmentation + export\n", + "[514/1053] tile_r000023_c000030\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.47it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.44it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 515/515 [00:15<00:00, 33.95it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "402it [00:01, 207.99it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "397it [00:00, 518.92it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 124/124 [00:01<00:00, 78.21it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 200/200 [00:00<00:00, 208.48it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000023_c000030_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000023_c000030_grain_stats.csv\n", + "done with segmentation + export\n", + "[515/1053] tile_r000023_c000031\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.48it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.48it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 631/631 [00:16<00:00, 37.49it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "515it [00:01, 375.33it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "513it [00:01, 407.28it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 159/159 [00:02<00:00, 61.53it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 249/249 [00:01<00:00, 232.83it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000023_c000031_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000023_c000031_grain_stats.csv\n", + "done with segmentation + export\n", + "[516/1053] tile_r000023_c000032\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.47it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.50it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 617/617 [00:16<00:00, 37.18it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "504it [00:01, 350.71it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "503it [00:01, 360.87it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 164/164 [00:02<00:00, 61.52it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 232/232 [00:01<00:00, 208.16it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000023_c000032_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000023_c000032_grain_stats.csv\n", + "done with segmentation + export\n", + "[517/1053] tile_r000023_c000033\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:01<00:00, 2.51it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.48it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 505/505 [00:14<00:00, 35.71it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "370it [00:01, 202.80it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "369it [00:01, 221.23it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 125/125 [00:02<00:00, 47.71it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 173/173 [00:01<00:00, 170.72it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000023_c000033_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000023_c000033_grain_stats.csv\n", + "done with segmentation + export\n", + "[518/1053] tile_r000023_c000034\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:01<00:00, 2.51it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.48it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 782/782 [00:20<00:00, 37.87it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "635it [00:01, 351.76it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "634it [00:01, 362.03it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 220/220 [00:02<00:00, 103.73it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 270/270 [00:01<00:00, 208.75it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000023_c000034_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000023_c000034_grain_stats.csv\n", + "done with segmentation + export\n", + "[519/1053] tile_r000023_c000035\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:01<00:00, 2.51it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.52it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 873/873 [00:22<00:00, 38.90it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "743it [00:02, 300.02it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "777it [00:02, 299.62it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 270/270 [00:03<00:00, 78.92it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 287/287 [00:01<00:00, 197.00it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000023_c000035_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000023_c000035_grain_stats.csv\n", + "done with segmentation + export\n", + "[520/1053] tile_r000023_c000036\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.48it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.42it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 618/618 [00:15<00:00, 39.53it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "518it [00:02, 257.68it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "541it [00:02, 258.32it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 182/182 [00:02<00:00, 66.27it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 191/191 [00:01<00:00, 182.64it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000023_c000036_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000023_c000036_grain_stats.csv\n", + "done with segmentation + export\n", + "[521/1053] tile_r000023_c000037\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:01<00:00, 2.50it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.29it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 514/514 [00:13<00:00, 39.12it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "420it [00:01, 254.72it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "447it [00:01, 271.80it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 155/155 [00:02<00:00, 68.14it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 161/161 [00:00<00:00, 193.59it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000023_c000037_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000023_c000037_grain_stats.csv\n", + "done with segmentation + export\n", + "[522/1053] tile_r000023_c000038\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:01<00:00, 2.53it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.52it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 530/530 [00:13<00:00, 39.58it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "425it [00:01, 274.58it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "454it [00:01, 276.39it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 161/161 [00:02<00:00, 71.33it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 166/166 [00:00<00:00, 197.23it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000023_c000038_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000023_c000038_grain_stats.csv\n", + "done with segmentation + export\n", + "[523/1053] tile_r000023_c000039\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.49it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.40it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 479/479 [00:12<00:00, 38.64it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "376it [00:01, 252.80it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "406it [00:01, 250.99it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 141/141 [00:02<00:00, 63.00it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 147/147 [00:00<00:00, 186.64it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000023_c000039_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000023_c000039_grain_stats.csv\n", + "done with segmentation + export\n", + "[524/1053] tile_r000023_c000040\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.50it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.49it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 416/416 [00:10<00:00, 40.29it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "359it [00:01, 322.70it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "380it [00:01, 324.58it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 139/139 [00:01<00:00, 81.46it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 142/142 [00:00<00:00, 198.78it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000023_c000040_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000023_c000040_grain_stats.csv\n", + "done with segmentation + export\n", + "[525/1053] tile_r000023_c000041\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.49it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.52it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 525/525 [00:13<00:00, 39.37it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "435it [00:01, 292.57it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "434it [00:01, 299.84it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 133/133 [00:02<00:00, 65.96it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 168/168 [00:00<00:00, 193.95it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000023_c000041_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000023_c000041_grain_stats.csv\n", + "done with segmentation + export\n", + "[526/1053] tile_r000023_c000042\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.48it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.47it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 807/807 [00:20<00:00, 39.92it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "719it [00:02, 299.31it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "717it [00:02, 310.40it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 231/231 [00:02<00:00, 78.10it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 291/291 [00:01<00:00, 210.78it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000023_c000042_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000023_c000042_grain_stats.csv\n", + "done with segmentation + export\n", + "[527/1053] tile_r000023_c000043\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:01<00:00, 2.52it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.32it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 541/541 [00:14<00:00, 38.47it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "442it [00:01, 263.09it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "440it [00:01, 270.90it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 130/130 [00:02<00:00, 53.78it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 157/157 [00:00<00:00, 178.32it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000023_c000043_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000023_c000043_grain_stats.csv\n", + "done with segmentation + export\n", + "[528/1053] tile_r000023_c000044\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:01<00:00, 2.53it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.15it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 637/637 [00:17<00:00, 35.64it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "485it [00:02, 177.31it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "481it [00:02, 197.31it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 129/129 [00:03<00:00, 38.40it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 174/174 [00:01<00:00, 171.36it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000023_c000044_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000023_c000044_grain_stats.csv\n", + "done with segmentation + export\n", + "[529/1053] tile_r000023_c000045\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.43it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.49it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 597/597 [00:15<00:00, 39.78it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "512it [00:02, 252.68it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "536it [00:02, 248.00it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 191/191 [00:02<00:00, 73.96it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 200/200 [00:01<00:00, 187.43it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000023_c000045_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000023_c000045_grain_stats.csv\n", + "done with segmentation + export\n", + "[530/1053] tile_r000023_c000046\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.46it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.46it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 489/489 [00:13<00:00, 35.40it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "375it [00:01, 201.17it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "397it [00:01, 199.91it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 125/125 [00:02<00:00, 41.82it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 131/131 [00:00<00:00, 168.54it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000023_c000046_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000023_c000046_grain_stats.csv\n", + "done with segmentation + export\n", + "[531/1053] tile_r000023_c000047\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.48it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.46it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 633/633 [00:16<00:00, 38.93it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "529it [00:02, 183.69it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "528it [00:02, 187.45it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 145/145 [00:03<00:00, 37.68it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 175/175 [00:01<00:00, 170.53it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000023_c000047_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000023_c000047_grain_stats.csv\n", + "done with segmentation + export\n", + "[532/1053] tile_r000023_c000048\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:01<00:00, 2.51it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.54it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 648/648 [00:17<00:00, 36.39it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "523it [00:02, 177.34it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "547it [00:03, 174.38it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 170/170 [00:03<00:00, 43.63it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 175/175 [00:01<00:00, 168.64it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000023_c000048_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000023_c000048_grain_stats.csv\n", + "done with segmentation + export\n", + "[533/1053] tile_r000023_c000049\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:01<00:00, 2.53it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.47it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 564/564 [00:16<00:00, 35.03it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "397it [00:02, 189.52it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "419it [00:02, 192.88it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 136/136 [00:02<00:00, 50.60it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 139/139 [00:00<00:00, 159.23it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000023_c000049_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000023_c000049_grain_stats.csv\n", + "done with segmentation + export\n", + "[534/1053] tile_r000023_c000050\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.48it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.48it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 754/754 [00:18<00:00, 39.69it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "623it [00:03, 205.62it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "659it [00:03, 205.96it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 229/229 [00:03<00:00, 58.97it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 237/237 [00:01<00:00, 175.15it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000023_c000050_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000023_c000050_grain_stats.csv\n", + "done with segmentation + export\n", + "[535/1053] tile_r000023_c000051\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:01<00:00, 2.52it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.49it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 883/883 [00:22<00:00, 39.80it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "774it [00:03, 233.81it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "807it [00:03, 234.37it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 277/277 [00:04<00:00, 65.89it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 289/289 [00:01<00:00, 189.49it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000023_c000051_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000023_c000051_grain_stats.csv\n", + "done with segmentation + export\n", + "[536/1053] tile_r000023_c000052\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:01<00:00, 2.53it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.48it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 1059/1059 [00:26<00:00, 40.41it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "927it [00:03, 245.54it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "959it [00:03, 252.34it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 344/344 [00:04<00:00, 77.09it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 350/350 [00:01<00:00, 202.99it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000023_c000052_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000023_c000052_grain_stats.csv\n", + "done with segmentation + export\n", + "[537/1053] tile_r000023_c000053\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.47it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.57it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 1058/1058 [00:26<00:00, 39.86it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "894it [00:04, 217.99it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "893it [00:04, 216.34it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 311/311 [00:04<00:00, 64.06it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 339/339 [00:01<00:00, 193.41it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000023_c000053_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000023_c000053_grain_stats.csv\n", + "done with segmentation + export\n", + "[538/1053] tile_r000023_c000054\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:01<00:00, 2.51it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.51it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 939/939 [00:24<00:00, 38.49it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "795it [00:05, 149.57it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "813it [00:05, 151.09it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 273/273 [00:06<00:00, 40.23it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 284/284 [00:01<00:00, 181.13it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n", + "Warning: len(gdf)=284 != len(grain_stats)=283 -> truncating to 283\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000023_c000054_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000023_c000054_grain_stats.csv\n", + "done with segmentation + export\n", + "[539/1053] tile_r000023_c000055\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.49it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.53it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 962/962 [00:24<00:00, 39.87it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "823it [00:04, 192.98it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "839it [00:04, 193.16it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 289/289 [00:05<00:00, 56.23it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 291/291 [00:01<00:00, 182.40it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000023_c000055_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000023_c000055_grain_stats.csv\n", + "done with segmentation + export\n", + "[540/1053] tile_r000023_c000056\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.49it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.43it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 519/519 [00:13<00:00, 38.20it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "431it [00:02, 167.20it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "442it [00:02, 162.03it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 144/144 [00:03<00:00, 45.84it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 147/147 [00:00<00:00, 170.34it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000023_c000056_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000023_c000056_grain_stats.csv\n", + "done with segmentation + export\n", + "[541/1053] tile_r000023_c000057\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.47it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.51it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 30/30 [00:01<00:00, 28.91it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "6it [00:00, 812.69it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "6it [00:00, 884.78it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 3/3 [00:00<00:00, 407.35it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 3/3 [00:00<00:00, 247.87it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000023_c000057_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000023_c000057_grain_stats.csv\n", + "done with segmentation + export\n", + "[542/1053] tile_r000024_c000010\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.46it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.50it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 17/17 [00:00<00:00, 25.76it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "7it [00:00, 737.58it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "8it [00:00, 618.74it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 3/3 [00:00<00:00, 73.48it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:00<00:00, 124.39it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000024_c000010_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000024_c000010_grain_stats.csv\n", + "done with segmentation + export\n", + "[543/1053] tile_r000024_c000011\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.48it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.46it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 284/284 [00:09<00:00, 30.82it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "197it [00:01, 175.14it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "195it [00:01, 187.86it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 46/46 [00:01<00:00, 26.70it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 66/66 [00:00<00:00, 134.32it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000024_c000011_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000024_c000011_grain_stats.csv\n", + "done with segmentation + export\n", + "[544/1053] tile_r000024_c000012\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.50it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.50it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 495/495 [00:12<00:00, 38.08it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "370it [00:02, 178.28it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "390it [00:02, 174.63it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 120/120 [00:03<00:00, 38.10it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 127/127 [00:00<00:00, 141.94it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n", + "Warning: len(gdf)=127 != len(grain_stats)=126 -> truncating to 126\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000024_c000012_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000024_c000012_grain_stats.csv\n", + "done with segmentation + export\n", + "[545/1053] tile_r000024_c000013\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.44it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.51it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 724/724 [00:18<00:00, 38.99it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "581it [00:02, 227.14it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "600it [00:02, 226.97it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 202/202 [00:03<00:00, 59.94it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 216/216 [00:01<00:00, 164.36it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000024_c000013_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000024_c000013_grain_stats.csv\n", + "done with segmentation + export\n", + "[546/1053] tile_r000024_c000014\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:01<00:00, 2.51it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.48it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 796/796 [00:20<00:00, 39.62it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "670it [00:03, 186.68it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "685it [00:03, 187.00it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 220/220 [00:04<00:00, 46.74it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 225/225 [00:01<00:00, 161.75it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000024_c000014_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000024_c000014_grain_stats.csv\n", + "done with segmentation + export\n", + "[547/1053] tile_r000024_c000015\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:01<00:00, 2.51it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.52it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 829/829 [00:20<00:00, 39.79it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "703it [00:03, 184.20it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "724it [00:03, 184.40it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 235/235 [00:04<00:00, 50.37it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 243/243 [00:01<00:00, 167.80it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000024_c000015_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000024_c000015_grain_stats.csv\n", + "done with segmentation + export\n", + "[548/1053] tile_r000024_c000016\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.42it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.54it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 793/793 [00:20<00:00, 38.62it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "656it [00:03, 191.51it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "675it [00:03, 192.46it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 221/221 [00:04<00:00, 51.35it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 229/229 [00:01<00:00, 167.59it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000024_c000016_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000024_c000016_grain_stats.csv\n", + "done with segmentation + export\n", + "[549/1053] tile_r000024_c000017\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.43it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.55it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 713/713 [00:18<00:00, 39.39it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "588it [00:03, 187.05it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "586it [00:03, 191.47it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 175/175 [00:03<00:00, 44.47it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 205/205 [00:01<00:00, 159.95it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000024_c000017_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000024_c000017_grain_stats.csv\n", + "done with segmentation + export\n", + "[550/1053] tile_r000024_c000018\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.47it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.44it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 581/581 [00:14<00:00, 38.78it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "481it [00:02, 202.19it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "501it [00:02, 204.17it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 161/161 [00:03<00:00, 48.97it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 170/170 [00:01<00:00, 169.20it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000024_c000018_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000024_c000018_grain_stats.csv\n", + "done with segmentation + export\n", + "[551/1053] tile_r000024_c000019\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.48it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.47it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 759/759 [00:19<00:00, 39.13it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "640it [00:02, 239.75it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "663it [00:02, 240.55it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 216/216 [00:03<00:00, 58.97it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 222/222 [00:01<00:00, 174.83it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000024_c000019_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000024_c000019_grain_stats.csv\n", + "done with segmentation + export\n", + "[552/1053] tile_r000024_c000020\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:01<00:00, 2.52it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.49it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 750/750 [00:18<00:00, 40.04it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "642it [00:02, 273.75it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "667it [00:02, 273.24it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 235/235 [00:03<00:00, 70.38it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 246/246 [00:01<00:00, 186.29it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000024_c000020_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000024_c000020_grain_stats.csv\n", + "done with segmentation + export\n", + "[553/1053] tile_r000024_c000021\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:14<00:00, 2.86s/it]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.17it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 1261/1261 [00:31<00:00, 39.78it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "1112it [00:02, 402.11it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "1169it [00:02, 389.72it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 482/482 [00:03<00:00, 137.83it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 495/495 [00:02<00:00, 241.39it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000024_c000021_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000024_c000021_grain_stats.csv\n", + "done with segmentation + export\n", + "[554/1053] tile_r000024_c000022\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.30it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.29it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 1597/1597 [00:39<00:00, 40.80it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "1494it [00:03, 476.49it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "1542it [00:03, 488.76it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 660/660 [00:03<00:00, 189.34it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 670/670 [00:02<00:00, 262.76it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000024_c000022_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000024_c000022_grain_stats.csv\n", + "done with segmentation + export\n", + "[555/1053] tile_r000024_c000023\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.38it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.41it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 1307/1307 [00:32<00:00, 40.61it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "1211it [00:02, 546.32it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "1210it [00:02, 540.04it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 487/487 [00:02<00:00, 185.97it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 555/555 [00:02<00:00, 266.72it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000024_c000023_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000024_c000023_grain_stats.csv\n", + "done with segmentation + export\n", + "[556/1053] tile_r000024_c000024\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.41it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.41it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 1154/1154 [00:28<00:00, 40.69it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "1047it [00:01, 559.57it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "1113it [00:02, 546.68it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 471/471 [00:02<00:00, 192.04it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 482/482 [00:01<00:00, 259.28it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000024_c000024_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000024_c000024_grain_stats.csv\n", + "done with segmentation + export\n", + "[557/1053] tile_r000024_c000025\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.23it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.47it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 825/825 [00:22<00:00, 37.48it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "728it [00:01, 435.14it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "727it [00:01, 457.51it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 259/259 [00:02<00:00, 112.58it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 339/339 [00:01<00:00, 233.89it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000024_c000025_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000024_c000025_grain_stats.csv\n", + "done with segmentation + export\n", + "[558/1053] tile_r000024_c000026\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.42it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.47it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 476/476 [00:13<00:00, 34.72it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "365it [00:01, 226.73it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "363it [00:01, 270.73it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 105/105 [00:02<00:00, 37.83it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 158/158 [00:00<00:00, 183.05it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000024_c000026_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000024_c000026_grain_stats.csv\n", + "done with segmentation + export\n", + "[559/1053] tile_r000024_c000027\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.43it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.45it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 254/254 [00:08<00:00, 28.65it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "144it [00:01, 82.53it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "143it [00:01, 87.06it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 30/30 [00:04<00:00, 7.30it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 56/56 [00:00<00:00, 111.86it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000024_c000027_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000024_c000027_grain_stats.csv\n", + "done with segmentation + export\n", + "[560/1053] tile_r000024_c000028\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.50it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.48it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 361/361 [00:11<00:00, 32.28it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "242it [00:03, 73.66it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "217it [00:00, 289.01it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 46/46 [00:02<00:00, 22.51it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 119/119 [00:00<00:00, 141.83it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000024_c000028_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000024_c000028_grain_stats.csv\n", + "done with segmentation + export\n", + "[561/1053] tile_r000024_c000029\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.47it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.47it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 394/394 [00:12<00:00, 32.18it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "280it [00:04, 59.13it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "269it [00:03, 76.95it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 53/53 [00:06<00:00, 7.65it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 135/135 [00:00<00:00, 168.61it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000024_c000029_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000024_c000029_grain_stats.csv\n", + "done with segmentation + export\n", + "[562/1053] tile_r000024_c000030\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.42it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.45it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 450/450 [00:12<00:00, 35.02it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "316it [00:00, 521.40it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "384it [00:00, 503.60it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 155/155 [00:01<00:00, 97.35it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 170/170 [00:00<00:00, 197.35it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000024_c000030_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000024_c000030_grain_stats.csv\n", + "done with segmentation + export\n", + "[563/1053] tile_r000024_c000031\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.45it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.45it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 587/587 [00:18<00:00, 31.89it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "417it [00:03, 113.75it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "412it [00:01, 357.17it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 116/116 [00:01<00:00, 60.37it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 194/194 [00:00<00:00, 212.50it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000024_c000031_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000024_c000031_grain_stats.csv\n", + "done with segmentation + export\n", + "[564/1053] tile_r000024_c000032\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.45it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.47it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 688/688 [00:18<00:00, 37.98it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "603it [00:05, 119.35it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "43it [00:00, 2828.61it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "0it [00:00, ?it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 43/43 [00:00<00:00, 137.28it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000024_c000032_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000024_c000032_grain_stats.csv\n", + "done with segmentation + export\n", + "[565/1053] tile_r000024_c000033\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:01<00:00, 2.50it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.46it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 830/830 [00:20<00:00, 40.22it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "715it [00:01, 403.89it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "764it [00:01, 390.36it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 299/299 [00:02<00:00, 118.71it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 309/309 [00:01<00:00, 214.53it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000024_c000033_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000024_c000033_grain_stats.csv\n", + "done with segmentation + export\n", + "[566/1053] tile_r000024_c000034\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.43it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.42it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 682/682 [00:17<00:00, 38.80it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "557it [00:01, 316.91it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "556it [00:01, 334.90it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 175/175 [00:02<00:00, 66.54it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 226/226 [00:01<00:00, 192.90it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000024_c000034_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000024_c000034_grain_stats.csv\n", + "done with segmentation + export\n", + "[567/1053] tile_r000024_c000035\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.48it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.46it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 671/671 [00:17<00:00, 39.17it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "561it [00:01, 315.24it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "599it [00:01, 299.69it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 217/217 [00:02<00:00, 87.03it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 224/224 [00:01<00:00, 200.72it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000024_c000035_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000024_c000035_grain_stats.csv\n", + "done with segmentation + export\n", + "[568/1053] tile_r000024_c000036\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.45it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.48it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 577/577 [00:14<00:00, 39.65it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "484it [00:01, 353.95it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "522it [00:01, 350.24it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 195/195 [00:01<00:00, 99.06it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 200/200 [00:01<00:00, 194.52it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000024_c000036_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000024_c000036_grain_stats.csv\n", + "done with segmentation + export\n", + "[569/1053] tile_r000024_c000037\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.47it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.49it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 718/718 [00:18<00:00, 39.29it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "605it [00:02, 253.71it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "600it [00:02, 269.20it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 193/193 [00:02<00:00, 69.35it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 235/235 [00:01<00:00, 190.54it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000024_c000037_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000024_c000037_grain_stats.csv\n", + "done with segmentation + export\n", + "[570/1053] tile_r000024_c000038\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.49it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.49it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 812/812 [00:20<00:00, 39.77it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "714it [00:02, 304.98it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "713it [00:02, 312.46it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 238/238 [00:02<00:00, 85.70it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 287/287 [00:01<00:00, 203.91it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n", + "Warning: len(gdf)=287 != len(grain_stats)=286 -> truncating to 286\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000024_c000038_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000024_c000038_grain_stats.csv\n", + "done with segmentation + export\n", + "[571/1053] tile_r000024_c000039\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:01<00:00, 2.54it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.52it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 869/869 [00:21<00:00, 40.24it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "790it [00:02, 330.42it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "845it [00:02, 320.87it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 321/321 [00:03<00:00, 106.67it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 332/332 [00:01<00:00, 214.13it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000024_c000039_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000024_c000039_grain_stats.csv\n", + "done with segmentation + export\n", + "[572/1053] tile_r000024_c000040\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:01<00:00, 2.52it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.49it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 640/640 [00:15<00:00, 40.47it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "561it [00:02, 269.63it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "588it [00:02, 277.88it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 203/203 [00:02<00:00, 68.01it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 209/209 [00:01<00:00, 190.29it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000024_c000040_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000024_c000040_grain_stats.csv\n", + "done with segmentation + export\n", + "[573/1053] tile_r000024_c000041\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:01<00:00, 2.52it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.52it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 534/534 [00:13<00:00, 39.13it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "433it [00:01, 261.58it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "450it [00:01, 258.94it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 145/145 [00:02<00:00, 58.66it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 146/146 [00:00<00:00, 182.35it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000024_c000041_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000024_c000041_grain_stats.csv\n", + "done with segmentation + export\n", + "[574/1053] tile_r000024_c000042\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:01<00:00, 2.52it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.51it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 332/332 [00:10<00:00, 30.47it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "210it [00:00, 411.50it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "229it [00:00, 421.58it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 87/87 [00:00<00:00, 96.23it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 88/88 [00:00<00:00, 179.12it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000024_c000042_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000024_c000042_grain_stats.csv\n", + "done with segmentation + export\n", + "[575/1053] tile_r000024_c000043\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.49it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.49it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 601/601 [00:15<00:00, 37.79it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "485it [00:01, 336.99it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "517it [00:01, 338.27it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 189/189 [00:02<00:00, 76.62it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 199/199 [00:00<00:00, 221.21it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n", + "Warning: len(gdf)=199 != len(grain_stats)=198 -> truncating to 198\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000024_c000043_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000024_c000043_grain_stats.csv\n", + "done with segmentation + export\n", + "[576/1053] tile_r000024_c000044\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:01<00:00, 2.53it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.47it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 430/430 [00:16<00:00, 26.37it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "234it [00:08, 28.79it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "26it [00:00, 32.45it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 1/1 [00:02<00:00, 2.48s/it]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 18/18 [00:00<00:00, 91.17it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000024_c000044_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000024_c000044_grain_stats.csv\n", + "done with segmentation + export\n", + "[577/1053] tile_r000024_c000045\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.50it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.45it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 509/509 [00:13<00:00, 37.65it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "397it [00:01, 270.26it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "425it [00:01, 269.29it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 154/154 [00:01<00:00, 81.70it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 156/156 [00:00<00:00, 184.33it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000024_c000045_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000024_c000045_grain_stats.csv\n", + "done with segmentation + export\n", + "[578/1053] tile_r000024_c000046\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:01<00:00, 2.54it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.52it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 326/326 [00:09<00:00, 33.35it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "244it [00:01, 181.10it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "254it [00:01, 162.18it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 79/79 [00:01<00:00, 43.71it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 81/81 [00:00<00:00, 155.46it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000024_c000046_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000024_c000046_grain_stats.csv\n", + "done with segmentation + export\n", + "[579/1053] tile_r000024_c000047\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.50it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.54it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 536/536 [00:14<00:00, 38.11it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "425it [00:02, 188.97it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "447it [00:02, 190.94it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 140/140 [00:03<00:00, 43.75it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 150/150 [00:00<00:00, 166.00it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000024_c000047_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000024_c000047_grain_stats.csv\n", + "done with segmentation + export\n", + "[580/1053] tile_r000024_c000048\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.28it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.41it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 495/495 [00:12<00:00, 38.85it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "400it [00:02, 188.90it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "428it [00:02, 186.15it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 129/129 [00:02<00:00, 44.77it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 138/138 [00:00<00:00, 162.08it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000024_c000048_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000024_c000048_grain_stats.csv\n", + "done with segmentation + export\n", + "[581/1053] tile_r000024_c000049\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.48it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.46it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 671/671 [00:17<00:00, 37.97it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "532it [00:03, 168.28it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "530it [00:03, 175.06it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 137/137 [00:04<00:00, 32.64it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 185/185 [00:01<00:00, 161.74it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000024_c000049_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000024_c000049_grain_stats.csv\n", + "done with segmentation + export\n", + "[582/1053] tile_r000024_c000050\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:01<00:00, 2.52it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.52it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 433/433 [00:11<00:00, 36.70it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "308it [00:01, 247.47it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "329it [00:01, 254.72it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 111/111 [00:01<00:00, 60.82it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 114/114 [00:00<00:00, 158.75it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000024_c000050_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000024_c000050_grain_stats.csv\n", + "done with segmentation + export\n", + "[583/1053] tile_r000024_c000051\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:01<00:00, 2.51it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.54it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 731/731 [00:19<00:00, 38.23it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "614it [00:02, 232.49it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "612it [00:02, 259.05it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 202/202 [00:03<00:00, 65.33it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 237/237 [00:01<00:00, 184.10it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000024_c000051_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000024_c000051_grain_stats.csv\n", + "done with segmentation + export\n", + "[584/1053] tile_r000024_c000052\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.48it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.55it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 964/964 [00:24<00:00, 39.83it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "803it [00:03, 226.76it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "830it [00:03, 226.33it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 297/297 [00:04<00:00, 65.25it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 304/304 [00:01<00:00, 189.89it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000024_c000052_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000024_c000052_grain_stats.csv\n", + "done with segmentation + export\n", + "[585/1053] tile_r000024_c000053\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.48it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.47it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 984/984 [00:24<00:00, 39.49it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "837it [00:04, 205.19it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "852it [00:04, 206.67it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 300/300 [00:04<00:00, 63.19it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 305/305 [00:01<00:00, 190.21it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000024_c000053_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000024_c000053_grain_stats.csv\n", + "done with segmentation + export\n", + "[586/1053] tile_r000024_c000054\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.46it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.50it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 750/750 [00:19<00:00, 37.80it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "620it [00:04, 154.59it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "616it [00:03, 175.41it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 189/189 [00:04<00:00, 41.88it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 218/218 [00:01<00:00, 174.06it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000024_c000054_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000024_c000054_grain_stats.csv\n", + "done with segmentation + export\n", + "[587/1053] tile_r000024_c000055\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.46it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.48it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 265/265 [00:07<00:00, 37.36it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "218it [00:01, 176.14it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "222it [00:01, 177.54it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 76/76 [00:01<00:00, 51.90it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 78/78 [00:00<00:00, 181.77it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000024_c000055_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000024_c000055_grain_stats.csv\n", + "done with segmentation + export\n", + "[588/1053] tile_r000025_c000008\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.44it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.46it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "0it [00:00, ?it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "0it [00:00, ?it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "0it [00:00, ?it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "0it [00:00, ?it/s]\n", + "0it [00:00, ?it/s]\n", + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n", + "No grains found -> skipping histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000025_c000008_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000025_c000008_grain_stats.csv\n", + "done with segmentation + export\n", + "[589/1053] tile_r000025_c000009\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:01<00:00, 2.51it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.51it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 37/37 [00:01<00:00, 28.40it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "22it [00:00, 122.68it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "22it [00:00, 127.37it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:00<00:00, 14.55it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:00<00:00, 128.90it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000025_c000009_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000025_c000009_grain_stats.csv\n", + "done with segmentation + export\n", + "[590/1053] tile_r000025_c000010\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.49it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.49it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 162/162 [00:04<00:00, 33.56it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "98it [00:00, 282.71it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "105it [00:00, 293.08it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 32/32 [00:00<00:00, 48.04it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 32/32 [00:00<00:00, 148.79it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000025_c000010_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000025_c000010_grain_stats.csv\n", + "done with segmentation + export\n", + "[591/1053] tile_r000025_c000011\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:01<00:00, 2.51it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.50it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 375/375 [00:10<00:00, 36.36it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "253it [00:01, 221.16it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "266it [00:01, 223.78it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 79/79 [00:02<00:00, 37.32it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 90/90 [00:00<00:00, 146.17it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000025_c000011_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000025_c000011_grain_stats.csv\n", + "done with segmentation + export\n", + "[592/1053] tile_r000025_c000012\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.47it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.50it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 451/451 [00:12<00:00, 36.62it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "372it [00:02, 149.30it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "370it [00:02, 158.62it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 92/92 [00:03<00:00, 27.29it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 123/123 [00:00<00:00, 133.23it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000025_c000012_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000025_c000012_grain_stats.csv\n", + "done with segmentation + export\n", + "[593/1053] tile_r000025_c000013\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.48it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.49it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 729/729 [00:19<00:00, 38.16it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "584it [00:02, 226.03it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "583it [00:02, 230.40it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 172/172 [00:03<00:00, 48.13it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 213/213 [00:01<00:00, 169.04it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000025_c000013_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000025_c000013_grain_stats.csv\n", + "done with segmentation + export\n", + "[594/1053] tile_r000025_c000014\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:01<00:00, 2.50it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.53it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 725/725 [00:18<00:00, 38.35it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "569it [00:03, 186.54it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "568it [00:02, 190.28it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 161/161 [00:03<00:00, 40.39it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 197/197 [00:01<00:00, 158.65it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000025_c000014_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000025_c000014_grain_stats.csv\n", + "done with segmentation + export\n", + "[595/1053] tile_r000025_c000015\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:01<00:00, 2.52it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.49it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 684/684 [00:17<00:00, 38.62it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "538it [00:02, 180.82it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "558it [00:03, 179.54it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 176/176 [00:03<00:00, 45.40it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 189/189 [00:01<00:00, 155.24it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000025_c000015_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000025_c000015_grain_stats.csv\n", + "done with segmentation + export\n", + "[596/1053] tile_r000025_c000016\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:01<00:00, 2.53it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.51it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 760/760 [00:19<00:00, 39.25it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "608it [00:03, 187.19it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "624it [00:03, 186.86it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 202/202 [00:04<00:00, 48.72it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 210/210 [00:01<00:00, 163.38it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000025_c000016_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000025_c000016_grain_stats.csv\n", + "done with segmentation + export\n", + "[597/1053] tile_r000025_c000017\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:01<00:00, 2.52it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.52it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 773/773 [00:19<00:00, 39.12it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "672it [00:03, 203.37it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "700it [00:03, 203.53it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 238/238 [00:04<00:00, 56.68it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 247/247 [00:01<00:00, 174.88it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000025_c000017_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000025_c000017_grain_stats.csv\n", + "done with segmentation + export\n", + "[598/1053] tile_r000025_c000018\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.46it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.52it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 763/763 [00:19<00:00, 39.65it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "631it [00:03, 185.15it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "630it [00:03, 187.51it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 189/189 [00:04<00:00, 45.04it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 217/217 [00:01<00:00, 157.61it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000025_c000018_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000025_c000018_grain_stats.csv\n", + "done with segmentation + export\n", + "[599/1053] tile_r000025_c000019\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.49it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.51it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 669/669 [00:17<00:00, 38.76it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "551it [00:03, 177.26it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "573it [00:03, 177.14it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 181/181 [00:03<00:00, 45.69it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 193/193 [00:01<00:00, 160.41it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000025_c000019_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000025_c000019_grain_stats.csv\n", + "done with segmentation + export\n", + "[600/1053] tile_r000025_c000020\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.49it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.51it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 704/704 [00:18<00:00, 38.75it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "587it [00:02, 196.74it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "607it [00:03, 195.82it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 196/196 [00:03<00:00, 53.44it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 202/202 [00:01<00:00, 167.53it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000025_c000020_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000025_c000020_grain_stats.csv\n", + "done with segmentation + export\n", + "[601/1053] tile_r000025_c000021\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.46it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.55it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 640/640 [00:16<00:00, 39.49it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "530it [00:02, 250.28it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "529it [00:02, 253.97it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 168/168 [00:02<00:00, 57.92it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 203/203 [00:01<00:00, 190.46it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000025_c000021_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000025_c000021_grain_stats.csv\n", + "done with segmentation + export\n", + "[602/1053] tile_r000025_c000022\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.49it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.50it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 1112/1112 [00:27<00:00, 40.33it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "972it [00:02, 364.28it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "1009it [00:02, 357.70it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 398/398 [00:03<00:00, 123.22it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 408/408 [00:01<00:00, 233.38it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000025_c000022_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000025_c000022_grain_stats.csv\n", + "done with segmentation + export\n", + "[603/1053] tile_r000025_c000023\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:01<00:00, 2.53it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.49it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 1582/1582 [00:38<00:00, 40.94it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "1425it [00:03, 400.63it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "1467it [00:03, 402.68it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 613/613 [00:03<00:00, 170.97it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 621/621 [00:02<00:00, 248.41it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000025_c000023_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000025_c000023_grain_stats.csv\n", + "done with segmentation + export\n", + "[604/1053] tile_r000025_c000024\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.45it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.52it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 998/998 [00:25<00:00, 39.50it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "893it [00:03, 237.01it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "889it [00:03, 268.76it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 327/327 [00:06<00:00, 52.81it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 397/397 [00:01<00:00, 232.76it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000025_c000024_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000025_c000024_grain_stats.csv\n", + "done with segmentation + export\n", + "[605/1053] tile_r000025_c000025\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.46it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.49it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 795/795 [00:20<00:00, 38.87it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "642it [00:01, 378.52it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "690it [00:01, 369.19it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 273/273 [00:02<00:00, 105.36it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 282/282 [00:01<00:00, 208.61it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000025_c000025_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000025_c000025_grain_stats.csv\n", + "done with segmentation + export\n", + "[606/1053] tile_r000025_c000026\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.48it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.51it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 125/125 [00:03<00:00, 33.72it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "87it [00:00, 335.66it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "111it [00:00, 334.81it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 45/45 [00:01<00:00, 44.23it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 54/54 [00:00<00:00, 122.96it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000025_c000026_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000025_c000026_grain_stats.csv\n", + "done with segmentation + export\n", + "[607/1053] tile_r000025_c000027\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.46it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.47it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 350/350 [00:10<00:00, 32.08it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "233it [00:01, 177.18it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "226it [00:00, 362.46it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 53/53 [00:01<00:00, 33.30it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 113/113 [00:00<00:00, 151.33it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000025_c000027_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000025_c000027_grain_stats.csv\n", + "done with segmentation + export\n", + "[608/1053] tile_r000025_c000028\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:01<00:00, 2.56it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.53it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 622/622 [00:17<00:00, 35.13it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "446it [00:01, 401.47it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "488it [00:01, 420.26it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 187/187 [00:01<00:00, 101.96it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 200/200 [00:00<00:00, 224.52it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n", + "Warning: len(gdf)=200 != len(grain_stats)=199 -> truncating to 199\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000025_c000028_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000025_c000028_grain_stats.csv\n", + "done with segmentation + export\n", + "[609/1053] tile_r000025_c000029\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.47it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.48it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 732/732 [00:19<00:00, 36.96it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "603it [00:01, 338.23it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "651it [00:01, 344.25it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 250/250 [00:02<00:00, 97.58it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 261/261 [00:01<00:00, 207.75it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000025_c000029_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000025_c000029_grain_stats.csv\n", + "done with segmentation + export\n", + "[610/1053] tile_r000025_c000030\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:01<00:00, 2.54it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.50it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 746/746 [00:19<00:00, 37.82it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "608it [00:02, 292.45it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "645it [00:02, 286.01it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 228/228 [00:02<00:00, 80.52it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 239/239 [00:01<00:00, 199.55it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000025_c000030_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000025_c000030_grain_stats.csv\n", + "done with segmentation + export\n", + "[611/1053] tile_r000025_c000031\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.43it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.52it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 726/726 [00:17<00:00, 40.55it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "628it [00:01, 362.78it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "663it [00:01, 357.61it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 254/254 [00:02<00:00, 109.02it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 260/260 [00:01<00:00, 206.42it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000025_c000031_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000025_c000031_grain_stats.csv\n", + "done with segmentation + export\n", + "[612/1053] tile_r000025_c000032\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.41it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.52it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 955/955 [00:24<00:00, 39.76it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "826it [00:03, 268.10it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "825it [00:03, 265.13it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 266/266 [00:03<00:00, 69.48it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 314/314 [00:01<00:00, 204.90it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000025_c000032_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000025_c000032_grain_stats.csv\n", + "done with segmentation + export\n", + "[613/1053] tile_r000025_c000033\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:01<00:00, 2.51it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.54it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 744/744 [00:19<00:00, 38.55it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "613it [00:02, 276.05it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "664it [00:02, 272.73it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 236/236 [00:03<00:00, 65.11it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 249/249 [00:01<00:00, 195.77it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000025_c000033_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000025_c000033_grain_stats.csv\n", + "done with segmentation + export\n", + "[614/1053] tile_r000025_c000034\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.49it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.53it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 704/704 [00:17<00:00, 39.16it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "571it [00:02, 275.22it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "605it [00:02, 273.60it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 214/214 [00:02<00:00, 80.88it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 217/217 [00:01<00:00, 179.86it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000025_c000034_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000025_c000034_grain_stats.csv\n", + "done with segmentation + export\n", + "[615/1053] tile_r000025_c000035\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.48it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.47it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 626/626 [00:15<00:00, 39.41it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "517it [00:02, 232.74it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "540it [00:02, 230.93it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 191/191 [00:02<00:00, 65.35it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 201/201 [00:01<00:00, 193.38it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000025_c000035_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000025_c000035_grain_stats.csv\n", + "done with segmentation + export\n", + "[616/1053] tile_r000025_c000036\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.46it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.49it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 711/711 [00:18<00:00, 39.46it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "589it [00:02, 239.98it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "626it [00:02, 239.57it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 225/225 [00:03<00:00, 66.37it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 230/230 [00:01<00:00, 185.52it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000025_c000036_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000025_c000036_grain_stats.csv\n", + "done with segmentation + export\n", + "[617/1053] tile_r000025_c000037\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.49it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.50it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 703/703 [00:17<00:00, 40.59it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "609it [00:02, 287.65it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "637it [00:02, 279.00it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 228/228 [00:02<00:00, 77.12it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 236/236 [00:01<00:00, 201.64it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000025_c000037_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000025_c000037_grain_stats.csv\n", + "done with segmentation + export\n", + "[618/1053] tile_r000025_c000038\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.50it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.45it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 788/788 [00:19<00:00, 40.22it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "694it [00:02, 329.67it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "742it [00:02, 321.16it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 282/282 [00:02<00:00, 99.07it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 286/286 [00:01<00:00, 212.31it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000025_c000038_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000025_c000038_grain_stats.csv\n", + "done with segmentation + export\n", + "[619/1053] tile_r000025_c000039\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.47it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.40it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 428/428 [00:11<00:00, 38.56it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "352it [00:01, 287.42it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "370it [00:01, 291.22it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 131/131 [00:01<00:00, 75.92it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 133/133 [00:00<00:00, 180.71it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000025_c000039_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000025_c000039_grain_stats.csv\n", + "done with segmentation + export\n", + "[620/1053] tile_r000025_c000040\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.44it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.47it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 594/594 [00:15<00:00, 38.70it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "499it [00:01, 329.30it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "531it [00:01, 331.37it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 193/193 [00:02<00:00, 88.31it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 196/196 [00:00<00:00, 199.83it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000025_c000040_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000025_c000040_grain_stats.csv\n", + "done with segmentation + export\n", + "[621/1053] tile_r000025_c000041\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.45it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.48it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 532/532 [00:13<00:00, 39.39it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "430it [00:01, 237.46it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "447it [00:01, 239.52it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 151/151 [00:02<00:00, 56.87it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 153/153 [00:00<00:00, 197.58it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000025_c000041_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000025_c000041_grain_stats.csv\n", + "done with segmentation + export\n", + "[622/1053] tile_r000025_c000042\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.47it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.54it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 662/662 [00:16<00:00, 39.15it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "566it [00:01, 288.12it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "565it [00:01, 297.90it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 191/191 [00:02<00:00, 78.43it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 227/227 [00:01<00:00, 196.94it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000025_c000042_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000025_c000042_grain_stats.csv\n", + "done with segmentation + export\n", + "[623/1053] tile_r000025_c000043\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.44it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.47it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 658/658 [00:16<00:00, 39.55it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "552it [00:02, 263.84it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "585it [00:02, 264.84it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 205/205 [00:02<00:00, 76.47it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 207/207 [00:01<00:00, 185.57it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000025_c000043_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000025_c000043_grain_stats.csv\n", + "done with segmentation + export\n", + "[624/1053] tile_r000025_c000044\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.49it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.47it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 378/378 [00:10<00:00, 36.21it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "304it [00:01, 210.66it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "321it [00:01, 201.32it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 102/102 [00:01<00:00, 51.42it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 103/103 [00:00<00:00, 157.61it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000025_c000044_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000025_c000044_grain_stats.csv\n", + "done with segmentation + export\n", + "[625/1053] tile_r000025_c000045\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.36it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.49it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 593/593 [00:15<00:00, 39.23it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "494it [00:02, 212.40it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "493it [00:02, 212.42it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 143/143 [00:03<00:00, 46.13it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 186/186 [00:01<00:00, 175.97it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000025_c000045_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000025_c000045_grain_stats.csv\n", + "done with segmentation + export\n", + "[626/1053] tile_r000025_c000046\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.49it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.49it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 409/409 [00:10<00:00, 37.95it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "307it [00:01, 206.95it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "319it [00:01, 207.95it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 96/96 [00:02<00:00, 41.04it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 103/103 [00:00<00:00, 153.02it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000025_c000046_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000025_c000046_grain_stats.csv\n", + "done with segmentation + export\n", + "[627/1053] tile_r000025_c000047\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.47it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.51it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 452/452 [00:12<00:00, 35.60it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "305it [00:02, 113.15it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "301it [00:02, 133.53it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 75/75 [00:03<00:00, 22.62it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 99/99 [00:00<00:00, 142.35it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000025_c000047_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000025_c000047_grain_stats.csv\n", + "done with segmentation + export\n", + "[628/1053] tile_r000025_c000048\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.45it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.48it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 493/493 [00:13<00:00, 37.41it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "381it [00:02, 156.81it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "402it [00:02, 156.28it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 116/116 [00:03<00:00, 33.28it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 121/121 [00:00<00:00, 140.97it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000025_c000048_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000025_c000048_grain_stats.csv\n", + "done with segmentation + export\n", + "[629/1053] tile_r000025_c000049\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.45it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.56it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 411/411 [00:12<00:00, 31.82it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "277it [00:01, 210.30it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "298it [00:01, 205.84it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 99/99 [00:02<00:00, 46.27it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 106/106 [00:00<00:00, 146.43it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000025_c000049_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000025_c000049_grain_stats.csv\n", + "done with segmentation + export\n", + "[630/1053] tile_r000025_c000050\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.45it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.47it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 567/567 [00:16<00:00, 34.74it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "413it [00:02, 187.15it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "412it [00:02, 185.79it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 118/118 [00:03<00:00, 36.52it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 146/146 [00:00<00:00, 165.70it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000025_c000050_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000025_c000050_grain_stats.csv\n", + "done with segmentation + export\n", + "[631/1053] tile_r000025_c000051\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.50it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.51it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 836/836 [00:21<00:00, 38.27it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "725it [00:04, 159.45it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "721it [00:04, 156.77it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 230/230 [00:06<00:00, 36.90it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 251/251 [00:01<00:00, 178.48it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000025_c000051_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000025_c000051_grain_stats.csv\n", + "done with segmentation + export\n", + "[632/1053] tile_r000025_c000052\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.42it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.48it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 861/861 [00:22<00:00, 38.31it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "692it [00:04, 163.09it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "691it [00:04, 165.59it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 226/226 [00:04<00:00, 46.75it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 248/248 [00:01<00:00, 177.05it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000025_c000052_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000025_c000052_grain_stats.csv\n", + "done with segmentation + export\n", + "[633/1053] tile_r000025_c000053\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.46it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.47it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 438/438 [00:12<00:00, 36.49it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "347it [00:03, 105.53it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "337it [00:02, 121.52it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 90/90 [00:04<00:00, 21.20it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 113/113 [00:00<00:00, 148.41it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000025_c000053_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000025_c000053_grain_stats.csv\n", + "done with segmentation + export\n", + "[634/1053] tile_r000025_c000054\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.48it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.47it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 66/66 [00:02<00:00, 27.31it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "10it [00:00, 259.25it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "11it [00:00, 265.73it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:00<00:00, 44.39it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:00<00:00, 81.76it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000025_c000054_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000025_c000054_grain_stats.csv\n", + "done with segmentation + export\n", + "[635/1053] tile_r000026_c000007\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.46it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.51it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 6/6 [00:00<00:00, 16.99it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "0it [00:00, ?it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "0it [00:00, ?it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "0it [00:00, ?it/s]\n", + "0it [00:00, ?it/s]\n", + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n", + "No grains found -> skipping histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000026_c000007_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000026_c000007_grain_stats.csv\n", + "done with segmentation + export\n", + "[636/1053] tile_r000026_c000008\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:01<00:00, 2.51it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.43it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 42/42 [00:01<00:00, 21.53it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "1it [00:00, 1805.55it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2it [00:00, 725.85it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 1/1 [00:00<00:00, 278.54it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 1/1 [00:00<00:00, 163.22it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000026_c000008_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000026_c000008_grain_stats.csv\n", + "done with segmentation + export\n", + "[637/1053] tile_r000026_c000009\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.47it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.53it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 169/169 [00:05<00:00, 32.48it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "103it [00:00, 296.57it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "112it [00:00, 338.39it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 37/37 [00:00<00:00, 57.41it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 39/39 [00:00<00:00, 164.06it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000026_c000009_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000026_c000009_grain_stats.csv\n", + "done with segmentation + export\n", + "[638/1053] tile_r000026_c000010\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.43it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.50it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 419/419 [00:11<00:00, 36.35it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "317it [00:01, 169.46it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "333it [00:01, 184.52it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 101/101 [00:02<00:00, 37.03it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 110/110 [00:00<00:00, 130.49it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000026_c000010_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000026_c000010_grain_stats.csv\n", + "done with segmentation + export\n", + "[639/1053] tile_r000026_c000011\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.46it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.49it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 434/434 [00:11<00:00, 36.77it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "323it [00:01, 198.13it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "338it [00:01, 201.46it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 103/103 [00:02<00:00, 38.17it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 112/112 [00:00<00:00, 145.00it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000026_c000011_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000026_c000011_grain_stats.csv\n", + "done with segmentation + export\n", + "[640/1053] tile_r000026_c000012\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.43it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.50it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 510/510 [00:13<00:00, 37.11it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "402it [00:02, 176.43it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "420it [00:02, 173.43it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 121/121 [00:03<00:00, 32.62it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 130/130 [00:00<00:00, 145.14it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000026_c000012_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000026_c000012_grain_stats.csv\n", + "done with segmentation + export\n", + "[641/1053] tile_r000026_c000013\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.48it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.51it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 691/691 [00:18<00:00, 36.96it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "565it [00:03, 184.02it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "564it [00:03, 186.11it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 160/160 [00:04<00:00, 39.59it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 190/190 [00:01<00:00, 160.65it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000026_c000013_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000026_c000013_grain_stats.csv\n", + "done with segmentation + export\n", + "[642/1053] tile_r000026_c000014\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.37it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.56it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 709/709 [00:18<00:00, 38.21it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "574it [00:02, 196.23it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "596it [00:03, 197.08it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 188/188 [00:03<00:00, 48.75it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 195/195 [00:01<00:00, 160.44it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000026_c000014_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000026_c000014_grain_stats.csv\n", + "done with segmentation + export\n", + "[643/1053] tile_r000026_c000015\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.43it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.49it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 579/579 [00:15<00:00, 37.17it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "439it [00:02, 181.78it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "462it [00:02, 178.31it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 148/148 [00:03<00:00, 41.93it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 161/161 [00:01<00:00, 148.62it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000026_c000015_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000026_c000015_grain_stats.csv\n", + "done with segmentation + export\n", + "[644/1053] tile_r000026_c000016\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.46it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.50it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 603/603 [00:15<00:00, 38.46it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "486it [00:02, 184.08it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "485it [00:02, 193.25it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 129/129 [00:03<00:00, 35.57it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 161/161 [00:01<00:00, 137.98it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000026_c000016_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000026_c000016_grain_stats.csv\n", + "done with segmentation + export\n", + "[645/1053] tile_r000026_c000017\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.49it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.49it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 667/667 [00:17<00:00, 38.94it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "541it [00:03, 178.46it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "553it [00:03, 177.29it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 166/166 [00:03<00:00, 42.35it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 169/169 [00:01<00:00, 156.40it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000026_c000017_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000026_c000017_grain_stats.csv\n", + "done with segmentation + export\n", + "[646/1053] tile_r000026_c000018\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.45it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.48it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 768/768 [00:19<00:00, 38.75it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "638it [00:03, 185.04it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "656it [00:03, 185.78it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 212/212 [00:04<00:00, 50.77it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 220/220 [00:01<00:00, 162.31it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000026_c000018_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000026_c000018_grain_stats.csv\n", + "done with segmentation + export\n", + "[647/1053] tile_r000026_c000019\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.41it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.49it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 694/694 [00:17<00:00, 38.68it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "568it [00:03, 157.36it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "567it [00:03, 161.18it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 163/163 [00:04<00:00, 37.91it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 193/193 [00:01<00:00, 153.53it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000026_c000019_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000026_c000019_grain_stats.csv\n", + "done with segmentation + export\n", + "[648/1053] tile_r000026_c000020\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.47it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.51it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 475/475 [00:12<00:00, 38.48it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "391it [00:02, 191.46it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "405it [00:02, 192.86it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 131/131 [00:02<00:00, 44.93it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 140/140 [00:00<00:00, 148.48it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000026_c000020_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000026_c000020_grain_stats.csv\n", + "done with segmentation + export\n", + "[649/1053] tile_r000026_c000021\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:01<00:00, 2.51it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.50it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 506/506 [00:12<00:00, 39.03it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "414it [00:02, 204.22it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "429it [00:02, 202.76it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 140/140 [00:02<00:00, 49.62it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 146/146 [00:00<00:00, 147.12it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000026_c000021_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000026_c000021_grain_stats.csv\n", + "done with segmentation + export\n", + "[650/1053] tile_r000026_c000022\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.47it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.50it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 654/654 [00:16<00:00, 38.51it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "506it [00:02, 225.59it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "525it [00:02, 225.92it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 172/172 [00:03<00:00, 53.08it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 185/185 [00:01<00:00, 169.40it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000026_c000022_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000026_c000022_grain_stats.csv\n", + "done with segmentation + export\n", + "[651/1053] tile_r000026_c000023\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.49it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.46it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 913/913 [00:23<00:00, 39.29it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "772it [00:02, 300.90it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "771it [00:02, 301.19it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 261/261 [00:03<00:00, 81.05it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 306/306 [00:01<00:00, 212.32it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000026_c000023_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000026_c000023_grain_stats.csv\n", + "done with segmentation + export\n", + "[652/1053] tile_r000026_c000024\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.48it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.52it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 1220/1220 [00:30<00:00, 40.42it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "1093it [00:03, 325.55it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "1136it [00:03, 316.34it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 453/453 [00:03<00:00, 124.57it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 460/460 [00:02<00:00, 226.09it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000026_c000024_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000026_c000024_grain_stats.csv\n", + "done with segmentation + export\n", + "[653/1053] tile_r000026_c000025\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.46it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.49it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 1166/1166 [00:29<00:00, 39.46it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "1035it [00:03, 316.13it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "1074it [00:03, 309.31it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 420/420 [00:03<00:00, 120.67it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 430/430 [00:01<00:00, 216.27it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000026_c000025_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000026_c000025_grain_stats.csv\n", + "done with segmentation + export\n", + "[654/1053] tile_r000026_c000026\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.47it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.51it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 996/996 [00:24<00:00, 39.85it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "898it [00:02, 319.25it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "934it [00:02, 320.15it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 370/370 [00:03<00:00, 119.63it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 379/379 [00:01<00:00, 216.82it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000026_c000026_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000026_c000026_grain_stats.csv\n", + "done with segmentation + export\n", + "[655/1053] tile_r000026_c000027\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.45it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.50it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 1020/1020 [00:25<00:00, 39.71it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "889it [00:02, 331.72it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "932it [00:02, 322.97it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 358/358 [00:03<00:00, 106.10it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 376/376 [00:01<00:00, 218.76it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000026_c000027_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000026_c000027_grain_stats.csv\n", + "done with segmentation + export\n", + "[656/1053] tile_r000026_c000028\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.46it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.43it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 1060/1060 [00:26<00:00, 39.92it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "899it [00:02, 319.47it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "943it [00:02, 317.81it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 359/359 [00:03<00:00, 99.76it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 374/374 [00:01<00:00, 213.72it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000026_c000028_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000026_c000028_grain_stats.csv\n", + "done with segmentation + export\n", + "[657/1053] tile_r000026_c000029\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.49it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.45it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 941/941 [00:23<00:00, 39.48it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "816it [00:02, 291.53it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "862it [00:02, 289.52it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 324/324 [00:03<00:00, 93.38it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 328/328 [00:01<00:00, 203.84it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000026_c000029_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000026_c000029_grain_stats.csv\n", + "done with segmentation + export\n", + "[658/1053] tile_r000026_c000030\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.37it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.48it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 827/827 [00:20<00:00, 39.73it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "718it [00:02, 263.35it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "716it [00:02, 273.28it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 231/231 [00:03<00:00, 66.07it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 273/273 [00:01<00:00, 205.88it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000026_c000030_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000026_c000030_grain_stats.csv\n", + "done with segmentation + export\n", + "[659/1053] tile_r000026_c000031\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.44it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.43it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 754/754 [00:18<00:00, 39.75it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "629it [00:02, 278.37it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "674it [00:02, 277.59it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 241/241 [00:03<00:00, 78.12it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 246/246 [00:01<00:00, 197.06it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000026_c000031_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000026_c000031_grain_stats.csv\n", + "done with segmentation + export\n", + "[660/1053] tile_r000026_c000032\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.44it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.52it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 570/570 [00:14<00:00, 39.46it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "463it [00:02, 227.14it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "485it [00:01, 246.97it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 162/162 [00:02<00:00, 58.94it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 164/164 [00:00<00:00, 185.67it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000026_c000032_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000026_c000032_grain_stats.csv\n", + "done with segmentation + export\n", + "[661/1053] tile_r000026_c000033\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:01<00:00, 2.51it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.50it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 687/687 [00:17<00:00, 40.01it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "580it [00:02, 248.27it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "607it [00:02, 250.90it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 210/210 [00:03<00:00, 68.37it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 213/213 [00:01<00:00, 178.62it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000026_c000033_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000026_c000033_grain_stats.csv\n", + "done with segmentation + export\n", + "[662/1053] tile_r000026_c000034\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.48it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.48it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 710/710 [00:17<00:00, 39.65it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "584it [00:01, 324.73it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "613it [00:01, 313.95it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 233/233 [00:02<00:00, 96.31it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 236/236 [00:01<00:00, 194.29it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000026_c000034_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000026_c000034_grain_stats.csv\n", + "done with segmentation + export\n", + "[663/1053] tile_r000026_c000035\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:01<00:00, 2.52it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.48it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 940/940 [00:23<00:00, 40.67it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "846it [00:03, 273.45it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "892it [00:03, 265.37it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 319/319 [00:03<00:00, 86.25it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 322/322 [00:01<00:00, 209.10it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000026_c000035_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000026_c000035_grain_stats.csv\n", + "done with segmentation + export\n", + "[664/1053] tile_r000026_c000036\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.45it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.48it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 710/710 [00:17<00:00, 40.10it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "598it [00:01, 339.82it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "643it [00:01, 338.52it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 244/244 [00:02<00:00, 101.23it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 247/247 [00:01<00:00, 210.31it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000026_c000036_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000026_c000036_grain_stats.csv\n", + "done with segmentation + export\n", + "[665/1053] tile_r000026_c000037\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.15it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.18it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 581/581 [00:14<00:00, 39.80it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "503it [00:01, 252.19it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "526it [00:02, 244.55it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 186/186 [00:02<00:00, 65.34it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 192/192 [00:01<00:00, 178.80it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000026_c000037_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000026_c000037_grain_stats.csv\n", + "done with segmentation + export\n", + "[666/1053] tile_r000026_c000038\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.15it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.18it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 533/533 [00:13<00:00, 40.06it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "464it [00:01, 277.34it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "492it [00:01, 282.51it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 181/181 [00:02<00:00, 71.51it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 182/182 [00:01<00:00, 166.74it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000026_c000038_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000026_c000038_grain_stats.csv\n", + "done with segmentation + export\n", + "[667/1053] tile_r000026_c000039\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.26it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.27it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 567/567 [00:14<00:00, 38.90it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "466it [00:01, 274.37it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "498it [00:01, 272.33it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 185/185 [00:02<00:00, 76.29it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 187/187 [00:00<00:00, 192.14it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000026_c000039_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000026_c000039_grain_stats.csv\n", + "done with segmentation + export\n", + "[668/1053] tile_r000026_c000040\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.29it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.35it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 494/494 [00:12<00:00, 39.43it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "406it [00:01, 259.59it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "426it [00:01, 256.92it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 148/148 [00:02<00:00, 62.10it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 154/154 [00:00<00:00, 160.87it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000026_c000040_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000026_c000040_grain_stats.csv\n", + "done with segmentation + export\n", + "[669/1053] tile_r000026_c000041\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.26it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.33it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 499/499 [00:12<00:00, 38.64it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "414it [00:01, 243.79it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "442it [00:01, 242.92it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 148/148 [00:02<00:00, 53.79it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 154/154 [00:00<00:00, 170.06it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000026_c000041_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000026_c000041_grain_stats.csv\n", + "done with segmentation + export\n", + "[670/1053] tile_r000026_c000042\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.33it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.37it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 538/538 [00:13<00:00, 39.48it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "447it [00:02, 217.34it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "478it [00:02, 205.97it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 151/151 [00:03<00:00, 48.63it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 155/155 [00:00<00:00, 176.97it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000026_c000042_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000026_c000042_grain_stats.csv\n", + "done with segmentation + export\n", + "[671/1053] tile_r000026_c000043\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.35it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.40it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 557/557 [00:14<00:00, 38.49it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "455it [00:01, 233.65it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "483it [00:02, 233.35it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 163/163 [00:02<00:00, 55.14it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 166/166 [00:00<00:00, 171.93it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000026_c000043_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000026_c000043_grain_stats.csv\n", + "done with segmentation + export\n", + "[672/1053] tile_r000026_c000044\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.34it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.40it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 499/499 [00:13<00:00, 37.81it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "385it [00:01, 197.80it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "400it [00:01, 202.76it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 128/128 [00:02<00:00, 44.87it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 134/134 [00:00<00:00, 153.18it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000026_c000044_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000026_c000044_grain_stats.csv\n", + "done with segmentation + export\n", + "[673/1053] tile_r000026_c000045\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.31it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.31it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 352/352 [00:09<00:00, 38.35it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "288it [00:01, 174.54it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "286it [00:01, 174.58it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 80/80 [00:02<00:00, 33.57it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 93/93 [00:00<00:00, 154.97it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000026_c000045_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000026_c000045_grain_stats.csv\n", + "done with segmentation + export\n", + "[674/1053] tile_r000026_c000046\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.42it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.43it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 363/363 [00:09<00:00, 37.54it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "279it [00:01, 203.07it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "294it [00:01, 195.25it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 94/94 [00:02<00:00, 44.73it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 96/96 [00:00<00:00, 139.66it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000026_c000046_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000026_c000046_grain_stats.csv\n", + "done with segmentation + export\n", + "[675/1053] tile_r000026_c000047\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.40it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.45it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 403/403 [00:10<00:00, 37.28it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "301it [00:01, 172.06it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "314it [00:01, 174.58it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 93/93 [00:02<00:00, 34.14it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 101/101 [00:00<00:00, 151.91it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000026_c000047_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000026_c000047_grain_stats.csv\n", + "done with segmentation + export\n", + "[676/1053] tile_r000026_c000048\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.37it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.45it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 433/433 [00:11<00:00, 37.15it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "322it [00:01, 172.71it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "337it [00:01, 175.58it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 99/99 [00:03<00:00, 32.82it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 103/103 [00:00<00:00, 146.91it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000026_c000048_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000026_c000048_grain_stats.csv\n", + "done with segmentation + export\n", + "[677/1053] tile_r000026_c000049\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.48it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.25it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 579/579 [00:17<00:00, 32.84it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "400it [00:02, 137.00it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "399it [00:02, 139.56it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 102/102 [00:04<00:00, 23.49it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 121/121 [00:00<00:00, 145.73it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n", + "Warning: len(gdf)=121 != len(grain_stats)=120 -> truncating to 120\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000026_c000049_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000026_c000049_grain_stats.csv\n", + "done with segmentation + export\n", + "[678/1053] tile_r000026_c000050\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.39it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.43it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 688/688 [00:22<00:00, 31.04it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "510it [00:02, 180.11it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "509it [00:02, 187.88it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 145/145 [00:05<00:00, 25.43it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 182/182 [00:01<00:00, 153.86it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000026_c000050_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000026_c000050_grain_stats.csv\n", + "done with segmentation + export\n", + "[679/1053] tile_r000026_c000051\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.38it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.42it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 704/704 [00:18<00:00, 38.29it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "576it [00:02, 200.60it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "594it [00:02, 200.19it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 198/198 [00:03<00:00, 52.80it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 202/202 [00:01<00:00, 157.15it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000026_c000051_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000026_c000051_grain_stats.csv\n", + "done with segmentation + export\n", + "[680/1053] tile_r000026_c000052\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.42it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.41it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 259/259 [00:06<00:00, 37.02it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "208it [00:01, 192.81it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "212it [00:01, 198.11it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 69/69 [00:01<00:00, 46.41it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 76/76 [00:00<00:00, 156.94it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000026_c000052_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000026_c000052_grain_stats.csv\n", + "done with segmentation + export\n", + "[681/1053] tile_r000026_c000053\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.48it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.42it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 1/1 [00:00<00:00, 6.70it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "0it [00:00, ?it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "0it [00:00, ?it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "0it [00:00, ?it/s]\n", + "0it [00:00, ?it/s]\n", + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n", + "No grains found -> skipping histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000026_c000053_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000026_c000053_grain_stats.csv\n", + "done with segmentation + export\n", + "[682/1053] tile_r000027_c000005\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.36it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.48it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "0it [00:00, ?it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "0it [00:00, ?it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "0it [00:00, ?it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "0it [00:00, ?it/s]\n", + "0it [00:00, ?it/s]\n", + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n", + "No grains found -> skipping histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000027_c000005_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000027_c000005_grain_stats.csv\n", + "done with segmentation + export\n", + "[683/1053] tile_r000027_c000006\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.42it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.49it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 155/155 [00:04<00:00, 32.58it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "121it [00:00, 229.55it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "129it [00:00, 229.92it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 38/38 [00:00<00:00, 41.18it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 44/44 [00:00<00:00, 168.88it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000027_c000006_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000027_c000006_grain_stats.csv\n", + "done with segmentation + export\n", + "[684/1053] tile_r000027_c000007\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.33it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.39it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 269/269 [00:08<00:00, 32.30it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "197it [00:00, 258.64it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "203it [00:00, 276.41it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 63/63 [00:01<00:00, 48.13it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 66/66 [00:00<00:00, 167.02it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000027_c000007_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000027_c000007_grain_stats.csv\n", + "done with segmentation + export\n", + "[685/1053] tile_r000027_c000008\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.46it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.44it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 329/329 [00:08<00:00, 37.24it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "265it [00:01, 221.30it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "286it [00:01, 223.57it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 94/94 [00:02<00:00, 41.54it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 99/99 [00:00<00:00, 163.07it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000027_c000008_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000027_c000008_grain_stats.csv\n", + "done with segmentation + export\n", + "[686/1053] tile_r000027_c000009\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.45it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.44it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 443/443 [00:11<00:00, 37.89it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "330it [00:01, 259.56it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "351it [00:01, 260.92it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 112/112 [00:02<00:00, 52.93it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 117/117 [00:00<00:00, 165.58it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000027_c000009_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000027_c000009_grain_stats.csv\n", + "done with segmentation + export\n", + "[687/1053] tile_r000027_c000010\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.33it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.35it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 477/477 [00:12<00:00, 37.09it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "364it [00:01, 194.72it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "363it [00:01, 222.25it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 99/99 [00:03<00:00, 29.94it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 137/137 [00:00<00:00, 155.72it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000027_c000010_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000027_c000010_grain_stats.csv\n", + "done with segmentation + export\n", + "[688/1053] tile_r000027_c000011\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.43it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.48it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 568/568 [00:15<00:00, 37.11it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "432it [00:01, 255.21it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "457it [00:01, 257.24it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 150/150 [00:02<00:00, 53.44it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 160/160 [00:00<00:00, 170.09it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000027_c000011_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000027_c000011_grain_stats.csv\n", + "done with segmentation + export\n", + "[689/1053] tile_r000027_c000012\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.36it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.50it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 468/468 [00:12<00:00, 36.86it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "377it [00:02, 185.95it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "376it [00:01, 206.29it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 104/104 [00:02<00:00, 39.14it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 136/136 [00:00<00:00, 145.45it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000027_c000012_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000027_c000012_grain_stats.csv\n", + "done with segmentation + export\n", + "[690/1053] tile_r000027_c000013\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.49it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.40it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 468/468 [00:12<00:00, 37.95it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "363it [00:01, 191.41it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "375it [00:01, 190.39it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 103/103 [00:03<00:00, 32.70it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 113/113 [00:00<00:00, 146.28it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000027_c000013_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000027_c000013_grain_stats.csv\n", + "done with segmentation + export\n", + "[691/1053] tile_r000027_c000014\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.42it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.45it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 574/574 [00:15<00:00, 36.83it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "441it [00:02, 175.93it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "440it [00:02, 183.98it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 118/118 [00:03<00:00, 34.62it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 144/144 [00:01<00:00, 142.14it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000027_c000014_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000027_c000014_grain_stats.csv\n", + "done with segmentation + export\n", + "[692/1053] tile_r000027_c000015\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.48it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.42it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 572/572 [00:15<00:00, 37.93it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "427it [00:02, 207.49it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "426it [00:02, 201.83it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 132/132 [00:02<00:00, 50.77it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 161/161 [00:00<00:00, 162.85it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000027_c000015_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000027_c000015_grain_stats.csv\n", + "done with segmentation + export\n", + "[693/1053] tile_r000027_c000016\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.44it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.52it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 660/660 [00:17<00:00, 37.10it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "547it [00:02, 197.27it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "576it [00:02, 198.25it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 187/187 [00:03<00:00, 51.30it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 190/190 [00:01<00:00, 163.85it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000027_c000016_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000027_c000016_grain_stats.csv\n", + "done with segmentation + export\n", + "[694/1053] tile_r000027_c000017\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.46it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.46it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 653/653 [00:16<00:00, 38.63it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "551it [00:02, 217.84it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "572it [00:02, 204.58it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 190/190 [00:03<00:00, 57.22it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 199/199 [00:01<00:00, 156.79it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000027_c000017_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000027_c000017_grain_stats.csv\n", + "done with segmentation + export\n", + "[695/1053] tile_r000027_c000018\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.42it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.45it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 680/680 [00:17<00:00, 38.87it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "575it [00:03, 149.00it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "591it [00:03, 150.86it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 171/171 [00:05<00:00, 31.08it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 183/183 [00:01<00:00, 157.15it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsshome1/0B/di54doz/.local/share/mamba/envs/segmenteverygrain/lib/python3.10/site-packages/segmenteverygrain/segmenteverygrain.py:2218: RuntimeWarning: divide by zero encountered in log2\n", + " phi_minor = -np.log2(minor_axis_length)\n", + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000027_c000018_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000027_c000018_grain_stats.csv\n", + "done with segmentation + export\n", + "[696/1053] tile_r000027_c000019\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.47it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.44it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 607/607 [00:15<00:00, 38.03it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "432it [00:02, 207.29it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "459it [00:02, 210.42it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 161/161 [00:02<00:00, 60.55it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 168/168 [00:01<00:00, 164.20it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000027_c000019_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000027_c000019_grain_stats.csv\n", + "done with segmentation + export\n", + "[697/1053] tile_r000027_c000020\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.43it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.47it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 565/565 [00:14<00:00, 38.06it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "445it [00:02, 163.85it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "444it [00:02, 166.45it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 124/124 [00:03<00:00, 36.74it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 146/146 [00:00<00:00, 150.54it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000027_c000020_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000027_c000020_grain_stats.csv\n", + "done with segmentation + export\n", + "[698/1053] tile_r000027_c000021\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.41it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.47it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 672/672 [00:17<00:00, 39.00it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "570it [00:02, 194.30it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "595it [00:03, 192.84it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 196/196 [00:03<00:00, 52.91it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 204/204 [00:01<00:00, 168.86it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000027_c000021_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000027_c000021_grain_stats.csv\n", + "done with segmentation + export\n", + "[699/1053] tile_r000027_c000022\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.48it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.50it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 623/623 [00:16<00:00, 38.93it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "495it [00:02, 206.07it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "513it [00:02, 207.15it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 173/173 [00:03<00:00, 55.23it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 179/179 [00:01<00:00, 163.13it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000027_c000022_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000027_c000022_grain_stats.csv\n", + "done with segmentation + export\n", + "[700/1053] tile_r000027_c000023\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.49it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.51it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 761/761 [00:19<00:00, 39.47it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "621it [00:03, 197.05it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "620it [00:03, 204.50it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 191/191 [00:03<00:00, 48.15it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 234/234 [00:01<00:00, 173.91it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000027_c000023_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000027_c000023_grain_stats.csv\n", + "done with segmentation + export\n", + "[701/1053] tile_r000027_c000024\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.48it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.50it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 745/745 [00:19<00:00, 39.12it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "592it [00:02, 219.24it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "620it [00:02, 220.31it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 208/208 [00:03<00:00, 58.13it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 216/216 [00:01<00:00, 179.18it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000027_c000024_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000027_c000024_grain_stats.csv\n", + "done with segmentation + export\n", + "[702/1053] tile_r000027_c000025\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.50it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.51it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 819/819 [00:21<00:00, 38.25it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "671it [00:03, 210.42it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "670it [00:03, 213.31it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 201/201 [00:04<00:00, 44.91it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 254/254 [00:01<00:00, 190.69it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000027_c000025_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000027_c000025_grain_stats.csv\n", + "done with segmentation + export\n", + "[703/1053] tile_r000027_c000026\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.44it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.47it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 800/800 [00:20<00:00, 39.79it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "672it [00:02, 265.55it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "702it [00:02, 260.57it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 233/233 [00:03<00:00, 65.04it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 242/242 [00:01<00:00, 188.61it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000027_c000026_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000027_c000026_grain_stats.csv\n", + "done with segmentation + export\n", + "[704/1053] tile_r000027_c000027\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:01<00:00, 2.51it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.50it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 750/750 [00:19<00:00, 39.10it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "639it [00:02, 253.76it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "676it [00:02, 247.29it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 229/229 [00:03<00:00, 63.90it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 239/239 [00:01<00:00, 187.22it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n", + "Warning: len(gdf)=239 != len(grain_stats)=238 -> truncating to 238\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000027_c000027_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000027_c000027_grain_stats.csv\n", + "done with segmentation + export\n", + "[705/1053] tile_r000027_c000028\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.46it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.49it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 741/741 [00:18<00:00, 39.88it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "633it [00:02, 240.53it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "661it [00:02, 242.85it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 227/227 [00:03<00:00, 65.08it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 231/231 [00:01<00:00, 177.42it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000027_c000028_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000027_c000028_grain_stats.csv\n", + "done with segmentation + export\n", + "[706/1053] tile_r000027_c000029\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.48it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.52it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 515/515 [00:13<00:00, 39.46it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "430it [00:01, 232.55it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "453it [00:02, 219.93it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 155/155 [00:02<00:00, 60.06it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 157/157 [00:00<00:00, 178.05it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000027_c000029_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000027_c000029_grain_stats.csv\n", + "done with segmentation + export\n", + "[707/1053] tile_r000027_c000030\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.41it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.50it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 663/663 [00:17<00:00, 38.28it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "555it [00:02, 240.25it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "576it [00:02, 235.46it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 189/189 [00:03<00:00, 56.84it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 196/196 [00:01<00:00, 182.50it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000027_c000030_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000027_c000030_grain_stats.csv\n", + "done with segmentation + export\n", + "[708/1053] tile_r000027_c000031\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.39it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.54it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 650/650 [00:16<00:00, 39.04it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "541it [00:02, 232.63it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "540it [00:02, 232.24it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 164/164 [00:03<00:00, 52.56it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 203/203 [00:01<00:00, 181.88it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000027_c000031_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000027_c000031_grain_stats.csv\n", + "done with segmentation + export\n", + "[709/1053] tile_r000027_c000032\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.44it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.50it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 664/664 [00:17<00:00, 38.96it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "542it [00:02, 229.70it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "576it [00:02, 219.75it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 189/189 [00:03<00:00, 56.95it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 193/193 [00:01<00:00, 175.76it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000027_c000032_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000027_c000032_grain_stats.csv\n", + "done with segmentation + export\n", + "[710/1053] tile_r000027_c000033\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.47it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.45it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 558/558 [00:14<00:00, 38.20it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "439it [00:01, 238.94it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "470it [00:01, 242.81it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 162/162 [00:02<00:00, 59.60it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 168/168 [00:00<00:00, 176.89it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000027_c000033_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000027_c000033_grain_stats.csv\n", + "done with segmentation + export\n", + "[711/1053] tile_r000027_c000034\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.46it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.52it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 648/648 [00:16<00:00, 40.10it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "554it [00:02, 251.08it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "580it [00:02, 241.06it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 195/195 [00:03<00:00, 59.31it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 203/203 [00:01<00:00, 186.32it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000027_c000034_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000027_c000034_grain_stats.csv\n", + "done with segmentation + export\n", + "[712/1053] tile_r000027_c000035\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.47it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.50it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 562/562 [00:14<00:00, 39.05it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "469it [00:01, 253.17it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "491it [00:02, 243.36it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 170/170 [00:02<00:00, 62.10it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 172/172 [00:00<00:00, 185.89it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000027_c000035_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000027_c000035_grain_stats.csv\n", + "done with segmentation + export\n", + "[713/1053] tile_r000027_c000036\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:01<00:00, 2.55it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.46it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 621/621 [00:15<00:00, 40.14it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "529it [00:02, 249.64it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "564it [00:02, 243.06it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 196/196 [00:02<00:00, 67.80it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 200/200 [00:01<00:00, 175.49it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000027_c000036_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000027_c000036_grain_stats.csv\n", + "done with segmentation + export\n", + "[714/1053] tile_r000027_c000037\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.45it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.49it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 564/564 [00:14<00:00, 38.81it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "449it [00:01, 254.31it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "477it [00:01, 251.57it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 160/160 [00:02<00:00, 59.71it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 164/164 [00:00<00:00, 182.05it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000027_c000037_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000027_c000037_grain_stats.csv\n", + "done with segmentation + export\n", + "[715/1053] tile_r000027_c000038\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.45it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.54it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 579/579 [00:14<00:00, 39.40it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "473it [00:01, 249.37it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "501it [00:01, 258.75it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 166/166 [00:02<00:00, 60.00it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 169/169 [00:01<00:00, 155.39it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000027_c000038_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000027_c000038_grain_stats.csv\n", + "done with segmentation + export\n", + "[716/1053] tile_r000027_c000039\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.45it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.49it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 579/579 [00:14<00:00, 40.05it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "483it [00:01, 290.39it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "511it [00:01, 282.52it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 180/180 [00:02<00:00, 72.62it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 187/187 [00:01<00:00, 179.81it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000027_c000039_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000027_c000039_grain_stats.csv\n", + "done with segmentation + export\n", + "[717/1053] tile_r000027_c000040\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.43it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.59it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 480/480 [00:12<00:00, 39.12it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "413it [00:02, 162.18it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "411it [00:02, 163.75it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 112/112 [00:03<00:00, 30.50it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 137/137 [00:00<00:00, 151.47it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsshome1/0B/di54doz/.local/share/mamba/envs/segmenteverygrain/lib/python3.10/site-packages/segmenteverygrain/segmenteverygrain.py:2218: RuntimeWarning: divide by zero encountered in log2\n", + " phi_minor = -np.log2(minor_axis_length)\n", + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000027_c000040_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000027_c000040_grain_stats.csv\n", + "done with segmentation + export\n", + "[718/1053] tile_r000027_c000041\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.48it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.53it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 443/443 [00:11<00:00, 37.67it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "348it [00:01, 222.12it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "366it [00:01, 224.11it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 118/118 [00:02<00:00, 47.42it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 121/121 [00:00<00:00, 167.93it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000027_c000041_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000027_c000041_grain_stats.csv\n", + "done with segmentation + export\n", + "[719/1053] tile_r000027_c000042\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:01<00:00, 2.52it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.50it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 535/535 [00:14<00:00, 38.04it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "417it [00:02, 194.91it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "438it [00:02, 191.64it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 132/132 [00:03<00:00, 40.79it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 136/136 [00:00<00:00, 148.40it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000027_c000042_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000027_c000042_grain_stats.csv\n", + "done with segmentation + export\n", + "[720/1053] tile_r000027_c000043\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.41it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.47it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 500/500 [00:12<00:00, 38.52it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "387it [00:01, 224.34it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "407it [00:01, 215.03it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 125/125 [00:02<00:00, 44.10it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 134/134 [00:00<00:00, 155.10it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000027_c000043_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000027_c000043_grain_stats.csv\n", + "done with segmentation + export\n", + "[721/1053] tile_r000027_c000044\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:01<00:00, 2.50it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.51it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 409/409 [00:11<00:00, 36.18it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "311it [00:02, 130.43it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "327it [00:02, 134.63it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 100/100 [00:03<00:00, 26.28it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 102/102 [00:00<00:00, 142.68it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000027_c000044_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000027_c000044_grain_stats.csv\n", + "done with segmentation + export\n", + "[722/1053] tile_r000027_c000045\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:01<00:00, 2.51it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.51it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 479/479 [00:12<00:00, 38.48it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "400it [00:02, 164.05it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "420it [00:02, 160.20it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 124/124 [00:03<00:00, 36.54it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 128/128 [00:00<00:00, 135.82it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000027_c000045_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000027_c000045_grain_stats.csv\n", + "done with segmentation + export\n", + "[723/1053] tile_r000027_c000046\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:01<00:00, 2.53it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.48it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 517/517 [00:13<00:00, 37.62it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "413it [00:02, 152.51it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "412it [00:02, 150.98it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 102/102 [00:04<00:00, 24.40it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 133/133 [00:00<00:00, 137.65it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000027_c000046_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000027_c000046_grain_stats.csv\n", + "done with segmentation + export\n", + "[724/1053] tile_r000027_c000047\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.46it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.48it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 407/407 [00:11<00:00, 36.70it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "323it [00:02, 141.66it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "322it [00:02, 143.02it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 83/83 [00:03<00:00, 24.23it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 103/103 [00:00<00:00, 122.25it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000027_c000047_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000027_c000047_grain_stats.csv\n", + "done with segmentation + export\n", + "[725/1053] tile_r000027_c000048\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.42it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.51it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 451/451 [00:11<00:00, 37.77it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "348it [00:02, 164.39it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "370it [00:02, 155.82it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 120/120 [00:03<00:00, 37.16it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 124/124 [00:00<00:00, 140.20it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000027_c000048_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000027_c000048_grain_stats.csv\n", + "done with segmentation + export\n", + "[726/1053] tile_r000027_c000049\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.43it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.48it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 684/684 [00:18<00:00, 36.04it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "520it [00:02, 187.26it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "518it [00:02, 185.65it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 157/157 [00:03<00:00, 42.73it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 182/182 [00:01<00:00, 161.40it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000027_c000049_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000027_c000049_grain_stats.csv\n", + "done with segmentation + export\n", + "[727/1053] tile_r000027_c000050\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.43it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.53it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 426/426 [00:11<00:00, 36.07it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "321it [00:03, 105.67it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "317it [00:02, 145.77it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 82/82 [00:03<00:00, 23.88it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 107/107 [00:00<00:00, 128.72it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000027_c000050_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000027_c000050_grain_stats.csv\n", + "done with segmentation + export\n", + "[728/1053] tile_r000027_c000051\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.46it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.49it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 68/68 [00:02<00:00, 27.59it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "17it [00:00, 290.50it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "20it [00:00, 285.81it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 7/7 [00:00<00:00, 47.84it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 9/9 [00:00<00:00, 123.02it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000027_c000051_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000027_c000051_grain_stats.csv\n", + "done with segmentation + export\n", + "[729/1053] tile_r000028_c000004\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.43it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.48it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 46/46 [00:01<00:00, 31.11it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "22it [00:00, 214.50it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "22it [00:00, 215.42it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:00<00:00, 23.73it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:00<00:00, 125.17it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000028_c000004_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000028_c000004_grain_stats.csv\n", + "done with segmentation + export\n", + "[730/1053] tile_r000028_c000005\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:01<00:00, 2.51it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.50it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 353/353 [00:10<00:00, 34.17it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "290it [00:00, 325.81it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "289it [00:00, 331.99it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 85/85 [00:01<00:00, 55.66it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 113/113 [00:00<00:00, 189.60it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000028_c000005_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000028_c000005_grain_stats.csv\n", + "done with segmentation + export\n", + "[731/1053] tile_r000028_c000006\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:01<00:00, 2.52it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.50it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 546/546 [00:14<00:00, 37.72it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "448it [00:01, 246.38it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "472it [00:01, 236.49it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 147/147 [00:03<00:00, 47.05it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 159/159 [00:00<00:00, 164.33it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000028_c000006_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000028_c000006_grain_stats.csv\n", + "done with segmentation + export\n", + "[732/1053] tile_r000028_c000007\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.42it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.53it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 539/539 [00:13<00:00, 38.90it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "458it [00:01, 299.78it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "478it [00:01, 298.19it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 155/155 [00:02<00:00, 58.73it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 166/166 [00:00<00:00, 175.45it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000028_c000007_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000028_c000007_grain_stats.csv\n", + "done with segmentation + export\n", + "[733/1053] tile_r000028_c000008\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.49it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.46it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 593/593 [00:15<00:00, 38.17it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "503it [00:02, 211.73it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "501it [00:02, 238.48it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 140/140 [00:03<00:00, 42.14it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 186/186 [00:01<00:00, 162.80it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n", + "Warning: len(gdf)=186 != len(grain_stats)=185 -> truncating to 185\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000028_c000008_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000028_c000008_grain_stats.csv\n", + "done with segmentation + export\n", + "[734/1053] tile_r000028_c000009\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.48it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.52it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 474/474 [00:12<00:00, 36.74it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "341it [00:01, 273.19it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "339it [00:01, 293.79it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 89/89 [00:02<00:00, 44.03it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 132/132 [00:00<00:00, 178.36it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000028_c000009_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000028_c000009_grain_stats.csv\n", + "done with segmentation + export\n", + "[735/1053] tile_r000028_c000010\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:01<00:00, 2.51it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.48it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 373/373 [00:10<00:00, 33.93it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "244it [00:00, 310.38it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "255it [00:00, 309.63it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 88/88 [00:01<00:00, 63.17it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 95/95 [00:00<00:00, 171.66it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000028_c000010_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000028_c000010_grain_stats.csv\n", + "done with segmentation + export\n", + "[736/1053] tile_r000028_c000011\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.48it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.46it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 620/620 [00:16<00:00, 36.95it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "492it [00:02, 211.91it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "489it [00:02, 226.29it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 135/135 [00:03<00:00, 42.10it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 179/179 [00:01<00:00, 163.34it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000028_c000011_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000028_c000011_grain_stats.csv\n", + "done with segmentation + export\n", + "[737/1053] tile_r000028_c000012\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.48it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.51it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 669/669 [00:17<00:00, 37.82it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "536it [00:02, 198.96it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "564it [00:02, 197.59it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 174/174 [00:04<00:00, 41.77it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 195/195 [00:01<00:00, 153.45it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n", + "Warning: len(gdf)=195 != len(grain_stats)=194 -> truncating to 194\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000028_c000012_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000028_c000012_grain_stats.csv\n", + "done with segmentation + export\n", + "[738/1053] tile_r000028_c000013\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.32it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.40it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 657/657 [00:17<00:00, 37.19it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "529it [00:02, 216.19it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "560it [00:02, 218.86it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 177/177 [00:03<00:00, 49.03it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 189/189 [00:01<00:00, 170.33it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n", + "Warning: len(gdf)=189 != len(grain_stats)=188 -> truncating to 188\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsshome1/0B/di54doz/.local/share/mamba/envs/segmenteverygrain/lib/python3.10/site-packages/segmenteverygrain/segmenteverygrain.py:2218: RuntimeWarning: divide by zero encountered in log2\n", + " phi_minor = -np.log2(minor_axis_length)\n", + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000028_c000013_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000028_c000013_grain_stats.csv\n", + "done with segmentation + export\n", + "[739/1053] tile_r000028_c000014\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.48it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.50it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 635/635 [00:16<00:00, 37.56it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "488it [00:02, 198.93it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "487it [00:02, 214.56it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 130/130 [00:03<00:00, 38.87it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 169/169 [00:01<00:00, 156.81it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000028_c000014_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000028_c000014_grain_stats.csv\n", + "done with segmentation + export\n", + "[740/1053] tile_r000028_c000015\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.48it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.54it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 485/485 [00:13<00:00, 35.93it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "345it [00:01, 192.52it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "362it [00:01, 185.71it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 115/115 [00:02<00:00, 40.64it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 119/119 [00:00<00:00, 152.50it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000028_c000015_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000028_c000015_grain_stats.csv\n", + "done with segmentation + export\n", + "[741/1053] tile_r000028_c000016\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.48it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.48it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 576/576 [00:15<00:00, 36.93it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "452it [00:02, 191.58it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "474it [00:02, 193.81it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 148/148 [00:03<00:00, 45.57it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 154/154 [00:00<00:00, 157.66it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000028_c000016_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000028_c000016_grain_stats.csv\n", + "done with segmentation + export\n", + "[742/1053] tile_r000028_c000017\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.49it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.48it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 495/495 [00:13<00:00, 36.05it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "380it [00:02, 176.32it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "378it [00:02, 183.39it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 101/101 [00:03<00:00, 32.36it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 120/120 [00:00<00:00, 139.19it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000028_c000017_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000028_c000017_grain_stats.csv\n", + "done with segmentation + export\n", + "[743/1053] tile_r000028_c000018\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.48it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.48it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 442/442 [00:12<00:00, 36.40it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "344it [00:02, 159.90it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "359it [00:02, 162.10it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 103/103 [00:03<00:00, 33.42it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 112/112 [00:00<00:00, 145.36it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000028_c000018_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000028_c000018_grain_stats.csv\n", + "done with segmentation + export\n", + "[744/1053] tile_r000028_c000019\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.50it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.52it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 506/506 [00:13<00:00, 37.84it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "386it [00:02, 172.45it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "404it [00:02, 175.42it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 119/119 [00:03<00:00, 37.75it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 121/121 [00:00<00:00, 149.65it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000028_c000019_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000028_c000019_grain_stats.csv\n", + "done with segmentation + export\n", + "[745/1053] tile_r000028_c000020\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.50it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.52it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 551/551 [00:15<00:00, 36.71it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "446it [00:02, 155.42it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "467it [00:03, 152.93it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 135/135 [00:03<00:00, 33.82it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 149/149 [00:00<00:00, 154.88it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000028_c000020_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000028_c000020_grain_stats.csv\n", + "done with segmentation + export\n", + "[746/1053] tile_r000028_c000021\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:01<00:00, 2.54it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.44it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 392/392 [00:10<00:00, 38.29it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "300it [00:01, 179.37it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "315it [00:01, 181.88it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 97/97 [00:02<00:00, 41.58it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 101/101 [00:00<00:00, 148.65it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000028_c000021_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000028_c000021_grain_stats.csv\n", + "done with segmentation + export\n", + "[747/1053] tile_r000028_c000022\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.47it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.49it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 801/801 [00:20<00:00, 39.28it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "680it [00:03, 185.70it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "697it [00:03, 184.87it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 227/227 [00:04<00:00, 47.77it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 234/234 [00:01<00:00, 175.89it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000028_c000022_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000028_c000022_grain_stats.csv\n", + "done with segmentation + export\n", + "[748/1053] tile_r000028_c000023\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.46it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.43it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 728/728 [00:18<00:00, 39.77it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "640it [00:03, 203.30it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "667it [00:03, 199.92it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 227/227 [00:04<00:00, 56.58it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 236/236 [00:01<00:00, 171.92it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000028_c000023_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000028_c000023_grain_stats.csv\n", + "done with segmentation + export\n", + "[749/1053] tile_r000028_c000024\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:01<00:00, 2.50it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.48it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 824/824 [00:20<00:00, 39.65it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "713it [00:03, 205.19it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "712it [00:03, 210.48it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 231/231 [00:03<00:00, 58.55it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 264/264 [00:01<00:00, 170.85it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000028_c000024_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000028_c000024_grain_stats.csv\n", + "done with segmentation + export\n", + "[750/1053] tile_r000028_c000025\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:01<00:00, 2.56it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.54it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 922/922 [00:23<00:00, 39.93it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "776it [00:03, 227.66it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "775it [00:03, 233.08it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 247/247 [00:04<00:00, 60.31it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 281/281 [00:01<00:00, 179.10it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000028_c000025_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000028_c000025_grain_stats.csv\n", + "done with segmentation + export\n", + "[751/1053] tile_r000028_c000026\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.48it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.57it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 765/765 [00:19<00:00, 39.05it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "650it [00:03, 201.15it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "676it [00:03, 211.06it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 226/226 [00:04<00:00, 55.88it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 239/239 [00:01<00:00, 175.69it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000028_c000026_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000028_c000026_grain_stats.csv\n", + "done with segmentation + export\n", + "[752/1053] tile_r000028_c000027\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:01<00:00, 2.50it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.49it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 639/639 [00:16<00:00, 39.17it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "508it [00:02, 240.15it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "528it [00:02, 239.20it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 186/186 [00:03<00:00, 59.56it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 188/188 [00:01<00:00, 175.33it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000028_c000027_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000028_c000027_grain_stats.csv\n", + "done with segmentation + export\n", + "[753/1053] tile_r000028_c000028\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.46it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.50it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 756/756 [00:19<00:00, 38.98it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "600it [00:02, 256.58it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "637it [00:02, 254.88it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 215/215 [00:03<00:00, 65.50it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 226/226 [00:01<00:00, 185.09it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000028_c000028_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000028_c000028_grain_stats.csv\n", + "done with segmentation + export\n", + "[754/1053] tile_r000028_c000029\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.49it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.48it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 705/705 [00:18<00:00, 38.93it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "562it [00:02, 255.18it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "592it [00:02, 258.27it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 196/196 [00:03<00:00, 56.98it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 202/202 [00:01<00:00, 182.59it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000028_c000029_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000028_c000029_grain_stats.csv\n", + "done with segmentation + export\n", + "[755/1053] tile_r000028_c000030\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:01<00:00, 2.53it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.47it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 720/720 [00:18<00:00, 38.79it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "602it [00:02, 235.72it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "632it [00:02, 237.09it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 195/195 [00:03<00:00, 49.50it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 205/205 [00:01<00:00, 186.21it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000028_c000030_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000028_c000030_grain_stats.csv\n", + "done with segmentation + export\n", + "[756/1053] tile_r000028_c000031\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.49it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.50it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 669/669 [00:16<00:00, 40.01it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "544it [00:02, 218.32it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "571it [00:02, 218.81it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 180/180 [00:03<00:00, 48.17it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 187/187 [00:01<00:00, 180.22it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000028_c000031_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000028_c000031_grain_stats.csv\n", + "done with segmentation + export\n", + "[757/1053] tile_r000028_c000032\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.43it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.49it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 668/668 [00:17<00:00, 39.05it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "548it [00:02, 230.28it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "577it [00:02, 229.42it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 189/189 [00:03<00:00, 55.74it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 197/197 [00:01<00:00, 179.53it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000028_c000032_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000028_c000032_grain_stats.csv\n", + "done with segmentation + export\n", + "[758/1053] tile_r000028_c000033\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.44it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.50it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 624/624 [00:16<00:00, 38.88it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "505it [00:02, 203.17it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "536it [00:02, 211.06it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 177/177 [00:03<00:00, 46.30it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 183/183 [00:01<00:00, 176.01it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000028_c000033_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000028_c000033_grain_stats.csv\n", + "done with segmentation + export\n", + "[759/1053] tile_r000028_c000034\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.45it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.53it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 645/645 [00:16<00:00, 38.62it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "504it [00:02, 246.58it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "538it [00:02, 248.58it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 180/180 [00:02<00:00, 61.64it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 184/184 [00:01<00:00, 178.38it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000028_c000034_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000028_c000034_grain_stats.csv\n", + "done with segmentation + export\n", + "[760/1053] tile_r000028_c000035\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.40it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.53it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 485/485 [00:12<00:00, 38.30it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "348it [00:01, 251.05it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "367it [00:01, 252.14it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 122/122 [00:02<00:00, 56.32it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 124/124 [00:00<00:00, 164.77it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000028_c000035_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000028_c000035_grain_stats.csv\n", + "done with segmentation + export\n", + "[761/1053] tile_r000028_c000036\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:01<00:00, 2.52it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.45it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 391/391 [00:10<00:00, 37.47it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "289it [00:01, 247.12it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "305it [00:01, 246.69it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 103/103 [00:01<00:00, 55.75it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 105/105 [00:00<00:00, 149.13it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000028_c000036_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000028_c000036_grain_stats.csv\n", + "done with segmentation + export\n", + "[762/1053] tile_r000028_c000037\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.47it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.51it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 642/642 [00:16<00:00, 39.86it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "512it [00:02, 218.77it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "553it [00:02, 224.24it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 188/188 [00:03<00:00, 57.22it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 194/194 [00:01<00:00, 178.67it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000028_c000037_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000028_c000037_grain_stats.csv\n", + "done with segmentation + export\n", + "[763/1053] tile_r000028_c000038\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.50it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.48it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 465/465 [00:12<00:00, 38.42it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "384it [00:01, 205.56it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "402it [00:01, 209.23it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 122/122 [00:02<00:00, 44.17it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 125/125 [00:00<00:00, 163.65it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000028_c000038_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000028_c000038_grain_stats.csv\n", + "done with segmentation + export\n", + "[764/1053] tile_r000028_c000039\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:01<00:00, 2.51it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.51it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 547/547 [00:13<00:00, 39.54it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "441it [00:02, 193.62it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "461it [00:02, 193.80it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 142/142 [00:03<00:00, 43.46it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 145/145 [00:00<00:00, 159.61it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000028_c000039_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000028_c000039_grain_stats.csv\n", + "done with segmentation + export\n", + "[765/1053] tile_r000028_c000040\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.48it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.30it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 621/621 [00:15<00:00, 39.89it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "547it [00:03, 142.68it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "546it [00:03, 147.05it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 147/147 [00:05<00:00, 27.56it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 170/170 [00:01<00:00, 161.96it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000028_c000040_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000028_c000040_grain_stats.csv\n", + "done with segmentation + export\n", + "[766/1053] tile_r000028_c000041\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.45it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.49it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 468/468 [00:12<00:00, 38.59it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "368it [00:02, 180.31it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "386it [00:02, 169.45it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 117/117 [00:03<00:00, 38.31it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 121/121 [00:00<00:00, 151.37it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000028_c000041_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000028_c000041_grain_stats.csv\n", + "done with segmentation + export\n", + "[767/1053] tile_r000028_c000042\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.49it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.48it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 377/377 [00:14<00:00, 26.40it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "210it [00:01, 159.97it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "209it [00:01, 171.22it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 49/49 [00:02<00:00, 22.44it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 62/62 [00:00<00:00, 142.59it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000028_c000042_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000028_c000042_grain_stats.csv\n", + "done with segmentation + export\n", + "[768/1053] tile_r000028_c000043\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.49it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.50it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 383/383 [00:10<00:00, 37.38it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "268it [00:01, 149.71it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "280it [00:01, 152.63it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 80/80 [00:02<00:00, 29.72it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 84/84 [00:00<00:00, 134.88it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000028_c000043_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000028_c000043_grain_stats.csv\n", + "done with segmentation + export\n", + "[769/1053] tile_r000028_c000044\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:01<00:00, 2.53it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.51it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 437/437 [00:11<00:00, 38.48it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "361it [00:02, 161.69it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "378it [00:02, 164.75it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 108/108 [00:03<00:00, 31.60it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 111/111 [00:00<00:00, 153.86it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000028_c000044_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000028_c000044_grain_stats.csv\n", + "done with segmentation + export\n", + "[770/1053] tile_r000028_c000045\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.47it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.38it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 466/466 [00:12<00:00, 36.05it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "351it [00:02, 128.18it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "347it [00:02, 165.91it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 84/84 [00:04<00:00, 20.42it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 126/126 [00:00<00:00, 152.59it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000028_c000045_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000028_c000045_grain_stats.csv\n", + "done with segmentation + export\n", + "[771/1053] tile_r000028_c000046\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.45it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.49it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 421/421 [00:12<00:00, 34.84it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "266it [00:01, 143.19it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "278it [00:01, 144.70it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 73/73 [00:03<00:00, 24.29it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 84/84 [00:00<00:00, 129.57it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n", + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000028_c000046_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000028_c000046_grain_stats.csv\n", + "done with segmentation + export\n", + "[772/1053] tile_r000028_c000047\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:01<00:00, 2.51it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.51it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 598/598 [00:16<00:00, 36.60it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "472it [00:03, 155.59it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "490it [00:03, 161.17it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 148/148 [00:04<00:00, 34.08it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 159/159 [00:01<00:00, 153.61it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000028_c000047_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000028_c000047_grain_stats.csv\n", + "done with segmentation + export\n", + "[773/1053] tile_r000028_c000048\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:01<00:00, 2.53it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.47it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 596/596 [00:16<00:00, 36.77it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "452it [00:02, 176.55it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "450it [00:02, 194.57it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 138/138 [00:03<00:00, 42.55it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 159/159 [00:01<00:00, 154.37it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000028_c000048_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000028_c000048_grain_stats.csv\n", + "done with segmentation + export\n", + "[774/1053] tile_r000028_c000049\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.45it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.52it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 291/291 [00:08<00:00, 35.09it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "215it [00:01, 170.71it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "219it [00:01, 170.93it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 60/60 [00:02<00:00, 28.80it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 64/64 [00:00<00:00, 126.47it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000028_c000049_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000028_c000049_grain_stats.csv\n", + "done with segmentation + export\n", + "[775/1053] tile_r000028_c000050\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:01<00:00, 2.50it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.50it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 9/9 [00:00<00:00, 21.12it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "0it [00:00, ?it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "0it [00:00, ?it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "0it [00:00, ?it/s]\n", + "0it [00:00, ?it/s]\n", + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n", + "No grains found -> skipping histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000028_c000050_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000028_c000050_grain_stats.csv\n", + "done with segmentation + export\n", + "[776/1053] tile_r000029_c000004\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:01<00:00, 2.54it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.50it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 20/20 [00:00<00:00, 26.00it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2it [00:00, 3211.57it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "4it [00:00, 641.21it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 2/2 [00:00<00:00, 230.18it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 2/2 [00:00<00:00, 145.92it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000029_c000004_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000029_c000004_grain_stats.csv\n", + "done with segmentation + export\n", + "[777/1053] tile_r000029_c000005\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.49it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.50it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 559/559 [00:14<00:00, 39.52it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "500it [00:01, 283.12it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "499it [00:01, 314.71it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 158/158 [00:02<00:00, 63.48it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 203/203 [00:01<00:00, 188.86it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000029_c000005_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000029_c000005_grain_stats.csv\n", + "done with segmentation + export\n", + "[778/1053] tile_r000029_c000006\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.49it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.48it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 610/610 [00:15<00:00, 38.33it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "519it [00:01, 279.04it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "545it [00:01, 283.48it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 182/182 [00:02<00:00, 63.91it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 194/194 [00:01<00:00, 187.59it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000029_c000006_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000029_c000006_grain_stats.csv\n", + "done with segmentation + export\n", + "[779/1053] tile_r000029_c000007\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.49it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.51it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 596/596 [00:17<00:00, 33.43it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "468it [00:01, 296.67it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "466it [00:01, 326.78it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 144/144 [00:02<00:00, 60.87it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 185/185 [00:01<00:00, 176.54it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n", + "Warning: len(gdf)=185 != len(grain_stats)=184 -> truncating to 184\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000029_c000007_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000029_c000007_grain_stats.csv\n", + "done with segmentation + export\n", + "[780/1053] tile_r000029_c000008\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:01<00:00, 2.54it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.50it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 544/544 [00:14<00:00, 37.10it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "419it [00:01, 357.04it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "445it [00:01, 351.90it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 158/158 [00:01<00:00, 79.00it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 165/165 [00:00<00:00, 186.74it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000029_c000008_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000029_c000008_grain_stats.csv\n", + "done with segmentation + export\n", + "[781/1053] tile_r000029_c000009\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.47it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.47it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 563/563 [00:15<00:00, 35.48it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "418it [00:01, 316.24it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "440it [00:01, 316.88it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 152/152 [00:02<00:00, 67.03it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 163/163 [00:00<00:00, 170.01it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000029_c000009_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000029_c000009_grain_stats.csv\n", + "done with segmentation + export\n", + "[782/1053] tile_r000029_c000010\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.50it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.53it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 512/512 [00:13<00:00, 37.05it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "406it [00:01, 266.11it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "405it [00:01, 275.89it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 123/123 [00:02<00:00, 56.75it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 169/169 [00:00<00:00, 175.44it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000029_c000010_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000029_c000010_grain_stats.csv\n", + "done with segmentation + export\n", + "[783/1053] tile_r000029_c000011\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.43it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.50it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 597/597 [00:16<00:00, 37.07it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "489it [00:01, 281.13it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "519it [00:01, 269.40it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 174/174 [00:03<00:00, 57.42it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 181/181 [00:00<00:00, 182.67it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n", + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000029_c000011_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000029_c000011_grain_stats.csv\n", + "done with segmentation + export\n", + "[784/1053] tile_r000029_c000012\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.50it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.46it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 575/575 [00:15<00:00, 38.04it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "445it [00:01, 279.63it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "443it [00:01, 301.54it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 139/139 [00:02<00:00, 58.84it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 177/177 [00:01<00:00, 174.43it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000029_c000012_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000029_c000012_grain_stats.csv\n", + "done with segmentation + export\n", + "[785/1053] tile_r000029_c000013\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.48it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.50it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 675/675 [00:17<00:00, 38.29it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "562it [00:02, 237.30it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "587it [00:02, 252.29it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 203/203 [00:03<00:00, 62.22it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 210/210 [00:01<00:00, 179.00it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000029_c000013_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000029_c000013_grain_stats.csv\n", + "done with segmentation + export\n", + "[786/1053] tile_r000029_c000014\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.47it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.40it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 701/701 [00:18<00:00, 38.50it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "568it [00:02, 201.13it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "565it [00:02, 212.95it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 163/163 [00:04<00:00, 38.19it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 201/201 [00:01<00:00, 168.65it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000029_c000014_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000029_c000014_grain_stats.csv\n", + "done with segmentation + export\n", + "[787/1053] tile_r000029_c000015\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.50it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.47it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 719/719 [00:19<00:00, 37.69it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "604it [00:03, 157.02it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "602it [00:03, 174.78it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 169/169 [00:04<00:00, 38.41it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 201/201 [00:01<00:00, 164.19it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000029_c000015_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000029_c000015_grain_stats.csv\n", + "done with segmentation + export\n", + "[788/1053] tile_r000029_c000016\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.44it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.53it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 470/470 [00:12<00:00, 37.71it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "347it [00:01, 185.17it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "346it [00:01, 175.70it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 92/92 [00:02<00:00, 32.29it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 120/120 [00:00<00:00, 159.82it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000029_c000016_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000029_c000016_grain_stats.csv\n", + "done with segmentation + export\n", + "[789/1053] tile_r000029_c000017\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:01<00:00, 2.52it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.47it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 515/515 [00:13<00:00, 37.91it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "408it [00:01, 227.13it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "406it [00:01, 242.14it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 109/109 [00:02<00:00, 39.21it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 150/150 [00:00<00:00, 164.46it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000029_c000017_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000029_c000017_grain_stats.csv\n", + "done with segmentation + export\n", + "[790/1053] tile_r000029_c000018\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.48it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.48it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 516/516 [00:13<00:00, 37.85it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "412it [00:02, 160.51it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "435it [00:02, 156.70it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 128/128 [00:03<00:00, 36.90it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 132/132 [00:00<00:00, 149.19it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000029_c000018_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000029_c000018_grain_stats.csv\n", + "done with segmentation + export\n", + "[791/1053] tile_r000029_c000019\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:01<00:00, 2.51it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.41it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 375/375 [00:10<00:00, 36.63it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "255it [00:01, 171.42it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "274it [00:01, 172.15it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 82/82 [00:02<00:00, 33.70it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 89/89 [00:00<00:00, 149.06it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000029_c000019_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000029_c000019_grain_stats.csv\n", + "done with segmentation + export\n", + "[792/1053] tile_r000029_c000020\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.48it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.53it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 472/472 [00:12<00:00, 36.62it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "320it [00:01, 215.82it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "335it [00:01, 214.51it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 111/111 [00:02<00:00, 46.97it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 114/114 [00:00<00:00, 146.16it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000029_c000020_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000029_c000020_grain_stats.csv\n", + "done with segmentation + export\n", + "[793/1053] tile_r000029_c000021\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.41it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.53it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 431/431 [00:11<00:00, 36.76it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "321it [00:01, 170.99it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "348it [00:01, 174.72it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 108/108 [00:02<00:00, 38.53it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 116/116 [00:00<00:00, 144.31it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000029_c000021_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000029_c000021_grain_stats.csv\n", + "done with segmentation + export\n", + "[794/1053] tile_r000029_c000022\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.49it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.51it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 503/503 [00:13<00:00, 37.93it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "366it [00:02, 179.72it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "383it [00:02, 179.96it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 113/113 [00:03<00:00, 36.65it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 120/120 [00:00<00:00, 126.82it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000029_c000022_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000029_c000022_grain_stats.csv\n", + "done with segmentation + export\n", + "[795/1053] tile_r000029_c000023\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.44it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.54it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 727/727 [00:18<00:00, 39.30it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "580it [00:03, 174.01it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "599it [00:03, 176.10it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 187/187 [00:04<00:00, 43.76it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 195/195 [00:01<00:00, 156.65it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000029_c000023_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000029_c000023_grain_stats.csv\n", + "done with segmentation + export\n", + "[796/1053] tile_r000029_c000024\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:01<00:00, 2.51it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.55it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 625/625 [00:16<00:00, 38.32it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "505it [00:02, 201.56it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "519it [00:02, 202.74it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 167/167 [00:03<00:00, 50.19it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 170/170 [00:01<00:00, 157.58it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000029_c000024_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000029_c000024_grain_stats.csv\n", + "done with segmentation + export\n", + "[797/1053] tile_r000029_c000025\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:01<00:00, 2.52it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.49it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 778/778 [00:19<00:00, 40.08it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "678it [00:03, 181.42it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "702it [00:03, 180.83it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 229/229 [00:04<00:00, 48.76it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 235/235 [00:01<00:00, 171.46it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000029_c000025_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000029_c000025_grain_stats.csv\n", + "done with segmentation + export\n", + "[798/1053] tile_r000029_c000026\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:01<00:00, 2.51it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.54it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 709/709 [00:17<00:00, 39.97it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "623it [00:02, 216.94it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "650it [00:03, 215.90it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 221/221 [00:03<00:00, 60.36it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 225/225 [00:01<00:00, 168.49it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000029_c000026_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000029_c000026_grain_stats.csv\n", + "done with segmentation + export\n", + "[799/1053] tile_r000029_c000027\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.44it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.49it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 589/589 [00:15<00:00, 38.23it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "481it [00:02, 193.66it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "509it [00:02, 193.89it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 167/167 [00:03<00:00, 43.36it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 172/172 [00:01<00:00, 161.82it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000029_c000027_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000029_c000027_grain_stats.csv\n", + "done with segmentation + export\n", + "[800/1053] tile_r000029_c000028\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.48it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.46it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 719/719 [00:18<00:00, 39.60it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "599it [00:02, 223.22it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "628it [00:02, 222.22it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 205/205 [00:03<00:00, 51.40it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 217/217 [00:01<00:00, 175.67it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000029_c000028_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000029_c000028_grain_stats.csv\n", + "done with segmentation + export\n", + "[801/1053] tile_r000029_c000029\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.35it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.51it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 692/692 [00:17<00:00, 38.67it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "567it [00:02, 196.47it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "603it [00:03, 200.27it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 194/194 [00:04<00:00, 47.79it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 202/202 [00:01<00:00, 173.30it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000029_c000029_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000029_c000029_grain_stats.csv\n", + "done with segmentation + export\n", + "[802/1053] tile_r000029_c000030\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.46it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.53it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 649/649 [00:17<00:00, 37.99it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "508it [00:02, 201.83it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "535it [00:02, 203.00it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 176/176 [00:03<00:00, 50.05it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 184/184 [00:01<00:00, 165.69it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000029_c000030_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000029_c000030_grain_stats.csv\n", + "done with segmentation + export\n", + "[803/1053] tile_r000029_c000031\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.48it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.45it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 680/680 [00:17<00:00, 39.61it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "583it [00:02, 230.82it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "622it [00:02, 228.35it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 203/203 [00:03<00:00, 56.37it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 208/208 [00:01<00:00, 169.82it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000029_c000031_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000029_c000031_grain_stats.csv\n", + "done with segmentation + export\n", + "[804/1053] tile_r000029_c000032\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.50it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.53it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 600/600 [00:15<00:00, 39.11it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "491it [00:02, 201.63it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "516it [00:02, 203.08it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 164/164 [00:03<00:00, 47.54it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 167/167 [00:01<00:00, 164.31it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000029_c000032_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000029_c000032_grain_stats.csv\n", + "done with segmentation + export\n", + "[805/1053] tile_r000029_c000033\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:01<00:00, 2.51it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.52it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 504/504 [00:12<00:00, 39.26it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "408it [00:01, 211.47it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "421it [00:01, 213.27it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 123/123 [00:02<00:00, 41.91it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 132/132 [00:00<00:00, 166.37it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000029_c000033_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000029_c000033_grain_stats.csv\n", + "done with segmentation + export\n", + "[806/1053] tile_r000029_c000034\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.34it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.45it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 553/553 [00:13<00:00, 39.79it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "465it [00:02, 219.70it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "482it [00:02, 220.60it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 154/154 [00:03<00:00, 50.10it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 160/160 [00:00<00:00, 160.11it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000029_c000034_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000029_c000034_grain_stats.csv\n", + "done with segmentation + export\n", + "[807/1053] tile_r000029_c000035\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.45it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.58it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 515/515 [00:13<00:00, 39.30it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "436it [00:02, 205.49it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "454it [00:02, 206.81it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 146/146 [00:02<00:00, 48.83it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 152/152 [00:00<00:00, 162.56it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000029_c000035_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000029_c000035_grain_stats.csv\n", + "done with segmentation + export\n", + "[808/1053] tile_r000029_c000036\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.48it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.53it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 478/478 [00:12<00:00, 39.03it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "387it [00:02, 188.68it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "408it [00:02, 189.89it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 123/123 [00:03<00:00, 39.59it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 125/125 [00:00<00:00, 154.94it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000029_c000036_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000029_c000036_grain_stats.csv\n", + "done with segmentation + export\n", + "[809/1053] tile_r000029_c000037\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.48it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.50it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 367/367 [00:09<00:00, 37.90it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "275it [00:01, 180.25it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "287it [00:01, 181.11it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 91/91 [00:02<00:00, 36.31it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 93/93 [00:00<00:00, 157.43it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000029_c000037_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000029_c000037_grain_stats.csv\n", + "done with segmentation + export\n", + "[810/1053] tile_r000029_c000038\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.33it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.39it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 412/412 [00:10<00:00, 38.96it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "332it [00:01, 181.17it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "346it [00:02, 169.41it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 96/96 [00:03<00:00, 31.02it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 99/99 [00:00<00:00, 149.79it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000029_c000038_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000029_c000038_grain_stats.csv\n", + "done with segmentation + export\n", + "[811/1053] tile_r000029_c000039\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.42it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.56it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 438/438 [00:11<00:00, 38.83it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "359it [00:01, 187.33it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "378it [00:02, 186.90it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 116/116 [00:02<00:00, 40.45it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 120/120 [00:00<00:00, 154.46it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000029_c000039_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000029_c000039_grain_stats.csv\n", + "done with segmentation + export\n", + "[812/1053] tile_r000029_c000040\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.46it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.41it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 314/314 [00:08<00:00, 37.59it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "246it [00:01, 183.61it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "255it [00:01, 187.07it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 78/78 [00:02<00:00, 36.52it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 80/80 [00:00<00:00, 148.10it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000029_c000040_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000029_c000040_grain_stats.csv\n", + "done with segmentation + export\n", + "[813/1053] tile_r000029_c000041\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.47it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.48it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 351/351 [00:09<00:00, 35.55it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "256it [00:01, 144.41it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "268it [00:01, 147.57it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 73/73 [00:02<00:00, 27.19it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 74/74 [00:00<00:00, 138.49it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000029_c000041_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000029_c000041_grain_stats.csv\n", + "done with segmentation + export\n", + "[814/1053] tile_r000029_c000042\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.47it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.49it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 421/421 [00:11<00:00, 36.60it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "318it [00:02, 158.09it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "332it [00:02, 160.14it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 98/98 [00:03<00:00, 30.15it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 102/102 [00:00<00:00, 137.81it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000029_c000042_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000029_c000042_grain_stats.csv\n", + "done with segmentation + export\n", + "[815/1053] tile_r000029_c000043\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:01<00:00, 2.51it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.41it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 367/367 [00:10<00:00, 36.53it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "288it [00:02, 126.93it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "284it [00:02, 130.84it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 68/68 [00:03<00:00, 20.49it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 89/89 [00:00<00:00, 138.19it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000029_c000043_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000029_c000043_grain_stats.csv\n", + "done with segmentation + export\n", + "[816/1053] tile_r000029_c000044\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.49it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.48it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 487/487 [00:13<00:00, 36.52it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "348it [00:02, 169.84it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "359it [00:02, 171.59it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 107/107 [00:03<00:00, 34.93it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 112/112 [00:00<00:00, 152.12it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000029_c000044_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000029_c000044_grain_stats.csv\n", + "done with segmentation + export\n", + "[817/1053] tile_r000029_c000045\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:01<00:00, 2.50it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.53it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 586/586 [00:17<00:00, 34.07it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "420it [00:02, 183.89it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "447it [00:02, 183.32it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 134/134 [00:03<00:00, 39.21it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 142/142 [00:00<00:00, 151.91it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000029_c000045_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000029_c000045_grain_stats.csv\n", + "done with segmentation + export\n", + "[818/1053] tile_r000029_c000046\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.48it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.48it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 564/564 [00:15<00:00, 36.52it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "409it [00:03, 128.53it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "406it [00:03, 127.58it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 110/110 [00:04<00:00, 24.85it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 135/135 [00:01<00:00, 127.05it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000029_c000046_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000029_c000046_grain_stats.csv\n", + "done with segmentation + export\n", + "[819/1053] tile_r000029_c000047\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:01<00:00, 2.51it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.48it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 586/586 [00:15<00:00, 37.48it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "474it [00:04, 114.97it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "493it [00:04, 115.81it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 140/140 [00:05<00:00, 26.83it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 146/146 [00:01<00:00, 144.33it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000029_c000047_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000029_c000047_grain_stats.csv\n", + "done with segmentation + export\n", + "[820/1053] tile_r000029_c000048\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.46it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.54it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 153/153 [00:04<00:00, 34.73it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "110it [00:00, 115.77it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "112it [00:00, 116.73it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 27/27 [00:02<00:00, 9.35it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 31/31 [00:00<00:00, 107.00it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000029_c000048_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000029_c000048_grain_stats.csv\n", + "done with segmentation + export\n", + "[821/1053] tile_r000030_c000005\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:01<00:00, 2.50it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.48it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 356/356 [00:10<00:00, 34.65it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "270it [00:00, 284.41it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "269it [00:00, 340.32it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 83/83 [00:01<00:00, 65.64it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 108/108 [00:00<00:00, 188.43it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000030_c000005_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000030_c000005_grain_stats.csv\n", + "done with segmentation + export\n", + "[822/1053] tile_r000030_c000006\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.49it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.50it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 647/647 [00:17<00:00, 37.14it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "496it [00:01, 257.65it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "534it [00:02, 257.10it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 175/175 [00:03<00:00, 55.90it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 181/181 [00:00<00:00, 191.61it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000030_c000006_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000030_c000006_grain_stats.csv\n", + "done with segmentation + export\n", + "[823/1053] tile_r000030_c000007\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:01<00:00, 2.52it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.54it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 627/627 [00:17<00:00, 35.39it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "505it [00:01, 294.87it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "504it [00:01, 305.24it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 152/152 [00:03<00:00, 50.10it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 195/195 [00:01<00:00, 175.09it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n", + "Warning: len(gdf)=195 != len(grain_stats)=192 -> truncating to 192\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000030_c000007_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000030_c000007_grain_stats.csv\n", + "done with segmentation + export\n", + "[824/1053] tile_r000030_c000008\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.48it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.37it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 651/651 [00:17<00:00, 38.01it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "548it [00:02, 230.85it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "543it [00:01, 274.37it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 151/151 [00:03<00:00, 40.57it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 198/198 [00:01<00:00, 175.10it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000030_c000008_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000030_c000008_grain_stats.csv\n", + "done with segmentation + export\n", + "[825/1053] tile_r000030_c000009\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.48it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.50it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 657/657 [00:16<00:00, 39.40it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "544it [00:01, 277.36it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "541it [00:02, 265.39it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 167/167 [00:03<00:00, 53.11it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 205/205 [00:01<00:00, 192.14it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000030_c000009_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000030_c000009_grain_stats.csv\n", + "done with segmentation + export\n", + "[826/1053] tile_r000030_c000010\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.49it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.49it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 711/711 [00:18<00:00, 37.57it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "581it [00:02, 270.72it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "620it [00:02, 272.49it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 211/211 [00:03<00:00, 64.15it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 225/225 [00:01<00:00, 190.90it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n", + "Warning: len(gdf)=225 != len(grain_stats)=224 -> truncating to 224\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000030_c000010_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000030_c000010_grain_stats.csv\n", + "done with segmentation + export\n", + "[827/1053] tile_r000030_c000011\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.48it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.51it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 777/777 [00:21<00:00, 35.48it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "624it [00:02, 291.48it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "623it [00:02, 296.41it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 189/189 [00:03<00:00, 58.26it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 240/240 [00:01<00:00, 192.28it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000030_c000011_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000030_c000011_grain_stats.csv\n", + "done with segmentation + export\n", + "[828/1053] tile_r000030_c000012\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.47it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.49it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 561/561 [00:14<00:00, 38.06it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "450it [00:01, 309.58it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "486it [00:01, 313.67it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 168/168 [00:02<00:00, 69.26it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 174/174 [00:00<00:00, 187.34it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000030_c000012_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000030_c000012_grain_stats.csv\n", + "done with segmentation + export\n", + "[829/1053] tile_r000030_c000013\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.30it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.50it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 713/713 [00:20<00:00, 35.64it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "568it [00:02, 229.92it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "567it [00:02, 237.21it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 159/159 [00:04<00:00, 38.89it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 216/216 [00:01<00:00, 175.63it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000030_c000013_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000030_c000013_grain_stats.csv\n", + "done with segmentation + export\n", + "[830/1053] tile_r000030_c000014\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:01<00:00, 2.53it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.50it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 669/669 [00:19<00:00, 35.13it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "528it [00:02, 198.69it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "524it [00:02, 233.54it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 154/154 [00:03<00:00, 42.23it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 193/193 [00:01<00:00, 174.67it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n", + "Warning: len(gdf)=193 != len(grain_stats)=192 -> truncating to 192\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000030_c000014_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000030_c000014_grain_stats.csv\n", + "done with segmentation + export\n", + "[831/1053] tile_r000030_c000015\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.49it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.50it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 693/693 [00:18<00:00, 37.91it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "552it [00:02, 254.18it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "549it [00:01, 280.03it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 150/150 [00:03<00:00, 46.18it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 216/216 [00:01<00:00, 189.06it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000030_c000015_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000030_c000015_grain_stats.csv\n", + "done with segmentation + export\n", + "[832/1053] tile_r000030_c000016\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:01<00:00, 2.52it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.36it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 723/723 [00:20<00:00, 35.61it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "554it [00:02, 201.58it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "553it [00:02, 215.85it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 156/156 [00:03<00:00, 41.28it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 190/190 [00:01<00:00, 180.80it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000030_c000016_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000030_c000016_grain_stats.csv\n", + "done with segmentation + export\n", + "[833/1053] tile_r000030_c000017\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.48it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.57it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 521/521 [00:13<00:00, 37.60it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "402it [00:02, 174.02it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "418it [00:02, 178.45it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 125/125 [00:03<00:00, 36.72it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 134/134 [00:00<00:00, 159.21it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000030_c000017_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000030_c000017_grain_stats.csv\n", + "done with segmentation + export\n", + "[834/1053] tile_r000030_c000018\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.10it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.13it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 531/531 [00:15<00:00, 35.26it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "384it [00:02, 170.04it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "411it [00:02, 170.24it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 130/130 [00:03<00:00, 37.84it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 141/141 [00:00<00:00, 146.81it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000030_c000018_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000030_c000018_grain_stats.csv\n", + "done with segmentation + export\n", + "[835/1053] tile_r000030_c000019\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.11it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.11it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 402/402 [00:11<00:00, 36.23it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "294it [00:01, 160.02it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "311it [00:01, 165.95it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 99/99 [00:02<00:00, 34.13it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 107/107 [00:00<00:00, 138.28it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000030_c000019_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000030_c000019_grain_stats.csv\n", + "done with segmentation + export\n", + "[836/1053] tile_r000030_c000020\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.08it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.15it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 386/386 [00:10<00:00, 36.31it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "275it [00:01, 157.55it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "289it [00:01, 161.93it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 86/86 [00:03<00:00, 28.49it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 91/91 [00:00<00:00, 142.81it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000030_c000020_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000030_c000020_grain_stats.csv\n", + "done with segmentation + export\n", + "[837/1053] tile_r000030_c000021\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 1.89it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.11it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 421/421 [00:11<00:00, 36.31it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "310it [00:02, 130.14it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "326it [00:02, 132.59it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 87/87 [00:03<00:00, 22.57it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 95/95 [00:00<00:00, 137.48it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000030_c000021_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000030_c000021_grain_stats.csv\n", + "done with segmentation + export\n", + "[838/1053] tile_r000030_c000022\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.12it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.13it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 489/489 [00:12<00:00, 37.92it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "393it [00:02, 177.05it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "409it [00:02, 177.78it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 132/132 [00:03<00:00, 36.40it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 143/143 [00:01<00:00, 141.52it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000030_c000022_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000030_c000022_grain_stats.csv\n", + "done with segmentation + export\n", + "[839/1053] tile_r000030_c000023\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.14it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.18it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 650/650 [00:16<00:00, 38.51it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "528it [00:02, 182.79it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "550it [00:03, 182.02it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 167/167 [00:04<00:00, 39.34it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 184/184 [00:01<00:00, 157.93it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000030_c000023_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000030_c000023_grain_stats.csv\n", + "done with segmentation + export\n", + "[840/1053] tile_r000030_c000024\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.17it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.27it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 765/765 [00:19<00:00, 38.65it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "641it [00:03, 171.39it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "666it [00:03, 172.56it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 219/219 [00:04<00:00, 45.25it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 226/226 [00:01<00:00, 163.31it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000030_c000024_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000030_c000024_grain_stats.csv\n", + "done with segmentation + export\n", + "[841/1053] tile_r000030_c000025\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.21it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.26it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 706/706 [00:18<00:00, 37.73it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "572it [00:03, 164.30it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "588it [00:03, 164.76it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 183/183 [00:04<00:00, 40.81it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 192/192 [00:01<00:00, 155.38it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000030_c000025_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000030_c000025_grain_stats.csv\n", + "done with segmentation + export\n", + "[842/1053] tile_r000030_c000026\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.28it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.27it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 612/612 [00:15<00:00, 38.69it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "508it [00:02, 181.92it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "533it [00:02, 182.56it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 174/174 [00:03<00:00, 45.98it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 179/179 [00:01<00:00, 162.92it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000030_c000026_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000030_c000026_grain_stats.csv\n", + "done with segmentation + export\n", + "[843/1053] tile_r000030_c000027\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.25it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.35it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 683/683 [00:17<00:00, 38.47it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "554it [00:02, 194.18it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "589it [00:03, 192.50it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 197/197 [00:03<00:00, 51.70it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 201/201 [00:01<00:00, 160.32it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsshome1/0B/di54doz/.local/share/mamba/envs/segmenteverygrain/lib/python3.10/site-packages/segmenteverygrain/segmenteverygrain.py:2218: RuntimeWarning: divide by zero encountered in log2\n", + " phi_minor = -np.log2(minor_axis_length)\n", + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000030_c000027_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000030_c000027_grain_stats.csv\n", + "done with segmentation + export\n", + "[844/1053] tile_r000030_c000028\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.27it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.31it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 637/637 [00:16<00:00, 38.26it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "499it [00:03, 164.82it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "498it [00:03, 160.66it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 137/137 [00:04<00:00, 31.79it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 179/179 [00:01<00:00, 158.03it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000030_c000028_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000030_c000028_grain_stats.csv\n", + "done with segmentation + export\n", + "[845/1053] tile_r000030_c000029\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.26it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.32it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 540/540 [00:14<00:00, 37.96it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "446it [00:02, 183.54it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "469it [00:02, 187.71it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 157/157 [00:03<00:00, 47.86it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 161/161 [00:01<00:00, 156.98it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000030_c000029_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000030_c000029_grain_stats.csv\n", + "done with segmentation + export\n", + "[846/1053] tile_r000030_c000030\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.36it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.37it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 554/554 [00:14<00:00, 37.68it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "449it [00:02, 163.37it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "467it [00:02, 164.74it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 137/137 [00:04<00:00, 33.24it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 143/143 [00:00<00:00, 148.29it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000030_c000030_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000030_c000030_grain_stats.csv\n", + "done with segmentation + export\n", + "[847/1053] tile_r000030_c000031\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.34it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.36it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 570/570 [00:14<00:00, 38.87it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "476it [00:02, 170.92it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "502it [00:02, 169.22it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 159/159 [00:04<00:00, 39.44it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 165/165 [00:01<00:00, 163.45it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000030_c000031_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000030_c000031_grain_stats.csv\n", + "done with segmentation + export\n", + "[848/1053] tile_r000030_c000032\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.37it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.35it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 557/557 [00:14<00:00, 37.65it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "441it [00:02, 152.21it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "438it [00:02, 165.58it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 115/115 [00:03<00:00, 29.59it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 143/143 [00:00<00:00, 156.57it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000030_c000032_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000030_c000032_grain_stats.csv\n", + "done with segmentation + export\n", + "[849/1053] tile_r000030_c000033\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.35it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.37it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 491/491 [00:12<00:00, 37.95it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "402it [00:02, 142.27it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "395it [00:02, 158.29it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 109/109 [00:03<00:00, 29.33it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 133/133 [00:00<00:00, 153.69it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000030_c000033_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000030_c000033_grain_stats.csv\n", + "done with segmentation + export\n", + "[850/1053] tile_r000030_c000034\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.38it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.45it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 374/374 [00:09<00:00, 37.81it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "278it [00:01, 168.53it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "299it [00:01, 172.12it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 94/94 [00:02<00:00, 37.93it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 96/96 [00:00<00:00, 151.73it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000030_c000034_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000030_c000034_grain_stats.csv\n", + "done with segmentation + export\n", + "[851/1053] tile_r000030_c000035\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.34it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.47it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 480/480 [00:12<00:00, 37.52it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "382it [00:02, 145.25it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "378it [00:02, 159.43it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 95/95 [00:03<00:00, 26.34it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 117/117 [00:00<00:00, 144.35it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n", + "Warning: len(gdf)=117 != len(grain_stats)=116 -> truncating to 116\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000030_c000035_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000030_c000035_grain_stats.csv\n", + "done with segmentation + export\n", + "[852/1053] tile_r000030_c000036\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.31it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.40it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 385/385 [00:10<00:00, 36.57it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "260it [00:01, 152.57it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "275it [00:01, 153.58it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 83/83 [00:02<00:00, 29.40it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 89/89 [00:00<00:00, 140.05it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000030_c000036_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000030_c000036_grain_stats.csv\n", + "done with segmentation + export\n", + "[853/1053] tile_r000030_c000037\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.36it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.43it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 318/318 [00:08<00:00, 37.42it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "217it [00:01, 157.89it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "228it [00:01, 157.48it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 71/71 [00:02<00:00, 30.19it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 75/75 [00:00<00:00, 142.54it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000030_c000037_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000030_c000037_grain_stats.csv\n", + "done with segmentation + export\n", + "[854/1053] tile_r000030_c000038\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.37it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.41it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 396/396 [00:11<00:00, 35.90it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "289it [00:01, 182.77it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "302it [00:01, 184.61it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 91/91 [00:02<00:00, 33.81it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 94/94 [00:00<00:00, 142.09it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000030_c000038_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000030_c000038_grain_stats.csv\n", + "done with segmentation + export\n", + "[855/1053] tile_r000030_c000039\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.40it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.42it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 337/337 [00:08<00:00, 37.59it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "248it [00:01, 148.62it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "257it [00:01, 151.71it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 75/75 [00:02<00:00, 29.68it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 77/77 [00:00<00:00, 135.99it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000030_c000039_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000030_c000039_grain_stats.csv\n", + "done with segmentation + export\n", + "[856/1053] tile_r000030_c000040\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.38it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.40it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 351/351 [00:09<00:00, 36.64it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "276it [00:01, 147.70it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "270it [00:01, 166.73it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 71/71 [00:02<00:00, 27.68it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 90/90 [00:00<00:00, 138.02it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000030_c000040_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000030_c000040_grain_stats.csv\n", + "done with segmentation + export\n", + "[857/1053] tile_r000030_c000041\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.40it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.48it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 434/434 [00:11<00:00, 36.54it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "323it [00:02, 154.97it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "339it [00:02, 155.79it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 98/98 [00:03<00:00, 28.08it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 106/106 [00:00<00:00, 136.70it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000030_c000041_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000030_c000041_grain_stats.csv\n", + "done with segmentation + export\n", + "[858/1053] tile_r000030_c000042\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.41it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.40it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 428/428 [00:11<00:00, 37.23it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "318it [00:01, 160.82it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "341it [00:02, 166.92it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 106/106 [00:02<00:00, 36.00it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 112/112 [00:00<00:00, 140.27it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000030_c000042_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000030_c000042_grain_stats.csv\n", + "done with segmentation + export\n", + "[859/1053] tile_r000030_c000043\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.39it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.42it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 598/598 [00:15<00:00, 37.45it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "477it [00:03, 121.71it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "474it [00:03, 132.44it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 120/120 [00:04<00:00, 24.33it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 153/153 [00:01<00:00, 143.17it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000030_c000043_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000030_c000043_grain_stats.csv\n", + "done with segmentation + export\n", + "[860/1053] tile_r000030_c000044\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.39it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.44it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 629/629 [00:19<00:00, 31.62it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "459it [00:03, 120.83it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "456it [00:03, 141.88it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 111/111 [00:05<00:00, 19.64it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 143/143 [00:00<00:00, 144.66it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000030_c000044_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000030_c000044_grain_stats.csv\n", + "done with segmentation + export\n", + "[861/1053] tile_r000030_c000045\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.40it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.41it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 539/539 [00:16<00:00, 33.39it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "406it [00:03, 115.11it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "400it [00:03, 127.40it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 102/102 [00:04<00:00, 22.84it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 124/124 [00:01<00:00, 122.68it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000030_c000045_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000030_c000045_grain_stats.csv\n", + "done with segmentation + export\n", + "[862/1053] tile_r000030_c000046\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.40it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.46it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 402/402 [00:10<00:00, 36.85it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "330it [00:02, 111.52it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "329it [00:02, 112.84it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 81/81 [00:04<00:00, 19.67it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 97/97 [00:00<00:00, 118.78it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000030_c000046_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000030_c000046_grain_stats.csv\n", + "done with segmentation + export\n", + "[863/1053] tile_r000030_c000047\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.39it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.43it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 57/57 [00:01<00:00, 30.58it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "27it [00:00, 167.27it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "29it [00:00, 165.31it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 7/7 [00:00<00:00, 22.75it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 8/8 [00:00<00:00, 109.85it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000030_c000047_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000030_c000047_grain_stats.csv\n", + "done with segmentation + export\n", + "[864/1053] tile_r000031_c000005\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.40it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.36it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 180/180 [00:05<00:00, 32.70it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "142it [00:00, 405.10it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "151it [00:00, 405.99it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 55/55 [00:00<00:00, 91.85it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 58/58 [00:00<00:00, 194.20it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000031_c000005_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000031_c000005_grain_stats.csv\n", + "done with segmentation + export\n", + "[865/1053] tile_r000031_c000006\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.39it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.43it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 714/714 [00:19<00:00, 37.11it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "598it [00:02, 291.26it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "597it [00:01, 302.79it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 164/164 [00:03<00:00, 48.03it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 231/231 [00:01<00:00, 183.50it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n", + "Warning: len(gdf)=231 != len(grain_stats)=230 -> truncating to 230\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000031_c000006_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000031_c000006_grain_stats.csv\n", + "done with segmentation + export\n", + "[866/1053] tile_r000031_c000007\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.46it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.46it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 563/563 [00:16<00:00, 35.05it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "411it [00:01, 400.83it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "409it [00:00, 422.04it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 127/127 [00:01<00:00, 76.16it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 170/170 [00:00<00:00, 204.45it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000031_c000007_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000031_c000007_grain_stats.csv\n", + "done with segmentation + export\n", + "[867/1053] tile_r000031_c000008\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.41it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.41it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 635/635 [00:16<00:00, 37.85it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "516it [00:01, 286.48it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "561it [00:01, 285.94it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 197/197 [00:02<00:00, 66.75it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 207/207 [00:01<00:00, 186.00it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n", + "Warning: len(gdf)=207 != len(grain_stats)=206 -> truncating to 206\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000031_c000008_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000031_c000008_grain_stats.csv\n", + "done with segmentation + export\n", + "[868/1053] tile_r000031_c000009\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.40it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.44it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 663/663 [00:17<00:00, 37.32it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "523it [00:01, 354.95it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "565it [00:01, 347.41it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 207/207 [00:02<00:00, 80.39it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 222/222 [00:01<00:00, 196.62it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000031_c000009_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000031_c000009_grain_stats.csv\n", + "done with segmentation + export\n", + "[869/1053] tile_r000031_c000010\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.42it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.42it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 545/545 [00:14<00:00, 37.40it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "425it [00:01, 354.96it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "424it [00:01, 369.62it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 140/140 [00:01<00:00, 81.80it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 171/171 [00:00<00:00, 193.64it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000031_c000010_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000031_c000010_grain_stats.csv\n", + "done with segmentation + export\n", + "[870/1053] tile_r000031_c000011\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.41it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.25it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 690/690 [00:18<00:00, 37.13it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "572it [00:02, 237.65it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "570it [00:02, 264.18it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 168/168 [00:03<00:00, 50.66it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 215/215 [00:01<00:00, 180.28it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000031_c000011_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000031_c000011_grain_stats.csv\n", + "done with segmentation + export\n", + "[871/1053] tile_r000031_c000012\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.47it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.48it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 657/657 [00:17<00:00, 37.59it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "554it [00:02, 222.76it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "594it [00:02, 231.23it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 196/196 [00:03<00:00, 51.96it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 202/202 [00:01<00:00, 190.21it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000031_c000012_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000031_c000012_grain_stats.csv\n", + "done with segmentation + export\n", + "[872/1053] tile_r000031_c000013\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.43it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.48it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 619/619 [00:18<00:00, 34.30it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "454it [00:01, 295.64it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "490it [00:01, 294.81it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 171/171 [00:02<00:00, 60.47it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 185/185 [00:01<00:00, 184.57it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000031_c000013_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000031_c000013_grain_stats.csv\n", + "done with segmentation + export\n", + "[873/1053] tile_r000031_c000014\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.41it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.42it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 525/525 [00:15<00:00, 34.15it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "388it [00:01, 228.48it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "386it [00:01, 293.50it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 111/111 [00:02<00:00, 49.08it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 161/161 [00:00<00:00, 175.74it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000031_c000014_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000031_c000014_grain_stats.csv\n", + "done with segmentation + export\n", + "[874/1053] tile_r000031_c000015\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.42it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.44it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 489/489 [00:13<00:00, 35.20it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "348it [00:01, 276.23it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "368it [00:01, 286.83it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 120/120 [00:02<00:00, 52.40it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 133/133 [00:00<00:00, 166.39it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n", + "Warning: len(gdf)=133 != len(grain_stats)=132 -> truncating to 132\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000031_c000015_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000031_c000015_grain_stats.csv\n", + "done with segmentation + export\n", + "[875/1053] tile_r000031_c000016\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.44it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.46it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 578/578 [00:16<00:00, 35.32it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "456it [00:01, 236.26it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "455it [00:01, 245.78it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 134/134 [00:02<00:00, 45.45it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 167/167 [00:00<00:00, 184.97it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000031_c000016_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000031_c000016_grain_stats.csv\n", + "done with segmentation + export\n", + "[876/1053] tile_r000031_c000017\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.46it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.46it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 593/593 [00:16<00:00, 36.58it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "481it [00:02, 204.33it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "474it [00:02, 222.55it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 134/134 [00:03<00:00, 37.39it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 173/173 [00:01<00:00, 167.00it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000031_c000017_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000031_c000017_grain_stats.csv\n", + "done with segmentation + export\n", + "[877/1053] tile_r000031_c000018\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.43it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.45it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 646/646 [00:17<00:00, 36.62it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "511it [00:02, 224.80it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "537it [00:02, 221.74it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 167/167 [00:04<00:00, 41.48it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 176/176 [00:01<00:00, 167.94it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000031_c000018_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000031_c000018_grain_stats.csv\n", + "done with segmentation + export\n", + "[878/1053] tile_r000031_c000019\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.45it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.52it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 464/464 [00:12<00:00, 37.63it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "394it [00:02, 133.95it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "385it [00:02, 189.74it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 104/104 [00:03<00:00, 32.26it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 141/141 [00:00<00:00, 150.54it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000031_c000019_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000031_c000019_grain_stats.csv\n", + "done with segmentation + export\n", + "[879/1053] tile_r000031_c000020\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.43it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.49it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 390/390 [00:10<00:00, 37.45it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "277it [00:01, 226.71it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "293it [00:01, 225.56it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 94/94 [00:01<00:00, 52.11it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 98/98 [00:00<00:00, 149.40it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000031_c000020_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000031_c000020_grain_stats.csv\n", + "done with segmentation + export\n", + "[880/1053] tile_r000031_c000021\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.49it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.47it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 421/421 [00:11<00:00, 37.61it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "325it [00:01, 195.35it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "345it [00:01, 195.03it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 105/105 [00:02<00:00, 41.84it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 110/110 [00:00<00:00, 151.47it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n", + "Warning: len(gdf)=110 != len(grain_stats)=109 -> truncating to 109\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000031_c000021_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000031_c000021_grain_stats.csv\n", + "done with segmentation + export\n", + "[881/1053] tile_r000031_c000022\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.33it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.30it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 565/565 [00:14<00:00, 37.67it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "441it [00:02, 175.84it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "466it [00:02, 177.16it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 140/140 [00:03<00:00, 38.13it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 145/145 [00:00<00:00, 148.65it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000031_c000022_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000031_c000022_grain_stats.csv\n", + "done with segmentation + export\n", + "[882/1053] tile_r000031_c000023\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.45it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.44it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 584/584 [00:15<00:00, 38.55it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "494it [00:02, 182.37it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "513it [00:03, 168.77it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 160/160 [00:03<00:00, 42.21it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 169/169 [00:01<00:00, 153.52it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000031_c000023_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000031_c000023_grain_stats.csv\n", + "done with segmentation + export\n", + "[883/1053] tile_r000031_c000024\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.41it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.46it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 575/575 [00:15<00:00, 38.11it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "453it [00:02, 197.84it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "481it [00:02, 197.65it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 155/155 [00:03<00:00, 48.10it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 157/157 [00:01<00:00, 155.90it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000031_c000024_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000031_c000024_grain_stats.csv\n", + "done with segmentation + export\n", + "[884/1053] tile_r000031_c000025\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.48it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.45it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 625/625 [00:16<00:00, 37.33it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "488it [00:02, 182.58it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "503it [00:02, 181.54it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 161/161 [00:03<00:00, 40.52it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 170/170 [00:01<00:00, 163.12it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n", + "Warning: len(gdf)=170 != len(grain_stats)=169 -> truncating to 169\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000031_c000025_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000031_c000025_grain_stats.csv\n", + "done with segmentation + export\n", + "[885/1053] tile_r000031_c000026\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.45it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.48it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 562/562 [00:14<00:00, 37.60it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "438it [00:03, 141.40it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "437it [00:03, 143.91it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 114/114 [00:04<00:00, 26.18it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 133/133 [00:00<00:00, 139.01it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000031_c000026_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000031_c000026_grain_stats.csv\n", + "done with segmentation + export\n", + "[886/1053] tile_r000031_c000027\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.31it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.26it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 616/616 [00:15<00:00, 38.64it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "479it [00:02, 197.49it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "499it [00:02, 198.56it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 157/157 [00:03<00:00, 45.70it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 165/165 [00:01<00:00, 160.57it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000031_c000027_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000031_c000027_grain_stats.csv\n", + "done with segmentation + export\n", + "[887/1053] tile_r000031_c000028\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:01<00:00, 2.50it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.49it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 620/620 [00:16<00:00, 38.72it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "497it [00:02, 196.31it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "526it [00:02, 189.21it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 171/171 [00:03<00:00, 49.13it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 177/177 [00:01<00:00, 158.58it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000031_c000028_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000031_c000028_grain_stats.csv\n", + "done with segmentation + export\n", + "[888/1053] tile_r000031_c000029\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.49it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.49it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 586/586 [00:15<00:00, 38.40it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "471it [00:02, 179.41it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "502it [00:02, 178.64it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 166/166 [00:03<00:00, 45.02it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 173/173 [00:01<00:00, 164.17it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000031_c000029_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000031_c000029_grain_stats.csv\n", + "done with segmentation + export\n", + "[889/1053] tile_r000031_c000030\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.47it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.53it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 594/594 [00:15<00:00, 37.73it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "478it [00:02, 192.76it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "504it [00:02, 193.95it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 161/161 [00:03<00:00, 44.41it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 164/164 [00:00<00:00, 166.73it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n", + "Warning: len(gdf)=164 != len(grain_stats)=163 -> truncating to 163\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000031_c000030_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000031_c000030_grain_stats.csv\n", + "done with segmentation + export\n", + "[890/1053] tile_r000031_c000031\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.45it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.49it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 514/514 [00:13<00:00, 38.17it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "411it [00:01, 209.71it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "433it [00:02, 210.40it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 141/141 [00:02<00:00, 48.07it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 148/148 [00:00<00:00, 165.13it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000031_c000031_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000031_c000031_grain_stats.csv\n", + "done with segmentation + export\n", + "[891/1053] tile_r000031_c000032\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.45it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.49it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 506/506 [00:13<00:00, 36.71it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "395it [00:02, 176.82it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "419it [00:02, 176.63it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 127/127 [00:03<00:00, 36.10it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 136/136 [00:00<00:00, 149.66it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000031_c000032_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000031_c000032_grain_stats.csv\n", + "done with segmentation + export\n", + "[892/1053] tile_r000031_c000033\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.44it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.42it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 556/556 [00:14<00:00, 38.11it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "408it [00:02, 149.15it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "401it [00:02, 189.75it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 106/106 [00:03<00:00, 32.77it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 132/132 [00:00<00:00, 160.48it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000031_c000033_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000031_c000033_grain_stats.csv\n", + "done with segmentation + export\n", + "[893/1053] tile_r000031_c000034\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.18it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.54it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 418/418 [00:10<00:00, 38.17it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "305it [00:01, 221.94it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "333it [00:01, 224.05it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 115/115 [00:02<00:00, 50.87it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 119/119 [00:00<00:00, 161.53it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000031_c000034_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000031_c000034_grain_stats.csv\n", + "done with segmentation + export\n", + "[894/1053] tile_r000031_c000035\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.50it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.49it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 364/364 [00:10<00:00, 35.99it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "233it [00:01, 167.00it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "239it [00:01, 171.57it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 70/70 [00:02<00:00, 30.98it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 73/73 [00:00<00:00, 131.45it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000031_c000035_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000031_c000035_grain_stats.csv\n", + "done with segmentation + export\n", + "[895/1053] tile_r000031_c000036\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.41it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.45it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 443/443 [00:12<00:00, 36.73it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "306it [00:01, 184.87it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "326it [00:01, 186.63it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 99/99 [00:02<00:00, 36.71it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 100/100 [00:00<00:00, 152.77it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000031_c000036_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000031_c000036_grain_stats.csv\n", + "done with segmentation + export\n", + "[896/1053] tile_r000031_c000037\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.44it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.45it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 406/406 [00:10<00:00, 37.53it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "300it [00:01, 174.77it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "298it [00:01, 180.89it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 79/79 [00:02<00:00, 29.43it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 103/103 [00:00<00:00, 154.96it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000031_c000037_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000031_c000037_grain_stats.csv\n", + "done with segmentation + export\n", + "[897/1053] tile_r000031_c000038\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.47it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.50it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 194/194 [00:05<00:00, 35.38it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "104it [00:00, 244.83it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "112it [00:00, 246.04it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 37/37 [00:00<00:00, 43.14it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 39/39 [00:00<00:00, 111.33it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000031_c000038_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000031_c000038_grain_stats.csv\n", + "done with segmentation + export\n", + "[898/1053] tile_r000031_c000039\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.43it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.50it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 339/339 [00:09<00:00, 36.90it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "243it [00:01, 132.73it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "251it [00:01, 140.46it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 74/74 [00:02<00:00, 27.77it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 81/81 [00:00<00:00, 138.24it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000031_c000039_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000031_c000039_grain_stats.csv\n", + "done with segmentation + export\n", + "[899/1053] tile_r000031_c000040\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.47it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.48it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 376/376 [00:10<00:00, 36.51it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "278it [00:01, 173.74it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "295it [00:01, 173.15it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 91/91 [00:02<00:00, 34.11it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 98/98 [00:00<00:00, 146.80it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000031_c000040_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000031_c000040_grain_stats.csv\n", + "done with segmentation + export\n", + "[900/1053] tile_r000031_c000041\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:01<00:00, 2.51it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.49it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 525/525 [00:14<00:00, 35.95it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "417it [00:04, 86.79it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "412it [00:04, 91.25it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 102/102 [00:07<00:00, 14.01it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 135/135 [00:00<00:00, 146.12it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000031_c000041_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000031_c000041_grain_stats.csv\n", + "done with segmentation + export\n", + "[901/1053] tile_r000031_c000042\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:01<00:00, 2.51it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.54it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 480/480 [00:12<00:00, 37.20it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "356it [00:02, 145.27it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "354it [00:02, 147.26it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 90/90 [00:04<00:00, 21.97it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 113/113 [00:00<00:00, 137.15it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000031_c000042_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000031_c000042_grain_stats.csv\n", + "done with segmentation + export\n", + "[902/1053] tile_r000031_c000043\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:01<00:00, 2.51it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.43it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 663/663 [00:17<00:00, 36.93it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "506it [00:04, 122.15it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "492it [00:03, 147.74it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 121/121 [00:05<00:00, 23.09it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 152/152 [00:01<00:00, 135.48it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000031_c000043_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000031_c000043_grain_stats.csv\n", + "done with segmentation + export\n", + "[903/1053] tile_r000031_c000044\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:01<00:00, 2.51it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.53it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 571/571 [00:16<00:00, 34.90it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "437it [00:04, 96.43it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "448it [00:04, 96.32it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 115/115 [00:06<00:00, 16.88it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 130/130 [00:01<00:00, 127.94it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000031_c000044_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000031_c000044_grain_stats.csv\n", + "done with segmentation + export\n", + "[904/1053] tile_r000031_c000045\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.47it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.43it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 225/225 [00:06<00:00, 33.39it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "137it [00:00, 180.37it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "150it [00:00, 180.74it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 42/42 [00:01<00:00, 24.91it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 50/50 [00:00<00:00, 124.56it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000031_c000045_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000031_c000045_grain_stats.csv\n", + "done with segmentation + export\n", + "[905/1053] tile_r000031_c000046\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.45it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.48it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 1/1 [00:00<00:00, 6.63it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "0it [00:00, ?it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "0it [00:00, ?it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "0it [00:00, ?it/s]\n", + "0it [00:00, ?it/s]\n", + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n", + "No grains found -> skipping histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000031_c000046_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000031_c000046_grain_stats.csv\n", + "done with segmentation + export\n", + "[906/1053] tile_r000032_c000005\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.41it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.46it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 139/139 [00:04<00:00, 31.54it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "85it [00:00, 434.90it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "99it [00:00, 413.28it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 41/41 [00:00<00:00, 121.20it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 41/41 [00:00<00:00, 179.26it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000032_c000005_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000032_c000005_grain_stats.csv\n", + "done with segmentation + export\n", + "[907/1053] tile_r000032_c000006\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.48it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.45it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 656/656 [00:17<00:00, 38.01it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "557it [00:02, 266.73it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "553it [00:01, 297.25it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 157/157 [00:03<00:00, 44.39it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 218/218 [00:01<00:00, 182.27it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n", + "Warning: len(gdf)=218 != len(grain_stats)=216 -> truncating to 216\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000032_c000006_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000032_c000006_grain_stats.csv\n", + "done with segmentation + export\n", + "[908/1053] tile_r000032_c000007\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.44it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.49it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 634/634 [00:17<00:00, 35.94it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "499it [00:01, 319.24it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "498it [00:01, 340.15it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 151/151 [00:02<00:00, 62.93it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 202/202 [00:01<00:00, 193.88it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000032_c000007_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000032_c000007_grain_stats.csv\n", + "done with segmentation + export\n", + "[909/1053] tile_r000032_c000008\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.47it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.44it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 660/660 [00:17<00:00, 36.79it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "519it [00:02, 211.00it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "512it [00:01, 261.46it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 144/144 [00:03<00:00, 38.61it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 192/192 [00:01<00:00, 174.75it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n", + "Warning: len(gdf)=192 != len(grain_stats)=190 -> truncating to 190\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000032_c000008_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000032_c000008_grain_stats.csv\n", + "done with segmentation + export\n", + "[910/1053] tile_r000032_c000009\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.47it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.51it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 641/641 [00:17<00:00, 36.82it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "490it [00:01, 262.68it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "487it [00:01, 301.77it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 143/143 [00:02<00:00, 55.84it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 185/185 [00:01<00:00, 180.00it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n", + "Warning: len(gdf)=185 != len(grain_stats)=184 -> truncating to 184\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000032_c000009_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000032_c000009_grain_stats.csv\n", + "done with segmentation + export\n", + "[911/1053] tile_r000032_c000010\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:01<00:00, 2.56it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.44it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 624/624 [00:17<00:00, 35.89it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "480it [00:01, 259.76it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "511it [00:01, 280.57it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 170/170 [00:03<00:00, 56.34it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 188/188 [00:01<00:00, 185.06it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000032_c000010_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000032_c000010_grain_stats.csv\n", + "done with segmentation + export\n", + "[912/1053] tile_r000032_c000011\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.46it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.38it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 542/542 [00:15<00:00, 35.02it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "421it [00:01, 287.37it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "418it [00:01, 335.15it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 118/118 [00:02<00:00, 49.68it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 161/161 [00:00<00:00, 175.34it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n", + "Warning: len(gdf)=161 != len(grain_stats)=159 -> truncating to 159\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000032_c000011_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000032_c000011_grain_stats.csv\n", + "done with segmentation + export\n", + "[913/1053] tile_r000032_c000012\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.42it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.49it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 356/356 [00:11<00:00, 30.35it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "229it [00:00, 317.96it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "250it [00:00, 336.93it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 85/85 [00:01<00:00, 63.08it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 91/91 [00:00<00:00, 181.76it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000032_c000012_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000032_c000012_grain_stats.csv\n", + "done with segmentation + export\n", + "[914/1053] tile_r000032_c000013\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.46it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.44it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 432/432 [00:15<00:00, 28.23it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "282it [00:01, 253.29it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "304it [00:01, 257.91it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 105/105 [00:01<00:00, 55.90it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 112/112 [00:00<00:00, 156.01it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000032_c000013_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000032_c000013_grain_stats.csv\n", + "done with segmentation + export\n", + "[915/1053] tile_r000032_c000014\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.46it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.47it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 316/316 [00:10<00:00, 29.43it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "222it [00:01, 214.01it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "240it [00:01, 220.33it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 76/76 [00:01<00:00, 40.74it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 77/77 [00:00<00:00, 156.73it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000032_c000014_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000032_c000014_grain_stats.csv\n", + "done with segmentation + export\n", + "[916/1053] tile_r000032_c000015\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.26it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.47it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 344/344 [00:11<00:00, 29.60it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "232it [00:00, 255.76it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "243it [00:00, 266.03it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 76/76 [00:01<00:00, 50.80it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 79/79 [00:00<00:00, 167.33it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000032_c000015_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000032_c000015_grain_stats.csv\n", + "done with segmentation + export\n", + "[917/1053] tile_r000032_c000016\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.49it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.41it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 452/452 [00:15<00:00, 28.70it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "300it [00:01, 274.77it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "322it [00:01, 286.12it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 105/105 [00:01<00:00, 55.84it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 108/108 [00:00<00:00, 170.43it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000032_c000016_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000032_c000016_grain_stats.csv\n", + "done with segmentation + export\n", + "[918/1053] tile_r000032_c000017\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.47it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.49it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 528/528 [00:14<00:00, 35.26it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "416it [00:02, 188.88it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "442it [00:02, 191.02it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 136/136 [00:03<00:00, 36.36it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 152/152 [00:00<00:00, 166.20it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000032_c000017_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000032_c000017_grain_stats.csv\n", + "done with segmentation + export\n", + "[919/1053] tile_r000032_c000018\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.48it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.55it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 605/605 [00:16<00:00, 36.26it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "477it [00:02, 212.96it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "474it [00:02, 220.29it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 125/125 [00:03<00:00, 32.36it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 168/168 [00:01<00:00, 158.49it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000032_c000018_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000032_c000018_grain_stats.csv\n", + "done with segmentation + export\n", + "[920/1053] tile_r000032_c000019\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.45it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.49it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 538/538 [00:14<00:00, 37.35it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "429it [00:01, 227.19it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "455it [00:02, 227.33it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 147/147 [00:02<00:00, 49.12it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 162/162 [00:01<00:00, 152.43it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n", + "Warning: len(gdf)=162 != len(grain_stats)=161 -> truncating to 161\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000032_c000019_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000032_c000019_grain_stats.csv\n", + "done with segmentation + export\n", + "[921/1053] tile_r000032_c000020\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.49it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.49it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 641/641 [00:17<00:00, 36.31it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "528it [00:03, 158.34it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "522it [00:02, 175.20it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 151/151 [00:04<00:00, 36.74it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 172/172 [00:01<00:00, 162.75it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000032_c000020_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000032_c000020_grain_stats.csv\n", + "done with segmentation + export\n", + "[922/1053] tile_r000032_c000021\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.49it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.47it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 715/715 [00:18<00:00, 38.33it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "595it [00:03, 194.97it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "620it [00:03, 196.14it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 204/204 [00:04<00:00, 49.32it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 214/214 [00:01<00:00, 170.37it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000032_c000021_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000032_c000021_grain_stats.csv\n", + "done with segmentation + export\n", + "[923/1053] tile_r000032_c000022\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.41it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.33it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 527/527 [00:13<00:00, 37.86it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "393it [00:02, 192.97it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "405it [00:02, 193.41it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 126/126 [00:02<00:00, 42.25it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 138/138 [00:00<00:00, 152.75it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000032_c000022_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000032_c000022_grain_stats.csv\n", + "done with segmentation + export\n", + "[924/1053] tile_r000032_c000023\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.44it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.46it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 564/564 [00:15<00:00, 36.92it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "418it [00:02, 140.34it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "417it [00:02, 154.25it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 113/113 [00:03<00:00, 29.66it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 141/141 [00:00<00:00, 150.81it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000032_c000023_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000032_c000023_grain_stats.csv\n", + "done with segmentation + export\n", + "[925/1053] tile_r000032_c000024\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.46it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.49it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 664/664 [00:16<00:00, 39.07it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "557it [00:02, 191.88it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "574it [00:02, 191.42it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 183/183 [00:03<00:00, 48.13it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 192/192 [00:01<00:00, 162.49it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000032_c000024_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000032_c000024_grain_stats.csv\n", + "done with segmentation + export\n", + "[926/1053] tile_r000032_c000025\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.47it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.48it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 542/542 [00:14<00:00, 36.90it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "444it [00:02, 203.09it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "464it [00:02, 200.43it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 153/153 [00:02<00:00, 54.73it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 160/160 [00:01<00:00, 159.04it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000032_c000025_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000032_c000025_grain_stats.csv\n", + "done with segmentation + export\n", + "[927/1053] tile_r000032_c000026\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.47it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.47it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 544/544 [00:14<00:00, 38.67it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "444it [00:02, 199.43it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "464it [00:02, 199.97it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 150/150 [00:03<00:00, 46.97it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 160/160 [00:01<00:00, 154.44it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000032_c000026_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000032_c000026_grain_stats.csv\n", + "done with segmentation + export\n", + "[928/1053] tile_r000032_c000027\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:01<00:00, 2.51it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.47it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 506/506 [00:13<00:00, 36.66it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "410it [00:02, 176.38it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "427it [00:02, 178.26it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 133/133 [00:03<00:00, 39.55it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 141/141 [00:00<00:00, 149.36it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000032_c000027_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000032_c000027_grain_stats.csv\n", + "done with segmentation + export\n", + "[929/1053] tile_r000032_c000028\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.46it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.50it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 555/555 [00:14<00:00, 37.58it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "405it [00:02, 194.58it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "425it [00:02, 193.72it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 137/137 [00:02<00:00, 46.03it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 142/142 [00:00<00:00, 159.01it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000032_c000028_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000032_c000028_grain_stats.csv\n", + "done with segmentation + export\n", + "[930/1053] tile_r000032_c000029\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.47it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.43it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 532/532 [00:13<00:00, 38.21it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "454it [00:02, 210.28it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "486it [00:02, 210.39it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 161/161 [00:03<00:00, 50.33it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 173/173 [00:01<00:00, 163.37it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000032_c000029_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000032_c000029_grain_stats.csv\n", + "done with segmentation + export\n", + "[931/1053] tile_r000032_c000030\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.47it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.50it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 445/445 [00:11<00:00, 37.73it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "324it [00:01, 170.20it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "341it [00:01, 170.53it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 108/108 [00:02<00:00, 41.61it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 109/109 [00:00<00:00, 153.04it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000032_c000030_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000032_c000030_grain_stats.csv\n", + "done with segmentation + export\n", + "[932/1053] tile_r000032_c000031\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.44it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.36it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 497/497 [00:13<00:00, 37.87it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "380it [00:02, 157.26it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "402it [00:02, 160.43it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 129/129 [00:03<00:00, 35.31it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 132/132 [00:00<00:00, 152.34it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000032_c000031_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000032_c000031_grain_stats.csv\n", + "done with segmentation + export\n", + "[933/1053] tile_r000032_c000032\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.42it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.52it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 371/371 [00:09<00:00, 37.19it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "256it [00:01, 190.08it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "273it [00:01, 191.81it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 84/84 [00:02<00:00, 39.41it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 87/87 [00:00<00:00, 145.66it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000032_c000032_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000032_c000032_grain_stats.csv\n", + "done with segmentation + export\n", + "[934/1053] tile_r000032_c000033\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.45it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.44it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 310/310 [00:08<00:00, 35.07it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "210it [00:01, 166.07it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "219it [00:01, 166.39it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 62/62 [00:02<00:00, 29.48it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 64/64 [00:00<00:00, 146.62it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000032_c000033_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000032_c000033_grain_stats.csv\n", + "done with segmentation + export\n", + "[935/1053] tile_r000032_c000034\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.48it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.46it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 370/370 [00:10<00:00, 36.93it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "275it [00:02, 126.00it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "287it [00:02, 127.56it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 93/93 [00:03<00:00, 29.46it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 95/95 [00:00<00:00, 149.40it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000032_c000034_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000032_c000034_grain_stats.csv\n", + "done with segmentation + export\n", + "[936/1053] tile_r000032_c000035\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.50it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.50it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 238/238 [00:06<00:00, 36.75it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "175it [00:00, 185.66it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "182it [00:00, 185.47it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 56/56 [00:01<00:00, 34.91it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 60/60 [00:00<00:00, 121.17it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000032_c000035_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000032_c000035_grain_stats.csv\n", + "done with segmentation + export\n", + "[937/1053] tile_r000032_c000036\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:01<00:00, 2.50it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.46it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 353/353 [00:09<00:00, 37.28it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "235it [00:01, 125.15it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "249it [00:01, 139.87it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 68/68 [00:03<00:00, 20.87it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 72/72 [00:00<00:00, 139.78it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000032_c000036_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000032_c000036_grain_stats.csv\n", + "done with segmentation + export\n", + "[938/1053] tile_r000032_c000037\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.48it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.51it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 359/359 [00:09<00:00, 36.69it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "265it [00:01, 198.71it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "280it [00:01, 199.28it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 87/87 [00:02<00:00, 39.60it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 93/93 [00:00<00:00, 146.32it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000032_c000037_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000032_c000037_grain_stats.csv\n", + "done with segmentation + export\n", + "[939/1053] tile_r000032_c000038\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.50it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.54it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 302/302 [00:08<00:00, 36.08it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "214it [00:01, 141.35it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "224it [00:01, 145.46it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 66/66 [00:02<00:00, 29.20it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 68/68 [00:00<00:00, 139.71it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000032_c000038_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000032_c000038_grain_stats.csv\n", + "done with segmentation + export\n", + "[940/1053] tile_r000032_c000039\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.49it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.49it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 489/489 [00:12<00:00, 38.25it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "390it [00:02, 145.18it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "389it [00:02, 144.17it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 105/105 [00:03<00:00, 29.89it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 127/127 [00:00<00:00, 145.31it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000032_c000039_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000032_c000039_grain_stats.csv\n", + "done with segmentation + export\n", + "[941/1053] tile_r000032_c000040\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.44it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.50it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 487/487 [00:13<00:00, 35.91it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "359it [00:02, 160.90it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "374it [00:02, 162.53it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 111/111 [00:03<00:00, 32.21it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 120/120 [00:00<00:00, 136.29it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000032_c000040_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000032_c000040_grain_stats.csv\n", + "done with segmentation + export\n", + "[942/1053] tile_r000032_c000041\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.49it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.41it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 531/531 [00:14<00:00, 36.91it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "419it [00:03, 137.88it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "418it [00:02, 140.39it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 108/108 [00:04<00:00, 23.68it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 130/130 [00:00<00:00, 134.39it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000032_c000041_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000032_c000041_grain_stats.csv\n", + "done with segmentation + export\n", + "[943/1053] tile_r000032_c000042\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.29it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.33it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 524/524 [00:13<00:00, 37.64it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "410it [00:03, 119.29it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "418it [00:03, 119.87it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 123/123 [00:04<00:00, 28.18it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 131/131 [00:01<00:00, 127.82it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000032_c000042_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000032_c000042_grain_stats.csv\n", + "done with segmentation + export\n", + "[944/1053] tile_r000032_c000043\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.46it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.52it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 408/408 [00:11<00:00, 36.11it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "313it [00:02, 108.13it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "309it [00:02, 119.34it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 75/75 [00:04<00:00, 17.18it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 100/100 [00:00<00:00, 120.01it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000032_c000043_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000032_c000043_grain_stats.csv\n", + "done with segmentation + export\n", + "[945/1053] tile_r000032_c000044\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.48it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.49it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 79/79 [00:02<00:00, 28.82it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "26it [00:00, 354.67it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "32it [00:00, 314.99it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 12/12 [00:00<00:00, 70.25it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 13/13 [00:00<00:00, 96.04it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000032_c000044_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000032_c000044_grain_stats.csv\n", + "done with segmentation + export\n", + "[946/1053] tile_r000033_c000005\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.47it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.46it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 58/58 [00:01<00:00, 30.62it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "22it [00:00, 471.82it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "23it [00:00, 484.01it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 8/8 [00:00<00:00, 76.36it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 8/8 [00:00<00:00, 175.13it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000033_c000005_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000033_c000005_grain_stats.csv\n", + "done with segmentation + export\n", + "[947/1053] tile_r000033_c000006\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.47it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.44it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 592/592 [00:18<00:00, 32.76it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "459it [00:02, 222.92it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "453it [00:01, 267.62it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 114/114 [00:03<00:00, 30.35it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 172/172 [00:00<00:00, 179.25it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n", + "Warning: len(gdf)=172 != len(grain_stats)=170 -> truncating to 170\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000033_c000006_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000033_c000006_grain_stats.csv\n", + "done with segmentation + export\n", + "[948/1053] tile_r000033_c000007\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.44it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.52it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 588/588 [00:18<00:00, 32.45it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "440it [00:01, 318.72it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "479it [00:01, 311.44it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 158/158 [00:02<00:00, 57.20it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 171/171 [00:00<00:00, 181.56it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n", + "Warning: len(gdf)=171 != len(grain_stats)=169 -> truncating to 169\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000033_c000007_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000033_c000007_grain_stats.csv\n", + "done with segmentation + export\n", + "[949/1053] tile_r000033_c000008\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:01<00:00, 2.50it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.48it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 676/676 [00:21<00:00, 31.34it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "492it [00:01, 252.10it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "487it [00:01, 285.08it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 127/127 [00:03<00:00, 41.80it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 193/193 [00:01<00:00, 185.31it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000033_c000008_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000033_c000008_grain_stats.csv\n", + "done with segmentation + export\n", + "[950/1053] tile_r000033_c000009\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.47it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.48it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 668/668 [00:19<00:00, 34.72it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "522it [00:02, 230.95it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "518it [00:01, 264.89it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 147/147 [00:03<00:00, 44.40it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 204/204 [00:01<00:00, 177.15it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n", + "Warning: len(gdf)=204 != len(grain_stats)=202 -> truncating to 202\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000033_c000009_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000033_c000009_grain_stats.csv\n", + "done with segmentation + export\n", + "[951/1053] tile_r000033_c000010\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.49it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.49it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 480/480 [00:16<00:00, 28.96it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "302it [00:01, 259.29it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "294it [00:00, 302.53it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 75/75 [00:02<00:00, 35.08it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 122/122 [00:00<00:00, 170.46it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000033_c000010_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000033_c000010_grain_stats.csv\n", + "done with segmentation + export\n", + "[952/1053] tile_r000033_c000011\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.44it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.46it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 391/391 [00:12<00:00, 30.43it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "252it [00:00, 387.58it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "273it [00:00, 379.18it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 97/97 [00:01<00:00, 77.15it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 103/103 [00:00<00:00, 173.96it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000033_c000011_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000033_c000011_grain_stats.csv\n", + "done with segmentation + export\n", + "[953/1053] tile_r000033_c000012\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.45it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.47it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 323/323 [00:10<00:00, 30.08it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "223it [00:00, 336.28it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "247it [00:00, 327.48it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 84/84 [00:01<00:00, 68.26it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 89/89 [00:00<00:00, 172.07it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000033_c000012_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000033_c000012_grain_stats.csv\n", + "done with segmentation + export\n", + "[954/1053] tile_r000033_c000013\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:01<00:00, 2.51it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.40it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 232/232 [00:07<00:00, 32.47it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "150it [00:00, 225.53it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "160it [00:00, 227.99it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 48/48 [00:01<00:00, 38.74it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 51/51 [00:00<00:00, 156.93it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000033_c000013_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000033_c000013_grain_stats.csv\n", + "done with segmentation + export\n", + "[955/1053] tile_r000033_c000014\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:01<00:00, 2.51it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.49it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 184/184 [00:06<00:00, 30.03it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "76it [00:00, 159.02it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "88it [00:00, 167.40it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 28/28 [00:01<00:00, 26.32it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 33/33 [00:00<00:00, 102.70it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000033_c000014_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000033_c000014_grain_stats.csv\n", + "done with segmentation + export\n", + "[956/1053] tile_r000033_c000015\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.49it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.54it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 271/271 [00:09<00:00, 28.81it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "165it [00:00, 236.03it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "194it [00:00, 239.97it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 68/68 [00:01<00:00, 54.55it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 70/70 [00:00<00:00, 127.90it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000033_c000015_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000033_c000015_grain_stats.csv\n", + "done with segmentation + export\n", + "[957/1053] tile_r000033_c000016\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.46it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.52it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 344/344 [00:12<00:00, 28.28it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "196it [00:01, 172.68it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "212it [00:01, 177.62it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 68/68 [00:02<00:00, 28.57it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 69/69 [00:00<00:00, 160.85it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000033_c000016_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000033_c000016_grain_stats.csv\n", + "done with segmentation + export\n", + "[958/1053] tile_r000033_c000017\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.46it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.54it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 382/382 [00:11<00:00, 33.46it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "256it [00:00, 301.83it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "280it [00:00, 306.96it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 99/99 [00:01<00:00, 68.45it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 101/101 [00:00<00:00, 171.33it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000033_c000017_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000033_c000017_grain_stats.csv\n", + "done with segmentation + export\n", + "[959/1053] tile_r000033_c000018\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.24it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.37it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 501/501 [00:13<00:00, 36.41it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "405it [00:01, 239.23it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "417it [00:01, 239.09it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 130/130 [00:02<00:00, 50.25it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 145/145 [00:00<00:00, 169.31it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000033_c000018_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000033_c000018_grain_stats.csv\n", + "done with segmentation + export\n", + "[960/1053] tile_r000033_c000019\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.47it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.46it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 555/555 [00:14<00:00, 38.28it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "470it [00:02, 233.91it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "497it [00:02, 234.73it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 170/170 [00:02<00:00, 58.99it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 178/178 [00:01<00:00, 151.17it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000033_c000019_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000033_c000019_grain_stats.csv\n", + "done with segmentation + export\n", + "[961/1053] tile_r000033_c000020\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.49it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.51it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 741/741 [00:19<00:00, 38.85it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "619it [00:02, 216.31it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "649it [00:02, 217.76it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 210/210 [00:03<00:00, 55.39it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 222/222 [00:01<00:00, 172.67it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000033_c000020_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000033_c000020_grain_stats.csv\n", + "done with segmentation + export\n", + "[962/1053] tile_r000033_c000021\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.45it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.51it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 618/618 [00:16<00:00, 38.19it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "515it [00:02, 185.42it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "538it [00:02, 188.80it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 177/177 [00:04<00:00, 43.31it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 186/186 [00:01<00:00, 169.84it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000033_c000021_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000033_c000021_grain_stats.csv\n", + "done with segmentation + export\n", + "[963/1053] tile_r000033_c000022\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.46it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.44it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 799/799 [00:20<00:00, 39.21it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "675it [00:03, 206.33it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "702it [00:03, 205.39it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 217/217 [00:04<00:00, 45.40it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 228/228 [00:01<00:00, 170.94it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000033_c000022_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000033_c000022_grain_stats.csv\n", + "done with segmentation + export\n", + "[964/1053] tile_r000033_c000023\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.41it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.47it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 715/715 [00:18<00:00, 38.31it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "571it [00:03, 160.17it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "589it [00:03, 160.28it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 172/172 [00:04<00:00, 38.39it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 184/184 [00:01<00:00, 160.22it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000033_c000023_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000033_c000023_grain_stats.csv\n", + "done with segmentation + export\n", + "[965/1053] tile_r000033_c000024\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.44it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.53it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 480/480 [00:13<00:00, 36.62it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "352it [00:01, 233.78it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "366it [00:01, 232.59it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 119/119 [00:02<00:00, 52.96it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 127/127 [00:00<00:00, 156.74it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000033_c000024_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000033_c000024_grain_stats.csv\n", + "done with segmentation + export\n", + "[966/1053] tile_r000033_c000025\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.43it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.50it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 543/543 [00:15<00:00, 35.66it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "403it [00:02, 151.50it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "401it [00:02, 156.84it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 107/107 [00:03<00:00, 29.92it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 135/135 [00:00<00:00, 150.32it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000033_c000025_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000033_c000025_grain_stats.csv\n", + "done with segmentation + export\n", + "[967/1053] tile_r000033_c000026\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.46it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.51it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 642/642 [00:17<00:00, 37.45it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "508it [00:02, 189.53it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "530it [00:02, 192.78it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 168/168 [00:03<00:00, 46.63it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 177/177 [00:01<00:00, 160.83it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000033_c000026_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000033_c000026_grain_stats.csv\n", + "done with segmentation + export\n", + "[968/1053] tile_r000033_c000027\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.44it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.47it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 518/518 [00:13<00:00, 39.01it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "430it [00:02, 208.01it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "429it [00:02, 211.72it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 126/126 [00:02<00:00, 42.95it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 155/155 [00:01<00:00, 150.64it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000033_c000027_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000033_c000027_grain_stats.csv\n", + "done with segmentation + export\n", + "[969/1053] tile_r000033_c000028\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.32it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.48it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 624/624 [00:16<00:00, 38.91it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "520it [00:02, 179.06it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "519it [00:02, 181.17it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 137/137 [00:04<00:00, 33.51it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 182/182 [00:01<00:00, 164.69it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000033_c000028_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000033_c000028_grain_stats.csv\n", + "done with segmentation + export\n", + "[970/1053] tile_r000033_c000029\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:01<00:00, 2.51it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.52it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 584/584 [00:14<00:00, 39.42it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "491it [00:02, 184.55it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "523it [00:02, 201.49it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 175/175 [00:03<00:00, 50.19it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 180/180 [00:01<00:00, 157.60it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000033_c000029_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000033_c000029_grain_stats.csv\n", + "done with segmentation + export\n", + "[971/1053] tile_r000033_c000030\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.46it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.53it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 446/446 [00:11<00:00, 37.72it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "351it [00:01, 191.37it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "368it [00:01, 194.83it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 110/110 [00:02<00:00, 38.09it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 119/119 [00:00<00:00, 153.73it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000033_c000030_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000033_c000030_grain_stats.csv\n", + "done with segmentation + export\n", + "[972/1053] tile_r000033_c000031\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:01<00:00, 2.50it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.45it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 533/533 [00:13<00:00, 38.23it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "415it [00:02, 187.16it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "436it [00:02, 188.13it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 137/137 [00:03<00:00, 41.58it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 147/147 [00:00<00:00, 153.72it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000033_c000031_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000033_c000031_grain_stats.csv\n", + "done with segmentation + export\n", + "[973/1053] tile_r000033_c000032\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:01<00:00, 2.52it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.51it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 289/289 [00:07<00:00, 37.04it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "217it [00:01, 140.44it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "229it [00:01, 143.28it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 63/63 [00:02<00:00, 25.33it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 71/71 [00:00<00:00, 126.71it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000033_c000032_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000033_c000032_grain_stats.csv\n", + "done with segmentation + export\n", + "[974/1053] tile_r000033_c000033\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:01<00:00, 2.55it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.48it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 224/224 [00:06<00:00, 37.24it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "153it [00:00, 156.53it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "156it [00:01, 155.42it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 41/41 [00:01<00:00, 23.78it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 42/42 [00:00<00:00, 120.87it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000033_c000033_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000033_c000033_grain_stats.csv\n", + "done with segmentation + export\n", + "[975/1053] tile_r000033_c000034\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.45it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.31it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 397/397 [00:10<00:00, 37.42it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "298it [00:01, 166.76it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "316it [00:01, 167.33it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 94/94 [00:02<00:00, 33.11it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 99/99 [00:00<00:00, 134.63it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000033_c000034_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000033_c000034_grain_stats.csv\n", + "done with segmentation + export\n", + "[976/1053] tile_r000033_c000035\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.45it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.46it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 312/312 [00:08<00:00, 37.01it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "237it [00:01, 160.88it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "251it [00:01, 160.82it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 79/79 [00:02<00:00, 32.56it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 82/82 [00:00<00:00, 136.54it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n", + "Warning: len(gdf)=82 != len(grain_stats)=81 -> truncating to 81\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000033_c000035_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000033_c000035_grain_stats.csv\n", + "done with segmentation + export\n", + "[977/1053] tile_r000033_c000036\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.49it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.47it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 321/321 [00:08<00:00, 35.88it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "202it [00:01, 142.14it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "213it [00:01, 144.01it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 61/61 [00:02<00:00, 25.03it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 63/63 [00:00<00:00, 143.09it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000033_c000036_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000033_c000036_grain_stats.csv\n", + "done with segmentation + export\n", + "[978/1053] tile_r000033_c000037\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:01<00:00, 2.50it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.51it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 261/261 [00:07<00:00, 37.01it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "179it [00:01, 168.45it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "190it [00:01, 170.14it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 59/59 [00:01<00:00, 32.99it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 62/62 [00:00<00:00, 138.35it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n", + "Warning: len(gdf)=62 != len(grain_stats)=61 -> truncating to 61\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000033_c000037_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000033_c000037_grain_stats.csv\n", + "done with segmentation + export\n", + "[979/1053] tile_r000033_c000038\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.48it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.50it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 450/450 [00:12<00:00, 36.95it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "325it [00:02, 115.58it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "346it [00:02, 116.37it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 98/98 [00:04<00:00, 21.86it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 105/105 [00:00<00:00, 126.67it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n", + "Warning: len(gdf)=105 != len(grain_stats)=104 -> truncating to 104\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000033_c000038_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000033_c000038_grain_stats.csv\n", + "done with segmentation + export\n", + "[980/1053] tile_r000033_c000039\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:01<00:00, 2.52it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.52it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 491/491 [00:13<00:00, 37.50it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "396it [00:02, 142.75it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "394it [00:02, 145.25it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 110/110 [00:03<00:00, 29.51it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 133/133 [00:00<00:00, 140.13it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000033_c000039_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000033_c000039_grain_stats.csv\n", + "done with segmentation + export\n", + "[981/1053] tile_r000033_c000040\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:01<00:00, 2.52it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.50it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 589/589 [00:15<00:00, 37.60it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "461it [00:03, 117.45it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "459it [00:03, 123.11it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 129/129 [00:04<00:00, 26.94it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 144/144 [00:01<00:00, 137.31it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000033_c000040_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000033_c000040_grain_stats.csv\n", + "done with segmentation + export\n", + "[982/1053] tile_r000033_c000041\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.50it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.50it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 556/556 [00:15<00:00, 36.86it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "431it [00:03, 128.26it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "427it [00:03, 139.23it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 112/112 [00:04<00:00, 25.38it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 131/131 [00:01<00:00, 129.24it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000033_c000041_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000033_c000041_grain_stats.csv\n", + "done with segmentation + export\n", + "[983/1053] tile_r000033_c000042\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.19it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.11it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 283/283 [00:08<00:00, 33.80it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "205it [00:02, 80.72it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "191it [00:01, 121.38it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 41/41 [00:02<00:00, 14.37it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 57/57 [00:00<00:00, 109.86it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000033_c000042_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000033_c000042_grain_stats.csv\n", + "done with segmentation + export\n", + "[984/1053] tile_r000033_c000043\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.49it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.48it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 15/15 [00:00<00:00, 23.72it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "0it [00:00, ?it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "0it [00:00, ?it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "0it [00:00, ?it/s]\n", + "0it [00:00, ?it/s]\n", + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n", + "No grains found -> skipping histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000033_c000043_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000033_c000043_grain_stats.csv\n", + "done with segmentation + export\n", + "[985/1053] tile_r000034_c000006\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:01<00:00, 2.52it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.43it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 136/136 [00:04<00:00, 30.88it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "67it [00:00, 554.72it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "76it [00:00, 546.28it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 28/28 [00:00<00:00, 105.62it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 29/29 [00:00<00:00, 216.54it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000034_c000006_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000034_c000006_grain_stats.csv\n", + "done with segmentation + export\n", + "[986/1053] tile_r000034_c000007\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.48it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.47it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 499/499 [00:15<00:00, 33.21it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "366it [00:01, 227.65it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "359it [00:01, 310.26it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 102/102 [00:02<00:00, 50.62it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 142/142 [00:00<00:00, 182.43it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000034_c000007_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000034_c000007_grain_stats.csv\n", + "done with segmentation + export\n", + "[987/1053] tile_r000034_c000008\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.47it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.46it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 431/431 [00:12<00:00, 34.63it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "285it [00:00, 329.67it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "326it [00:00, 326.74it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 119/119 [00:01<00:00, 75.07it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 130/130 [00:00<00:00, 159.73it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000034_c000008_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000034_c000008_grain_stats.csv\n", + "done with segmentation + export\n", + "[988/1053] tile_r000034_c000009\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:01<00:00, 2.51it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.52it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 470/470 [00:13<00:00, 34.22it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "344it [00:01, 312.18it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "376it [00:01, 310.78it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 132/132 [00:02<00:00, 54.44it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 143/143 [00:00<00:00, 158.07it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000034_c000009_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000034_c000009_grain_stats.csv\n", + "done with segmentation + export\n", + "[989/1053] tile_r000034_c000010\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.42it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.52it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 357/357 [00:10<00:00, 34.31it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "265it [00:00, 325.98it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "286it [00:00, 328.23it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 98/98 [00:01<00:00, 66.51it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 106/106 [00:00<00:00, 183.68it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000034_c000010_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000034_c000010_grain_stats.csv\n", + "done with segmentation + export\n", + "[990/1053] tile_r000034_c000011\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.44it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.47it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 307/307 [00:08<00:00, 34.94it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "233it [00:00, 345.32it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "232it [00:00, 360.99it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 59/59 [00:01<00:00, 41.88it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 102/102 [00:00<00:00, 183.17it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n", + "Warning: len(gdf)=102 != len(grain_stats)=101 -> truncating to 101\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000034_c000011_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000034_c000011_grain_stats.csv\n", + "done with segmentation + export\n", + "[991/1053] tile_r000034_c000012\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.49it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.53it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 300/300 [00:09<00:00, 31.24it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "213it [00:00, 298.64it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "212it [00:00, 361.24it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 62/62 [00:01<00:00, 58.41it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 91/91 [00:00<00:00, 178.54it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000034_c000012_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000034_c000012_grain_stats.csv\n", + "done with segmentation + export\n", + "[992/1053] tile_r000034_c000013\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:01<00:00, 2.51it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.51it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 221/221 [00:07<00:00, 28.24it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "122it [00:00, 369.73it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "134it [00:00, 378.77it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 45/45 [00:00<00:00, 68.86it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 49/49 [00:00<00:00, 181.39it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000034_c000013_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000034_c000013_grain_stats.csv\n", + "done with segmentation + export\n", + "[993/1053] tile_r000034_c000014\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:01<00:00, 2.51it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.46it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 120/120 [00:04<00:00, 28.72it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "50it [00:00, 236.59it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "55it [00:00, 248.23it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 17/17 [00:00<00:00, 42.82it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 18/18 [00:00<00:00, 130.71it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000034_c000014_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000034_c000014_grain_stats.csv\n", + "done with segmentation + export\n", + "[994/1053] tile_r000034_c000015\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.49it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.47it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 228/228 [00:07<00:00, 29.69it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "140it [00:00, 268.95it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "150it [00:00, 273.32it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 45/45 [00:00<00:00, 46.32it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 51/51 [00:00<00:00, 149.55it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000034_c000015_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000034_c000015_grain_stats.csv\n", + "done with segmentation + export\n", + "[995/1053] tile_r000034_c000016\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.32it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.34it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 231/231 [00:07<00:00, 32.37it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "155it [00:00, 180.92it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "152it [00:00, 254.63it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 34/34 [00:01<00:00, 17.27it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 62/62 [00:00<00:00, 164.90it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000034_c000016_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000034_c000016_grain_stats.csv\n", + "done with segmentation + export\n", + "[996/1053] tile_r000034_c000017\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.46it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.47it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 285/285 [00:08<00:00, 34.13it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "199it [00:00, 235.81it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "214it [00:00, 243.36it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 73/73 [00:01<00:00, 48.15it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 76/76 [00:00<00:00, 152.78it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000034_c000017_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000034_c000017_grain_stats.csv\n", + "done with segmentation + export\n", + "[997/1053] tile_r000034_c000018\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.46it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.48it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 456/456 [00:12<00:00, 35.47it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "320it [00:01, 253.15it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "336it [00:01, 230.83it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 108/108 [00:02<00:00, 47.10it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 114/114 [00:00<00:00, 157.26it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000034_c000018_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000034_c000018_grain_stats.csv\n", + "done with segmentation + export\n", + "[998/1053] tile_r000034_c000019\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.41it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.47it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 573/573 [00:15<00:00, 37.31it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "435it [00:01, 254.81it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "474it [00:01, 260.17it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 165/165 [00:02<00:00, 62.92it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 169/169 [00:00<00:00, 186.25it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000034_c000019_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000034_c000019_grain_stats.csv\n", + "done with segmentation + export\n", + "[999/1053] tile_r000034_c000020\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.49it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.51it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 583/583 [00:15<00:00, 37.75it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "439it [00:02, 219.06it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "474it [00:02, 230.06it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 160/160 [00:02<00:00, 59.61it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 176/176 [00:01<00:00, 172.12it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000034_c000020_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000034_c000020_grain_stats.csv\n", + "done with segmentation + export\n", + "[1000/1053] tile_r000034_c000021\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.50it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.47it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 756/756 [00:19<00:00, 39.36it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "650it [00:02, 290.02it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "680it [00:02, 289.01it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 243/243 [00:03<00:00, 76.69it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 263/263 [00:01<00:00, 195.13it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000034_c000021_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000034_c000021_grain_stats.csv\n", + "done with segmentation + export\n", + "[1001/1053] tile_r000034_c000022\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:01<00:00, 2.51it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.50it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 630/630 [00:16<00:00, 37.67it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "543it [00:03, 179.81it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "542it [00:02, 180.95it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 154/154 [00:03<00:00, 41.39it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 192/192 [00:01<00:00, 164.27it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000034_c000022_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000034_c000022_grain_stats.csv\n", + "done with segmentation + export\n", + "[1002/1053] tile_r000034_c000023\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.49it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.53it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 633/633 [00:16<00:00, 38.16it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "517it [00:02, 190.27it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "516it [00:02, 193.45it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 152/152 [00:03<00:00, 40.00it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 179/179 [00:01<00:00, 153.74it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000034_c000023_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000034_c000023_grain_stats.csv\n", + "done with segmentation + export\n", + "[1003/1053] tile_r000034_c000024\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.45it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.49it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 561/561 [00:14<00:00, 37.62it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "438it [00:01, 231.21it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "460it [00:01, 234.33it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 152/152 [00:02<00:00, 52.80it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 166/166 [00:01<00:00, 153.43it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n", + "Warning: len(gdf)=166 != len(grain_stats)=165 -> truncating to 165\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000034_c000024_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000034_c000024_grain_stats.csv\n", + "done with segmentation + export\n", + "[1004/1053] tile_r000034_c000025\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:01<00:00, 2.51it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.46it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 456/456 [00:11<00:00, 38.75it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "354it [00:01, 189.56it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "372it [00:02, 179.85it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 118/118 [00:02<00:00, 44.40it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 122/122 [00:00<00:00, 149.44it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000034_c000025_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000034_c000025_grain_stats.csv\n", + "done with segmentation + export\n", + "[1005/1053] tile_r000034_c000026\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.47it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.45it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 564/564 [00:16<00:00, 33.86it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "393it [00:02, 194.45it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "415it [00:02, 195.29it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 130/130 [00:03<00:00, 41.61it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 141/141 [00:00<00:00, 158.65it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n", + "Warning: len(gdf)=141 != len(grain_stats)=140 -> truncating to 140\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000034_c000026_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000034_c000026_grain_stats.csv\n", + "done with segmentation + export\n", + "[1006/1053] tile_r000034_c000027\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.47it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.48it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 399/399 [00:10<00:00, 38.16it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "295it [00:01, 184.40it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "309it [00:01, 180.40it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 99/99 [00:02<00:00, 39.50it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 104/104 [00:00<00:00, 146.42it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000034_c000027_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000034_c000027_grain_stats.csv\n", + "done with segmentation + export\n", + "[1007/1053] tile_r000034_c000028\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:01<00:00, 2.51it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.49it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 481/481 [00:12<00:00, 37.80it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "396it [00:01, 207.74it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "421it [00:02, 207.05it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 139/139 [00:02<00:00, 50.90it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 145/145 [00:00<00:00, 162.66it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000034_c000028_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000034_c000028_grain_stats.csv\n", + "done with segmentation + export\n", + "[1008/1053] tile_r000035_c000014\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:01<00:00, 2.53it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.48it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 254/254 [00:07<00:00, 32.66it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "137it [00:00, 267.73it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "151it [00:00, 243.96it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 51/51 [00:00<00:00, 54.84it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 56/56 [00:00<00:00, 131.74it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000035_c000014_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000035_c000014_grain_stats.csv\n", + "done with segmentation + export\n", + "[1009/1053] tile_r000035_c000015\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.50it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.37it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 246/246 [00:08<00:00, 28.28it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "161it [00:01, 118.40it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "158it [00:00, 199.20it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 35/35 [00:02<00:00, 13.75it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 66/66 [00:00<00:00, 137.84it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000035_c000015_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000035_c000015_grain_stats.csv\n", + "done with segmentation + export\n", + "[1010/1053] tile_r000035_c000016\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.33it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.48it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 321/321 [00:09<00:00, 34.02it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "210it [00:00, 234.73it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "229it [00:00, 242.60it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 74/74 [00:01<00:00, 54.49it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 78/78 [00:00<00:00, 146.23it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000035_c000016_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000035_c000016_grain_stats.csv\n", + "done with segmentation + export\n", + "[1011/1053] tile_r000035_c000017\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.47it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.44it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 422/422 [00:12<00:00, 34.70it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "290it [00:01, 159.07it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "289it [00:01, 178.82it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 77/77 [00:03<00:00, 23.72it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 107/107 [00:00<00:00, 140.02it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000035_c000017_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000035_c000017_grain_stats.csv\n", + "done with segmentation + export\n", + "[1012/1053] tile_r000035_c000018\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.49it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.47it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 521/521 [00:14<00:00, 36.84it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "391it [00:01, 260.35it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "417it [00:01, 259.07it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 139/139 [00:02<00:00, 56.60it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 154/154 [00:00<00:00, 174.99it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000035_c000018_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000035_c000018_grain_stats.csv\n", + "done with segmentation + export\n", + "[1013/1053] tile_r000035_c000019\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:01<00:00, 2.52it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.49it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 516/516 [00:13<00:00, 37.57it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "395it [00:01, 271.66it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "422it [00:01, 280.19it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 153/153 [00:02<00:00, 73.76it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 160/160 [00:00<00:00, 177.16it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000035_c000019_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000035_c000019_grain_stats.csv\n", + "done with segmentation + export\n", + "[1014/1053] tile_r000035_c000020\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.50it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.53it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 798/798 [00:20<00:00, 38.23it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "664it [00:02, 244.10it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "663it [00:02, 253.44it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 201/201 [00:03<00:00, 52.15it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 251/251 [00:01<00:00, 184.42it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000035_c000020_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000035_c000020_grain_stats.csv\n", + "done with segmentation + export\n", + "[1015/1053] tile_r000035_c000021\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:01<00:00, 2.55it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.48it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 720/720 [00:19<00:00, 37.37it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "584it [00:02, 252.42it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "620it [00:02, 250.79it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 208/208 [00:04<00:00, 48.71it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 224/224 [00:01<00:00, 186.33it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000035_c000021_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000035_c000021_grain_stats.csv\n", + "done with segmentation + export\n", + "[1016/1053] tile_r000035_c000022\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.46it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.49it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 731/731 [00:18<00:00, 39.58it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "596it [00:02, 282.28it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "593it [00:02, 244.29it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 181/181 [00:03<00:00, 57.14it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 225/225 [00:01<00:00, 191.33it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000035_c000022_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000035_c000022_grain_stats.csv\n", + "done with segmentation + export\n", + "[1017/1053] tile_r000035_c000023\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:01<00:00, 2.51it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.52it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 724/724 [00:18<00:00, 38.92it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "599it [00:02, 263.38it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "636it [00:02, 264.57it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 227/227 [00:02<00:00, 78.02it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 233/233 [00:01<00:00, 192.73it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000035_c000023_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000035_c000023_grain_stats.csv\n", + "done with segmentation + export\n", + "[1018/1053] tile_r000035_c000024\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:01<00:00, 2.52it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.54it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 596/596 [00:15<00:00, 37.59it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "489it [00:02, 204.14it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "488it [00:02, 208.17it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 140/140 [00:03<00:00, 44.00it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 177/177 [00:01<00:00, 168.69it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000035_c000024_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000035_c000024_grain_stats.csv\n", + "done with segmentation + export\n", + "[1019/1053] tile_r000035_c000025\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.45it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.47it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 592/592 [00:15<00:00, 37.65it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "482it [00:02, 196.53it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "481it [00:02, 199.75it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 136/136 [00:03<00:00, 42.25it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 167/167 [00:00<00:00, 175.67it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000035_c000025_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000035_c000025_grain_stats.csv\n", + "done with segmentation + export\n", + "[1020/1053] tile_r000035_c000026\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.49it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.34it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 557/557 [00:14<00:00, 37.26it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "425it [00:02, 211.58it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "440it [00:02, 213.76it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 142/142 [00:03<00:00, 46.64it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 147/147 [00:00<00:00, 164.04it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000035_c000026_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000035_c000026_grain_stats.csv\n", + "done with segmentation + export\n", + "[1021/1053] tile_r000035_c000027\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.47it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.46it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 471/471 [00:13<00:00, 36.02it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "355it [00:01, 228.64it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "387it [00:01, 229.28it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 131/131 [00:02<00:00, 54.30it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 134/134 [00:00<00:00, 155.54it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000035_c000027_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000035_c000027_grain_stats.csv\n", + "done with segmentation + export\n", + "[1022/1053] tile_r000036_c000015\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.38it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.32it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 280/280 [00:10<00:00, 27.06it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "193it [00:01, 173.10it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "191it [00:00, 386.23it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 48/48 [00:00<00:00, 51.35it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 110/110 [00:00<00:00, 144.56it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000036_c000015_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000036_c000015_grain_stats.csv\n", + "done with segmentation + export\n", + "[1023/1053] tile_r000036_c000016\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.48it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.48it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 470/470 [00:14<00:00, 32.59it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "341it [00:01, 187.48it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "338it [00:01, 326.60it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 82/82 [00:02<00:00, 37.65it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 153/153 [00:00<00:00, 165.25it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000036_c000016_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000036_c000016_grain_stats.csv\n", + "done with segmentation + export\n", + "[1024/1053] tile_r000036_c000017\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:01<00:00, 2.51it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.46it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 585/585 [00:16<00:00, 36.28it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "458it [00:01, 329.26it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "455it [00:01, 376.19it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 138/138 [00:02<00:00, 57.78it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 199/199 [00:01<00:00, 184.52it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000036_c000017_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000036_c000017_grain_stats.csv\n", + "done with segmentation + export\n", + "[1025/1053] tile_r000036_c000018\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:01<00:00, 2.51it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.57it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 734/734 [00:18<00:00, 39.07it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "625it [00:01, 332.91it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "685it [00:02, 328.03it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 257/257 [00:02<00:00, 86.56it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 264/264 [00:01<00:00, 208.98it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000036_c000018_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000036_c000018_grain_stats.csv\n", + "done with segmentation + export\n", + "[1026/1053] tile_r000036_c000019\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.39it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.50it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 706/706 [00:18<00:00, 39.21it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "590it [00:01, 428.28it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "632it [00:01, 424.00it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 243/243 [00:02<00:00, 108.60it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 257/257 [00:01<00:00, 211.78it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000036_c000019_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000036_c000019_grain_stats.csv\n", + "done with segmentation + export\n", + "[1027/1053] tile_r000036_c000020\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.49it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.46it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 794/794 [00:21<00:00, 37.44it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "661it [00:02, 307.41it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "705it [00:02, 307.88it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 254/254 [00:03<00:00, 79.09it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 268/268 [00:01<00:00, 196.47it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000036_c000020_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000036_c000020_grain_stats.csv\n", + "done with segmentation + export\n", + "[1028/1053] tile_r000036_c000021\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.44it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.48it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 644/644 [00:16<00:00, 38.64it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "534it [00:01, 306.68it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "560it [00:01, 307.72it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 202/202 [00:02<00:00, 77.20it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 212/212 [00:01<00:00, 188.31it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000036_c000021_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000036_c000021_grain_stats.csv\n", + "done with segmentation + export\n", + "[1029/1053] tile_r000036_c000022\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.44it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.48it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 748/748 [00:19<00:00, 38.81it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "619it [00:02, 273.69it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "618it [00:02, 277.01it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 193/193 [00:03<00:00, 61.27it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 240/240 [00:01<00:00, 182.37it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000036_c000022_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000036_c000022_grain_stats.csv\n", + "done with segmentation + export\n", + "[1030/1053] tile_r000036_c000023\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.47it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.48it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 795/795 [00:20<00:00, 39.54it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "690it [00:02, 287.18it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "728it [00:02, 271.93it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 263/263 [00:03<00:00, 76.92it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 275/275 [00:01<00:00, 190.33it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000036_c000023_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000036_c000023_grain_stats.csv\n", + "done with segmentation + export\n", + "[1031/1053] tile_r000036_c000024\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:01<00:00, 2.53it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.46it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 712/712 [00:18<00:00, 39.47it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "633it [00:02, 262.72it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "669it [00:02, 262.69it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 237/237 [00:03<00:00, 70.59it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 247/247 [00:01<00:00, 176.78it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000036_c000024_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000036_c000024_grain_stats.csv\n", + "done with segmentation + export\n", + "[1032/1053] tile_r000036_c000025\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.47it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.42it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 684/684 [00:17<00:00, 38.86it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "587it [00:02, 198.08it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "619it [00:03, 198.31it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 203/203 [00:04<00:00, 49.14it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 213/213 [00:01<00:00, 174.02it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000036_c000025_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000036_c000025_grain_stats.csv\n", + "done with segmentation + export\n", + "[1033/1053] tile_r000037_c000015\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:01<00:00, 2.52it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.49it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 359/359 [00:12<00:00, 29.44it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "256it [00:01, 178.75it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "253it [00:00, 415.19it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 62/62 [00:01<00:00, 49.11it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 135/135 [00:00<00:00, 147.83it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000037_c000015_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000037_c000015_grain_stats.csv\n", + "done with segmentation + export\n", + "[1034/1053] tile_r000037_c000016\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:01<00:00, 2.51it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.44it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 404/404 [00:33<00:00, 12.17it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "302it [00:01, 284.52it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "301it [00:00, 349.72it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 76/76 [00:02<00:00, 25.66it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 141/141 [00:00<00:00, 189.26it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000037_c000016_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000037_c000016_grain_stats.csv\n", + "done with segmentation + export\n", + "[1035/1053] tile_r000037_c000017\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.10it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.07it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 570/570 [00:17<00:00, 32.02it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "434it [00:01, 243.18it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "432it [00:01, 313.78it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 125/125 [00:03<00:00, 34.94it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 189/189 [00:01<00:00, 184.65it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n", + "Warning: len(gdf)=189 != len(grain_stats)=188 -> truncating to 188\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000037_c000017_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000037_c000017_grain_stats.csv\n", + "done with segmentation + export\n", + "[1036/1053] tile_r000037_c000018\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.06it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.08it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 879/879 [00:24<00:00, 36.56it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "748it [00:02, 305.94it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "745it [00:02, 356.47it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 245/245 [00:03<00:00, 72.32it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 323/323 [00:01<00:00, 196.51it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000037_c000018_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000037_c000018_grain_stats.csv\n", + "done with segmentation + export\n", + "[1037/1053] tile_r000037_c000019\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 1.97it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.03it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 899/899 [00:23<00:00, 38.01it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "796it [00:02, 308.63it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "795it [00:02, 331.69it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 278/278 [00:03<00:00, 79.31it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 328/328 [00:01<00:00, 215.17it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000037_c000019_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000037_c000019_grain_stats.csv\n", + "done with segmentation + export\n", + "[1038/1053] tile_r000037_c000020\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.12it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.20it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 669/669 [00:17<00:00, 38.61it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "559it [00:02, 244.35it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "598it [00:02, 243.42it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 210/210 [00:03<00:00, 58.23it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 216/216 [00:01<00:00, 197.58it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000037_c000020_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000037_c000020_grain_stats.csv\n", + "done with segmentation + export\n", + "[1039/1053] tile_r000037_c000021\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.11it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.16it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 737/737 [00:19<00:00, 37.80it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "627it [00:02, 274.84it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "626it [00:02, 279.12it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 189/189 [00:03<00:00, 49.41it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 252/252 [00:01<00:00, 193.88it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000037_c000021_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000037_c000021_grain_stats.csv\n", + "done with segmentation + export\n", + "[1040/1053] tile_r000037_c000022\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.19it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.21it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 657/657 [00:17<00:00, 38.21it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "548it [00:01, 280.71it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "588it [00:02, 278.87it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 206/206 [00:03<00:00, 60.52it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 218/218 [00:01<00:00, 189.89it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000037_c000022_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000037_c000022_grain_stats.csv\n", + "done with segmentation + export\n", + "[1041/1053] tile_r000037_c000023\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.18it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.26it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 817/817 [00:21<00:00, 38.23it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "724it [00:02, 262.67it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "723it [00:02, 268.37it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 229/229 [00:03<00:00, 57.26it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 289/289 [00:01<00:00, 197.54it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000037_c000023_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000037_c000023_grain_stats.csv\n", + "done with segmentation + export\n", + "[1042/1053] tile_r000038_c000016\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.24it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.29it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 785/785 [00:20<00:00, 38.22it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "682it [00:01, 394.85it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "680it [00:01, 419.30it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 232/232 [00:02<00:00, 93.12it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 293/293 [00:01<00:00, 223.81it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000038_c000016_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000038_c000016_grain_stats.csv\n", + "done with segmentation + export\n", + "[1043/1053] tile_r000038_c000017\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.25it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.31it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 721/721 [00:18<00:00, 39.44it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "623it [00:01, 391.07it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "622it [00:01, 407.88it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 206/206 [00:02<00:00, 91.64it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 274/274 [00:01<00:00, 215.64it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000038_c000017_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000038_c000017_grain_stats.csv\n", + "done with segmentation + export\n", + "[1044/1053] tile_r000038_c000018\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.20it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.26it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 721/721 [00:19<00:00, 37.63it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "614it [00:01, 414.02it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "669it [00:01, 418.70it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 256/256 [00:02<00:00, 92.46it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 268/268 [00:01<00:00, 212.97it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000038_c000018_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000038_c000018_grain_stats.csv\n", + "done with segmentation + export\n", + "[1045/1053] tile_r000038_c000019\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.32it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.38it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 815/815 [00:20<00:00, 39.06it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "720it [00:01, 422.88it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "719it [00:01, 457.80it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 246/246 [00:02<00:00, 105.43it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 319/319 [00:01<00:00, 222.11it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000038_c000019_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000038_c000019_grain_stats.csv\n", + "done with segmentation + export\n", + "[1046/1053] tile_r000038_c000020\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.17it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.26it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 805/805 [00:20<00:00, 40.24it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "717it [00:01, 373.59it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "716it [00:01, 376.18it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 249/249 [00:02<00:00, 93.21it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 304/304 [00:01<00:00, 209.54it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000038_c000020_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000038_c000020_grain_stats.csv\n", + "done with segmentation + export\n", + "[1047/1053] tile_r000038_c000021\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.37it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.39it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 723/723 [00:19<00:00, 37.82it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "615it [00:02, 296.11it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "651it [00:02, 295.35it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 231/231 [00:02<00:00, 77.87it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 238/238 [00:01<00:00, 187.19it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000038_c000021_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000038_c000021_grain_stats.csv\n", + "done with segmentation + export\n", + "[1048/1053] tile_r000038_c000022\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.29it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.45it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 587/587 [00:15<00:00, 38.45it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "489it [00:02, 216.39it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "488it [00:02, 224.03it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 142/142 [00:03<00:00, 45.46it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 194/194 [00:01<00:00, 164.14it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000038_c000022_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000038_c000022_grain_stats.csv\n", + "done with segmentation + export\n", + "[1049/1053] tile_r000039_c000017\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.37it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.40it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 940/940 [00:23<00:00, 39.86it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "841it [00:01, 421.98it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "840it [00:01, 451.36it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 306/306 [00:02<00:00, 121.75it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 370/370 [00:01<00:00, 234.73it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000039_c000017_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000039_c000017_grain_stats.csv\n", + "done with segmentation + export\n", + "[1050/1053] tile_r000039_c000018\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.33it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.41it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 1108/1108 [00:28<00:00, 39.49it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "988it [00:02, 426.97it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "1062it [00:02, 417.28it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 423/423 [00:03<00:00, 134.68it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 436/436 [00:01<00:00, 219.99it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000039_c000018_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000039_c000018_grain_stats.csv\n", + "done with segmentation + export\n", + "[1051/1053] tile_r000039_c000019\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.35it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.40it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 813/813 [00:20<00:00, 38.94it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "682it [00:02, 327.82it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "679it [00:01, 372.16it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 226/226 [00:02<00:00, 86.89it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 299/299 [00:01<00:00, 211.61it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000039_c000019_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000039_c000019_grain_stats.csv\n", + "done with segmentation + export\n", + "[1052/1053] tile_r000039_c000020\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.29it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.41it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 754/754 [00:19<00:00, 38.46it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "642it [00:02, 295.70it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "641it [00:02, 312.81it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 204/204 [00:02<00:00, 69.19it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 270/270 [00:01<00:00, 198.85it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000039_c000020_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000039_c000020_grain_stats.csv\n", + "done with segmentation + export\n", + "[1053/1053] tile_r000040_c000018\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.40it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.40it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 932/932 [00:23<00:00, 39.37it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "826it [00:02, 401.71it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "880it [00:02, 403.60it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 343/343 [00:03<00:00, 112.89it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 349/349 [00:01<00:00, 242.90it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n", + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/ouputgpkg/tile_r000040_c000018_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/csv_stats/tile_r000040_c000018_grain_stats.csv\n", + "done with segmentation + export\n", + "Saved runtime metrics CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/runtime_metrics.csv\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " t_total_s t_unet_s t_label_s t_sam_s t_export_s \\\n", + "count 1053.000000 1053.000000 1053.000000 1053.000000 1053.000000 \n", + "mean 34.429307 5.187793 0.712705 26.472839 2.000390 \n", + "std 12.145334 0.491151 0.124814 11.782959 0.349426 \n", + "min 5.994651 4.981573 0.025233 0.101764 0.713906 \n", + "25% 27.856576 5.084024 0.663316 20.031752 1.926130 \n", + "50% 34.527694 5.124797 0.723726 26.511136 2.021397 \n", + "75% 40.852592 5.179260 0.777807 32.761168 2.131798 \n", + "max 97.578874 17.851711 1.042117 89.912478 7.922703 \n", + "\n", + " n_prompts_used n_grains \n", + "count 1053.000000 1053.000000 \n", + "mean 616.000950 195.086420 \n", + "std 305.531201 121.225543 \n", + "min 0.000000 0.000000 \n", + "25% 426.000000 115.000000 \n", + "50% 589.000000 176.000000 \n", + "75% 765.000000 248.000000 \n", + "max 1679.000000 698.000000 \n", + "\n", + "READY TABLE:\n", + "\n", + " metric value\n", + " n_tiles_total 1053.000\n", + " n_tiles_ok 1053.000\n", + " n_tiles_skipped_nodata 0.000\n", + " n_tiles_error 0.000\n", + "pixel_size_mm_per_px (median) 2.806\n", + " min_area_px (median) 39.905\n", + " total_runtime_min 604.234\n", + " tiles_per_min 1.743\n", + " total_s_per_tile (median) 34.528\n", + " unet_s (median) 5.125\n", + " label_s (median) 0.724\n", + " sam_s (median) 26.511\n", + " export_s (median) 2.021\n", + " prompts_used (median) 589.000\n", + " grains (median) 176.000\n", + "\n", + "Saved ready table CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_1/runtime_summary_ready_table.csv\n", + "PROCESS: F22_2 tiles: 905\n", + "Found 905 tiles in /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_2 (folder root)\n", + "CRS: EPSG:32643\n", + "Pixel size: 0.002806 units/px (~2.806 mm/px)\n", + "2 cm => minor_px=7.13 px -> min_area_px=39.9 px^2\n", + "[1/905] tile_r000004_c000027\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.35it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.40it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 11/11 [00:00<00:00, 23.70it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2it [00:00, 819.52it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2it [00:00, 912.20it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 1/1 [00:00<00:00, 273.92it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 1/1 [00:00<00:00, 202.20it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_2/ouputgpkg/tile_r000004_c000027_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_2/csv_stats/tile_r000004_c000027_grain_stats.csv\n", + "done with segmentation + export\n", + "[2/905] tile_r000004_c000028\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.43it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.45it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 590/590 [00:16<00:00, 35.86it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "477it [00:01, 339.15it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "495it [00:01, 333.70it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 185/185 [00:01<00:00, 103.46it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 200/200 [00:01<00:00, 189.63it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_2/ouputgpkg/tile_r000004_c000028_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_2/csv_stats/tile_r000004_c000028_grain_stats.csv\n", + "done with segmentation + export\n", + "[3/905] tile_r000004_c000029\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.47it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.41it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 847/847 [00:22<00:00, 38.32it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "749it [00:02, 277.17it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "748it [00:02, 280.61it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 233/233 [00:03<00:00, 60.18it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 289/289 [00:01<00:00, 200.41it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n", + "Warning: len(gdf)=289 != len(grain_stats)=288 -> truncating to 288\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_2/ouputgpkg/tile_r000004_c000029_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_2/csv_stats/tile_r000004_c000029_grain_stats.csv\n", + "done with segmentation + export\n", + "[4/905] tile_r000004_c000030\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.43it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.45it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 285/285 [00:08<00:00, 33.17it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "191it [00:00, 234.78it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "205it [00:00, 257.25it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 66/66 [00:01<00:00, 57.47it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 68/68 [00:00<00:00, 182.82it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_2/ouputgpkg/tile_r000004_c000030_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_2/csv_stats/tile_r000004_c000030_grain_stats.csv\n", + "done with segmentation + export\n", + "[5/905] tile_r000004_c000031\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.37it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.44it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 27/27 [00:01<00:00, 26.24it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "6it [00:00, 652.51it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "7it [00:00, 633.85it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 3/3 [00:00<00:00, 149.12it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 3/3 [00:00<00:00, 157.84it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_2/ouputgpkg/tile_r000004_c000031_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_2/csv_stats/tile_r000004_c000031_grain_stats.csv\n", + "done with segmentation + export\n", + "[6/905] tile_r000005_c000026\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.37it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.51it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 283/283 [00:08<00:00, 34.58it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "211it [00:00, 417.24it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "227it [00:00, 411.97it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 93/93 [00:00<00:00, 145.00it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 95/95 [00:00<00:00, 229.33it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_2/ouputgpkg/tile_r000005_c000026_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_2/csv_stats/tile_r000005_c000026_grain_stats.csv\n", + "done with segmentation + export\n", + "[7/905] tile_r000005_c000027\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.44it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.44it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 1014/1014 [00:26<00:00, 38.04it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "913it [00:02, 397.42it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "964it [00:02, 386.87it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 381/381 [00:02<00:00, 131.87it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 391/391 [00:01<00:00, 224.19it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_2/ouputgpkg/tile_r000005_c000027_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_2/csv_stats/tile_r000005_c000027_grain_stats.csv\n", + "done with segmentation + export\n", + "[8/905] tile_r000005_c000028\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.41it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.44it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 1115/1115 [00:28<00:00, 39.77it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "957it [00:03, 306.71it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "990it [00:03, 292.04it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 357/357 [00:03<00:00, 91.24it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 374/374 [00:01<00:00, 208.07it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_2/ouputgpkg/tile_r000005_c000028_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_2/csv_stats/tile_r000005_c000028_grain_stats.csv\n", + "done with segmentation + export\n", + "[9/905] tile_r000005_c000029\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.41it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.42it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 880/880 [00:21<00:00, 40.06it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "773it [00:02, 357.50it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "823it [00:02, 350.74it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 298/298 [00:03<00:00, 94.32it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 310/310 [00:01<00:00, 208.98it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_2/ouputgpkg/tile_r000005_c000029_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_2/csv_stats/tile_r000005_c000029_grain_stats.csv\n", + "done with segmentation + export\n", + "[10/905] tile_r000005_c000030\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.45it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.49it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 434/434 [00:11<00:00, 37.83it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "361it [00:01, 260.29it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "379it [00:01, 270.91it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 135/135 [00:01<00:00, 68.04it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 137/137 [00:00<00:00, 185.41it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_2/ouputgpkg/tile_r000005_c000030_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_2/csv_stats/tile_r000005_c000030_grain_stats.csv\n", + "done with segmentation + export\n", + "[11/905] tile_r000005_c000031\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:01<00:00, 2.50it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.46it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 356/356 [00:10<00:00, 33.61it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "286it [00:01, 261.05it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "298it [00:01, 261.35it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 96/96 [00:01<00:00, 54.85it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 100/100 [00:00<00:00, 173.84it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_2/ouputgpkg/tile_r000005_c000031_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_2/csv_stats/tile_r000005_c000031_grain_stats.csv\n", + "done with segmentation + export\n", + "[12/905] tile_r000006_c000024\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.47it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.44it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 33/33 [00:01<00:00, 28.82it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "16it [00:00, 544.90it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "18it [00:00, 531.17it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 8/8 [00:00<00:00, 153.31it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 8/8 [00:00<00:00, 186.84it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_2/ouputgpkg/tile_r000006_c000024_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_2/csv_stats/tile_r000006_c000024_grain_stats.csv\n", + "done with segmentation + export\n", + "[13/905] tile_r000006_c000025\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.42it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.46it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 547/547 [00:15<00:00, 34.24it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "475it [00:00, 534.74it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "507it [00:00, 528.91it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 207/207 [00:01<00:00, 159.99it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 222/222 [00:00<00:00, 252.27it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_2/ouputgpkg/tile_r000006_c000025_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_2/csv_stats/tile_r000006_c000025_grain_stats.csv\n", + "done with segmentation + export\n", + "[14/905] tile_r000006_c000026\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.45it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.51it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 854/854 [00:22<00:00, 37.58it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "723it [00:03, 180.88it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "718it [00:03, 224.08it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 258/258 [00:04<00:00, 57.19it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 312/312 [00:01<00:00, 225.12it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_2/ouputgpkg/tile_r000006_c000026_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_2/csv_stats/tile_r000006_c000026_grain_stats.csv\n", + "done with segmentation + export\n", + "[15/905] tile_r000006_c000027\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:01<00:00, 2.50it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.49it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 928/928 [00:23<00:00, 38.98it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "829it [00:01, 436.62it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "828it [00:01, 449.61it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 286/286 [00:02<00:00, 106.23it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 355/355 [00:01<00:00, 220.75it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_2/ouputgpkg/tile_r000006_c000027_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_2/csv_stats/tile_r000006_c000027_grain_stats.csv\n", + "done with segmentation + export\n", + "[16/905] tile_r000006_c000028\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.45it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.49it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 698/698 [00:18<00:00, 38.45it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "598it [00:01, 335.05it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "632it [00:01, 336.32it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 225/225 [00:02<00:00, 88.06it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 234/234 [00:01<00:00, 213.23it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_2/ouputgpkg/tile_r000006_c000028_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_2/csv_stats/tile_r000006_c000028_grain_stats.csv\n", + "done with segmentation + export\n", + "[17/905] tile_r000006_c000029\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.45it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.44it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 474/474 [00:12<00:00, 39.23it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "391it [00:01, 372.08it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "415it [00:01, 369.98it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 153/153 [00:01<00:00, 94.54it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 160/160 [00:00<00:00, 198.82it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_2/ouputgpkg/tile_r000006_c000029_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_2/csv_stats/tile_r000006_c000029_grain_stats.csv\n", + "done with segmentation + export\n", + "[18/905] tile_r000006_c000030\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.46it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.49it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 283/283 [00:07<00:00, 37.05it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "214it [00:00, 360.58it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "229it [00:00, 364.75it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 82/82 [00:01<00:00, 77.77it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 85/85 [00:00<00:00, 182.77it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_2/ouputgpkg/tile_r000006_c000030_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_2/csv_stats/tile_r000006_c000030_grain_stats.csv\n", + "done with segmentation + export\n", + "[19/905] tile_r000006_c000031\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.45it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.48it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 330/330 [00:08<00:00, 37.19it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "235it [00:00, 312.17it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "251it [00:00, 309.96it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 86/86 [00:01<00:00, 63.20it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 91/91 [00:00<00:00, 164.04it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_2/ouputgpkg/tile_r000006_c000031_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_2/csv_stats/tile_r000006_c000031_grain_stats.csv\n", + "done with segmentation + export\n", + "[20/905] tile_r000006_c000032\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.49it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.48it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 84/84 [00:02<00:00, 31.62it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "43it [00:00, 378.93it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "47it [00:00, 374.69it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 18/18 [00:00<00:00, 92.18it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 18/18 [00:00<00:00, 165.11it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_2/ouputgpkg/tile_r000006_c000032_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_2/csv_stats/tile_r000006_c000032_grain_stats.csv\n", + "done with segmentation + export\n", + "[21/905] tile_r000007_c000022\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.47it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.44it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 11/11 [00:00<00:00, 24.30it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "4it [00:00, 602.89it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "5it [00:00, 333.93it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 2/2 [00:00<00:00, 142.91it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 2/2 [00:00<00:00, 183.52it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_2/ouputgpkg/tile_r000007_c000022_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_2/csv_stats/tile_r000007_c000022_grain_stats.csv\n", + "done with segmentation + export\n", + "[22/905] tile_r000007_c000023\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.42it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.49it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 166/166 [00:04<00:00, 34.60it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "120it [00:00, 512.20it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "128it [00:00, 455.31it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 49/49 [00:00<00:00, 110.73it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 50/50 [00:00<00:00, 229.72it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_2/ouputgpkg/tile_r000007_c000023_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_2/csv_stats/tile_r000007_c000023_grain_stats.csv\n", + "done with segmentation + export\n", + "[23/905] tile_r000007_c000024\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.32it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.41it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 322/322 [00:08<00:00, 37.88it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "284it [00:00, 602.09it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "308it [00:00, 588.89it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 123/123 [00:00<00:00, 129.21it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 128/128 [00:00<00:00, 238.88it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n", + "Warning: len(gdf)=128 != len(grain_stats)=126 -> truncating to 126\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_2/ouputgpkg/tile_r000007_c000024_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_2/csv_stats/tile_r000007_c000024_grain_stats.csv\n", + "done with segmentation + export\n", + "[24/905] tile_r000007_c000025\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.47it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.48it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 379/379 [00:10<00:00, 37.29it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "310it [00:00, 689.78it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "337it [00:00, 651.18it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 143/143 [00:00<00:00, 202.86it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 147/147 [00:00<00:00, 255.78it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_2/ouputgpkg/tile_r000007_c000025_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_2/csv_stats/tile_r000007_c000025_grain_stats.csv\n", + "done with segmentation + export\n", + "[25/905] tile_r000007_c000026\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.44it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.42it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 204/204 [00:06<00:00, 29.64it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "108it [00:00, 282.03it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "111it [00:00, 281.88it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 34/34 [00:01<00:00, 32.20it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 39/39 [00:00<00:00, 169.75it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_2/ouputgpkg/tile_r000007_c000026_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_2/csv_stats/tile_r000007_c000026_grain_stats.csv\n", + "done with segmentation + export\n", + "[26/905] tile_r000007_c000027\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.33it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.35it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 527/527 [00:14<00:00, 36.85it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "436it [00:01, 327.55it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "433it [00:01, 363.29it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 130/130 [00:01<00:00, 66.03it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 162/162 [00:00<00:00, 198.06it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_2/ouputgpkg/tile_r000007_c000027_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_2/csv_stats/tile_r000007_c000027_grain_stats.csv\n", + "done with segmentation + export\n", + "[27/905] tile_r000007_c000028\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.45it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.50it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 513/513 [00:13<00:00, 37.04it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "431it [00:01, 358.65it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "453it [00:01, 362.42it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 157/157 [00:01<00:00, 79.37it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 163/163 [00:00<00:00, 204.78it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_2/ouputgpkg/tile_r000007_c000028_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_2/csv_stats/tile_r000007_c000028_grain_stats.csv\n", + "done with segmentation + export\n", + "[28/905] tile_r000007_c000029\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.42it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.20it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 306/306 [00:08<00:00, 38.11it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "244it [00:00, 305.04it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "243it [00:00, 317.09it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 72/72 [00:01<00:00, 52.73it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 87/87 [00:00<00:00, 188.13it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsshome1/0B/di54doz/.local/share/mamba/envs/segmenteverygrain/lib/python3.10/site-packages/segmenteverygrain/segmenteverygrain.py:2217: RuntimeWarning: divide by zero encountered in log2\n", + " phi_major = -np.log2(major_axis_length) # major axis length in phi scale\n", + "/dss/dsshome1/0B/di54doz/.local/share/mamba/envs/segmenteverygrain/lib/python3.10/site-packages/segmenteverygrain/segmenteverygrain.py:2218: RuntimeWarning: divide by zero encountered in log2\n", + " phi_minor = -np.log2(minor_axis_length)\n", + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_2/ouputgpkg/tile_r000007_c000029_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_2/csv_stats/tile_r000007_c000029_grain_stats.csv\n", + "done with segmentation + export\n", + "[29/905] tile_r000007_c000030\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.48it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.42it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 397/397 [00:10<00:00, 39.26it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "333it [00:01, 314.60it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "347it [00:01, 318.79it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 119/119 [00:01<00:00, 62.77it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 123/123 [00:00<00:00, 191.16it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_2/ouputgpkg/tile_r000007_c000030_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_2/csv_stats/tile_r000007_c000030_grain_stats.csv\n", + "done with segmentation + export\n", + "[30/905] tile_r000007_c000031\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.48it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.51it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 267/267 [00:07<00:00, 37.90it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "193it [00:00, 357.42it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "200it [00:00, 362.96it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 70/70 [00:00<00:00, 76.02it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 74/74 [00:00<00:00, 186.68it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_2/ouputgpkg/tile_r000007_c000031_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_2/csv_stats/tile_r000007_c000031_grain_stats.csv\n", + "done with segmentation + export\n", + "[31/905] tile_r000007_c000032\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.33it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.31it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 165/165 [00:04<00:00, 35.70it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "124it [00:00, 225.08it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "123it [00:00, 229.94it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 34/34 [00:00<00:00, 35.64it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 43/43 [00:00<00:00, 164.73it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_2/ouputgpkg/tile_r000007_c000032_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_2/csv_stats/tile_r000007_c000032_grain_stats.csv\n", + "done with segmentation + export\n", + "[32/905] tile_r000008_c000021\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:01<00:00, 2.52it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.34it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 86/86 [00:02<00:00, 35.31it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "63it [00:00, 858.61it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "76it [00:00, 801.30it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 33/33 [00:00<00:00, 243.22it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 33/33 [00:00<00:00, 268.37it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_2/ouputgpkg/tile_r000008_c000021_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_2/csv_stats/tile_r000008_c000021_grain_stats.csv\n", + "done with segmentation + export\n", + "[33/905] tile_r000008_c000022\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.47it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.38it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 259/259 [00:07<00:00, 33.72it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "193it [00:00, 663.42it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "214it [00:00, 672.15it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 94/94 [00:00<00:00, 232.03it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 94/94 [00:00<00:00, 264.78it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_2/ouputgpkg/tile_r000008_c000022_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_2/csv_stats/tile_r000008_c000022_grain_stats.csv\n", + "done with segmentation + export\n", + "[34/905] tile_r000008_c000023\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:01<00:00, 2.52it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.49it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 78/78 [00:02<00:00, 29.78it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "26it [00:00, 587.45it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "30it [00:00, 529.88it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 14/14 [00:00<00:00, 307.82it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 14/14 [00:00<00:00, 243.76it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_2/ouputgpkg/tile_r000008_c000023_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_2/csv_stats/tile_r000008_c000023_grain_stats.csv\n", + "done with segmentation + export\n", + "[35/905] tile_r000008_c000024\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:01<00:00, 2.52it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.46it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 103/103 [00:05<00:00, 19.13it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "19it [00:00, 961.94it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "26it [00:00, 645.92it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 12/12 [00:00<00:00, 204.68it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 13/13 [00:00<00:00, 84.39it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_2/ouputgpkg/tile_r000008_c000024_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_2/csv_stats/tile_r000008_c000024_grain_stats.csv\n", + "done with segmentation + export\n", + "[36/905] tile_r000008_c000025\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.47it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.48it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 193/193 [00:07<00:00, 25.14it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "49it [00:00, 278.38it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "61it [00:00, 295.00it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 23/23 [00:00<00:00, 47.77it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 26/26 [00:00<00:00, 144.55it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_2/ouputgpkg/tile_r000008_c000025_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_2/csv_stats/tile_r000008_c000025_grain_stats.csv\n", + "done with segmentation + export\n", + "[37/905] tile_r000008_c000026\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.41it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.39it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 149/149 [00:07<00:00, 20.99it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "47it [00:00, 487.49it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "57it [00:00, 403.28it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 21/21 [00:00<00:00, 25.62it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 25/25 [00:00<00:00, 173.46it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_2/ouputgpkg/tile_r000008_c000026_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_2/csv_stats/tile_r000008_c000026_grain_stats.csv\n", + "done with segmentation + export\n", + "[38/905] tile_r000008_c000027\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.48it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.52it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 234/234 [00:07<00:00, 33.08it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "159it [00:00, 397.28it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "180it [00:00, 358.41it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 63/63 [00:00<00:00, 78.49it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 69/69 [00:00<00:00, 152.36it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_2/ouputgpkg/tile_r000008_c000027_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_2/csv_stats/tile_r000008_c000027_grain_stats.csv\n", + "done with segmentation + export\n", + "[39/905] tile_r000008_c000028\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.48it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.36it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 174/174 [00:04<00:00, 34.83it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "134it [00:00, 493.39it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "156it [00:00, 478.33it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 61/61 [00:00<00:00, 110.91it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 63/63 [00:00<00:00, 199.42it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_2/ouputgpkg/tile_r000008_c000028_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_2/csv_stats/tile_r000008_c000028_grain_stats.csv\n", + "done with segmentation + export\n", + "[40/905] tile_r000008_c000029\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:01<00:00, 2.50it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.51it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 182/182 [00:04<00:00, 37.58it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "139it [00:00, 436.65it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "149it [00:00, 444.69it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 53/53 [00:00<00:00, 80.25it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 55/55 [00:00<00:00, 196.14it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_2/ouputgpkg/tile_r000008_c000029_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_2/csv_stats/tile_r000008_c000029_grain_stats.csv\n", + "done with segmentation + export\n", + "[41/905] tile_r000008_c000030\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.49it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.45it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 167/167 [00:05<00:00, 31.52it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "110it [00:00, 434.20it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "121it [00:00, 438.78it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 47/47 [00:00<00:00, 91.32it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 48/48 [00:00<00:00, 191.07it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_2/ouputgpkg/tile_r000008_c000030_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_2/csv_stats/tile_r000008_c000030_grain_stats.csv\n", + "done with segmentation + export\n", + "[42/905] tile_r000008_c000031\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.48it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.48it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 174/174 [00:04<00:00, 39.28it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "142it [00:00, 488.27it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "154it [00:00, 485.28it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 62/62 [00:00<00:00, 109.07it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 63/63 [00:00<00:00, 201.37it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_2/ouputgpkg/tile_r000008_c000031_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_2/csv_stats/tile_r000008_c000031_grain_stats.csv\n", + "done with segmentation + export\n", + "[43/905] tile_r000008_c000032\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:01<00:00, 2.50it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.45it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 232/232 [00:06<00:00, 38.03it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "203it [00:00, 376.45it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "215it [00:00, 373.95it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 76/76 [00:00<00:00, 82.78it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 78/78 [00:00<00:00, 177.07it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_2/ouputgpkg/tile_r000008_c000032_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_2/csv_stats/tile_r000008_c000032_grain_stats.csv\n", + "done with segmentation + export\n", + "[44/905] tile_r000008_c000033\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.46it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.51it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 64/64 [00:01<00:00, 36.08it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "50it [00:00, 140.08it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "54it [00:00, 146.19it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 15/15 [00:00<00:00, 20.89it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 15/15 [00:00<00:00, 175.41it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_2/ouputgpkg/tile_r000008_c000033_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_2/csv_stats/tile_r000008_c000033_grain_stats.csv\n", + "done with segmentation + export\n", + "[45/905] tile_r000009_c000019\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.49it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.44it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 20/20 [00:00<00:00, 21.14it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "3it [00:00, 452.26it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "3it [00:00, 453.16it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 1/1 [00:00<00:00, 58.34it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 1/1 [00:00<00:00, 152.70it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n", + "Saved histogram plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_2/ouputgpkg/tile_r000009_c000019_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_2/csv_stats/tile_r000009_c000019_grain_stats.csv\n", + "done with segmentation + export\n", + "[46/905] tile_r000009_c000020\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.48it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.46it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 237/237 [00:07<00:00, 33.64it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "199it [00:00, 367.94it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "214it [00:00, 361.74it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 79/79 [00:00<00:00, 85.42it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 83/83 [00:00<00:00, 178.82it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_2/ouputgpkg/tile_r000009_c000020_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_2/csv_stats/tile_r000009_c000020_grain_stats.csv\n", + "done with segmentation + export\n", + "[47/905] tile_r000009_c000021\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:01<00:00, 2.56it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.50it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 98/98 [00:04<00:00, 22.61it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "33it [00:00, 336.63it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "37it [00:00, 332.37it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 15/15 [00:00<00:00, 113.26it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 15/15 [00:00<00:00, 119.54it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_2/ouputgpkg/tile_r000009_c000021_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_2/csv_stats/tile_r000009_c000021_grain_stats.csv\n", + "done with segmentation + export\n", + "[48/905] tile_r000009_c000022\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.32it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.33it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 78/78 [00:04<00:00, 18.59it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "0it [00:00, ?it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "0it [00:00, ?it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "0it [00:00, ?it/s]\n", + "0it [00:00, ?it/s]\n", + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n", + "No grains found -> skipping histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_2/ouputgpkg/tile_r000009_c000022_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_2/csv_stats/tile_r000009_c000022_grain_stats.csv\n", + "done with segmentation + export\n", + "[49/905] tile_r000009_c000023\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.46it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.51it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 291/291 [00:13<00:00, 21.94it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "195it [00:15, 12.65it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "193it [00:14, 12.97it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 49/49 [00:37<00:00, 1.32it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 71/71 [00:00<00:00, 151.51it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_2/ouputgpkg/tile_r000009_c000023_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_2/csv_stats/tile_r000009_c000023_grain_stats.csv\n", + "done with segmentation + export\n", + "[50/905] tile_r000009_c000024\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:01<00:00, 2.51it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.49it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 144/144 [00:07<00:00, 20.16it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "23it [00:00, 33.03it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "20it [00:00, 47.44it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 1/1 [00:01<00:00, 1.02s/it]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 12/12 [00:00<00:00, 75.33it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_2/ouputgpkg/tile_r000009_c000024_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_2/csv_stats/tile_r000009_c000024_grain_stats.csv\n", + "done with segmentation + export\n", + "[51/905] tile_r000009_c000025\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.49it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.46it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 66/66 [00:02<00:00, 24.39it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "45it [00:00, 76.03it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "43it [00:00, 425.27it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 12/12 [00:00<00:00, 34.88it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 24/24 [00:00<00:00, 67.94it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_2/ouputgpkg/tile_r000009_c000025_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_2/csv_stats/tile_r000009_c000025_grain_stats.csv\n", + "done with segmentation + export\n", + "[52/905] tile_r000009_c000026\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.49it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.51it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 113/113 [00:04<00:00, 26.18it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "67it [00:00, 158.46it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "89it [00:00, 156.68it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 31/31 [00:03<00:00, 9.59it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 40/40 [00:00<00:00, 86.32it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_2/ouputgpkg/tile_r000009_c000026_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_2/csv_stats/tile_r000009_c000026_grain_stats.csv\n", + "done with segmentation + export\n", + "[53/905] tile_r000009_c000027\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.50it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.49it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 226/226 [00:11<00:00, 18.86it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "95it [00:16, 5.83it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "83it [00:10, 8.02it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 7/7 [00:25<00:00, 3.59s/it]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 29/29 [00:00<00:00, 122.19it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_2/ouputgpkg/tile_r000009_c000027_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_2/csv_stats/tile_r000009_c000027_grain_stats.csv\n", + "done with segmentation + export\n", + "[54/905] tile_r000009_c000028\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.45it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.46it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 177/177 [00:07<00:00, 22.16it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "84it [00:02, 39.53it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "73it [00:00, 77.32it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 15/15 [00:02<00:00, 6.32it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 37/37 [00:00<00:00, 90.76it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_2/ouputgpkg/tile_r000009_c000028_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_2/csv_stats/tile_r000009_c000028_grain_stats.csv\n", + "done with segmentation + export\n", + "[55/905] tile_r000009_c000029\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.49it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.45it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 321/321 [00:11<00:00, 26.82it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "199it [00:04, 41.17it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "180it [00:02, 64.14it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 40/40 [00:06<00:00, 6.25it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 89/89 [00:00<00:00, 165.21it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_2/ouputgpkg/tile_r000009_c000029_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_2/csv_stats/tile_r000009_c000029_grain_stats.csv\n", + "done with segmentation + export\n", + "[56/905] tile_r000009_c000030\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.49it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.51it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 430/430 [00:13<00:00, 31.72it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "331it [00:01, 188.81it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "319it [00:01, 300.97it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 96/96 [00:01<00:00, 55.99it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 147/147 [00:00<00:00, 176.01it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_2/ouputgpkg/tile_r000009_c000030_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_2/csv_stats/tile_r000009_c000030_grain_stats.csv\n", + "done with segmentation + export\n", + "[57/905] tile_r000009_c000031\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.44it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.48it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 353/353 [00:10<00:00, 33.98it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "248it [00:01, 179.11it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "244it [00:00, 280.84it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 70/70 [00:01<00:00, 40.86it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 97/97 [00:00<00:00, 184.19it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_2/ouputgpkg/tile_r000009_c000031_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_2/csv_stats/tile_r000009_c000031_grain_stats.csv\n", + "done with segmentation + export\n", + "[58/905] tile_r000009_c000032\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.46it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.40it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 439/439 [00:12<00:00, 35.73it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "317it [00:00, 345.51it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "316it [00:00, 363.54it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 96/96 [00:01<00:00, 67.88it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 126/126 [00:00<00:00, 188.56it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_2/ouputgpkg/tile_r000009_c000032_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_2/csv_stats/tile_r000009_c000032_grain_stats.csv\n", + "done with segmentation + export\n", + "[59/905] tile_r000009_c000033\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.47it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.48it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 245/245 [00:07<00:00, 33.98it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "181it [00:00, 368.37it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "198it [00:00, 359.46it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 67/67 [00:00<00:00, 68.07it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 74/74 [00:00<00:00, 175.98it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_2/ouputgpkg/tile_r000009_c000033_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_2/csv_stats/tile_r000009_c000033_grain_stats.csv\n", + "done with segmentation + export\n", + "[60/905] tile_r000010_c000017\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:01<00:00, 2.51it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.48it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "0it [00:00, ?it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "0it [00:00, ?it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "0it [00:00, ?it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "0it [00:00, ?it/s]\n", + "0it [00:00, ?it/s]\n", + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n", + "No grains found -> skipping histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_2/ouputgpkg/tile_r000010_c000017_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_2/csv_stats/tile_r000010_c000017_grain_stats.csv\n", + "done with segmentation + export\n", + "[61/905] tile_r000010_c000018\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.47it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.45it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 55/55 [00:02<00:00, 23.55it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "4it [00:00, 356.10it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "4it [00:00, 385.23it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 1/1 [00:00<00:00, 39.11it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 2/2 [00:00<00:00, 108.85it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_2/ouputgpkg/tile_r000010_c000018_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_2/csv_stats/tile_r000010_c000018_grain_stats.csv\n", + "done with segmentation + export\n", + "[62/905] tile_r000010_c000019\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.47it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.48it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 345/345 [00:09<00:00, 35.95it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "294it [00:00, 434.14it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "312it [00:00, 425.20it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 127/127 [00:00<00:00, 137.24it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 128/128 [00:00<00:00, 215.10it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_2/ouputgpkg/tile_r000010_c000019_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_2/csv_stats/tile_r000010_c000019_grain_stats.csv\n", + "done with segmentation + export\n", + "[63/905] tile_r000010_c000020\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.45it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.34it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 112/112 [00:04<00:00, 25.13it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "24it [00:00, 122.47it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "27it [00:00, 117.86it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 9/9 [00:00<00:00, 15.26it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 11/11 [00:00<00:00, 74.86it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_2/ouputgpkg/tile_r000010_c000020_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_2/csv_stats/tile_r000010_c000020_grain_stats.csv\n", + "done with segmentation + export\n", + "[64/905] tile_r000010_c000021\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.43it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.45it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 548/548 [00:17<00:00, 31.51it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "355it [00:00, 566.53it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "374it [00:00, 563.43it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 156/156 [00:00<00:00, 206.22it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 158/158 [00:00<00:00, 271.59it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_2/ouputgpkg/tile_r000010_c000021_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_2/csv_stats/tile_r000010_c000021_grain_stats.csv\n", + "done with segmentation + export\n", + "[65/905] tile_r000010_c000022\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.46it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.19it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 704/704 [00:19<00:00, 36.87it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "602it [00:01, 560.33it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "601it [00:00, 642.94it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 227/227 [00:01<00:00, 185.97it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 298/298 [00:01<00:00, 269.68it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_2/ouputgpkg/tile_r000010_c000022_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_2/csv_stats/tile_r000010_c000022_grain_stats.csv\n", + "done with segmentation + export\n", + "[66/905] tile_r000010_c000023\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.49it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.49it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 434/434 [00:12<00:00, 35.58it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "312it [00:00, 583.97it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "334it [00:00, 564.10it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 138/138 [00:00<00:00, 149.53it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 146/146 [00:00<00:00, 222.23it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_2/ouputgpkg/tile_r000010_c000023_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_2/csv_stats/tile_r000010_c000023_grain_stats.csv\n", + "done with segmentation + export\n", + "[67/905] tile_r000010_c000024\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.50it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.42it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 161/161 [00:07<00:00, 21.78it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "43it [00:00, 45.48it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "38it [00:00, 111.54it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 6/6 [00:01<00:00, 5.39it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 17/17 [00:00<00:00, 83.98it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_2/ouputgpkg/tile_r000010_c000024_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_2/csv_stats/tile_r000010_c000024_grain_stats.csv\n", + "done with segmentation + export\n", + "[68/905] tile_r000010_c000025\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:01<00:00, 2.50it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.47it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 80/80 [00:03<00:00, 22.21it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "35it [00:00, 419.50it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "48it [00:00, 362.65it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 21/21 [00:00<00:00, 40.23it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 26/26 [00:00<00:00, 101.87it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_2/ouputgpkg/tile_r000010_c000025_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_2/csv_stats/tile_r000010_c000025_grain_stats.csv\n", + "done with segmentation + export\n", + "[69/905] tile_r000010_c000026\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.44it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.50it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 129/129 [00:05<00:00, 23.66it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "23it [00:00, 1396.22it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "36it [00:00, 777.72it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 17/17 [00:00<00:00, 195.64it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 17/17 [00:00<00:00, 164.02it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_2/ouputgpkg/tile_r000010_c000026_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_2/csv_stats/tile_r000010_c000026_grain_stats.csv\n", + "done with segmentation + export\n", + "[70/905] tile_r000010_c000027\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.49it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.46it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 148/148 [00:05<00:00, 25.64it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "56it [00:00, 123.78it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "55it [00:00, 216.87it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 9/9 [00:02<00:00, 3.00it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 33/33 [00:00<00:00, 134.91it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_2/ouputgpkg/tile_r000010_c000027_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_2/csv_stats/tile_r000010_c000027_grain_stats.csv\n", + "done with segmentation + export\n", + "[71/905] tile_r000010_c000028\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.41it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.37it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 219/219 [00:10<00:00, 21.80it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "122it [00:10, 11.67it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "105it [00:07, 13.45it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 17/17 [00:16<00:00, 1.06it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 42/42 [00:00<00:00, 101.04it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n", + "Warning: len(gdf)=42 != len(grain_stats)=41 -> truncating to 41\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_2/ouputgpkg/tile_r000010_c000028_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_2/csv_stats/tile_r000010_c000028_grain_stats.csv\n", + "done with segmentation + export\n", + "[72/905] tile_r000010_c000029\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.45it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.44it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 248/248 [00:09<00:00, 26.68it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "98it [00:00, 147.93it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "97it [00:00, 150.31it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 21/21 [00:01<00:00, 15.75it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 49/49 [00:00<00:00, 95.26it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_2/ouputgpkg/tile_r000010_c000029_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_2/csv_stats/tile_r000010_c000029_grain_stats.csv\n", + "done with segmentation + export\n", + "[73/905] tile_r000010_c000030\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.47it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.43it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 199/199 [00:07<00:00, 26.04it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "111it [00:01, 56.66it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "99it [00:00, 190.40it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 17/17 [00:01<00:00, 15.29it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 59/59 [00:00<00:00, 122.04it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_2/ouputgpkg/tile_r000010_c000030_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_2/csv_stats/tile_r000010_c000030_grain_stats.csv\n", + "done with segmentation + export\n", + "[74/905] tile_r000010_c000031\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:01<00:00, 2.51it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.44it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 159/159 [00:05<00:00, 27.84it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "63it [00:00, 135.53it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "80it [00:00, 142.23it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 28/28 [00:01<00:00, 15.31it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 32/32 [00:00<00:00, 105.01it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_2/ouputgpkg/tile_r000010_c000031_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_2/csv_stats/tile_r000010_c000031_grain_stats.csv\n", + "done with segmentation + export\n", + "[75/905] tile_r000010_c000032\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.44it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.28it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 174/174 [00:06<00:00, 26.63it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "101it [00:00, 206.87it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "134it [00:00, 204.44it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 55/55 [00:01<00:00, 27.84it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 62/62 [00:00<00:00, 113.61it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_2/ouputgpkg/tile_r000010_c000032_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_2/csv_stats/tile_r000010_c000032_grain_stats.csv\n", + "done with segmentation + export\n", + "[76/905] tile_r000010_c000033\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:01<00:00, 2.52it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.41it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 568/568 [00:16<00:00, 35.35it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "412it [00:01, 232.27it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "401it [00:01, 382.29it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 136/136 [00:01<00:00, 94.62it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 187/187 [00:00<00:00, 191.15it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_2/ouputgpkg/tile_r000010_c000033_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_2/csv_stats/tile_r000010_c000033_grain_stats.csv\n", + "done with segmentation + export\n", + "[77/905] tile_r000010_c000034\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.44it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.50it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 125/125 [00:03<00:00, 35.88it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "95it [00:00, 477.13it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "104it [00:00, 457.37it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 45/45 [00:00<00:00, 187.25it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 45/45 [00:00<00:00, 229.66it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_2/ouputgpkg/tile_r000010_c000034_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_2/csv_stats/tile_r000010_c000034_grain_stats.csv\n", + "done with segmentation + export\n", + "[78/905] tile_r000011_c000016\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.39it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.28it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 23/23 [00:01<00:00, 19.71it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "0it [00:00, ?it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "0it [00:00, ?it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "0it [00:00, ?it/s]\n", + "0it [00:00, ?it/s]\n", + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n", + "No grains found -> skipping histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_2/ouputgpkg/tile_r000011_c000016_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_2/csv_stats/tile_r000011_c000016_grain_stats.csv\n", + "done with segmentation + export\n", + "[79/905] tile_r000011_c000017\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.49it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.54it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 127/127 [00:04<00:00, 31.16it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "80it [00:00, 330.39it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "84it [00:00, 331.70it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 31/31 [00:00<00:00, 77.74it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 34/34 [00:00<00:00, 178.38it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_2/ouputgpkg/tile_r000011_c000017_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_2/csv_stats/tile_r000011_c000017_grain_stats.csv\n", + "done with segmentation + export\n", + "[80/905] tile_r000011_c000018\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.33it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.32it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 142/142 [00:05<00:00, 25.27it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "49it [00:00, 675.70it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "55it [00:00, 501.35it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 24/24 [00:00<00:00, 134.21it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 24/24 [00:00<00:00, 147.94it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_2/ouputgpkg/tile_r000011_c000018_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_2/csv_stats/tile_r000011_c000018_grain_stats.csv\n", + "done with segmentation + export\n", + "[81/905] tile_r000011_c000019\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.48it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.47it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 1319/1319 [00:32<00:00, 40.84it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "1223it [00:02, 530.69it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "1222it [00:02, 528.94it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 508/508 [00:02<00:00, 192.30it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 549/549 [00:02<00:00, 259.88it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_2/ouputgpkg/tile_r000011_c000019_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_2/csv_stats/tile_r000011_c000019_grain_stats.csv\n", + "done with segmentation + export\n", + "[82/905] tile_r000011_c000020\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.45it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.55it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 1411/1411 [00:37<00:00, 37.56it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "1249it [00:02, 536.76it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "1248it [00:02, 514.64it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 493/493 [00:02<00:00, 181.03it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 553/553 [00:02<00:00, 275.68it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_2/ouputgpkg/tile_r000011_c000020_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_2/csv_stats/tile_r000011_c000020_grain_stats.csv\n", + "done with segmentation + export\n", + "[83/905] tile_r000011_c000021\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.40it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.43it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 1678/1678 [00:42<00:00, 39.44it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "1554it [00:02, 603.90it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "1605it [00:02, 578.00it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 685/685 [00:03<00:00, 218.18it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 699/699 [00:02<00:00, 289.00it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_2/ouputgpkg/tile_r000011_c000021_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_2/csv_stats/tile_r000011_c000021_grain_stats.csv\n", + "done with segmentation + export\n", + "[84/905] tile_r000011_c000022\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:01<00:00, 2.53it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.46it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 798/798 [00:20<00:00, 38.49it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "724it [00:01, 614.83it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "788it [00:01, 602.82it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 336/336 [00:01<00:00, 206.69it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 341/341 [00:01<00:00, 257.00it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_2/ouputgpkg/tile_r000011_c000022_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_2/csv_stats/tile_r000011_c000022_grain_stats.csv\n", + "done with segmentation + export\n", + "[85/905] tile_r000011_c000023\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.44it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.42it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 803/803 [00:22<00:00, 35.46it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "667it [00:01, 363.01it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "665it [00:01, 395.47it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 237/237 [00:02<00:00, 93.82it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 304/304 [00:01<00:00, 225.46it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_2/ouputgpkg/tile_r000011_c000023_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_2/csv_stats/tile_r000011_c000023_grain_stats.csv\n", + "done with segmentation + export\n", + "[86/905] tile_r000011_c000024\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.47it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.47it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 702/702 [00:20<00:00, 34.96it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "613it [00:06, 94.38it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "61it [00:00, 63.60it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 1/1 [00:01<00:00, 1.72s/it]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 49/49 [00:00<00:00, 196.95it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_2/ouputgpkg/tile_r000011_c000024_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_2/csv_stats/tile_r000011_c000024_grain_stats.csv\n", + "done with segmentation + export\n", + "[87/905] tile_r000011_c000025\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:01<00:00, 2.52it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.49it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 472/472 [00:13<00:00, 34.89it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "343it [00:01, 268.00it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "341it [00:01, 310.17it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 102/102 [00:02<00:00, 48.07it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 150/150 [00:00<00:00, 181.50it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_2/ouputgpkg/tile_r000011_c000025_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_2/csv_stats/tile_r000011_c000025_grain_stats.csv\n", + "done with segmentation + export\n", + "[88/905] tile_r000011_c000026\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.43it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.49it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 254/254 [00:08<00:00, 29.80it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "148it [00:01, 113.74it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "142it [00:00, 207.66it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 28/28 [00:02<00:00, 11.70it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 64/64 [00:00<00:00, 102.01it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_2/ouputgpkg/tile_r000011_c000026_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_2/csv_stats/tile_r000011_c000026_grain_stats.csv\n", + "done with segmentation + export\n", + "[89/905] tile_r000011_c000027\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.46it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.45it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 321/321 [00:10<00:00, 31.57it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "209it [00:00, 266.27it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "208it [00:00, 288.86it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 52/52 [00:01<00:00, 28.57it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 107/107 [00:00<00:00, 128.45it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_2/ouputgpkg/tile_r000011_c000027_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_2/csv_stats/tile_r000011_c000027_grain_stats.csv\n", + "done with segmentation + export\n", + "[90/905] tile_r000011_c000028\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.46it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.47it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 283/283 [00:09<00:00, 29.84it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "188it [00:01, 98.02it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "179it [00:01, 149.11it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 35/35 [00:07<00:00, 4.54it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 81/81 [00:00<00:00, 94.27it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_2/ouputgpkg/tile_r000011_c000028_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_2/csv_stats/tile_r000011_c000028_grain_stats.csv\n", + "done with segmentation + export\n", + "[91/905] tile_r000011_c000029\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.42it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.51it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 317/317 [00:12<00:00, 25.34it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "176it [00:01, 106.83it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "168it [00:01, 89.33it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 35/35 [00:03<00:00, 11.37it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 74/74 [00:00<00:00, 133.11it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_2/ouputgpkg/tile_r000011_c000029_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_2/csv_stats/tile_r000011_c000029_grain_stats.csv\n", + "done with segmentation + export\n", + "[92/905] tile_r000011_c000030\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.47it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.48it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 417/417 [00:11<00:00, 35.26it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "339it [00:01, 176.79it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "333it [00:01, 204.62it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 83/83 [00:04<00:00, 18.30it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 138/138 [00:00<00:00, 140.33it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_2/ouputgpkg/tile_r000011_c000030_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_2/csv_stats/tile_r000011_c000030_grain_stats.csv\n", + "done with segmentation + export\n", + "[93/905] tile_r000011_c000031\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:01<00:00, 2.50it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.49it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 426/426 [00:12<00:00, 33.24it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "324it [00:01, 236.06it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "323it [00:01, 261.52it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 83/83 [00:02<00:00, 28.17it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 154/154 [00:01<00:00, 144.05it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_2/ouputgpkg/tile_r000011_c000031_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_2/csv_stats/tile_r000011_c000031_grain_stats.csv\n", + "done with segmentation + export\n", + "[94/905] tile_r000011_c000032\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.44it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.49it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 502/502 [00:16<00:00, 30.34it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "359it [00:00, 473.18it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "357it [00:00, 526.51it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 119/119 [00:01<00:00, 96.49it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 183/183 [00:00<00:00, 202.06it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_2/ouputgpkg/tile_r000011_c000032_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_2/csv_stats/tile_r000011_c000032_grain_stats.csv\n", + "done with segmentation + export\n", + "[95/905] tile_r000011_c000033\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.45it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.52it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 560/560 [00:16<00:00, 33.66it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "406it [00:01, 262.77it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "404it [00:01, 286.83it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 127/127 [00:02<00:00, 43.21it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 187/187 [00:00<00:00, 187.06it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_2/ouputgpkg/tile_r000011_c000033_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_2/csv_stats/tile_r000011_c000033_grain_stats.csv\n", + "done with segmentation + export\n", + "[96/905] tile_r000011_c000034\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.39it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.47it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 458/458 [00:12<00:00, 37.56it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "393it [00:01, 330.68it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "419it [00:01, 334.72it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 154/154 [00:01<00:00, 89.80it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 163/163 [00:00<00:00, 200.75it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_2/ouputgpkg/tile_r000011_c000034_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_2/csv_stats/tile_r000011_c000034_grain_stats.csv\n", + "done with segmentation + export\n", + "[97/905] tile_r000012_c000014\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.50it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.44it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 82/82 [00:02<00:00, 32.15it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "55it [00:00, 226.69it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "58it [00:00, 225.31it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 20/20 [00:00<00:00, 50.70it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 20/20 [00:00<00:00, 159.98it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_2/ouputgpkg/tile_r000012_c000014_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_2/csv_stats/tile_r000012_c000014_grain_stats.csv\n", + "done with segmentation + export\n", + "[98/905] tile_r000012_c000015\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.44it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.48it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 343/343 [00:08<00:00, 38.63it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "297it [00:01, 265.77it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "311it [00:01, 270.94it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 114/114 [00:01<00:00, 68.39it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 119/119 [00:00<00:00, 186.84it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_2/ouputgpkg/tile_r000012_c000015_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_2/csv_stats/tile_r000012_c000015_grain_stats.csv\n", + "done with segmentation + export\n", + "[99/905] tile_r000012_c000016\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.41it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.49it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 487/487 [00:13<00:00, 35.94it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "373it [00:01, 256.44it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "371it [00:01, 268.08it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 115/115 [00:02<00:00, 56.60it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 141/141 [00:00<00:00, 185.29it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_2/ouputgpkg/tile_r000012_c000016_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_2/csv_stats/tile_r000012_c000016_grain_stats.csv\n", + "done with segmentation + export\n", + "[100/905] tile_r000012_c000017\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.44it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.49it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 834/834 [00:21<00:00, 39.07it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "749it [00:02, 320.86it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "793it [00:02, 304.66it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 297/297 [00:03<00:00, 84.00it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 306/306 [00:01<00:00, 217.91it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_2/ouputgpkg/tile_r000012_c000017_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_2/csv_stats/tile_r000012_c000017_grain_stats.csv\n", + "done with segmentation + export\n", + "[101/905] tile_r000012_c000018\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.45it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.43it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 827/827 [00:21<00:00, 37.66it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "699it [00:01, 376.99it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "735it [00:01, 370.16it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 277/277 [00:02<00:00, 97.38it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 283/283 [00:01<00:00, 206.17it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_2/ouputgpkg/tile_r000012_c000018_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_2/csv_stats/tile_r000012_c000018_grain_stats.csv\n", + "done with segmentation + export\n", + "[102/905] tile_r000012_c000019\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.35it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.47it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 1468/1468 [00:36<00:00, 40.19it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "1352it [00:02, 549.97it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "1394it [00:02, 519.78it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 611/611 [00:02<00:00, 213.77it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 621/621 [00:02<00:00, 262.26it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_2/ouputgpkg/tile_r000012_c000019_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_2/csv_stats/tile_r000012_c000019_grain_stats.csv\n", + "done with segmentation + export\n", + "[103/905] tile_r000012_c000020\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.47it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.39it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 1615/1615 [00:40<00:00, 39.48it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "1471it [00:02, 577.33it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "1470it [00:02, 564.58it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 597/597 [00:02<00:00, 201.13it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 679/679 [00:02<00:00, 276.65it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_2/ouputgpkg/tile_r000012_c000020_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_2/csv_stats/tile_r000012_c000020_grain_stats.csv\n", + "done with segmentation + export\n", + "[104/905] tile_r000012_c000021\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.44it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.43it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 1406/1406 [00:36<00:00, 38.83it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "1299it [00:01, 655.77it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "1297it [00:01, 726.58it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 546/546 [00:02<00:00, 231.74it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 606/606 [00:02<00:00, 279.52it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_2/ouputgpkg/tile_r000012_c000021_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_2/csv_stats/tile_r000012_c000021_grain_stats.csv\n", + "done with segmentation + export\n", + "[105/905] tile_r000012_c000022\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.42it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.49it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 1098/1098 [00:28<00:00, 38.39it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "967it [00:03, 275.53it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "88it [00:00, 95.24it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 2/2 [00:02<00:00, 1.05s/it]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 65/65 [00:00<00:00, 198.64it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_2/ouputgpkg/tile_r000012_c000022_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_2/csv_stats/tile_r000012_c000022_grain_stats.csv\n", + "done with segmentation + export\n", + "[106/905] tile_r000012_c000023\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.39it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.45it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 1324/1324 [00:35<00:00, 36.93it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "1195it [00:02, 555.76it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "1261it [00:02, 548.23it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 540/540 [00:02<00:00, 199.89it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 552/552 [00:02<00:00, 264.27it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_2/ouputgpkg/tile_r000012_c000023_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_2/csv_stats/tile_r000012_c000023_grain_stats.csv\n", + "done with segmentation + export\n", + "[107/905] tile_r000012_c000024\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.41it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.43it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 756/756 [00:19<00:00, 37.98it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "666it [00:01, 360.02it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "661it [00:01, 423.95it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 226/226 [00:02<00:00, 88.01it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 310/310 [00:01<00:00, 226.82it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_2/ouputgpkg/tile_r000012_c000024_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_2/csv_stats/tile_r000012_c000024_grain_stats.csv\n", + "done with segmentation + export\n", + "[108/905] tile_r000012_c000025\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.38it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.42it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 997/997 [00:26<00:00, 37.17it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "831it [00:02, 293.99it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "824it [00:02, 359.14it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 287/287 [00:03<00:00, 74.00it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 375/375 [00:01<00:00, 220.64it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n", + "Warning: len(gdf)=375 != len(grain_stats)=371 -> truncating to 371\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_2/ouputgpkg/tile_r000012_c000025_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_2/csv_stats/tile_r000012_c000025_grain_stats.csv\n", + "done with segmentation + export\n", + "[109/905] tile_r000012_c000026\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.42it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.41it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 957/957 [00:25<00:00, 36.94it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "803it [00:01, 499.68it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "878it [00:01, 463.12it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 356/356 [00:02<00:00, 127.72it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 366/366 [00:01<00:00, 230.26it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_2/ouputgpkg/tile_r000012_c000026_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_2/csv_stats/tile_r000012_c000026_grain_stats.csv\n", + "done with segmentation + export\n", + "[110/905] tile_r000012_c000027\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.44it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.40it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 808/808 [00:21<00:00, 38.31it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "680it [00:01, 505.99it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "736it [00:01, 480.97it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 304/304 [00:02<00:00, 118.43it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 322/322 [00:01<00:00, 229.33it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_2/ouputgpkg/tile_r000012_c000027_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_2/csv_stats/tile_r000012_c000027_grain_stats.csv\n", + "done with segmentation + export\n", + "[111/905] tile_r000012_c000028\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.39it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.45it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 721/721 [00:19<00:00, 37.18it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "590it [00:01, 326.75it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "587it [00:01, 352.41it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 185/185 [00:03<00:00, 49.80it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 252/252 [00:01<00:00, 203.38it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_2/ouputgpkg/tile_r000012_c000028_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_2/csv_stats/tile_r000012_c000028_grain_stats.csv\n", + "done with segmentation + export\n", + "[112/905] tile_r000012_c000029\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.44it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.47it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 573/573 [00:15<00:00, 36.04it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "451it [00:01, 261.83it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "450it [00:01, 274.51it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 129/129 [00:04<00:00, 29.84it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 203/203 [00:01<00:00, 165.54it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n", + "Warning: len(gdf)=203 != len(grain_stats)=202 -> truncating to 202\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_2/ouputgpkg/tile_r000012_c000029_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_2/csv_stats/tile_r000012_c000029_grain_stats.csv\n", + "done with segmentation + export\n", + "[113/905] tile_r000012_c000030\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.49it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.42it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 664/664 [00:18<00:00, 36.63it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "557it [00:02, 229.19it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "553it [00:02, 249.62it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 148/148 [00:03<00:00, 37.48it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 231/231 [00:01<00:00, 178.69it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_2/ouputgpkg/tile_r000012_c000030_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_2/csv_stats/tile_r000012_c000030_grain_stats.csv\n", + "done with segmentation + export\n", + "[114/905] tile_r000012_c000031\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.43it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.40it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 639/639 [00:17<00:00, 36.27it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "509it [00:01, 274.19it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "563it [00:02, 281.49it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 202/202 [00:03<00:00, 51.90it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 224/224 [00:01<00:00, 191.99it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n", + "Warning: len(gdf)=224 != len(grain_stats)=223 -> truncating to 223\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_2/ouputgpkg/tile_r000012_c000031_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_2/csv_stats/tile_r000012_c000031_grain_stats.csv\n", + "done with segmentation + export\n", + "[115/905] tile_r000012_c000032\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.43it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.44it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 354/354 [00:11<00:00, 32.06it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "231it [00:00, 487.19it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "259it [00:00, 481.75it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 96/96 [00:01<00:00, 89.80it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 106/106 [00:00<00:00, 184.61it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_2/ouputgpkg/tile_r000012_c000032_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_2/csv_stats/tile_r000012_c000032_grain_stats.csv\n", + "done with segmentation + export\n", + "[116/905] tile_r000012_c000033\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.41it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.50it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 509/509 [00:14<00:00, 34.62it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "372it [00:01, 226.82it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "367it [00:01, 258.50it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 120/120 [00:02<00:00, 49.42it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 156/156 [00:00<00:00, 188.39it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_2/ouputgpkg/tile_r000012_c000033_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_2/csv_stats/tile_r000012_c000033_grain_stats.csv\n", + "done with segmentation + export\n", + "[117/905] tile_r000012_c000034\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.29it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.40it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 817/817 [00:21<00:00, 37.62it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "698it [00:01, 387.32it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "742it [00:02, 370.07it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 294/294 [00:02<00:00, 124.90it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 304/304 [00:01<00:00, 217.55it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_2/ouputgpkg/tile_r000012_c000034_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_2/csv_stats/tile_r000012_c000034_grain_stats.csv\n", + "done with segmentation + export\n", + "[118/905] tile_r000013_c000013\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.39it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.40it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 266/266 [00:07<00:00, 36.23it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "192it [00:00, 215.43it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "202it [00:00, 217.59it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 66/66 [00:01<00:00, 51.50it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 69/69 [00:00<00:00, 162.42it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_2/ouputgpkg/tile_r000013_c000013_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_2/csv_stats/tile_r000013_c000013_grain_stats.csv\n", + "done with segmentation + export\n", + "[119/905] tile_r000013_c000014\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.31it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.46it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 727/727 [00:18<00:00, 38.27it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "626it [00:02, 214.58it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "625it [00:02, 214.48it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 196/196 [00:03<00:00, 51.53it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 236/236 [00:01<00:00, 167.98it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_2/ouputgpkg/tile_r000013_c000014_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_2/csv_stats/tile_r000013_c000014_grain_stats.csv\n", + "done with segmentation + export\n", + "[120/905] tile_r000013_c000015\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.35it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.48it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 776/776 [00:19<00:00, 38.87it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "661it [00:02, 225.17it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "692it [00:03, 215.89it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 240/240 [00:03<00:00, 60.43it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 247/247 [00:01<00:00, 172.69it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_2/ouputgpkg/tile_r000013_c000015_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_2/csv_stats/tile_r000013_c000015_grain_stats.csv\n", + "done with segmentation + export\n", + "[121/905] tile_r000013_c000016\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.43it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.40it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 881/881 [00:22<00:00, 38.52it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "771it [00:03, 234.05it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "810it [00:03, 226.03it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 269/269 [00:04<00:00, 56.86it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 282/282 [00:01<00:00, 181.97it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_2/ouputgpkg/tile_r000013_c000016_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_2/csv_stats/tile_r000013_c000016_grain_stats.csv\n", + "done with segmentation + export\n", + "[122/905] tile_r000013_c000017\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.43it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.48it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 747/747 [00:19<00:00, 39.28it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "642it [00:02, 304.75it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "676it [00:02, 285.59it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 239/239 [00:03<00:00, 78.36it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 250/250 [00:01<00:00, 185.01it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_2/ouputgpkg/tile_r000013_c000017_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_2/csv_stats/tile_r000013_c000017_grain_stats.csv\n", + "done with segmentation + export\n", + "[123/905] tile_r000013_c000018\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:01<00:00, 2.51it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.46it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 823/823 [00:20<00:00, 39.46it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "707it [00:02, 299.39it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "743it [00:02, 296.26it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 263/263 [00:03<00:00, 77.94it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 271/271 [00:01<00:00, 180.96it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_2/ouputgpkg/tile_r000013_c000018_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_2/csv_stats/tile_r000013_c000018_grain_stats.csv\n", + "done with segmentation + export\n", + "[124/905] tile_r000013_c000019\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.43it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.53it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 1207/1207 [00:29<00:00, 40.66it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "1074it [00:02, 392.73it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "1111it [00:02, 389.78it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 444/444 [00:03<00:00, 135.25it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 453/453 [00:01<00:00, 228.47it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_2/ouputgpkg/tile_r000013_c000019_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_2/csv_stats/tile_r000013_c000019_grain_stats.csv\n", + "done with segmentation + export\n", + "[125/905] tile_r000013_c000020\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.48it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.45it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 1558/1558 [00:39<00:00, 39.68it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "1422it [00:02, 531.57it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "1421it [00:02, 538.24it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 584/584 [00:03<00:00, 187.23it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 651/651 [00:02<00:00, 267.12it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_2/ouputgpkg/tile_r000013_c000020_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_2/csv_stats/tile_r000013_c000020_grain_stats.csv\n", + "done with segmentation + export\n", + "[126/905] tile_r000013_c000021\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.41it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.41it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 1371/1371 [00:35<00:00, 38.66it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "1243it [00:01, 674.22it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "1304it [00:02, 634.29it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 576/576 [00:02<00:00, 262.52it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 583/583 [00:02<00:00, 281.67it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_2/ouputgpkg/tile_r000013_c000021_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_2/csv_stats/tile_r000013_c000021_grain_stats.csv\n", + "done with segmentation + export\n", + "[127/905] tile_r000013_c000022\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.36it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.36it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 913/913 [00:25<00:00, 35.17it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "786it [00:01, 600.77it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "785it [00:01, 687.10it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 283/283 [00:01<00:00, 171.70it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 396/396 [00:01<00:00, 265.32it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_2/ouputgpkg/tile_r000013_c000022_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_2/csv_stats/tile_r000013_c000022_grain_stats.csv\n", + "done with segmentation + export\n", + "[128/905] tile_r000013_c000023\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.40it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.44it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 901/901 [00:26<00:00, 33.85it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "789it [00:07, 103.94it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "780it [00:07, 110.71it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 265/265 [00:14<00:00, 18.75it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 370/370 [00:01<00:00, 231.13it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_2/ouputgpkg/tile_r000013_c000023_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_2/csv_stats/tile_r000013_c000023_grain_stats.csv\n", + "done with segmentation + export\n", + "[129/905] tile_r000013_c000024\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.44it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.48it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 1080/1080 [00:26<00:00, 40.70it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "971it [00:01, 656.20it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "1036it [00:01, 603.05it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 440/440 [00:02<00:00, 195.39it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 449/449 [00:01<00:00, 261.39it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_2/ouputgpkg/tile_r000013_c000024_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_2/csv_stats/tile_r000013_c000024_grain_stats.csv\n", + "done with segmentation + export\n", + "[130/905] tile_r000013_c000025\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.49it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.51it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 1045/1045 [00:27<00:00, 38.68it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "913it [00:01, 484.35it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "911it [00:01, 488.12it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 340/340 [00:02<00:00, 132.12it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 397/397 [00:01<00:00, 240.67it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_2/ouputgpkg/tile_r000013_c000025_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_2/csv_stats/tile_r000013_c000025_grain_stats.csv\n", + "done with segmentation + export\n", + "[131/905] tile_r000013_c000026\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.46it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.54it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 970/970 [00:24<00:00, 39.27it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "835it [00:01, 486.72it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "834it [00:01, 567.78it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 308/308 [00:02<00:00, 145.52it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 373/373 [00:01<00:00, 246.48it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_2/ouputgpkg/tile_r000013_c000026_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_2/csv_stats/tile_r000013_c000026_grain_stats.csv\n", + "done with segmentation + export\n", + "[132/905] tile_r000013_c000027\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.44it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.48it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 853/853 [00:22<00:00, 38.10it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "715it [00:01, 459.56it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "711it [00:01, 502.48it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 239/239 [00:02<00:00, 107.81it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 348/348 [00:01<00:00, 225.10it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_2/ouputgpkg/tile_r000013_c000027_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_2/csv_stats/tile_r000013_c000027_grain_stats.csv\n", + "done with segmentation + export\n", + "[133/905] tile_r000013_c000028\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.44it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.43it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 679/679 [00:18<00:00, 37.57it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "554it [00:01, 388.03it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "550it [00:01, 462.23it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 164/164 [00:02<00:00, 62.72it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 262/262 [00:01<00:00, 205.18it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n", + "Warning: len(gdf)=262 != len(grain_stats)=259 -> truncating to 259\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_2/ouputgpkg/tile_r000013_c000028_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_2/csv_stats/tile_r000013_c000028_grain_stats.csv\n", + "done with segmentation + export\n", + "[134/905] tile_r000013_c000029\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.40it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.49it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 589/589 [00:16<00:00, 36.04it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "470it [00:01, 262.14it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "464it [00:01, 301.39it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 116/116 [00:03<00:00, 31.33it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 201/201 [00:01<00:00, 167.14it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n", + "Warning: len(gdf)=201 != len(grain_stats)=200 -> truncating to 200\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_2/ouputgpkg/tile_r000013_c000029_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_2/csv_stats/tile_r000013_c000029_grain_stats.csv\n", + "done with segmentation + export\n", + "[135/905] tile_r000013_c000030\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.42it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.45it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 542/542 [00:14<00:00, 37.09it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "437it [00:01, 254.77it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "431it [00:01, 420.12it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 113/113 [00:02<00:00, 46.28it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 218/218 [00:01<00:00, 179.20it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n", + "Warning: len(gdf)=218 != len(grain_stats)=217 -> truncating to 217\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_2/ouputgpkg/tile_r000013_c000030_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_2/csv_stats/tile_r000013_c000030_grain_stats.csv\n", + "done with segmentation + export\n", + "[136/905] tile_r000013_c000031\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.44it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.49it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 523/523 [00:15<00:00, 34.59it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "438it [00:01, 348.55it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "434it [00:00, 495.34it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 128/128 [00:01<00:00, 66.03it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 215/215 [00:01<00:00, 212.42it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_2/ouputgpkg/tile_r000013_c000031_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_2/csv_stats/tile_r000013_c000031_grain_stats.csv\n", + "done with segmentation + export\n", + "[137/905] tile_r000013_c000032\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.47it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.54it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 554/554 [00:15<00:00, 36.65it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "464it [00:01, 439.49it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "463it [00:00, 499.41it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 141/141 [00:01<00:00, 90.79it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 229/229 [00:01<00:00, 198.31it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsshome1/0B/di54doz/.local/share/mamba/envs/segmenteverygrain/lib/python3.10/site-packages/segmenteverygrain/segmenteverygrain.py:2218: RuntimeWarning: divide by zero encountered in log2\n", + " phi_minor = -np.log2(minor_axis_length)\n", + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_2/ouputgpkg/tile_r000013_c000032_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_2/csv_stats/tile_r000013_c000032_grain_stats.csv\n", + "done with segmentation + export\n", + "[138/905] tile_r000013_c000033\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.47it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.46it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 525/525 [00:15<00:00, 32.90it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "371it [00:00, 482.62it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "422it [00:00, 449.28it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 170/170 [00:01<00:00, 119.67it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 182/182 [00:00<00:00, 189.10it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_2/ouputgpkg/tile_r000013_c000033_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_2/csv_stats/tile_r000013_c000033_grain_stats.csv\n", + "done with segmentation + export\n", + "[139/905] tile_r000013_c000034\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.48it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.43it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 821/821 [00:21<00:00, 37.93it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "692it [00:02, 231.88it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "745it [00:03, 227.24it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 278/278 [00:05<00:00, 53.34it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 290/290 [00:01<00:00, 213.18it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_2/ouputgpkg/tile_r000013_c000034_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_2/csv_stats/tile_r000013_c000034_grain_stats.csv\n", + "done with segmentation + export\n", + "[140/905] tile_r000013_c000035\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.34it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.54it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 30/30 [00:01<00:00, 28.64it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "14it [00:00, 679.96it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "19it [00:00, 613.77it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 8/8 [00:00<00:00, 141.62it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 8/8 [00:00<00:00, 217.04it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_2/ouputgpkg/tile_r000013_c000035_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_2/csv_stats/tile_r000013_c000035_grain_stats.csv\n", + "done with segmentation + export\n", + "[141/905] tile_r000014_c000011\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.37it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.46it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 12/12 [00:00<00:00, 21.28it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "1it [00:00, 1242.76it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2it [00:00, 480.12it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 1/1 [00:00<00:00, 184.00it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 1/1 [00:00<00:00, 117.32it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n", + "Saved histogram plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_2/ouputgpkg/tile_r000014_c000011_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_2/csv_stats/tile_r000014_c000011_grain_stats.csv\n", + "done with segmentation + export\n", + "[142/905] tile_r000014_c000012\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.38it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.47it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 345/345 [00:09<00:00, 37.50it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "271it [00:01, 173.75it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "283it [00:01, 176.07it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 81/81 [00:02<00:00, 36.96it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 91/91 [00:00<00:00, 146.54it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_2/ouputgpkg/tile_r000014_c000012_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_2/csv_stats/tile_r000014_c000012_grain_stats.csv\n", + "done with segmentation + export\n", + "[143/905] tile_r000014_c000013\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.47it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.49it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 795/795 [00:20<00:00, 39.35it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "691it [00:03, 199.58it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "712it [00:03, 197.19it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 223/223 [00:04<00:00, 46.51it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 236/236 [00:01<00:00, 155.45it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_2/ouputgpkg/tile_r000014_c000013_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_2/csv_stats/tile_r000014_c000013_grain_stats.csv\n", + "done with segmentation + export\n", + "[144/905] tile_r000014_c000014\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.42it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.47it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 774/774 [00:20<00:00, 38.58it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "627it [00:02, 211.84it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "638it [00:03, 206.74it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 218/218 [00:03<00:00, 59.51it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 231/231 [00:01<00:00, 164.00it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_2/ouputgpkg/tile_r000014_c000014_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_2/csv_stats/tile_r000014_c000014_grain_stats.csv\n", + "done with segmentation + export\n", + "[145/905] tile_r000014_c000015\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.49it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.50it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 867/867 [00:22<00:00, 38.87it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "744it [00:03, 202.35it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "743it [00:03, 201.81it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 231/231 [00:04<00:00, 51.61it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 256/256 [00:01<00:00, 171.56it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_2/ouputgpkg/tile_r000014_c000015_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_2/csv_stats/tile_r000014_c000015_grain_stats.csv\n", + "done with segmentation + export\n", + "[146/905] tile_r000014_c000016\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.39it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.45it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 724/724 [00:18<00:00, 38.89it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "627it [00:03, 178.96it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "626it [00:03, 183.37it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 181/181 [00:04<00:00, 40.60it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 214/214 [00:01<00:00, 156.37it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_2/ouputgpkg/tile_r000014_c000016_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_2/csv_stats/tile_r000014_c000016_grain_stats.csv\n", + "done with segmentation + export\n", + "[147/905] tile_r000014_c000017\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.48it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.46it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 703/703 [00:18<00:00, 38.42it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "562it [00:02, 200.23it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "561it [00:02, 194.14it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 165/165 [00:03<00:00, 45.34it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 199/199 [00:01<00:00, 162.65it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_2/ouputgpkg/tile_r000014_c000017_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_2/csv_stats/tile_r000014_c000017_grain_stats.csv\n", + "done with segmentation + export\n", + "[148/905] tile_r000014_c000018\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.46it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.47it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 893/893 [00:22<00:00, 38.88it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "757it [00:03, 227.65it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "781it [00:03, 221.39it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 270/270 [00:04<00:00, 66.25it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 283/283 [00:01<00:00, 182.69it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_2/ouputgpkg/tile_r000014_c000018_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_2/csv_stats/tile_r000014_c000018_grain_stats.csv\n", + "done with segmentation + export\n", + "[149/905] tile_r000014_c000019\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:01<00:00, 2.51it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.50it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 1017/1017 [00:25<00:00, 39.48it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "882it [00:02, 299.67it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "918it [00:03, 282.76it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 325/325 [00:04<00:00, 81.08it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 337/337 [00:01<00:00, 202.29it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_2/ouputgpkg/tile_r000014_c000019_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_2/csv_stats/tile_r000014_c000019_grain_stats.csv\n", + "done with segmentation + export\n", + "[150/905] tile_r000014_c000020\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.47it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.42it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 1315/1315 [00:34<00:00, 38.48it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "1136it [00:03, 367.13it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "1135it [00:03, 366.37it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 400/400 [00:03<00:00, 105.33it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 477/477 [00:02<00:00, 232.00it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_2/ouputgpkg/tile_r000014_c000020_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_2/csv_stats/tile_r000014_c000020_grain_stats.csv\n", + "done with segmentation + export\n", + "[151/905] tile_r000014_c000021\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.41it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.49it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 1619/1619 [00:40<00:00, 39.88it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "1500it [00:03, 487.75it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "1558it [00:03, 472.17it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 659/659 [00:03<00:00, 189.74it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 678/678 [00:02<00:00, 268.04it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_2/ouputgpkg/tile_r000014_c000021_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_2/csv_stats/tile_r000014_c000021_grain_stats.csv\n", + "done with segmentation + export\n", + "[152/905] tile_r000014_c000022\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.49it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.49it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 1028/1028 [00:26<00:00, 39.20it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "937it [00:01, 797.70it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "1008it [00:01, 797.52it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 452/452 [00:01<00:00, 277.11it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 458/458 [00:01<00:00, 288.24it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_2/ouputgpkg/tile_r000014_c000022_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_2/csv_stats/tile_r000014_c000022_grain_stats.csv\n", + "done with segmentation + export\n", + "[153/905] tile_r000014_c000023\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.01it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.09it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 1069/1069 [00:29<00:00, 36.86it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "940it [00:02, 442.73it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "936it [00:01, 531.96it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 353/353 [00:02<00:00, 129.73it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 431/431 [00:01<00:00, 258.68it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_2/ouputgpkg/tile_r000014_c000023_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_2/csv_stats/tile_r000014_c000023_grain_stats.csv\n", + "done with segmentation + export\n", + "[154/905] tile_r000014_c000024\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.01it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.05it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 889/889 [00:24<00:00, 36.97it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "737it [00:02, 350.28it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "731it [00:01, 474.28it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 276/276 [00:02<00:00, 108.96it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 340/340 [00:01<00:00, 230.59it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_2/ouputgpkg/tile_r000014_c000024_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_2/csv_stats/tile_r000014_c000024_grain_stats.csv\n", + "done with segmentation + export\n", + "[155/905] tile_r000014_c000025\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 1.99it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.03it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 729/729 [00:20<00:00, 36.06it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "588it [00:01, 405.18it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "584it [00:01, 528.81it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 212/212 [00:01<00:00, 112.69it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 261/261 [00:01<00:00, 210.75it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_2/ouputgpkg/tile_r000014_c000025_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_2/csv_stats/tile_r000014_c000025_grain_stats.csv\n", + "done with segmentation + export\n", + "[156/905] tile_r000014_c000026\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.07it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.16it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 760/760 [00:20<00:00, 37.22it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "639it [00:01, 334.19it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "637it [00:01, 370.25it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 221/221 [00:02<00:00, 80.62it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 278/278 [00:01<00:00, 214.73it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n", + "Warning: len(gdf)=278 != len(grain_stats)=276 -> truncating to 276\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_2/ouputgpkg/tile_r000014_c000026_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_2/csv_stats/tile_r000014_c000026_grain_stats.csv\n", + "done with segmentation + export\n", + "[157/905] tile_r000014_c000027\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.17it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.23it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 378/378 [00:11<00:00, 34.08it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "291it [00:01, 203.10it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "288it [00:01, 240.24it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 90/90 [00:03<00:00, 24.16it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 126/126 [00:00<00:00, 180.10it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n", + "Warning: len(gdf)=126 != len(grain_stats)=125 -> truncating to 125\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_2/ouputgpkg/tile_r000014_c000027_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_2/csv_stats/tile_r000014_c000027_grain_stats.csv\n", + "done with segmentation + export\n", + "[158/905] tile_r000014_c000028\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.18it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.21it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 229/229 [00:08<00:00, 28.04it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "154it [00:01, 85.19it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "145it [00:01, 118.37it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 23/23 [00:06<00:00, 3.55it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 66/66 [00:00<00:00, 114.10it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_2/ouputgpkg/tile_r000014_c000028_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_2/csv_stats/tile_r000014_c000028_grain_stats.csv\n", + "done with segmentation + export\n", + "[159/905] tile_r000014_c000029\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.15it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.23it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 356/356 [00:11<00:00, 31.96it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "240it [00:03, 77.11it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "221it [00:01, 191.18it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 53/53 [00:02<00:00, 21.91it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 115/115 [00:00<00:00, 136.22it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved histogram plot\n", + "Saved GPKG: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_2/ouputgpkg/tile_r000014_c000029_grains.gpkg\n", + "Saved stats CSV: /dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_2/csv_stats/tile_r000014_c000029_grain_stats.csv\n", + "done with segmentation + export\n", + "[160/905] tile_r000014_c000030\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.28it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.30it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 515/515 [00:15<00:00, 33.38it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "402it [00:02, 194.98it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "397it [00:00, 491.19it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 124/124 [00:01<00:00, 69.23it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 200/200 [00:00<00:00, 205.59it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n", + "Saved histogram plot\n", + "ERROR on tile_r000014_c000030 : Could not add feature to layer at index 0: sqlite3_exec(CREATE TABLE \"tile_r000014_c000030_grains\" ( \"fid\" INTEGER PRIMARY KEY AUTOINCREMENT NOT NULL, \"geom\" POLYGON, \"label\" INTEGER, \"area_px\" REAL, \"centroid_x\" REAL, \"centroid_y\" REAL, \"major_axis_px\" REAL, \"minor_axis_px\" REAL, \"major_axis_length\" REAL, \"minor_axis_length\" REAL, \"major_axis_mm\" REAL, \"minor_axis_mm\" REAL)) failed: disk I/O error\n", + "[161/905] tile_r000014_c000031\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.31it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.35it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 631/631 [00:17<00:00, 36.55it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "515it [00:01, 340.53it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "513it [00:01, 398.18it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 159/159 [00:02<00:00, 58.48it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 249/249 [00:01<00:00, 225.01it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "ERROR on tile_r000014_c000031 : [Errno 122] Disk quota exceeded: '/dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_2/histplots/tile_r000014_c000031_hist.png'\n", + "[162/905] tile_r000014_c000032\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.30it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.35it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 617/617 [00:16<00:00, 36.80it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "504it [00:01, 330.08it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "503it [00:01, 330.97it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 164/164 [00:02<00:00, 57.48it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 232/232 [00:01<00:00, 210.36it/s]\n", + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "ERROR on tile_r000014_c000032 : [Errno 122] Disk quota exceeded: '/dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_2/pebbleplots/tile_r000014_c000032.png'\n", + "[163/905] tile_r000014_c000033\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.30it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.33it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 503/503 [00:14<00:00, 35.07it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "369it [00:01, 197.71it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "368it [00:01, 216.39it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 125/125 [00:02<00:00, 45.27it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 174/174 [00:01<00:00, 159.41it/s]\n", + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "ERROR on tile_r000014_c000033 : [Errno 122] Disk quota exceeded: '/dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_2/pebbleplots/tile_r000014_c000033.png'\n", + "[164/905] tile_r000014_c000034\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.24it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.38it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 500/500 [00:13<00:00, 35.80it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "386it [00:01, 351.46it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "420it [00:01, 362.79it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 167/167 [00:01<00:00, 115.57it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 170/170 [00:00<00:00, 174.77it/s]\n", + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "ERROR on tile_r000014_c000034 : [Errno 122] Disk quota exceeded: '/dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_2/pebbleplots/tile_r000014_c000034.png'\n", + "[165/905] tile_r000015_c000010\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.34it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.45it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 17/17 [00:00<00:00, 24.33it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "7it [00:00, 492.88it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "8it [00:00, 423.66it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 3/3 [00:00<00:00, 47.83it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:00<00:00, 97.44it/s]\n", + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "ERROR on tile_r000015_c000010 : [Errno 122] Disk quota exceeded: '/dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_2/pebbleplots/tile_r000015_c000010.png'\n", + "[166/905] tile_r000015_c000011\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.31it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.37it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 284/284 [00:09<00:00, 29.95it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "197it [00:01, 167.40it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "195it [00:01, 177.08it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 46/46 [00:01<00:00, 25.58it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 66/66 [00:00<00:00, 131.89it/s]\n", + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "ERROR on tile_r000015_c000011 : [Errno 122] Disk quota exceeded: '/dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_2/pebbleplots/tile_r000015_c000011.png'\n", + "[167/905] tile_r000015_c000012\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.32it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.37it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 495/495 [00:13<00:00, 37.12it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "370it [00:02, 168.41it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "390it [00:02, 161.99it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 120/120 [00:03<00:00, 35.74it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 127/127 [00:00<00:00, 140.18it/s]\n", + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "ERROR on tile_r000015_c000012 : [Errno 122] Disk quota exceeded: '/dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_2/pebbleplots/tile_r000015_c000012.png'\n", + "[168/905] tile_r000015_c000013\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.33it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.33it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 724/724 [00:19<00:00, 38.10it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "581it [00:02, 219.54it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "599it [00:02, 213.00it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 202/202 [00:03<00:00, 56.82it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 216/216 [00:01<00:00, 159.80it/s]\n", + "/dss/dsstbyfs02/scratch/0B/di54doz/di54doz/tmp.SmPjnQCt8c/ipykernel_69166/2345050271.py:122: NodataShadowWarning: The dataset's nodata attribute is shadowing the alpha band. All masks will be determined by the nodata attribute\n", + " m = src.dataset_mask()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "ERROR on tile_r000015_c000013 : [Errno 122] Disk quota exceeded: '/dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain/F22_2/pebbleplots/tile_r000015_c000013.png'\n", + "[169/905] tile_r000015_c000014\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:02<00:00, 2.25it/s]\n", + "100%|██████████| 4/4 [00:01<00:00, 2.39it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 797/797 [00:20<00:00, 38.68it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "672it [00:03, 172.94it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "452it [00:02, 154.07it/s]\n" + ] + }, + { + "ename": "KeyboardInterrupt", + "evalue": "", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[6], line 30\u001b[0m\n\u001b[1;32m 27\u001b[0m \u001b[38;5;28;01mcontinue\u001b[39;00m\n\u001b[1;32m 29\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mPROCESS:\u001b[39m\u001b[38;5;124m\"\u001b[39m, os\u001b[38;5;241m.\u001b[39mpath\u001b[38;5;241m.\u001b[39mbasename(folder), \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mtiles:\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;28mlen\u001b[39m(tiles))\n\u001b[0;32m---> 30\u001b[0m \u001b[43mprocess_one_folder\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfolder\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 31\u001b[0m processed \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m \u001b[38;5;241m1\u001b[39m\n", + "Cell \u001b[0;32mIn[4], line 171\u001b[0m, in \u001b[0;36mprocess_one_folder\u001b[0;34m(folder)\u001b[0m\n\u001b[1;32m 169\u001b[0m \u001b[38;5;66;03m# ---- SAM segmentation ----\u001b[39;00m\n\u001b[1;32m 170\u001b[0m t \u001b[38;5;241m=\u001b[39m time\u001b[38;5;241m.\u001b[39mperf_counter()\n\u001b[0;32m--> 171\u001b[0m all_grains, labels, mask_all, grain_data, fig, ax \u001b[38;5;241m=\u001b[39m \u001b[43mseg\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msam_segmentation\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 172\u001b[0m \u001b[43m \u001b[49m\u001b[43msam\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 173\u001b[0m \u001b[43m \u001b[49m\u001b[43mimage\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 174\u001b[0m \u001b[43m \u001b[49m\u001b[43mimage_pred\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 175\u001b[0m \u001b[43m \u001b[49m\u001b[43mcoords\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 176\u001b[0m \u001b[43m \u001b[49m\u001b[43mlabels\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 177\u001b[0m \u001b[43m \u001b[49m\u001b[43mmin_area\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmin_area_px\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 178\u001b[0m \u001b[43m \u001b[49m\u001b[43mplot_image\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[1;32m 179\u001b[0m \u001b[43m \u001b[49m\u001b[43mremove_edge_grains\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[1;32m 180\u001b[0m \u001b[43m \u001b[49m\u001b[43mremove_large_objects\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[1;32m 181\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 182\u001b[0m rec[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mt_sam_s\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m time\u001b[38;5;241m.\u001b[39mperf_counter() \u001b[38;5;241m-\u001b[39m t\n\u001b[1;32m 183\u001b[0m rec[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mn_grains\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mint\u001b[39m(\u001b[38;5;28mlen\u001b[39m(all_grains))\n", + "File \u001b[0;32m~/.local/share/mamba/envs/segmenteverygrain/lib/python3.10/site-packages/segmenteverygrain/segmenteverygrain.py:806\u001b[0m, in \u001b[0;36msam_segmentation\u001b[0;34m(sam, image, image_pred, coords, labels, min_area, plot_image, remove_edge_grains, remove_large_objects, keep_edges)\u001b[0m\n\u001b[1;32m 803\u001b[0m all_grains \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mlist\u001b[39m(all_grains) \u001b[38;5;241m+\u001b[39m new_grains\n\u001b[1;32m 805\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mfinding overlapping polygons...\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m--> 806\u001b[0m new_grains, comps, g \u001b[38;5;241m=\u001b[39m \u001b[43mfind_connected_components\u001b[49m\u001b[43m(\u001b[49m\u001b[43mall_grains\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmin_area\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 808\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mfinding best polygons...\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m 809\u001b[0m all_grains \u001b[38;5;241m=\u001b[39m merge_overlapping_polygons(\n\u001b[1;32m 810\u001b[0m all_grains, new_grains, comps, min_area, image_pred\n\u001b[1;32m 811\u001b[0m )\n", + "File \u001b[0;32m~/.local/share/mamba/envs/segmenteverygrain/lib/python3.10/site-packages/segmenteverygrain/segmenteverygrain.py:930\u001b[0m, in \u001b[0;36mfind_connected_components\u001b[0;34m(all_grains, min_area)\u001b[0m\n\u001b[1;32m 910\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21mfind_connected_components\u001b[39m(all_grains, min_area):\n\u001b[1;32m 911\u001b[0m \u001b[38;5;250m \u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 912\u001b[0m \u001b[38;5;124;03m Finds connected components in a graph of overlapping polygons.\u001b[39;00m\n\u001b[1;32m 913\u001b[0m \n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 928\u001b[0m \u001b[38;5;124;03m The graph of overlapping polygons.\u001b[39;00m\n\u001b[1;32m 929\u001b[0m \u001b[38;5;124;03m \"\"\"\u001b[39;00m\n\u001b[0;32m--> 930\u001b[0m overlapping_polygons \u001b[38;5;241m=\u001b[39m \u001b[43mfind_overlapping_polygons\u001b[49m\u001b[43m(\u001b[49m\u001b[43mall_grains\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 931\u001b[0m g \u001b[38;5;241m=\u001b[39m nx\u001b[38;5;241m.\u001b[39mGraph(overlapping_polygons)\n\u001b[1;32m 932\u001b[0m comps \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mlist\u001b[39m(nx\u001b[38;5;241m.\u001b[39mconnected_components(g))\n", + "File \u001b[0;32m~/.local/share/mamba/envs/segmenteverygrain/lib/python3.10/site-packages/segmenteverygrain/segmenteverygrain.py:465\u001b[0m, in \u001b[0;36mfind_overlapping_polygons\u001b[0;34m(polygons, min_overlap)\u001b[0m\n\u001b[1;32m 463\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m warnings\u001b[38;5;241m.\u001b[39mcatch_warnings():\n\u001b[1;32m 464\u001b[0m warnings\u001b[38;5;241m.\u001b[39msimplefilter(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mignore\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;167;01mRuntimeWarning\u001b[39;00m)\n\u001b[0;32m--> 465\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[43mpoly1\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mis_valid\u001b[49m:\n\u001b[1;32m 466\u001b[0m poly1 \u001b[38;5;241m=\u001b[39m poly1\u001b[38;5;241m.\u001b[39mbuffer(\u001b[38;5;241m0\u001b[39m)\n\u001b[1;32m 467\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m poly2\u001b[38;5;241m.\u001b[39mis_valid:\n", + "File \u001b[0;32m~/.local/share/mamba/envs/segmenteverygrain/lib/python3.10/site-packages/shapely/geometry/base.py:755\u001b[0m, in \u001b[0;36mBaseGeometry.is_valid\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 749\u001b[0m \u001b[38;5;129m@property\u001b[39m\n\u001b[1;32m 750\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21mis_valid\u001b[39m(\u001b[38;5;28mself\u001b[39m):\n\u001b[1;32m 751\u001b[0m \u001b[38;5;250m \u001b[39m\u001b[38;5;124;03m\"\"\"True if the geometry is valid.\u001b[39;00m\n\u001b[1;32m 752\u001b[0m \n\u001b[1;32m 753\u001b[0m \u001b[38;5;124;03m The definition depends on sub-class.\u001b[39;00m\n\u001b[1;32m 754\u001b[0m \u001b[38;5;124;03m \"\"\"\u001b[39;00m\n\u001b[0;32m--> 755\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mbool\u001b[39m(\u001b[43mshapely\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mis_valid\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m)\u001b[49m)\n", + "File \u001b[0;32m~/.local/share/mamba/envs/segmenteverygrain/lib/python3.10/site-packages/shapely/decorators.py:88\u001b[0m, in \u001b[0;36mmultithreading_enabled..wrapped\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m 86\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m arr \u001b[38;5;129;01min\u001b[39;00m array_args:\n\u001b[1;32m 87\u001b[0m arr\u001b[38;5;241m.\u001b[39mflags\u001b[38;5;241m.\u001b[39mwriteable \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mFalse\u001b[39;00m\n\u001b[0;32m---> 88\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 89\u001b[0m \u001b[38;5;28;01mfinally\u001b[39;00m:\n\u001b[1;32m 90\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m arr, old_flag \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mzip\u001b[39m(array_args, old_flags):\n", + "File \u001b[0;32m~/.local/share/mamba/envs/segmenteverygrain/lib/python3.10/site-packages/shapely/predicates.py:463\u001b[0m, in \u001b[0;36mis_valid\u001b[0;34m(geometry, **kwargs)\u001b[0m\n\u001b[1;32m 461\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m warnings\u001b[38;5;241m.\u001b[39mcatch_warnings():\n\u001b[1;32m 462\u001b[0m warnings\u001b[38;5;241m.\u001b[39msimplefilter(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mignore\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m--> 463\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[43mlib\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mis_valid\u001b[49m\u001b[43m(\u001b[49m\u001b[43mgeometry\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 464\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m result\n", + "\u001b[0;31mKeyboardInterrupt\u001b[0m: " + ] + }, + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import os, glob\n", + "\n", + "BASE = \"/dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain\"\n", + "folders = sorted(glob.glob(os.path.join(BASE, \"F*\")))\n", + "\n", + "MAX_TO_PROCESS = 5\n", + "processed = 0\n", + "\n", + "for folder in folders:\n", + " if processed >= MAX_TO_PROCESS:\n", + " print(\"Reached MAX_TO_PROCESS, stopping.\")\n", + " break\n", + "\n", + " # 1) skip wenn schon Ergebnisse (GPKG) vorhanden\n", + " gpkg_files = glob.glob(os.path.join(folder, \"ouputgpkg\", \"*.gpkg\"))\n", + " if len(gpkg_files) > 0:\n", + " print(\"SKIP already has GPKG:\", os.path.basename(folder), \"n_gpkg:\", len(gpkg_files))\n", + " continue\n", + "\n", + " # 2) skip wenn noch keine tiles drin sind (inputtiles/ ODER folder-root)\n", + " tiles = glob.glob(os.path.join(folder, \"inputtiles\", \"*.tif\"))\n", + " if len(tiles) == 0:\n", + " tiles = glob.glob(os.path.join(folder, \"*.tif\"))\n", + "\n", + " if len(tiles) == 0:\n", + " print(\"SKIP no tiles yet:\", os.path.basename(folder))\n", + " continue\n", + "\n", + " print(\"PROCESS:\", os.path.basename(folder), \"tiles:\", len(tiles))\n", + " process_one_folder(folder)\n", + " processed += 1" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "4f0a5a52", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.19" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/notebooks/Segment_every_grain_w_georeferencing.ipynb b/notebooks/Segment_every_grain_w_georeferencing.ipynb index cd488f4..3f71d37 100644 --- a/notebooks/Segment_every_grain_w_georeferencing.ipynb +++ b/notebooks/Segment_every_grain_w_georeferencing.ipynb @@ -20,7 +20,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "id": "f2ebc518", "metadata": { "execution": { @@ -32,7 +32,23 @@ } }, "outputs": [], - "source": "import cv2\nfrom keras.utils import load_img\nfrom keras.saving import load_model\nimport matplotlib.pyplot as plt\nimport numpy as np\nimport pandas as pd\nfrom sam2.build_sam import build_sam2\nfrom sam2.sam2_image_predictor import SAM2ImagePredictor\nfrom skimage.measure import regionprops, regionprops_table\nfrom tqdm import trange, tqdm\n\nimport segmenteverygrain as seg\nimport segmenteverygrain.interactions as si\n\n%matplotlib qt" + "source": [ + "import cv2\n", + "from keras.utils import load_img\n", + "from keras.saving import load_model\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "import pandas as pd\n", + "from sam2.build_sam import build_sam2\n", + "from sam2.sam2_image_predictor import SAM2ImagePredictor\n", + "from skimage.measure import regionprops, regionprops_table\n", + "from tqdm import trange, tqdm\n", + "\n", + "import segmenteverygrain as seg\n", + "import segmenteverygrain.interactions as si\n", + "\n", + "# %matplotlib qt" + ] }, { "cell_type": "markdown", @@ -47,6 +63,19 @@ { "cell_type": "code", "execution_count": null, + "id": "00ada296", + "metadata": {}, + "outputs": [], + "source": [ + "# import sam2, os\n", + "# print(\"sam2 from:\", sam2.__file__)\n", + "# print(\"has config:\", os.path.exists(os.path.join(os.path.dirname(sam2.__file__), \"configs\", \"sam2.1\", \"sam2.1_hiera_l.yaml\")))\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 37, "id": "fe1bd2bc", "metadata": { "execution": { @@ -58,7 +87,26 @@ } }, "outputs": [], - "source": "# UNET model\nunet = load_model(\n \"../models/seg_model.keras\",\n custom_objects={\"weighted_crossentropy\": seg.weighted_crossentropy},\n)\n\n# Auto-detect device: CUDA for NVIDIA, MPS for Apple Silicon, CPU as fallback\nimport torch\nif torch.cuda.is_available():\n device = \"cuda\"\nelif torch.backends.mps.is_available():\n device = \"mps\"\nelse:\n device = \"cpu\"\n\n# SAM 2.1 checkpoints. Download from:\n# https://dl.fbaipublicfiles.com/segment_anything_2/092424/sam2.1_hiera_large.pt\nsam = build_sam2(\"configs/sam2.1/sam2.1_hiera_l.yaml\", \"../models/sam2.1_hiera_large.pt\", device=device)" + "source": [ + "# UNET model\n", + "unet = load_model(\n", + " \"../models/seg_model.keras\",\n", + " custom_objects={\"weighted_crossentropy\": seg.weighted_crossentropy},\n", + ")\n", + "\n", + "# Auto-detect device: CUDA for NVIDIA, MPS for Apple Silicon, CPU as fallback\n", + "import torch\n", + "if torch.cuda.is_available():\n", + " device = \"cuda\"\n", + "elif torch.backends.mps.is_available():\n", + " device = \"mps\"\n", + "else:\n", + " device = \"cpu\"\n", + "\n", + "# SAM 2.1 checkpoints. Download from:\n", + "# https://dl.fbaipublicfiles.com/segment_anything_2/092824/sam2.1_hiera_large.pt\n", + "sam = build_sam2(r\"C:\\Users\\leoni\\sam2\\sam2\\configs\\sam2.1\\sam2.1_hiera_l.yaml\", \"../models/sam2.1_hiera_large.pt\", device=device)\n" + ] }, { "cell_type": "markdown", @@ -76,7 +124,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 9, "id": "94bf3f94", "metadata": { "execution": { @@ -87,8 +135,111 @@ "shell.execute_reply.started": "2025-01-23T16:49:14.577999Z" } }, - "outputs": [], - "source": "fname = \"../examples/RI_T01_Grid_65/RI_T01_Grid_65.tif\"\nimage = np.array(load_img(fname))\nimage_pred = seg.predict_image(image, unet, I=256)\n\n# decreasing the 'dbs_max_dist' parameter results in more SAM prompts\n# (and longer processing times):\nlabels, coords = seg.label_grains(image, image_pred, dbs_max_dist=10.0)\n\n# SAM segmentation, using the point prompts from the Unet:\nall_grains, labels, mask_all, grain_data, fig, ax = seg.sam_segmentation(\n sam,\n image,\n image_pred,\n coords,\n labels,\n min_area=50.0,\n plot_image=True,\n remove_edge_grains=False,\n remove_large_objects=False,\n)" + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:03<00:00, 1.24it/s]\n", + "100%|██████████| 3/3 [00:01<00:00, 2.28it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 836/836 [00:57<00:00, 14.44it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "743it [00:05, 138.01it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 259/259 [00:08<00:00, 28.79it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 314/314 [00:01<00:00, 222.29it/s]\n" + ] + }, + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fname = r\"C:\\Users\\leoni\\kirgis_repo\\segmenteverygrain\\leonieexamples\\tile_r000006_c000025.tif\"\n", + "image = np.array(load_img(fname))\n", + "image_pred = seg.predict_image(image, unet, I=256)\n", + "\n", + "# decreasing the 'dbs_max_dist' parameter results in more SAM prompts\n", + "# (and longer processing times):\n", + "labels, coords = seg.label_grains(image, image_pred, dbs_max_dist=10.0)\n", + "\n", + "# SAM segmentation, using the point prompts from the Unet:\n", + "all_grains, labels, mask_all, grain_data, fig, ax = seg.sam_segmentation(\n", + " sam,\n", + " image,\n", + " image_pred,\n", + " coords,\n", + " labels,\n", + " min_area=50.0,\n", + " plot_image=True,\n", + " remove_edge_grains=False,\n", + " remove_large_objects=False,\n", + ")" + ] }, { "cell_type": "markdown", @@ -96,7 +247,24 @@ "metadata": { "tags": [] }, - "source": "## Interactive editing of segmentation results\n\nConvert the polygons to Grain objects and use the interactive `GrainPlot` interface:\n\n**Mouse controls:**\n- **Left-click** on existing grain: Select/unselect\n- **Left-click** in grain-free area: Instant grain creation\n- **Alt + Left-click**: Multi-prompt grain creation\n- **Alt + Right-click**: Background prompt for multi-prompt creation\n- **Ctrl** (hold): Temporarily hide grain masks\n\n**Keyboard controls:**\n- **d**: Delete selected grains\n- **m**: Merge selected grains\n- **z**: Undo last created grain\n- **Esc**: Clear selection and prompts" + "source": [ + "## Interactive editing of segmentation results\n", + "\n", + "Convert the polygons to Grain objects and use the interactive `GrainPlot` interface:\n", + "\n", + "**Mouse controls:**\n", + "- **Left-click** on existing grain: Select/unselect\n", + "- **Left-click** in grain-free area: Instant grain creation\n", + "- **Alt + Left-click**: Multi-prompt grain creation\n", + "- **Alt + Right-click**: Background prompt for multi-prompt creation\n", + "- **Ctrl** (hold): Temporarily hide grain masks\n", + "\n", + "**Keyboard controls:**\n", + "- **d**: Delete selected grains\n", + "- **m**: Merge selected grains\n", + "- **z**: Undo last created grain\n", + "- **Esc**: Clear selection and prompts" + ] }, { "cell_type": "code", @@ -111,18 +279,61 @@ "shell.execute_reply.started": "2025-01-23T16:51:01.655529Z" } }, - "outputs": [], - "source": "# Convert polygons to Grain objects\ngrains = si.polygons_to_grains(all_grains, image=image)\nfor g in tqdm(grains, desc='Measuring grains'):\n g.measure()\n\n# Set up SAM 2.1 predictor for adding new grains\npredictor = SAM2ImagePredictor(sam)\npredictor.set_image(image)\n\n# Create interactive plot\nplot = si.GrainPlot(\n grains,\n image=image,\n predictor=predictor,\n blit=True,\n color_palette='Paired',\n figsize=(15, 10),\n)\nplot.activate()" + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Measuring grains: 100%|██████████| 314/314 [00:00<00:00, 496.88it/s]\n", + "Measuring and drawing grains: 100%|██████████| 314/314 [00:01<00:00, 181.26it/s]\n" + ] + }, + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Convert polygons to Grain objects\n", + "grains = si.polygons_to_grains(all_grains, image=image)\n", + "for g in tqdm(grains, desc='Measuring grains'):\n", + " g.measure()\n", + "\n", + "# Set up SAM 2.1 predictor for adding new grains\n", + "predictor = SAM2ImagePredictor(sam)\n", + "predictor.set_image(image)\n", + "\n", + "# Create interactive plot\n", + "plot = si.GrainPlot(\n", + " grains,\n", + " image=image,\n", + " predictor=predictor,\n", + " blit=True,\n", + " color_palette='Paired',\n", + " figsize=(15, 10),\n", + ")\n", + "plot.draw_axes() #optional\n", + "plot.activate()#optional\n", + "plot.activate()" + ] }, { "cell_type": "markdown", "id": "5d08448d", "metadata": {}, - "source": "Run this cell when you are done with interactive editing to retrieve the updated grains and deactivate the interface:" + "source": [ + "Run this cell when you are done with interactive editing to retrieve the updated grains and deactivate the interface:" + ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 12, "id": "8b6987ae", "metadata": { "execution": { @@ -134,17 +345,29 @@ } }, "outputs": [], - "source": "# Retrieve updated grains and deactivate interactive mode\ngrains = plot.get_grains()\nplot.deactivate()\n\n# Convert Grain objects back to shapely polygons for georeferencing\nall_grains = [g.polygon for g in grains]\n\n# Recreate labels from the updated grains\nlabels, mask_all = seg.create_labeled_image(all_grains, image)" + "source": [ + "# Retrieve updated grains and deactivate interactive mode\n", + "grains = plot.get_grains()\n", + "plot.deactivate()\n", + "\n", + "# Convert Grain objects back to shapely polygons for georeferencing\n", + "all_grains = [g.polygon for g in grains]\n", + "\n", + "# Recreate labels from the updated grains\n", + "labels, mask_all = seg.create_labeled_image(all_grains, image)" + ] }, { "cell_type": "markdown", "id": "b1ef7d75", "metadata": {}, - "source": "Optionally, plot the updated set of grains with axes:" + "source": [ + "Optionally, plot the updated set of grains with axes:" + ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 14, "id": "9f7b8082", "metadata": { "execution": { @@ -156,7 +379,11 @@ } }, "outputs": [], - "source": "# Draw major and minor axes on each grain\nplot.draw_axes()" + "source": [ + "# Draw major and minor axes on each grain\n", + "plot.draw_axes()\n", + "plot.activate()" + ] }, { "cell_type": "markdown", @@ -170,11 +397,50 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 35, "id": "c4962ba6", "metadata": {}, - "outputs": [], - "source": "dirname = \"../examples/RI_T01_Grid_65/\"\n# write grayscale mask to PNG file\ncv2.imwrite(dirname + fname.split(\"/\")[-1][:-4] + \"_mask.png\", mask_all)\n# Save the image as a PNG file\ncv2.imwrite(\n dirname + fname.split(\"/\")[-1][:-4] + \"_image.png\",\n cv2.cvtColor(image, cv2.COLOR_BGR2RGB),\n)" + "outputs": [ + { + "data": { + "text/plain": [ + "True" + ] + }, + "execution_count": 35, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import os\n", + "os.makedirs(dirname, exist_ok=True)\n", + "\n", + "base = os.path.splitext(os.path.basename(fname))[0]\n", + "cv2.imwrite(os.path.join(dirname, base + \"_mask.png\"), mask_all)\n", + "cv2.imwrite(os.path.join(dirname, base + \"_image.png\"), cv2.cvtColor(image, cv2.COLOR_BGR2RGB))\n" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "id": "42028ead", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'C:\\\\Users\\\\leoni\\\\kirgis_repo\\\\segmenteverygrain\\\\leonieexamples\\\\seg_output'" + ] + }, + "execution_count": 36, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "dirname" + ] }, { "cell_type": "markdown", @@ -188,7 +454,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 19, "id": "16c934cc", "metadata": { "execution": { @@ -208,7 +474,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 20, "id": "26b2fc5b", "metadata": { "execution": { @@ -235,7 +501,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 21, "id": "13746f7b", "metadata": { "execution": { @@ -274,23 +540,23 @@ " \n", " \n", " 0\n", - " POLYGON ((339043.943 4686358.495, 339043.941 4...\n", + " POLYGON ((504126.367 4586252.318, 504126.364 4...\n", " \n", " \n", " 1\n", - " POLYGON ((339043.824 4686358.479, 339043.822 4...\n", + " POLYGON ((504127.068 4586251.52, 504127.064 45...\n", " \n", " \n", " 2\n", - " POLYGON ((339043.228 4686358.364, 339043.226 4...\n", + " POLYGON ((504125.429 4586251.478, 504125.425 4...\n", " \n", " \n", " 3\n", - " POLYGON ((339043.768 4686358.191, 339043.767 4...\n", + " POLYGON ((504125.009 4586250.917, 504125.005 4...\n", " \n", " \n", " 4\n", - " POLYGON ((339043.036 4686357.764, 339043.034 4...\n", + " POLYGON ((504125.755 4586250.007, 504125.751 4...\n", " \n", " \n", "\n", @@ -298,14 +564,14 @@ ], "text/plain": [ " geometry\n", - "0 POLYGON ((339043.943 4686358.495, 339043.941 4...\n", - "1 POLYGON ((339043.824 4686358.479, 339043.822 4...\n", - "2 POLYGON ((339043.228 4686358.364, 339043.226 4...\n", - "3 POLYGON ((339043.768 4686358.191, 339043.767 4...\n", - "4 POLYGON ((339043.036 4686357.764, 339043.034 4..." + "0 POLYGON ((504126.367 4586252.318, 504126.364 4...\n", + "1 POLYGON ((504127.068 4586251.52, 504127.064 45...\n", + "2 POLYGON ((504125.429 4586251.478, 504125.425 4...\n", + "3 POLYGON ((504125.009 4586250.917, 504125.005 4...\n", + "4 POLYGON ((504125.755 4586250.007, 504125.751 4..." ] }, - "execution_count": 15, + "execution_count": 21, "metadata": {}, "output_type": "execute_result" } @@ -320,7 +586,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 22, "id": "548b71c0", "metadata": { "execution": { @@ -365,62 +631,62 @@ " \n", " 0\n", " 1\n", - " 683.0\n", - " 182.021962\n", - " 1097.698389\n", - " 37.846881\n", - " 24.424525\n", + " 173.0\n", + " 43.843931\n", + " 385.780347\n", + " 18.761750\n", + " 12.211637\n", " \n", " \n", " 1\n", " 2\n", - " 676.0\n", - " 189.627219\n", - " 1041.396450\n", - " 37.309940\n", - " 23.467704\n", + " 671.0\n", + " 263.789866\n", + " 597.250373\n", + " 38.509497\n", + " 22.986435\n", " \n", " \n", " 2\n", " 3\n", - " 410.0\n", - " 255.880488\n", - " 719.348780\n", - " 30.057850\n", - " 17.937276\n", + " 986.0\n", + " 265.920892\n", + " 124.828600\n", + " 51.102041\n", + " 26.606650\n", " \n", " \n", " 3\n", " 4\n", - " 393.0\n", - " 348.519084\n", - " 1015.414758\n", - " 29.249796\n", - " 19.877529\n", + " 281.0\n", + " 441.487544\n", + " 7.345196\n", + " 21.567443\n", + " 18.410031\n", " \n", " \n", " 4\n", " 5\n", - " 354.0\n", - " 587.093220\n", - " 607.022599\n", - " 25.084603\n", - " 18.253541\n", + " 246.0\n", + " 707.508130\n", + " 204.626016\n", + " 36.181924\n", + " 9.695120\n", " \n", " \n", "\n", "" ], "text/plain": [ - " label area centroid-0 centroid-1 major_axis_length minor_axis_length\n", - "0 1 683.0 182.021962 1097.698389 37.846881 24.424525\n", - "1 2 676.0 189.627219 1041.396450 37.309940 23.467704\n", - "2 3 410.0 255.880488 719.348780 30.057850 17.937276\n", - "3 4 393.0 348.519084 1015.414758 29.249796 19.877529\n", - "4 5 354.0 587.093220 607.022599 25.084603 18.253541" + " label area centroid-0 centroid-1 major_axis_length minor_axis_length\n", + "0 1 173.0 43.843931 385.780347 18.761750 12.211637\n", + "1 2 671.0 263.789866 597.250373 38.509497 22.986435\n", + "2 3 986.0 265.920892 124.828600 51.102041 26.606650\n", + "3 4 281.0 441.487544 7.345196 21.567443 18.410031\n", + "4 5 246.0 707.508130 204.626016 36.181924 9.695120" ] }, - "execution_count": 16, + "execution_count": 22, "metadata": {}, "output_type": "execute_result" } @@ -441,7 +707,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 23, "id": "552ca32c", "metadata": { "execution": { @@ -464,7 +730,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 24, "id": "f1b8a302", "metadata": { "execution": { @@ -507,43 +773,43 @@ " \n", " \n", " 0\n", - " POLYGON ((339043.943 4686358.495, 339043.941 4...\n", - " 339043.917150\n", - " 4.686359e+06\n", - " 0.068124\n", - " 0.043964\n", + " POLYGON ((504126.367 4586252.318, 504126.364 4...\n", + " 504126.352522\n", + " 4.586252e+06\n", + " 0.065692\n", + " 0.042758\n", " \n", " \n", " 1\n", - " POLYGON ((339043.824 4686358.479, 339043.822 4...\n", - " 339043.815807\n", - " 4.686359e+06\n", - " 0.067158\n", - " 0.042242\n", + " POLYGON ((504127.068 4586251.52, 504127.064 45...\n", + " 504127.092963\n", + " 4.586252e+06\n", + " 0.134837\n", + " 0.080485\n", " \n", " \n", " 2\n", - " POLYGON ((339043.228 4686358.364, 339043.226 4...\n", - " 339043.236121\n", - " 4.686358e+06\n", - " 0.054104\n", - " 0.032287\n", + " POLYGON ((504125.429 4586251.478, 504125.425 4...\n", + " 504125.438826\n", + " 4.586252e+06\n", + " 0.178929\n", + " 0.093161\n", " \n", " \n", " 3\n", - " POLYGON ((339043.768 4686358.191, 339043.767 4...\n", - " 339043.769040\n", - " 4.686358e+06\n", - " 0.052650\n", - " 0.035780\n", + " POLYGON ((504125.009 4586250.917, 504125.005 4...\n", + " 504125.027469\n", + " 4.586251e+06\n", + " 0.075516\n", + " 0.064461\n", " \n", " \n", " 4\n", - " POLYGON ((339043.036 4686357.764, 339043.034 4...\n", - " 339043.033934\n", - " 4.686358e+06\n", - " 0.045152\n", - " 0.032856\n", + " POLYGON ((504125.755 4586250.007, 504125.751 4...\n", + " 504125.718228\n", + " 4.586250e+06\n", + " 0.126687\n", + " 0.033946\n", " \n", " \n", "\n", @@ -551,21 +817,21 @@ ], "text/plain": [ " geometry centroid_x \\\n", - "0 POLYGON ((339043.943 4686358.495, 339043.941 4... 339043.917150 \n", - "1 POLYGON ((339043.824 4686358.479, 339043.822 4... 339043.815807 \n", - "2 POLYGON ((339043.228 4686358.364, 339043.226 4... 339043.236121 \n", - "3 POLYGON ((339043.768 4686358.191, 339043.767 4... 339043.769040 \n", - "4 POLYGON ((339043.036 4686357.764, 339043.034 4... 339043.033934 \n", + "0 POLYGON ((504126.367 4586252.318, 504126.364 4... 504126.352522 \n", + "1 POLYGON ((504127.068 4586251.52, 504127.064 45... 504127.092963 \n", + "2 POLYGON ((504125.429 4586251.478, 504125.425 4... 504125.438826 \n", + "3 POLYGON ((504125.009 4586250.917, 504125.005 4... 504125.027469 \n", + "4 POLYGON ((504125.755 4586250.007, 504125.751 4... 504125.718228 \n", "\n", " centroid_y major_axis_length minor_axis_length \n", - "0 4.686359e+06 0.068124 0.043964 \n", - "1 4.686359e+06 0.067158 0.042242 \n", - "2 4.686358e+06 0.054104 0.032287 \n", - "3 4.686358e+06 0.052650 0.035780 \n", - "4 4.686358e+06 0.045152 0.032856 " + "0 4.586252e+06 0.065692 0.042758 \n", + "1 4.586252e+06 0.134837 0.080485 \n", + "2 4.586252e+06 0.178929 0.093161 \n", + "3 4.586251e+06 0.075516 0.064461 \n", + "4 4.586250e+06 0.126687 0.033946 " ] }, - "execution_count": 18, + "execution_count": 24, "metadata": {}, "output_type": "execute_result" } @@ -579,10 +845,21 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": 25, "id": "80ef2e7c", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "# plot grain size distribution\n", "# units need to be in mm!\n", @@ -596,7 +873,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 26, "id": "1a7a7dd3", "metadata": { "execution": { @@ -607,7 +884,18 @@ "shell.execute_reply.started": "2025-01-23T16:56:39.597169Z" } }, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "# check if everything looks good\n", "band1 = dataset.read(1)\n", @@ -623,7 +911,7 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 27, "id": "2e284de5", "metadata": { "execution": { @@ -642,7 +930,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 31, "id": "27f9b8b3", "metadata": { "execution": { @@ -654,7 +942,10 @@ } }, "outputs": [], - "source": "# write shapefile\ngdf.to_file(\"../examples/RI_T01_Grid_65/projected_grains.shp\")" + "source": [ + "# write shapefile\n", + "gdf.to_file(r\"C:\\Users\\leoni\\kirgis_repo\\segmenteverygrain\\leonieexamples\\seg_output\\projected_grains.gpkg\")" + ] }, { "cell_type": "code", @@ -677,7 +968,7 @@ ], "metadata": { "kernelspec": { - "display_name": "segmenteverygrain", + "display_name": "seg_2", "language": "python", "name": "python3" }, @@ -691,9 +982,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.22" + "version": "3.10.19" } }, "nbformat": 4, "nbformat_minor": 5 -} \ No newline at end of file +} diff --git a/notebooks/Segment_every_grain_w_georeferencing_leonie masking.ipynb b/notebooks/Segment_every_grain_w_georeferencing_leonie masking.ipynb new file mode 100644 index 0000000..876ceb7 --- /dev/null +++ b/notebooks/Segment_every_grain_w_georeferencing_leonie masking.ipynb @@ -0,0 +1,1500 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "623da314", + "metadata": {}, + "source": [ + "# Import packages" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "ff94825b", + "metadata": {}, + "outputs": [], + "source": [ + "import cv2\n", + "from keras.utils import load_img\n", + "from keras.saving import load_model\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "import pandas as pd\n", + "from sam2.build_sam import build_sam2\n", + "from sam2.sam2_image_predictor import SAM2ImagePredictor\n", + "from skimage.measure import regionprops, regionprops_table\n", + "from tqdm import trange, tqdm\n", + "\n", + "import segmenteverygrain as seg\n", + "import segmenteverygrain.interactions as si\n", + "import os\n", + "import rasterio\n", + "\n", + "# %matplotlib qt" + ] + }, + { + "cell_type": "markdown", + "id": "44e817f1", + "metadata": {}, + "source": [ + "## Load models" + ] + }, + { + "cell_type": "markdown", + "id": "442d52dc", + "metadata": {}, + "source": [ + "Maybe you need to clone first SAM2. Then add the yaml file in the bottom of the next chunk" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "3965da5f", + "metadata": {}, + "outputs": [], + "source": [ + "# UNET model\n", + "unet = load_model(\n", + " \"../models/seg_model.keras\",\n", + " custom_objects={\"weighted_crossentropy\": seg.weighted_crossentropy},\n", + ")\n", + "\n", + "# Auto-detect device: CUDA for NVIDIA, MPS for Apple Silicon, CPU as fallback\n", + "import torch\n", + "if torch.cuda.is_available():\n", + " device = \"cuda\"\n", + "elif torch.backends.mps.is_available():\n", + " device = \"mps\"\n", + "else:\n", + " device = \"cpu\"\n", + "\n", + "# SAM 2.1 checkpoints. Download from:\n", + "# https://dl.fbaipublicfiles.com/segment_anything_2/092824/sam2.1_hiera_large.pt\n", + "sam = build_sam2(r\"C:\\Users\\leoni\\sam2\\sam2\\configs\\sam2.1\\sam2.1_hiera_l.yaml\", r\"C:\\Users\\leoni\\segmenteverygrain\\models\\sam2.1_hiera_large.pt\", device=device)\n" + ] + }, + { + "cell_type": "markdown", + "id": "93a0c303", + "metadata": {}, + "source": [ + "## Set your directories" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "6856ddaf", + "metadata": {}, + "outputs": [], + "source": [ + "dir = os.chdir(\"C:/Users/leoni/kirgis_repo/segmenteverygrain/leonieexamples/2509_with_vegetation_mask_nodataset\")\n", + "dir = os.getcwd()\n", + "os.makedirs(\"ouputgpkg\", exist_ok=True)\n", + "os.makedirs(\"inputtiles\", exist_ok=True)\n", + "os.makedirs(\"plots\", exist_ok=True)\n", + "os.makedirs(\"csv_stats\", exist_ok=True)\n", + "\n", + "inputtiledir = os.path.join(dir, \"inputtiles/\")\n", + "ouputgpkg = os.path.join(dir, \"ouputgpkg/\")\n", + "csvdir = os.path.join(dir, \"csv_stats\")\n", + "plotdir = os.path.join(dir, \"plots/\")" + ] + }, + { + "cell_type": "markdown", + "id": "ed334819", + "metadata": {}, + "source": [ + "## Inlcuding masks" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "f3ecdeae", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Found 1 tiles\n", + "CRS: EPSG:32643\n", + "Pixel size: 0.003501 units/px (~3.501 mm/px)\n", + "2 cm => minor_px=5.71 px -> min_area_px=25.6 px^2\n", + "[1/1] tile_r000016_c000012\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 2.65it/s]\n", + "100%|██████████| 3/3 [00:01<00:00, 2.66it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + " 1%| | 66/9427 [00:04<11:11, 13.94it/s]\n" + ] + }, + { + "ename": "KeyboardInterrupt", + "evalue": "", + "output_type": "error", + "traceback": [ + "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[1;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)", + "Cell \u001b[1;32mIn[9], line 176\u001b[0m\n\u001b[0;32m 171\u001b[0m \u001b[38;5;28;01mcontinue\u001b[39;00m\n\u001b[0;32m 173\u001b[0m \u001b[38;5;66;03m# --------------------------------------------------\u001b[39;00m\n\u001b[0;32m 174\u001b[0m \u001b[38;5;66;03m# 8) SAM segmentation (arbeitet mit originalem RGB + maskierter Prediction)\u001b[39;00m\n\u001b[0;32m 175\u001b[0m \u001b[38;5;66;03m# --------------------------------------------------\u001b[39;00m\n\u001b[1;32m--> 176\u001b[0m all_grains, labels, mask_all, grain_data, fig, ax \u001b[38;5;241m=\u001b[39m \u001b[43mseg\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msam_segmentation\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m 177\u001b[0m \u001b[43m \u001b[49m\u001b[43msam\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 178\u001b[0m \u001b[43m \u001b[49m\u001b[43mimage_for_seg\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 179\u001b[0m \u001b[43m \u001b[49m\u001b[43mimage_pred\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 180\u001b[0m \u001b[43m \u001b[49m\u001b[43mcoords\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 181\u001b[0m \u001b[43m \u001b[49m\u001b[43mlabels\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 182\u001b[0m \u001b[43m \u001b[49m\u001b[43mmin_area\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmin_area_px\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 183\u001b[0m \u001b[43m \u001b[49m\u001b[43mplot_image\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[0;32m 184\u001b[0m \u001b[43m \u001b[49m\u001b[43mremove_edge_grains\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[0;32m 185\u001b[0m \u001b[43m \u001b[49m\u001b[43mremove_large_objects\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[0;32m 186\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 188\u001b[0m \u001b[38;5;66;03m# --------------------------------------------------\u001b[39;00m\n\u001b[0;32m 189\u001b[0m \u001b[38;5;66;03m# 9) Finaler Hard-Cut:\u001b[39;00m\n\u001b[0;32m 190\u001b[0m \u001b[38;5;66;03m# Garantiert: in maskierten Bereichen gibt es keine Segmente\u001b[39;00m\n\u001b[0;32m 191\u001b[0m \u001b[38;5;66;03m# --------------------------------------------------\u001b[39;00m\n\u001b[0;32m 192\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n", + "File \u001b[1;32mc:\\Users\\leoni\\micromamba\\envs\\seg_2\\lib\\site-packages\\segmenteverygrain\\segmenteverygrain.py:725\u001b[0m, in \u001b[0;36msam_segmentation\u001b[1;34m(sam, image, image_pred, coords, labels, min_area, plot_image, remove_edge_grains, remove_large_objects, keep_edges)\u001b[0m\n\u001b[0;32m 723\u001b[0m x \u001b[38;5;241m=\u001b[39m coords[i, \u001b[38;5;241m0\u001b[39m]\n\u001b[0;32m 724\u001b[0m y \u001b[38;5;241m=\u001b[39m coords[i, \u001b[38;5;241m1\u001b[39m]\n\u001b[1;32m--> 725\u001b[0m sx, sy, mask \u001b[38;5;241m=\u001b[39m \u001b[43mone_point_prompt\u001b[49m\u001b[43m(\u001b[49m\u001b[43mx\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43my\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mimage\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mpredictor\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 726\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m np\u001b[38;5;241m.\u001b[39mmax(mask) \u001b[38;5;241m>\u001b[39m \u001b[38;5;241m0\u001b[39m:\n\u001b[0;32m 727\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m remove_edge_grains:\n\u001b[0;32m 728\u001b[0m \u001b[38;5;66;03m# Determine which edges to check based on keep_edges\u001b[39;00m\n", + "File \u001b[1;32mc:\\Users\\leoni\\micromamba\\envs\\seg_2\\lib\\site-packages\\segmenteverygrain\\segmenteverygrain.py:383\u001b[0m, in \u001b[0;36mone_point_prompt\u001b[1;34m(x, y, image, predictor, ax)\u001b[0m\n\u001b[0;32m 381\u001b[0m sy \u001b[38;5;241m=\u001b[39m []\n\u001b[0;32m 382\u001b[0m mask \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39mzeros_like(image[:, :, \u001b[38;5;241m0\u001b[39m])\n\u001b[1;32m--> 383\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m sx, sy, \u001b[43mmask\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mastype\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mbool\u001b[39;49m\u001b[43m)\u001b[49m\n", + "\u001b[1;31mKeyboardInterrupt\u001b[0m: " + ] + } + ], + "source": [ + "# ==========================================================\n", + "# MASKEN-SEKTION (ab hier)\n", + "# 1 = NICHT segmentieren\n", + "# 0 oder nodata = segmentieren erlaubt\n", + "# ==========================================================\n", + "veg_mask_path = r\"C:\\Users\\leoni\\kirgis_repo\\veg-shad-wat-filtering\\1_uav-filters\\vegetation\\veg_masks_rgbvi_fallback\\25092025_Aktal_gravel_2_10m_OM_RGB_utm_coregistered_ws128_clipwater_vegmask_rgbvi.tif\"\n", + "shadow_mask_path = r\"C:\\Users\\leoni\\kirgis_repo\\veg-shad-wat-filtering\\1_uav-filters\\shadow\\0-02\\25092025_Aktal_gravel_2_10m_OM_RGB_utm_coregistered_ws128_clipwater_shadowmask.tif\"\n", + "\n", + "import glob\n", + "from rasterio.warp import reproject, Resampling\n", + "\n", + "def read_mask_for_tile(tile_path, full_mask_src, mask_band=1):\n", + " \"\"\"\n", + " Schneidet/projiziert die Vollmaske auf das Grid des Tiles.\n", + " Ergebnis: 2D-Array mit exakt gleicher Höhe/Breite wie das Tile.\n", + " \"\"\"\n", + " with rasterio.open(tile_path) as tsrc:\n", + " tile_mask = np.zeros((tsrc.height, tsrc.width), dtype=np.float32)\n", + "\n", + " reproject(\n", + " source=rasterio.band(full_mask_src, mask_band),\n", + " destination=tile_mask,\n", + " src_transform=full_mask_src.transform,\n", + " src_crs=full_mask_src.crs,\n", + " dst_transform=tsrc.transform,\n", + " dst_crs=tsrc.crs,\n", + " resampling=Resampling.nearest, # wichtig für binäre Masken\n", + " dst_nodata=np.nan\n", + " )\n", + "\n", + " return tile_mask\n", + "\n", + "\n", + "# --- collect tiles ---\n", + "tiles = sorted(glob.glob(os.path.join(inputtiledir, \"*.tif\")))\n", + "\n", + "print(f\"Found {len(tiles)} tiles\")\n", + "\n", + "if len(tiles) == 0:\n", + " raise RuntimeError(\"No tiles found.\")\n", + "\n", + "# --- read pixel size once from first tile ---\n", + "with rasterio.open(tiles[0]) as src:\n", + " xres, yres = src.res # CRS units per pixel (usually meters)\n", + " crs = src.crs\n", + "\n", + "gsd_units = float((xres + yres) / 2.0)\n", + "gsd_mm = gsd_units * 1000.0 # assumes CRS units are meters (e.g., UTM)\n", + "\n", + "minor_mm = 20.0 # 2 cm\n", + "minor_px = minor_mm / gsd_mm\n", + "\n", + "# area threshold from minor-axis (circle assumption)\n", + "min_area_px = float(np.pi * (minor_px / 2.0) ** 2)\n", + "\n", + "print(\"CRS:\", crs)\n", + "print(f\"Pixel size: {gsd_units:.6f} units/px (~{gsd_mm:.3f} mm/px)\")\n", + "print(f\"2 cm => minor_px={minor_px:.2f} px -> min_area_px={min_area_px:.1f} px^2\")\n", + "\n", + "\n", + "# --- loop (mit Masken + Shape-Fix + RGB bleibt UNVERÄNDERT) ---\n", + "# Ziel: segmentieren NUR wo Maske == 0 oder NoData\n", + "# NICHT segmentieren wo Maske == 1\n", + "# WICHTIG: RGB wird NICHT auf 0 gesetzt (weil 0 ein gültiger RGB-Wert sein kann).\n", + "# Stattdessen: image_pred maskieren + Prompts filtern + mask_all final hart beschneiden.\n", + "\n", + "with rasterio.open(veg_mask_path) as veg_src, rasterio.open(shadow_mask_path) as shadow_src:\n", + "\n", + " for i, fname in enumerate(tiles, start=1):\n", + " tile_id = os.path.splitext(os.path.basename(fname))[0]\n", + " print(f\"[{i}/{len(tiles)}] {tile_id}\")\n", + "\n", + " # --------------------------------------------------\n", + " # 1) load RGB tile (unverändert)\n", + " # --------------------------------------------------\n", + " image = np.array(load_img(fname)) # (H, W, 3) typischerweise\n", + "\n", + " # --------------------------------------------------\n", + " # 2) tile-aligned masks aus Vollmasken holen\n", + " # --------------------------------------------------\n", + " veg_tile = read_mask_for_tile(fname, veg_src, mask_band=1)\n", + " shadow_tile = read_mask_for_tile(fname, shadow_src, mask_band=1)\n", + "\n", + " # Deine Logik:\n", + " # 1 = IGNORE (nicht segmentieren)\n", + " # 0 oder NoData = segmentieren erlaubt\n", + " #\n", + " # (robust gegen 1/255 etc.: alles >0 wird als \"maskiert\" interpretiert)\n", + " veg_exclude = np.isfinite(veg_tile) & (veg_tile > 0)\n", + " shadow_exclude = np.isfinite(shadow_tile) & (shadow_tile > 0)\n", + "\n", + " exclude_mask = veg_exclude | shadow_exclude\n", + " valid_mask = ~exclude_mask # True = hier darf segmentiert werden\n", + "\n", + " # --------------------------------------------------\n", + " # 3) SHAPE CHECK / FIX (TIFF: load_img kann H/W anders liefern)\n", + " # --------------------------------------------------\n", + " img_hw = image.shape[:2] # (H, W)\n", + " mask_hw = valid_mask.shape # (H, W) erwartet\n", + "\n", + " if mask_hw != img_hw:\n", + " if mask_hw[::-1] == img_hw:\n", + " valid_mask = valid_mask.T\n", + " print(f\"Transposed valid_mask from {mask_hw} to {valid_mask.shape} to match image {img_hw}\")\n", + " else:\n", + " raise ValueError(\n", + " f\"Shape mismatch between image and valid_mask: \"\n", + " f\"image.shape[:2]={img_hw}, valid_mask.shape={mask_hw}\"\n", + " )\n", + "\n", + " if not np.any(valid_mask):\n", + " print(\"Tile fully masked by vegetation/shadow -> skipping\")\n", + " continue\n", + "\n", + " # --------------------------------------------------\n", + " # 4) UNet prediction\n", + " # --------------------------------------------------\n", + " image_pred = seg.predict_image(image, unet, I=256)\n", + "\n", + " # Falls prediction-Shape nicht passt -> valid_mask ggf. transponieren\n", + " pred_hw = image_pred.shape[:2]\n", + " if pred_hw != valid_mask.shape:\n", + " if valid_mask.shape[::-1] == pred_hw:\n", + " valid_mask = valid_mask.T\n", + " print(f\"Transposed valid_mask to match image_pred: {pred_hw}\")\n", + " else:\n", + " raise ValueError(\n", + " f\"Shape mismatch between image_pred and valid_mask: \"\n", + " f\"image_pred.shape[:2]={pred_hw}, valid_mask.shape={valid_mask.shape}\"\n", + " )\n", + "\n", + " # --------------------------------------------------\n", + " # 5) Maskierung VOR Segmentierung:\n", + " # -> Nur image_pred maskieren (Prompt-Quelle)\n", + " # -> RGB NICHT verändern!\n", + " # --------------------------------------------------\n", + " image_pred = np.array(image_pred, copy=True)\n", + " image_pred[~valid_mask] = 0\n", + "\n", + " image_for_seg = image # unverändert lassen\n", + "\n", + " # --------------------------------------------------\n", + " # 6) Prompts (Unet -> points)\n", + " # --------------------------------------------------\n", + " labels, coords = seg.label_grains(image_for_seg, image_pred, dbs_max_dist=10.0)\n", + "\n", + " # --------------------------------------------------\n", + " # 7) Prompts außerhalb valid_mask entfernen (Sicherheitsfilter)\n", + " # Achtung: labels kann 2D Label-Bild sein -> nicht immer filtern!\n", + " # --------------------------------------------------\n", + " coords = np.asarray(coords)\n", + "\n", + " if len(coords) > 0:\n", + " # Annahme: coords = (x, y)\n", + " x_idx = np.clip(np.round(coords[:, 0]).astype(int), 0, valid_mask.shape[1] - 1)\n", + " y_idx = np.clip(np.round(coords[:, 1]).astype(int), 0, valid_mask.shape[0] - 1)\n", + "\n", + " keep = valid_mask[y_idx, x_idx]\n", + " coords = coords[keep]\n", + "\n", + " # labels nur filtern, wenn es wirklich ein 1D Prompt-Labelarray ist\n", + " try:\n", + " labels_arr = np.asarray(labels)\n", + " if labels_arr.ndim == 1 and len(labels_arr) == len(keep):\n", + " labels = labels_arr[keep]\n", + " except Exception:\n", + " pass\n", + "\n", + " if len(coords) == 0:\n", + " print(\"No valid prompts after masking -> skipping tile\")\n", + " continue\n", + "\n", + " # --------------------------------------------------\n", + " # 8) SAM segmentation (arbeitet mit originalem RGB + maskierter Prediction)\n", + " # --------------------------------------------------\n", + " all_grains, labels, mask_all, grain_data, fig, ax = seg.sam_segmentation(\n", + " sam,\n", + " image_for_seg,\n", + " image_pred,\n", + " coords,\n", + " labels,\n", + " min_area=min_area_px,\n", + " plot_image=True,\n", + " remove_edge_grains=True,\n", + " remove_large_objects=True,\n", + " )\n", + "\n", + " # --------------------------------------------------\n", + " # 9) Finaler Hard-Cut:\n", + " # Garantiert: in maskierten Bereichen gibt es keine Segmente\n", + " # --------------------------------------------------\n", + " try:\n", + " mask_all = np.array(mask_all, copy=True)\n", + " if mask_all.shape != valid_mask.shape:\n", + " if mask_all.shape[::-1] == valid_mask.shape:\n", + " mask_all = mask_all.T\n", + " else:\n", + " print(f\"Warning: mask_all shape {mask_all.shape} != valid_mask shape {valid_mask.shape}\")\n", + "\n", + " mask_all[~valid_mask] = 0\n", + " except Exception as e:\n", + " print(f\"Could not apply final hard mask cut: {e}\")\n", + "\n", + " # --------------------------------------------------\n", + " # 10) Plot speichern\n", + " # --------------------------------------------------\n", + " figname = os.path.join(plotdir, f\"{tile_id}.png\")\n", + " fig.savefig(figname, dpi=200, bbox_inches=\"tight\")\n", + " plt.close(fig)\n", + "\n", + " print(\"Saved the Plot\")\n", + " print(\"done with segmentation\")" + ] + }, + { + "cell_type": "markdown", + "id": "dbbfdccd", + "metadata": {}, + "source": [ + "## Not including Masks and just saving the pngs" + ] + }, + { + "cell_type": "markdown", + "id": "f9193000", + "metadata": {}, + "source": [ + "## Run segmentation" + ] + }, + { + "cell_type": "markdown", + "id": "6d450ab5", + "metadata": {}, + "source": [ + "Collect tiles" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "e229e490", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Found 1 tiles\n", + "CRS: EPSG:32643\n", + "Pixel size: 0.003501 units/px (~3.501 mm/px)\n", + "2 cm => minor_px=5.71 px -> min_area_px=25.6 px^2\n", + "[1/1] tile_r000017_c000012\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 2.61it/s]\n", + "100%|██████████| 3/3 [00:01<00:00, 2.53it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 301/301 [00:21<00:00, 14.13it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "167it [00:00, 499.01it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "217it [00:00, 361.85it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 99/99 [00:00<00:00, 115.84it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 103/103 [00:00<00:00, 231.34it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved the Plot\n", + "done with segmentation\n" + ] + } + ], + "source": [ + "import os\n", + "import glob\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "from tensorflow.keras.utils import load_img\n", + "import rasterio\n", + "\n", + "# --- optional: prompt cap for SAM (None = no limit) ---\n", + "MAX_SAM_PROMPTS = 2500\n", + "PROMPT_SUBSAMPLE_MODE = \"random\" # \"random\" oder \"first\"\n", + "RNG_SEED = 42\n", + "rng = np.random.default_rng(RNG_SEED)\n", + "\n", + "# --- collect tiles ---\n", + "tiles = sorted(glob.glob(os.path.join(inputtiledir, \"*.tif\")))\n", + "\n", + "print(f\"Found {len(tiles)} tiles\")\n", + "\n", + "\n", + "if len(tiles) == 0:\n", + " raise RuntimeError(\"No tiles found.\")\n", + "\n", + "# --- read pixel size once from first tile ---\n", + "with rasterio.open(tiles[0]) as src:\n", + " xres, yres = src.res # CRS units per pixel (usually meters)\n", + " crs = src.crs\n", + "\n", + "gsd_units = float((xres + yres) / 2.0)\n", + "gsd_mm = gsd_units * 1000.0 # assumes CRS units are meters (e.g., UTM)\n", + "\n", + "minor_mm = 20.0 # 2 cm\n", + "minor_px = minor_mm / gsd_mm\n", + "\n", + "# area threshold from minor-axis (circle assumption)\n", + "min_area_px = float(np.pi * (minor_px / 2.0) ** 2)\n", + "\n", + "print(\"CRS:\", crs)\n", + "print(f\"Pixel size: {gsd_units:.6f} units/px (~{gsd_mm:.3f} mm/px)\")\n", + "print(f\"2 cm => minor_px={minor_px:.2f} px -> min_area_px={min_area_px:.1f} px^2\")\n", + "\n", + "\n", + "\n", + "# --- loop ---\n", + "for i, fname in enumerate(tiles, start=1):\n", + " tile_id = os.path.splitext(os.path.basename(fname))[0]\n", + " print(f\"[{i}/{len(tiles)}] {tile_id}\")\n", + "\n", + " # load + predict\n", + " image = np.array(load_img(fname))\n", + " image_pred = seg.predict_image(image, unet, I=256)\n", + "\n", + " # prompts (Unet -> points)\n", + " labels, coords = seg.label_grains(image, image_pred, dbs_max_dist=10.0)\n", + "\n", + " # --- optional: cap number of prompts before SAM ---\n", + " coords = np.asarray(coords)\n", + " if MAX_SAM_PROMPTS is not None and len(coords) > MAX_SAM_PROMPTS:\n", + " n_before = len(coords)\n", + " \n", + " if PROMPT_SUBSAMPLE_MODE == \"first\":\n", + " keep_idx = np.arange(MAX_SAM_PROMPTS)\n", + " else: # random\n", + " keep_idx = np.sort(rng.choice(len(coords), size=MAX_SAM_PROMPTS, replace=False))\n", + " \n", + " coords = coords[keep_idx]\n", + " \n", + " # labels nur mitfiltern, wenn es ein 1D prompt-label array ist\n", + " try:\n", + " labels_arr = np.asarray(labels)\n", + " if labels_arr.ndim == 1 and len(labels_arr) == n_before:\n", + " labels = labels_arr[keep_idx]\n", + " except Exception:\n", + " pass\n", + " \n", + " print(f\"Prompt cap active: reduced prompts from {n_before} -> {len(coords)}\")\n", + " \n", + " # SAM segmentation\n", + " all_grains, labels, mask_all, grain_data, fig, ax = seg.sam_segmentation(\n", + " sam,\n", + " image,\n", + " image_pred,\n", + " coords,\n", + " labels,\n", + " min_area=min_area_px,\n", + " plot_image=True,\n", + " remove_edge_grains=True,\n", + " remove_large_objects=True,\n", + " )\n", + " figname =os.path.join(plotdir, f\"{tile_id}.png\")\n", + "\n", + " fig.savefig(figname, dpi = 200, bbox_inches=\"tight\")\n", + " plt.close(fig)\n", + " print(\"Saved the Plot\")\n", + " print(\"done with segmentation\")\n", + "\n", + "\n", + "\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "id": "df758347", + "metadata": {}, + "source": [ + "## Not including Masks and saving all statistiks, plots and images and gpkgs" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "035ef937", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Found 7 tiles\n", + "CRS: EPSG:32643\n", + "Pixel size: 0.003501 units/px (~3.501 mm/px)\n", + "2 cm => minor_px=5.71 px -> min_area_px=25.6 px^2\n", + "[1/7] tile_r000006_c000024_masked\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:05<00:00, 1.32s/it]\n", + "100%|██████████| 3/3 [00:02<00:00, 1.12it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 726/726 [01:20<00:00, 8.99it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "541it [00:18, 29.25it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "552it [00:12, 43.67it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 191/191 [00:33<00:00, 5.74it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 217/217 [00:05<00:00, 36.83it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n", + "Saved histogram plot\n", + "Saved GPKG: C:\\Users\\leoni\\kirgis_repo\\segmenteverygrain\\leonieexamples\\2509_with_vegetation_mask_nodataset\\ouputgpkg/tile_r000006_c000024_masked_grains.gpkg\n", + "Saved stats CSV: C:\\Users\\leoni\\kirgis_repo\\segmenteverygrain\\leonieexamples\\2509_with_vegetation_mask_nodataset\\csv_stats\\tile_r000006_c000024_masked_grain_stats.csv\n", + "done with segmentation + export\n", + "[2/7] tile_r000006_c000025_masked\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:08<00:00, 2.06s/it]\n", + "100%|██████████| 3/3 [00:05<00:00, 2.00s/it]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 948/948 [02:58<00:00, 5.32it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "747it [00:06, 118.21it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "736it [00:05, 143.34it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 255/255 [00:08<00:00, 29.98it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 298/298 [00:01<00:00, 233.66it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n", + "Saved histogram plot\n", + "Saved GPKG: C:\\Users\\leoni\\kirgis_repo\\segmenteverygrain\\leonieexamples\\2509_with_vegetation_mask_nodataset\\ouputgpkg/tile_r000006_c000025_masked_grains.gpkg\n", + "Saved stats CSV: C:\\Users\\leoni\\kirgis_repo\\segmenteverygrain\\leonieexamples\\2509_with_vegetation_mask_nodataset\\csv_stats\\tile_r000006_c000025_masked_grain_stats.csv\n", + "done with segmentation + export\n", + "[3/7] tile_r000006_c000028_masked\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 2.58it/s]\n", + "100%|██████████| 3/3 [00:01<00:00, 2.64it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 257/257 [00:24<00:00, 10.29it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "55it [00:03, 14.31it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "50it [00:02, 16.76it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 7/7 [00:04<00:00, 1.42it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 19/19 [00:00<00:00, 58.78it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n", + "Saved histogram plot\n", + "Saved GPKG: C:\\Users\\leoni\\kirgis_repo\\segmenteverygrain\\leonieexamples\\2509_with_vegetation_mask_nodataset\\ouputgpkg/tile_r000006_c000028_masked_grains.gpkg\n", + "Saved stats CSV: C:\\Users\\leoni\\kirgis_repo\\segmenteverygrain\\leonieexamples\\2509_with_vegetation_mask_nodataset\\csv_stats\\tile_r000006_c000028_masked_grain_stats.csv\n", + "done with segmentation + export\n", + "[4/7] tile_r000008_c000015_masked\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 2.41it/s]\n", + "100%|██████████| 3/3 [00:01<00:00, 2.32it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 353/353 [00:35<00:00, 9.84it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "86it [00:03, 26.02it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "83it [00:01, 48.34it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 15/15 [00:03<00:00, 3.86it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 47/47 [00:00<00:00, 127.92it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n", + "Warning: len(gdf)=47 != len(grain_stats)=45 -> truncating to 45\n", + "Saved histogram plot\n", + "Saved GPKG: C:\\Users\\leoni\\kirgis_repo\\segmenteverygrain\\leonieexamples\\2509_with_vegetation_mask_nodataset\\ouputgpkg/tile_r000008_c000015_masked_grains.gpkg\n", + "Saved stats CSV: C:\\Users\\leoni\\kirgis_repo\\segmenteverygrain\\leonieexamples\\2509_with_vegetation_mask_nodataset\\csv_stats\\tile_r000008_c000015_masked_grain_stats.csv\n", + "done with segmentation + export\n", + "[5/7] tile_r000008_c000026_masked\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 2.38it/s]\n", + "100%|██████████| 3/3 [00:01<00:00, 2.50it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 915/915 [01:17<00:00, 11.76it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "705it [00:09, 75.77it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "693it [00:07, 96.47it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 206/206 [00:12<00:00, 16.37it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 250/250 [00:01<00:00, 174.38it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n", + "Saved histogram plot\n", + "Saved GPKG: C:\\Users\\leoni\\kirgis_repo\\segmenteverygrain\\leonieexamples\\2509_with_vegetation_mask_nodataset\\ouputgpkg/tile_r000008_c000026_masked_grains.gpkg\n", + "Saved stats CSV: C:\\Users\\leoni\\kirgis_repo\\segmenteverygrain\\leonieexamples\\2509_with_vegetation_mask_nodataset\\csv_stats\\tile_r000008_c000026_masked_grain_stats.csv\n", + "done with segmentation + export\n", + "[6/7] tile_r000008_c000031_masked\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 2.08it/s]\n", + "100%|██████████| 3/3 [00:01<00:00, 2.40it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 142/142 [00:13<00:00, 10.85it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "52it [00:00, 63.57it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "70it [00:00, 87.98it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 31/31 [00:01<00:00, 25.58it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 34/34 [00:00<00:00, 103.80it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n", + "Warning: len(gdf)=34 != len(grain_stats)=33 -> truncating to 33\n", + "Saved histogram plot\n", + "Saved GPKG: C:\\Users\\leoni\\kirgis_repo\\segmenteverygrain\\leonieexamples\\2509_with_vegetation_mask_nodataset\\ouputgpkg/tile_r000008_c000031_masked_grains.gpkg\n", + "Saved stats CSV: C:\\Users\\leoni\\kirgis_repo\\segmenteverygrain\\leonieexamples\\2509_with_vegetation_mask_nodataset\\csv_stats\\tile_r000008_c000031_masked_grain_stats.csv\n", + "done with segmentation + export\n", + "[7/7] tile_r000008_c000032_masked\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 2.44it/s]\n", + "100%|██████████| 3/3 [00:01<00:00, 2.40it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 395/395 [00:34<00:00, 11.40it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "218it [00:01, 117.90it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "215it [00:00, 263.18it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 71/71 [00:01<00:00, 43.06it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 104/104 [00:00<00:00, 231.29it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segmentation plot\n", + "Warning: len(gdf)=104 != len(grain_stats)=102 -> truncating to 102\n", + "Saved histogram plot\n", + "Saved GPKG: C:\\Users\\leoni\\kirgis_repo\\segmenteverygrain\\leonieexamples\\2509_with_vegetation_mask_nodataset\\ouputgpkg/tile_r000008_c000032_masked_grains.gpkg\n", + "Saved stats CSV: C:\\Users\\leoni\\kirgis_repo\\segmenteverygrain\\leonieexamples\\2509_with_vegetation_mask_nodataset\\csv_stats\\tile_r000008_c000032_masked_grain_stats.csv\n", + "done with segmentation + export\n" + ] + } + ], + "source": [ + "import os\n", + "import glob\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "from tensorflow.keras.utils import load_img\n", + "import rasterio\n", + "\n", + "# zusätzliche Imports für Nachbearbeitung (ohne deine Library-Zelle zu ändern)\n", + "import geopandas as gpd\n", + "from shapely.geometry import Polygon\n", + "from skimage.measure import regionprops_table\n", + "\n", + "# --- optional: prompt cap for SAM (None = no limit) ---\n", + "MAX_SAM_PROMPTS = 2500\n", + "PROMPT_SUBSAMPLE_MODE = \"random\" # \"random\" oder \"first\"\n", + "RNG_SEED = 42\n", + "rng = np.random.default_rng(RNG_SEED)\n", + "\n", + "# --- collect tiles ---\n", + "tiles = sorted(glob.glob(os.path.join(inputtiledir, \"*.tif\")))\n", + "\n", + "print(f\"Found {len(tiles)} tiles\")\n", + "\n", + "if len(tiles) == 0:\n", + " raise RuntimeError(\"No tiles found.\")\n", + "\n", + "# --- read pixel size once from first tile ---\n", + "with rasterio.open(tiles[0]) as src:\n", + " xres, yres = src.res # CRS units per pixel (usually meters)\n", + " crs = src.crs\n", + "\n", + "gsd_units = float((xres + yres) / 2.0)\n", + "gsd_mm = gsd_units * 1000.0 # assumes CRS units are meters (e.g., UTM)\n", + "\n", + "minor_mm = 20.0 # 2 cm\n", + "minor_px = minor_mm / gsd_mm\n", + "\n", + "# area threshold from minor-axis (circle assumption)\n", + "min_area_px = float(np.pi * (minor_px / 2.0) ** 2)\n", + "\n", + "print(\"CRS:\", crs)\n", + "print(f\"Pixel size: {gsd_units:.6f} units/px (~{gsd_mm:.3f} mm/px)\")\n", + "print(f\"2 cm => minor_px={minor_px:.2f} px -> min_area_px={min_area_px:.1f} px^2\")\n", + "\n", + "\n", + "# --- loop ---\n", + "for i, fname in enumerate(tiles, start=1):\n", + " tile_id = os.path.splitext(os.path.basename(fname))[0]\n", + " print(f\"[{i}/{len(tiles)}] {tile_id}\")\n", + "\n", + " # load + predict\n", + " image = np.array(load_img(fname))\n", + " image_pred = seg.predict_image(image, unet, I=256)\n", + "\n", + " # prompts (Unet -> points)\n", + " labels, coords = seg.label_grains(image, image_pred, dbs_max_dist=10.0)\n", + "\n", + " # --- optional: cap number of prompts before SAM ---\n", + " coords = np.asarray(coords)\n", + " if MAX_SAM_PROMPTS is not None and len(coords) > MAX_SAM_PROMPTS:\n", + " n_before = len(coords)\n", + "\n", + " if PROMPT_SUBSAMPLE_MODE == \"first\":\n", + " keep_idx = np.arange(MAX_SAM_PROMPTS)\n", + " else: # random\n", + " keep_idx = np.sort(rng.choice(len(coords), size=MAX_SAM_PROMPTS, replace=False))\n", + "\n", + " coords = coords[keep_idx]\n", + "\n", + " # labels nur mitfiltern, wenn es ein 1D prompt-label array ist\n", + " try:\n", + " labels_arr = np.asarray(labels)\n", + " if labels_arr.ndim == 1 and len(labels_arr) == n_before:\n", + " labels = labels_arr[keep_idx]\n", + " except Exception:\n", + " pass\n", + "\n", + " print(f\"Prompt cap active: reduced prompts from {n_before} -> {len(coords)}\")\n", + "\n", + " # SAM segmentation\n", + " all_grains, labels, mask_all, grain_data, fig, ax = seg.sam_segmentation(\n", + " sam,\n", + " image,\n", + " image_pred,\n", + " coords,\n", + " labels,\n", + " min_area=min_area_px,\n", + " plot_image=True,\n", + " remove_edge_grains=True,\n", + " remove_large_objects=True,\n", + " )\n", + "\n", + " # --------------------------------------------------\n", + " # 1) Segmentierungsplot speichern (dein bestehender Plot)\n", + " # --------------------------------------------------\n", + " seg_plot_path = os.path.join(plotdir, f\"{tile_id}.png\")\n", + " fig.savefig(seg_plot_path, dpi=200, bbox_inches=\"tight\")\n", + " plt.close(fig)\n", + " print(\"Saved segmentation plot\")\n", + "\n", + " # --------------------------------------------------\n", + " # 2) Polygone georeferenzieren (Pixel -> UTM / CRS des Tiles)\n", + " # --------------------------------------------------\n", + " with rasterio.open(fname) as dataset:\n", + " projected_polys = []\n", + " for grain in all_grains:\n", + " # grain.exterior.xy liefert (x_coords, y_coords) in Pixelkoordinaten\n", + " # hier: row = y, col = x\n", + " px_x = np.asarray(grain.exterior.xy[0])\n", + " px_y = np.asarray(grain.exterior.xy[1])\n", + "\n", + " x_proj, y_proj = rasterio.transform.xy(\n", + " dataset.transform,\n", + " px_y, # rows\n", + " px_x # cols\n", + " )\n", + "\n", + " poly = Polygon(np.vstack((x_proj, y_proj)).T)\n", + " projected_polys.append(poly)\n", + "\n", + " # GeoDataFrame erzeugen\n", + " gdf = gpd.GeoDataFrame({\"geometry\": projected_polys}, geometry=\"geometry\", crs=dataset.crs)\n", + "\n", + " # --------------------------------------------------\n", + " # 3) Eigenschaften / Statistiken aus labeled image\n", + " # --------------------------------------------------\n", + " # (intensity_image hier weggelassen, da nur geometrische Properties gebraucht werden)\n", + " props = regionprops_table(\n", + " labels.astype(\"int\"),\n", + " properties=(\"label\", \"area\", \"centroid\", \"major_axis_length\", \"minor_axis_length\"),\n", + " )\n", + " grain_stats = pd.DataFrame(props)\n", + "\n", + " # Falls aus irgendeinem Grund Anzahl nicht exakt passt, robust behandeln\n", + " if len(grain_stats) != len(gdf):\n", + " nmin = min(len(grain_stats), len(gdf))\n", + " print(f\"Warning: len(gdf)={len(gdf)} != len(grain_stats)={len(grain_stats)} -> truncating to {nmin}\")\n", + " gdf = gdf.iloc[:nmin].copy()\n", + " grain_stats = grain_stats.iloc[:nmin].copy()\n", + "\n", + " # Zentroiden (row,col) -> CRS coords\n", + " centroid_x, centroid_y = rasterio.transform.xy(\n", + " dataset.transform,\n", + " grain_stats[\"centroid-0\"].values, # rows\n", + " grain_stats[\"centroid-1\"].values, # cols\n", + " )\n", + "\n", + " # Pixelgröße aus Transform (X-Richtung); für quadratische Pixel ok\n", + " px_size_x = abs(dataset.transform.a)\n", + "\n", + " # Attribute in GDF schreiben\n", + " gdf[\"label\"] = grain_stats[\"label\"].values\n", + " gdf[\"area_px\"] = grain_stats[\"area\"].values\n", + " gdf[\"centroid_x\"] = centroid_x\n", + " gdf[\"centroid_y\"] = centroid_y\n", + " gdf[\"major_axis_px\"] = grain_stats[\"major_axis_length\"].values\n", + " gdf[\"minor_axis_px\"] = grain_stats[\"minor_axis_length\"].values\n", + " gdf[\"major_axis_length\"] = grain_stats[\"major_axis_length\"].values * px_size_x\n", + " gdf[\"minor_axis_length\"] = grain_stats[\"minor_axis_length\"].values * px_size_x\n", + " gdf[\"major_axis_mm\"] = gdf[\"major_axis_length\"] * 1000.0\n", + " gdf[\"minor_axis_mm\"] = gdf[\"minor_axis_length\"] * 1000.0\n", + "\n", + " # --------------------------------------------------\n", + " # 4) Histogramm-Plot speichern (NICHT den Segmentierungsplot überschreiben)\n", + " # --------------------------------------------------\n", + " if len(gdf) > 0:\n", + " fig_hist, ax_hist = seg.plot_histogram_of_axis_lengths(\n", + " gdf[\"major_axis_mm\"],\n", + " gdf[\"minor_axis_mm\"],\n", + " binsize=0.25,\n", + " xlimits=[8, 2 * 256],\n", + " )\n", + " hist_plot_path = os.path.join(plotdir, f\"{tile_id}_hist.png\") # eigener Name!\n", + " fig_hist.savefig(hist_plot_path, dpi=200, bbox_inches=\"tight\")\n", + " plt.close(fig_hist)\n", + " print(\"Saved histogram plot\")\n", + " else:\n", + " print(\"No grains found -> skipping histogram plot\")\n", + "\n", + " # --------------------------------------------------\n", + " # 5) GPKG + Statistik-CSV speichern\n", + " # --------------------------------------------------\n", + " gpkg_path = os.path.join(ouputgpkg, f\"{tile_id}_grains.gpkg\")\n", + " csv_path = os.path.join(csvdir, f\"{tile_id}_grain_stats.csv\")\n", + "\n", + " # GPKG\n", + " gdf.to_file(gpkg_path, driver=\"GPKG\")\n", + " print(f\"Saved GPKG: {gpkg_path}\")\n", + "\n", + " # CSV (ohne Geometrie; Geometrie steckt im GPKG)\n", + " stats_df = gdf.drop(columns=\"geometry\").copy()\n", + " stats_df.to_csv(csv_path, index=False)\n", + " print(f\"Saved stats CSV: {csv_path}\")\n", + "\n", + " print(\"done with segmentation + export\")" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "seg_2", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.19" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/notebooks/Segment_every_grain_w_georeferencing_leonie_maskingnotworking.ipynb b/notebooks/Segment_every_grain_w_georeferencing_leonie_maskingnotworking.ipynb new file mode 100644 index 0000000..24653c0 --- /dev/null +++ b/notebooks/Segment_every_grain_w_georeferencing_leonie_maskingnotworking.ipynb @@ -0,0 +1,919 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "623da314", + "metadata": {}, + "source": [ + "# Import packages" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "ff94825b", + "metadata": {}, + "outputs": [], + "source": [ + "import cv2\n", + "from keras.utils import load_img\n", + "from keras.saving import load_model\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "import pandas as pd\n", + "from sam2.build_sam import build_sam2\n", + "from sam2.sam2_image_predictor import SAM2ImagePredictor\n", + "from skimage.measure import regionprops, regionprops_table\n", + "from tqdm import trange, tqdm\n", + "\n", + "import segmenteverygrain as seg\n", + "import segmenteverygrain.interactions as si\n", + "import os\n", + "import rasterio\n", + "\n", + "# %matplotlib qt" + ] + }, + { + "cell_type": "markdown", + "id": "44e817f1", + "metadata": {}, + "source": [ + "## Load models" + ] + }, + { + "cell_type": "markdown", + "id": "442d52dc", + "metadata": {}, + "source": [ + "Maybe you need to clone first SAM2. Then add the yaml file in the bottom of the next chunk" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "3965da5f", + "metadata": {}, + "outputs": [], + "source": [ + "# UNET model\n", + "unet = load_model(\n", + " \"../models/seg_model.keras\",\n", + " custom_objects={\"weighted_crossentropy\": seg.weighted_crossentropy},\n", + ")\n", + "\n", + "# Auto-detect device: CUDA for NVIDIA, MPS for Apple Silicon, CPU as fallback\n", + "import torch\n", + "if torch.cuda.is_available():\n", + " device = \"cuda\"\n", + "elif torch.backends.mps.is_available():\n", + " device = \"mps\"\n", + "else:\n", + " device = \"cpu\"\n", + "\n", + "# SAM 2.1 checkpoints. Download from:\n", + "# https://dl.fbaipublicfiles.com/segment_anything_2/092824/sam2.1_hiera_large.pt\n", + "sam = build_sam2(r\"C:\\Users\\leoni\\sam2\\sam2\\configs\\sam2.1\\sam2.1_hiera_l.yaml\", r\"C:\\Users\\leoni\\segmenteverygrain\\models\\sam2.1_hiera_large.pt\", device=device)\n" + ] + }, + { + "cell_type": "markdown", + "id": "93a0c303", + "metadata": {}, + "source": [ + "## Set your directories" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "6856ddaf", + "metadata": {}, + "outputs": [], + "source": [ + "dir = os.chdir(\"C:/Users/leoni/kirgis_repo/segmenteverygrain/leonieexamples/test_if_mask_necessary\")\n", + "dir = os.getcwd()\n", + "os.makedirs(\"ouputgpkg\", exist_ok=True)\n", + "os.makedirs(\"inputtiles\", exist_ok=True)\n", + "os.makedirs(\"plots\", exist_ok=True)\n", + "\n", + "\n", + "inputtiledir = os.path.join(dir, \"inputtiles/\")\n", + "ouputgpkg = os.path.join(dir, \"ouputgpkg/\")\n", + "\n", + "plotdir = os.path.join(dir, \"plots/\")" + ] + }, + { + "cell_type": "markdown", + "id": "ed334819", + "metadata": {}, + "source": [ + "Input your masks of the flight" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "f3ecdeae", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Found 7 tiles\n", + "CRS: EPSG:32643\n", + "Pixel size: 0.003501 units/px (~3.501 mm/px)\n", + "2 cm => minor_px=5.71 px -> min_area_px=25.6 px^2\n", + "[1/7] tile_r000006_c000024\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 2.27it/s]\n", + "100%|██████████| 3/3 [00:01<00:00, 2.41it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 645/645 [00:45<00:00, 14.23it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "490it [00:04, 120.89it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "486it [00:03, 142.40it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 141/141 [00:06<00:00, 23.31it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 201/201 [00:00<00:00, 205.39it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved the Plot\n", + "done with segmentation\n", + "[2/7] tile_r000006_c000025\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:02<00:00, 1.96it/s]\n", + "100%|██████████| 3/3 [00:01<00:00, 2.39it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 820/820 [01:04<00:00, 12.72it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "634it [00:04, 143.80it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "626it [00:04, 151.08it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 174/174 [00:05<00:00, 30.67it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 281/281 [00:01<00:00, 228.77it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved the Plot\n", + "done with segmentation\n", + "[3/7] tile_r000006_c000028\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:02<00:00, 1.82it/s]\n", + "100%|██████████| 3/3 [00:01<00:00, 2.10it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 1444/1444 [02:28<00:00, 9.74it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "538it [03:31, 2.54it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "374it [00:54, 6.92it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 32/32 [01:32<00:00, 2.89s/it]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 45/45 [00:00<00:00, 142.42it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved the Plot\n", + "done with segmentation\n", + "[4/7] tile_r000008_c000015\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:01<00:00, 2.02it/s]\n", + "100%|██████████| 3/3 [00:01<00:00, 2.23it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 7282/7282 [13:56<00:00, 8.71it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2252it [34:49, 1.08it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "1555it [27:35, 1.06s/it]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + " 0%| | 0/3 [13:40 158\u001b[0m all_grains, labels, mask_all, grain_data, fig, ax \u001b[38;5;241m=\u001b[39m \u001b[43mseg\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msam_segmentation\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m 159\u001b[0m \u001b[43m \u001b[49m\u001b[43msam\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 160\u001b[0m \u001b[43m \u001b[49m\u001b[43mimage_for_seg\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 161\u001b[0m \u001b[43m \u001b[49m\u001b[43mimage_pred\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 162\u001b[0m \u001b[43m \u001b[49m\u001b[43mcoords\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 163\u001b[0m \u001b[43m \u001b[49m\u001b[43mlabels\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 164\u001b[0m \u001b[43m \u001b[49m\u001b[43mmin_area\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmin_area_px\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 165\u001b[0m \u001b[43m \u001b[49m\u001b[43mplot_image\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[0;32m 166\u001b[0m \u001b[43m \u001b[49m\u001b[43mremove_edge_grains\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[0;32m 167\u001b[0m \u001b[43m \u001b[49m\u001b[43mremove_large_objects\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[0;32m 168\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 170\u001b[0m \u001b[38;5;66;03m# Optional: finale Maske hart beschneiden (zusätzliche Sicherheit)\u001b[39;00m\n\u001b[0;32m 171\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n", + "File \u001b[1;32mc:\\Users\\leoni\\micromamba\\envs\\seg_2\\lib\\site-packages\\segmenteverygrain\\segmenteverygrain.py:809\u001b[0m, in \u001b[0;36msam_segmentation\u001b[1;34m(sam, image, image_pred, coords, labels, min_area, plot_image, remove_edge_grains, remove_large_objects, keep_edges)\u001b[0m\n\u001b[0;32m 806\u001b[0m new_grains, comps, g \u001b[38;5;241m=\u001b[39m find_connected_components(all_grains, min_area)\n\u001b[0;32m 808\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mfinding best polygons...\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m--> 809\u001b[0m all_grains \u001b[38;5;241m=\u001b[39m \u001b[43mmerge_overlapping_polygons\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m 810\u001b[0m \u001b[43m \u001b[49m\u001b[43mall_grains\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mnew_grains\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcomps\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmin_area\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mimage_pred\u001b[49m\n\u001b[0;32m 811\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 812\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(all_grains) \u001b[38;5;241m>\u001b[39m \u001b[38;5;241m0\u001b[39m:\n\u001b[0;32m 813\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mcreating labeled image...\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n", + "File \u001b[1;32mc:\\Users\\leoni\\micromamba\\envs\\seg_2\\lib\\site-packages\\segmenteverygrain\\segmenteverygrain.py:979\u001b[0m, in \u001b[0;36mmerge_overlapping_polygons\u001b[1;34m(all_grains, new_grains, comps, min_area, image_pred)\u001b[0m\n\u001b[0;32m 977\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m i \u001b[38;5;129;01min\u001b[39;00m comps[j]:\n\u001b[0;32m 978\u001b[0m polygons\u001b[38;5;241m.\u001b[39mappend(all_grains[i])\n\u001b[1;32m--> 979\u001b[0m most_similar_polygon \u001b[38;5;241m=\u001b[39m \u001b[43mpick_most_similar_polygon\u001b[49m\u001b[43m(\u001b[49m\u001b[43mpolygons\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 980\u001b[0m \u001b[38;5;66;03m# this next section is needed so that grains that are not covered by the 'most similar polygon' are not lost\u001b[39;00m\n\u001b[0;32m 981\u001b[0m diff_polys \u001b[38;5;241m=\u001b[39m []\n", + "File \u001b[1;32mc:\\Users\\leoni\\micromamba\\envs\\seg_2\\lib\\site-packages\\segmenteverygrain\\segmenteverygrain.py:656\u001b[0m, in \u001b[0;36mpick_most_similar_polygon\u001b[1;34m(polygons)\u001b[0m\n\u001b[0;32m 654\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m j, poly2 \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28menumerate\u001b[39m(polygons):\n\u001b[0;32m 655\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m i \u001b[38;5;241m!=\u001b[39m j:\n\u001b[1;32m--> 656\u001b[0m iou_scores\u001b[38;5;241m.\u001b[39mappend(\u001b[43mcalculate_iou\u001b[49m\u001b[43m(\u001b[49m\u001b[43mpoly1\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mpoly2\u001b[49m\u001b[43m)\u001b[49m)\n\u001b[0;32m 657\u001b[0m avg_iou_scores\u001b[38;5;241m.\u001b[39mappend(\u001b[38;5;28msum\u001b[39m(iou_scores) \u001b[38;5;241m/\u001b[39m \u001b[38;5;28mlen\u001b[39m(iou_scores))\n\u001b[0;32m 658\u001b[0m \u001b[38;5;66;03m# Find the polygon with the highest average IoU score\u001b[39;00m\n", + "File \u001b[1;32mc:\\Users\\leoni\\micromamba\\envs\\seg_2\\lib\\site-packages\\segmenteverygrain\\segmenteverygrain.py:629\u001b[0m, in \u001b[0;36mcalculate_iou\u001b[1;34m(poly1, poly2)\u001b[0m\n\u001b[0;32m 627\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m warnings\u001b[38;5;241m.\u001b[39mcatch_warnings():\n\u001b[0;32m 628\u001b[0m warnings\u001b[38;5;241m.\u001b[39msimplefilter(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mignore\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;167;01mRuntimeWarning\u001b[39;00m)\n\u001b[1;32m--> 629\u001b[0m intersection_area \u001b[38;5;241m=\u001b[39m \u001b[43mpoly1\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mintersection\u001b[49m\u001b[43m(\u001b[49m\u001b[43mpoly2\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241m.\u001b[39marea\n\u001b[0;32m 630\u001b[0m union_area \u001b[38;5;241m=\u001b[39m poly1\u001b[38;5;241m.\u001b[39munion(poly2)\u001b[38;5;241m.\u001b[39marea\n\u001b[0;32m 631\u001b[0m iou \u001b[38;5;241m=\u001b[39m intersection_area \u001b[38;5;241m/\u001b[39m union_area\n", + "File \u001b[1;32mc:\\Users\\leoni\\micromamba\\envs\\seg_2\\lib\\site-packages\\shapely\\decorators.py:173\u001b[0m, in \u001b[0;36mdeprecate_positional..decorator..wrapper\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m 171\u001b[0m \u001b[38;5;129m@wraps\u001b[39m(func)\n\u001b[0;32m 172\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21mwrapper\u001b[39m(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs):\n\u001b[1;32m--> 173\u001b[0m result \u001b[38;5;241m=\u001b[39m func(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[0;32m 175\u001b[0m n \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mlen\u001b[39m(args)\n\u001b[0;32m 176\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m n \u001b[38;5;241m>\u001b[39m warn_from:\n", + "File \u001b[1;32mc:\\Users\\leoni\\micromamba\\envs\\seg_2\\lib\\site-packages\\shapely\\geometry\\base.py:674\u001b[0m, in \u001b[0;36mBaseGeometry.intersection\u001b[1;34m(self, other, grid_size)\u001b[0m\n\u001b[0;32m 668\u001b[0m \u001b[38;5;129m@deprecate_positional\u001b[39m([\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mgrid_size\u001b[39m\u001b[38;5;124m\"\u001b[39m], category\u001b[38;5;241m=\u001b[39m\u001b[38;5;167;01mDeprecationWarning\u001b[39;00m)\n\u001b[0;32m 669\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21mintersection\u001b[39m(\u001b[38;5;28mself\u001b[39m, other, grid_size\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m):\n\u001b[0;32m 670\u001b[0m \u001b[38;5;250m \u001b[39m\u001b[38;5;124;03m\"\"\"Return the intersection of the geometries.\u001b[39;00m\n\u001b[0;32m 671\u001b[0m \n\u001b[0;32m 672\u001b[0m \u001b[38;5;124;03m Refer to `shapely.intersection` for full documentation.\u001b[39;00m\n\u001b[0;32m 673\u001b[0m \u001b[38;5;124;03m \"\"\"\u001b[39;00m\n\u001b[1;32m--> 674\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mshapely\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mintersection\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mother\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mgrid_size\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mgrid_size\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[1;32mc:\\Users\\leoni\\micromamba\\envs\\seg_2\\lib\\site-packages\\shapely\\decorators.py:173\u001b[0m, in \u001b[0;36mdeprecate_positional..decorator..wrapper\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m 171\u001b[0m \u001b[38;5;129m@wraps\u001b[39m(func)\n\u001b[0;32m 172\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21mwrapper\u001b[39m(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs):\n\u001b[1;32m--> 173\u001b[0m result \u001b[38;5;241m=\u001b[39m func(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[0;32m 175\u001b[0m n \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mlen\u001b[39m(args)\n\u001b[0;32m 176\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m n \u001b[38;5;241m>\u001b[39m warn_from:\n", + "File \u001b[1;32mc:\\Users\\leoni\\micromamba\\envs\\seg_2\\lib\\site-packages\\shapely\\decorators.py:88\u001b[0m, in \u001b[0;36mmultithreading_enabled..wrapped\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m 86\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m arr \u001b[38;5;129;01min\u001b[39;00m array_args:\n\u001b[0;32m 87\u001b[0m arr\u001b[38;5;241m.\u001b[39mflags\u001b[38;5;241m.\u001b[39mwriteable \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mFalse\u001b[39;00m\n\u001b[1;32m---> 88\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m func(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[0;32m 89\u001b[0m \u001b[38;5;28;01mfinally\u001b[39;00m:\n\u001b[0;32m 90\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m arr, old_flag \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mzip\u001b[39m(array_args, old_flags):\n", + "File \u001b[1;32mc:\\Users\\leoni\\micromamba\\envs\\seg_2\\lib\\site-packages\\shapely\\set_operations.py:168\u001b[0m, in \u001b[0;36mintersection\u001b[1;34m(a, b, grid_size, **kwargs)\u001b[0m\n\u001b[0;32m 164\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mgrid_size parameter only accepts scalar values\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m 166\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m lib\u001b[38;5;241m.\u001b[39mintersection_prec(a, b, grid_size, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[1;32m--> 168\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m lib\u001b[38;5;241m.\u001b[39mintersection(a, b, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n", + "\u001b[1;31mKeyboardInterrupt\u001b[0m: " + ] + } + ], + "source": [ + "# ==========================================================\n", + "# MASKEN-SEKTION (ab hier)\n", + "# 1 = NICHT segmentieren\n", + "# 0 oder nodata = segmentieren erlaubt\n", + "# ==========================================================\n", + "veg_mask_path = r\"C:\\Users\\leoni\\kirgis_repo\\veg-shad-wat-filtering\\1_uav-filters\\vegetation\\veg_masks_rgbvi_fallback\\25092025_Aktal_gravel_2_10m_OM_RGB_utm_coregistered_ws128_clipwater_vegmask_rgbvi.tif\"\n", + "shadow_mask_path = r\"C:\\Users\\leoni\\kirgis_repo\\veg-shad-wat-filtering\\1_uav-filters\\shadow\\0-02\\25092025_Aktal_gravel_2_10m_OM_RGB_utm_coregistered_ws128_clipwater_shadowmask.tif\"\n", + "\n", + "import glob\n", + "from rasterio.warp import reproject, Resampling\n", + "\n", + "def read_mask_for_tile(tile_path, full_mask_src, mask_band=1):\n", + " \"\"\"\n", + " Schneidet/projiziert die Vollmaske auf das Grid des Tiles.\n", + " Ergebnis: 2D-Array mit exakt gleicher Höhe/Breite wie das Tile.\n", + " \"\"\"\n", + " with rasterio.open(tile_path) as tsrc:\n", + " tile_mask = np.zeros((tsrc.height, tsrc.width), dtype=np.float32)\n", + "\n", + " reproject(\n", + " source=rasterio.band(full_mask_src, mask_band),\n", + " destination=tile_mask,\n", + " src_transform=full_mask_src.transform,\n", + " src_crs=full_mask_src.crs,\n", + " dst_transform=tsrc.transform,\n", + " dst_crs=tsrc.crs,\n", + " resampling=Resampling.nearest, # wichtig für binäre Masken\n", + " dst_nodata=np.nan\n", + " )\n", + "\n", + " return tile_mask\n", + "\n", + "\n", + "# --- collect tiles ---\n", + "tiles = sorted(glob.glob(os.path.join(inputtiledir, \"*.tif\")))\n", + "\n", + "print(f\"Found {len(tiles)} tiles\")\n", + "\n", + "if len(tiles) == 0:\n", + " raise RuntimeError(\"No tiles found.\")\n", + "\n", + "# --- read pixel size once from first tile ---\n", + "with rasterio.open(tiles[0]) as src:\n", + " xres, yres = src.res # CRS units per pixel (usually meters)\n", + " crs = src.crs\n", + "\n", + "gsd_units = float((xres + yres) / 2.0)\n", + "gsd_mm = gsd_units * 1000.0 # assumes CRS units are meters (e.g., UTM)\n", + "\n", + "minor_mm = 20.0 # 2 cm\n", + "minor_px = minor_mm / gsd_mm\n", + "\n", + "# area threshold from minor-axis (circle assumption)\n", + "min_area_px = float(np.pi * (minor_px / 2.0) ** 2)\n", + "\n", + "print(\"CRS:\", crs)\n", + "print(f\"Pixel size: {gsd_units:.6f} units/px (~{gsd_mm:.3f} mm/px)\")\n", + "print(f\"2 cm => minor_px={minor_px:.2f} px -> min_area_px={min_area_px:.1f} px^2\")\n", + "\n", + "\n", + "# --- loop (mit Masken + Shape-Fix + robustem labels-Handling) ---\n", + "with rasterio.open(veg_mask_path) as veg_src, rasterio.open(shadow_mask_path) as shadow_src:\n", + "\n", + " for i, fname in enumerate(tiles, start=1):\n", + " tile_id = os.path.splitext(os.path.basename(fname))[0]\n", + " print(f\"[{i}/{len(tiles)}] {tile_id}\")\n", + "\n", + " # load image\n", + " image = np.array(load_img(fname))\n", + "\n", + " # passende Tile-Masken aus den Vollmasken holen\n", + " veg_tile = read_mask_for_tile(fname, veg_src, mask_band=1)\n", + " shadow_tile = read_mask_for_tile(fname, shadow_src, mask_band=1)\n", + "\n", + " # Deine Logik:\n", + " # 1 = maskiert (nicht segmentieren)\n", + " # 0 oder nodata = erlaubt\n", + " veg_exclude = np.isclose(veg_tile, 1.0, equal_nan=False)\n", + " shadow_exclude = np.isclose(shadow_tile, 1.0, equal_nan=False)\n", + "\n", + " exclude_mask = veg_exclude | shadow_exclude # True = blockieren\n", + " valid_mask = ~exclude_mask # True = segmentieren erlaubt\n", + "\n", + " # --------------------------------------------------\n", + " # SHAPE CHECK / FIX (für TIFFs: H/W manchmal vertauscht)\n", + " # --------------------------------------------------\n", + " img_hw = image.shape[:2] # (H, W)\n", + " mask_hw = valid_mask.shape # erwartet auch (H, W)\n", + "\n", + " if mask_hw != img_hw:\n", + " if mask_hw[::-1] == img_hw:\n", + " valid_mask = valid_mask.T\n", + " print(f\"Transposed valid_mask from {mask_hw} to {valid_mask.shape} to match image {img_hw}\")\n", + " else:\n", + " raise ValueError(\n", + " f\"Shape mismatch between image and valid_mask: \"\n", + " f\"image.shape[:2]={img_hw}, valid_mask.shape={mask_hw}\"\n", + " )\n", + "\n", + " if not np.any(valid_mask):\n", + " print(\"Tile fully masked by vegetation/shadow -> skipping\")\n", + " continue\n", + "\n", + " # load + predict\n", + " image_pred = seg.predict_image(image, unet, I=256)\n", + "\n", + " # Zusätzlicher Check: falls prediction-Shape abweicht\n", + " pred_hw = image_pred.shape[:2]\n", + " if pred_hw != valid_mask.shape:\n", + " if valid_mask.shape[::-1] == pred_hw:\n", + " valid_mask = valid_mask.T\n", + " print(f\"Transposed valid_mask to match image_pred: {pred_hw}\")\n", + " else:\n", + " raise ValueError(\n", + " f\"Shape mismatch between image_pred and valid_mask: \"\n", + " f\"image_pred.shape[:2]={pred_hw}, valid_mask.shape={valid_mask.shape}\"\n", + " )\n", + "\n", + " # Maskierte Flächen schon VOR Prompt-Erzeugung ausschließen\n", + " image_pred = np.array(image_pred, copy=True)\n", + " image_pred[~valid_mask] = 0\n", + "\n", + " # Optional/empfohlen: Bild dort auch neutralisieren\n", + " image_for_seg = np.array(image, copy=True)\n", + " image_for_seg[~valid_mask] = 0\n", + "\n", + " # prompts (Unet -> points)\n", + " labels, coords = seg.label_grains(image_for_seg, image_pred, dbs_max_dist=10.0)\n", + "\n", + " # --------------------------------------------------\n", + " # Sicherheitsfilter: Prompts in maskierten Bereichen entfernen\n", + " # WICHTIG: labels kann 2D Label-Bild sein -> nicht immer mit keep filtern!\n", + " # --------------------------------------------------\n", + " coords = np.asarray(coords)\n", + "\n", + " if len(coords) > 0:\n", + " # Annahme: coords = (x, y)\n", + " x_idx = np.clip(np.round(coords[:, 0]).astype(int), 0, valid_mask.shape[1] - 1)\n", + " y_idx = np.clip(np.round(coords[:, 1]).astype(int), 0, valid_mask.shape[0] - 1)\n", + "\n", + " keep = valid_mask[y_idx, x_idx]\n", + " coords = coords[keep]\n", + "\n", + " # labels nur filtern, wenn es wirklich ein 1D Prompt-Labelarray ist\n", + " try:\n", + " labels_arr = np.asarray(labels)\n", + " if labels_arr.ndim == 1 and len(labels_arr) == len(keep):\n", + " labels = labels_arr[keep]\n", + " # sonst labels unverändert lassen (z.B. 2D Labelbild)\n", + " except Exception:\n", + " pass\n", + "\n", + " if len(coords) == 0:\n", + " print(\"No valid prompts after masking -> skipping tile\")\n", + " continue\n", + "\n", + " # SAM segmentation\n", + " all_grains, labels, mask_all, grain_data, fig, ax = seg.sam_segmentation(\n", + " sam,\n", + " image_for_seg,\n", + " image_pred,\n", + " coords,\n", + " labels,\n", + " min_area=min_area_px,\n", + " plot_image=True,\n", + " remove_edge_grains=True,\n", + " remove_large_objects=True,\n", + " )\n", + "\n", + " # Optional: finale Maske hart beschneiden (zusätzliche Sicherheit)\n", + " try:\n", + " mask_all = np.array(mask_all, copy=True)\n", + " if mask_all.shape != valid_mask.shape:\n", + " if mask_all.shape[::-1] == valid_mask.shape:\n", + " mask_all = mask_all.T\n", + " else:\n", + " print(f\"Warning: mask_all shape {mask_all.shape} != valid_mask shape {valid_mask.shape}\")\n", + " mask_all[~valid_mask] = 0\n", + " except Exception as e:\n", + " print(f\"Could not apply final hard mask cut: {e}\")\n", + "\n", + " # save plot\n", + " figname = os.path.join(plotdir, f\"{tile_id}.png\")\n", + " fig.savefig(figname, dpi=200, bbox_inches=\"tight\")\n", + " plt.close(fig)\n", + "\n", + " print(\"Saved the Plot\")\n", + " print(\"done with segmentation\")" + ] + }, + { + "cell_type": "markdown", + "id": "dbbfdccd", + "metadata": {}, + "source": [ + "## Not including Masks" + ] + }, + { + "cell_type": "markdown", + "id": "f9193000", + "metadata": {}, + "source": [ + "## Run segmentation" + ] + }, + { + "cell_type": "markdown", + "id": "6d450ab5", + "metadata": {}, + "source": [ + "Collect tiles" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "e229e490", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Found 1 tiles\n", + "CRS: EPSG:32643\n", + "Pixel size: 0.003501 units/px (~3.501 mm/px)\n", + "2 cm => minor_px=5.71 px -> min_area_px=25.6 px^2\n", + "[1/1] tile_r000008_c000026\n", + "segmenting image tiles...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:04<00:00, 1.17s/it]\n", + "100%|██████████| 3/3 [00:02<00:00, 1.24it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating masks using SAM...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 822/822 [01:33<00:00, 8.80it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "657it [00:26, 25.11it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding overlapping polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "645it [00:20, 32.00it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finding best polygons...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 200/200 [00:32<00:00, 6.17it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating labeled image...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 244/244 [00:01<00:00, 128.43it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved the Plot\n", + "done with segmentation\n" + ] + } + ], + "source": [ + "import os\n", + "import glob\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "from tensorflow.keras.utils import load_img\n", + "import rasterio\n", + "\n", + "# --- collect tiles ---\n", + "tiles = sorted(glob.glob(os.path.join(inputtiledir, \"*.tif\")))\n", + "\n", + "print(f\"Found {len(tiles)} tiles\")\n", + "\n", + "\n", + "if len(tiles) == 0:\n", + " raise RuntimeError(\"No tiles found.\")\n", + "\n", + "# --- read pixel size once from first tile ---\n", + "with rasterio.open(tiles[0]) as src:\n", + " xres, yres = src.res # CRS units per pixel (usually meters)\n", + " crs = src.crs\n", + "\n", + "gsd_units = float((xres + yres) / 2.0)\n", + "gsd_mm = gsd_units * 1000.0 # assumes CRS units are meters (e.g., UTM)\n", + "\n", + "minor_mm = 20.0 # 2 cm\n", + "minor_px = minor_mm / gsd_mm\n", + "\n", + "# area threshold from minor-axis (circle assumption)\n", + "min_area_px = float(np.pi * (minor_px / 2.0) ** 2)\n", + "\n", + "print(\"CRS:\", crs)\n", + "print(f\"Pixel size: {gsd_units:.6f} units/px (~{gsd_mm:.3f} mm/px)\")\n", + "print(f\"2 cm => minor_px={minor_px:.2f} px -> min_area_px={min_area_px:.1f} px^2\")\n", + "\n", + "\n", + "\n", + "# --- loop ---\n", + "for i, fname in enumerate(tiles, start=1):\n", + " tile_id = os.path.splitext(os.path.basename(fname))[0]\n", + " print(f\"[{i}/{len(tiles)}] {tile_id}\")\n", + "\n", + " # load + predict\n", + " image = np.array(load_img(fname))\n", + " image_pred = seg.predict_image(image, unet, I=256)\n", + "\n", + " # prompts (Unet -> points)\n", + " labels, coords = seg.label_grains(image, image_pred, dbs_max_dist=10.0)\n", + "\n", + " # SAM segmentation\n", + " all_grains, labels, mask_all, grain_data, fig, ax = seg.sam_segmentation(\n", + " sam,\n", + " image,\n", + " image_pred,\n", + " coords,\n", + " labels,\n", + " min_area=min_area_px,\n", + " plot_image=True,\n", + " remove_edge_grains=True,\n", + " remove_large_objects=True,\n", + " )\n", + " figname =os.path.join(plotdir, f\"{tile_id}.png\")\n", + "\n", + " fig.savefig(figname, dpi = 200, bbox_inches=\"tight\")\n", + " plt.close(fig)\n", + " print(\"Saved the Plot\")\n", + " print(\"done with segmentation\")\n", + "\n", + "\n", + "\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "035ef937", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "25.62514189487139" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "min_area_px" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "seg_2", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.19" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/slurm/job.sh b/slurm/job.sh new file mode 100644 index 0000000..adc9ac6 --- /dev/null +++ b/slurm/job.sh @@ -0,0 +1,18 @@ +#!/bin/bash +#SBATCH --job-name=seg_1 +#SBATCH --output=/dss/tbyscratch/0B/di54doz/seg/logs/seg_%j.out +#SBATCH --error=/dss/tbyscratch/0B/di54doz/seg/logs/seg_%j.err +#SBATCH --mail-type=END +#SBATCH --mail-user=leonie.sonntag@stud-mail.uni-wuerzburg.de +#SBATCH --account=pr94no-c +#SBATCH --clusters=hpda2 +#SBATCH --partition=hpda2_compute_gpu +#SBATCH --gres=gpu:1 +#SBATCH --nodes=1 +#SBATCH --ntasks=1 +#SBATCH --cpus-per-task=8 +#SBATCH --mem=500G +#SBATCH --time=24:00:00 + +module load micromamba +micromamba run -n segmenteverygrain python seg_1.py diff --git a/slurm/seg_1.py b/slurm/seg_1.py new file mode 100644 index 0000000..1f5f093 --- /dev/null +++ b/slurm/seg_1.py @@ -0,0 +1,493 @@ +# to prevent blocking all memory +import os +os.environ["TF_FORCE_GPU_ALLOW_GROWTH"] = "true" + +import tensorflow as tf +for gpu in tf.config.list_physical_devices("GPU"): + tf.config.experimental.set_memory_growth(gpu, True) + +print("TF GPUs:", tf.config.list_physical_devices("GPU")) + +import cv2 +from keras.utils import load_img +from keras.saving import load_model +import matplotlib.pyplot as plt +import numpy as np +import pandas as pd +from sam2.build_sam import build_sam2 +from sam2.sam2_image_predictor import SAM2ImagePredictor +from skimage.measure import regionprops, regionprops_table +from tqdm import trange, tqdm + +import segmenteverygrain as seg +import segmenteverygrain.interactions as si +import os +import rasterio +import time, traceback +import pandas as pd +import torch + +# %matplotlib qt + +import torch + +# 1) UNet laden (TF) +unet = load_model( + "../models/seg_model.keras", + custom_objects={"weighted_crossentropy": seg.weighted_crossentropy}, +) +print("UNet loaded") + +# 2) SAM2 laden (Torch) +device = "cuda" if torch.cuda.is_available() else "cpu" +cfg = "configs/sam2.1/sam2.1_hiera_l.yaml" +ckpt = "/dss/dsshome1/0B/di54doz/segmenteverygrain/models/sam2.1_hiera_large.pt" +sam = build_sam2(cfg, ckpt, device=device) +print("SAM2 loaded on", device) + +# 3) kurzer Speicher-Check +free, total = torch.cuda.mem_get_info() +print(f"torch reports free {free/1024**3:.1f} GB / total {total/1024**3:.1f} GB") + + +import os +import glob +import time +import traceback +import numpy as np +import matplotlib.pyplot as plt +from tensorflow.keras.utils import load_img +import rasterio + +import pandas as pd +import torch + +import geopandas as gpd +from shapely.geometry import Polygon +from skimage.measure import regionprops_table + +# ----------------------------- +# USER SETTINGS +# ----------------------------- +OUT_ROOT = "/dss/tbyscratch/0B/di54doz/seg" # alles hier rein +os.makedirs(OUT_ROOT, exist_ok=True) + +MAX_SAM_PROMPTS = 3000 +PROMPT_SUBSAMPLE_MODE = "random" # "random" oder "first" +RNG_SEED = 42 +rng = np.random.default_rng(RNG_SEED) + +# Save label mask TIFF always +SAVE_LABEL_TIF = True + +# Keep plots +SAVE_PEBBLE_PNG = True + +# Histogram only for sample (saves a lot of time) +SAVE_HIST_SAMPLE = True +N_HIST_PER_FOLDER = 100 # random tiles per folder for histogram PNGs + +# Save GPKG only for sample +SAVE_GPKG_SAMPLE = True +N_GPKG_PER_FOLDER = 100 # random tiles per folder + +# SEG plotting: avoid drawing ellipse axes from segmenteverygrain +SEG_PLOT_IMAGE = False # <- important: prevents a/b axes overlays + +def _gpu_free_gb(): + if torch.cuda.is_available(): + free, total = torch.cuda.mem_get_info() + return free / 1024**3 + return None + +def setup_out_folder(in_folder: str, out_root: str): + folder_name = os.path.basename(os.path.normpath(in_folder)) + out_folder = os.path.join(out_root, folder_name) + + out_gpkg = os.path.join(out_folder, "ouputgpkg") + out_csv = os.path.join(out_folder, "csv_stats") + out_plot = os.path.join(out_folder, "pebbleplots") + out_hist = os.path.join(out_folder, "histplots") + out_masks = os.path.join(out_folder, "masktifs") + + for p in [out_gpkg, out_csv, out_plot, out_hist, out_masks]: + os.makedirs(p, exist_ok=True) + + return out_folder, out_gpkg, out_csv, out_plot, out_hist, out_masks + +def collect_tiles(folder: str): + inputtiledir = os.path.join(folder, "inputtiles") + tiles_in_input = sorted(glob.glob(os.path.join(inputtiledir, "*.tif"))) + tiles_in_root = sorted(glob.glob(os.path.join(folder, "*.tif"))) + tiles = tiles_in_input if len(tiles_in_input) > 0 else tiles_in_root + + source = "inputtiles" if len(tiles_in_input) > 0 else "folder root" + print(f"Found {len(tiles)} tiles in {folder} ({source})") + + if len(tiles) == 0: + raise RuntimeError(f"No tiles found in {inputtiledir} or in {folder}") + + return tiles + +def process_one_folder(folder: str, out_root: str = OUT_ROOT): + tiles = collect_tiles(folder) + + out_folder, out_gpkg, out_csv, out_plot, out_hist, out_masks = setup_out_folder(folder, out_root) + print("OUT_FOLDER:", out_folder) + + tile_ids_all = [os.path.splitext(os.path.basename(p))[0] for p in tiles] + + # sample for GPKG export + if SAVE_GPKG_SAMPLE: + if len(tile_ids_all) <= N_GPKG_PER_FOLDER: + gpkg_tile_ids = set(tile_ids_all) + else: + gpkg_tile_ids = set(rng.choice(tile_ids_all, size=N_GPKG_PER_FOLDER, replace=False)) + print(f"GPKG sample: {len(gpkg_tile_ids)} / {len(tile_ids_all)} tiles") + else: + gpkg_tile_ids = set() + + # sample for histogram plots + if SAVE_HIST_SAMPLE: + if len(tile_ids_all) <= N_HIST_PER_FOLDER: + hist_tile_ids = set(tile_ids_all) + else: + hist_tile_ids = set(rng.choice(tile_ids_all, size=N_HIST_PER_FOLDER, replace=False)) + print(f"HIST sample: {len(hist_tile_ids)} / {len(tile_ids_all)} tiles") + else: + hist_tile_ids = set() + + # read pixel size once from first tile + with rasterio.open(tiles[0]) as src: + xres, yres = src.res + crs = src.crs + + gsd_units = float((xres + yres) / 2.0) + gsd_mm = gsd_units * 1000.0 + + minor_mm = 20.0 + minor_px = minor_mm / gsd_mm + min_area_px = float(np.pi * (minor_px / 2.0) ** 2) + + print("CRS:", crs) + print(f"Pixel size: {gsd_units:.6f} units/px (~{gsd_mm:.3f} mm/px)") + print(f"2 cm => minor_px={minor_px:.2f} px -> min_area_px={min_area_px:.1f} px^2") + + rows = [] + metrics_csv = os.path.join(out_folder, "runtime_metrics.csv") + pd.DataFrame([]).to_csv(metrics_csv, index=False) # creates empty file early + + for i, fname in enumerate(tiles, start=1): + tile_id = os.path.splitext(os.path.basename(fname))[0] + print(f"[{i}/{len(tiles)}] {tile_id}") + with open(os.path.join(out_folder, "_progress.txt"), "w") as f: + f.write(f"{i}/{len(tiles)} {tile_id}\n") + + t0 = time.perf_counter() + gpu_free_before = _gpu_free_gb() + + rec = { + "folder": os.path.basename(folder), + "tile_id": tile_id, + "fname": fname, + "status": None, + + "crs": str(crs), + "pixel_size_units_per_px": gsd_units, + "pixel_size_mm_per_px": gsd_mm, + "minor_mm_threshold": minor_mm, + "minor_px_threshold": minor_px, + "min_area_px": min_area_px, + + "H": None, + "W": None, + "n_prompts_before": None, + "n_prompts_used": None, + "prompt_cap_used": None, + "n_grains": None, + + "t_unet_s": None, + "t_label_s": None, + "t_sam_s": None, + "t_export_s": None, + "t_total_s": None, + + "gpu_free_gb_before": gpu_free_before, + "gpu_free_gb_after": None, + + "error_msg": None, + "traceback_head": None, + } + + try: + # nodata check + with rasterio.open(fname) as src: + m = src.dataset_mask() + if not np.any(m): + print(" -> skipped (100% Nodata)") + rec["status"] = "skip_nodata" + rec["t_total_s"] = time.perf_counter() - t0 + rec["gpu_free_gb_after"] = _gpu_free_gb() + rows.append(rec) + continue + + # load + predict (UNet) + t = time.perf_counter() + image = np.array(load_img(fname)) + rec["H"], rec["W"] = int(image.shape[0]), int(image.shape[1]) + image_pred = seg.predict_image(image, unet, I=256) + rec["t_unet_s"] = time.perf_counter() - t + + # prompts + t = time.perf_counter() + labels_pts, coords = seg.label_grains(image, image_pred, dbs_max_dist=10.0) + rec["t_label_s"] = time.perf_counter() - t + + coords = np.asarray(coords) + rec["n_prompts_before"] = int(len(coords)) + rec["prompt_cap_used"] = False + + if MAX_SAM_PROMPTS is not None and len(coords) > MAX_SAM_PROMPTS: + rec["prompt_cap_used"] = True + n_before = len(coords) + + if PROMPT_SUBSAMPLE_MODE == "first": + keep_idx = np.arange(MAX_SAM_PROMPTS) + else: + keep_idx = np.sort(rng.choice(len(coords), size=MAX_SAM_PROMPTS, replace=False)) + + coords = coords[keep_idx] + + try: + labels_arr = np.asarray(labels_pts) + if labels_arr.ndim == 1 and len(labels_arr) == n_before: + labels_pts = labels_arr[keep_idx] + except Exception: + pass + + print(f"Prompt cap active: reduced prompts from {n_before} -> {len(coords)}") + + rec["n_prompts_used"] = int(len(coords)) + + # SAM segmentation + t = time.perf_counter() + all_grains, labels, mask_all, grain_data, fig, ax = seg.sam_segmentation( + sam, + image, + image_pred, + coords, + labels_pts, + min_area=min_area_px, + plot_image=SEG_PLOT_IMAGE, # <- no built-in axis overlays + remove_edge_grains=True, + remove_large_objects=True, + ) + rec["t_sam_s"] = time.perf_counter() - t + rec["n_grains"] = int(len(all_grains)) + + # export/post + t = time.perf_counter() + + # 1) pebble PNG (fallback plot, no axes, no a/b axis overlays) + if SAVE_PEBBLE_PNG: + seg_plot_path = os.path.join(out_plot, f"{tile_id}.png") + + if fig is None: + fig, ax = plt.subplots(figsize=(8, 8)) + ax.imshow(image) + for poly in all_grains: + x, y = poly.exterior.xy + ax.plot(x, y, linewidth=0.8) + ax.axis("off") + else: + try: + for a in fig.axes: + a.axis("off") + except Exception: + pass + + fig.savefig(seg_plot_path, dpi=200, bbox_inches="tight") + plt.close(fig) + + # 2) label mask GeoTIFF (0..N), georeferenced, filtered result from SEG + if SAVE_LABEL_TIF: + with rasterio.open(fname) as ds: + prof = ds.profile.copy() + prof.update(driver="GTiff", count=1, dtype="uint32", compress="lzw") + out_mask = os.path.join(out_masks, f"{tile_id}_labels.tif") + with rasterio.open(out_mask, "w", **prof) as dst: + dst.write(labels.astype("uint32"), 1) + + # 3) stats from labels (fast) + with rasterio.open(fname) as dataset: + px_size_x = abs(dataset.transform.a) + + props = regionprops_table( + labels.astype("int"), + properties=("label", "area", "centroid", "major_axis_length", "minor_axis_length"), + ) + grain_stats = pd.DataFrame(props) + + if len(grain_stats) > 0: + centroid_x, centroid_y = rasterio.transform.xy( + dataset.transform, + grain_stats["centroid-0"].values, + grain_stats["centroid-1"].values, + ) + + grain_stats["centroid_x"] = centroid_x + grain_stats["centroid_y"] = centroid_y + grain_stats.rename(columns={"area": "area_px"}, inplace=True) + grain_stats["major_axis_px"] = grain_stats["major_axis_length"] + grain_stats["minor_axis_px"] = grain_stats["minor_axis_length"] + grain_stats["major_axis_length"] = grain_stats["major_axis_length"] * px_size_x + grain_stats["minor_axis_length"] = grain_stats["minor_axis_length"] * px_size_x + grain_stats["major_axis_mm"] = grain_stats["major_axis_length"] * 1000.0 + grain_stats["minor_axis_mm"] = grain_stats["minor_axis_length"] * 1000.0 + + out_cols = [ + "label", "area_px", "centroid_x", "centroid_y", + "major_axis_px", "minor_axis_px", + "major_axis_length", "minor_axis_length", + "major_axis_mm", "minor_axis_mm", + ] + grain_stats_out = grain_stats[out_cols].copy() + else: + grain_stats_out = pd.DataFrame(columns=[ + "label","area_px","centroid_x","centroid_y", + "major_axis_px","minor_axis_px", + "major_axis_length","minor_axis_length", + "major_axis_mm","minor_axis_mm" + ]) + + csv_path = os.path.join(out_csv, f"{tile_id}_grain_stats.csv") + grain_stats_out.to_csv(csv_path, index=False) + + # 4) histogram PNG only for sampled tiles + if SAVE_HIST_SAMPLE and (tile_id in hist_tile_ids) and len(grain_stats_out) > 0: + fig_hist, ax_hist = seg.plot_histogram_of_axis_lengths( + grain_stats_out["major_axis_mm"], + grain_stats_out["minor_axis_mm"], + binsize=0.25, + xlimits=[8, 2 * 256], + ) + hist_plot_path = os.path.join(out_hist, f"{tile_id}_hist.png") + fig_hist.savefig(hist_plot_path, dpi=200, bbox_inches="tight") + plt.close(fig_hist) + + # 5) GPKG only for sampled tiles + if SAVE_GPKG_SAMPLE and (tile_id in gpkg_tile_ids): + with rasterio.open(fname) as dataset: + projected_polys = [] + for grain in all_grains: + px_x = np.asarray(grain.exterior.xy[0]) + px_y = np.asarray(grain.exterior.xy[1]) + x_proj, y_proj = rasterio.transform.xy(dataset.transform, px_y, px_x) + poly = Polygon(np.vstack((x_proj, y_proj)).T) + projected_polys.append(poly) + + gdf = gpd.GeoDataFrame({"geometry": projected_polys}, geometry="geometry", crs=dataset.crs) + + gpkg_path = os.path.join(out_gpkg, f"{tile_id}_grains.gpkg") + gdf.to_file(gpkg_path, driver="GPKG") + + rec["t_export_s"] = time.perf_counter() - t + rec["status"] = "ok" + rec["t_total_s"] = time.perf_counter() - t0 + rec["gpu_free_gb_after"] = _gpu_free_gb() + rows.append(rec) + pd.DataFrame(rows).to_csv(metrics_csv, index=False) + + except Exception as e: + rec["status"] = "error" + rec["t_total_s"] = time.perf_counter() - t0 + rec["gpu_free_gb_after"] = _gpu_free_gb() + rec["error_msg"] = str(e) + rec["traceback_head"] = traceback.format_exc(limit=8) + rows.append(rec) + pd.DataFrame(rows).to_csv(metrics_csv, index=False) + print("ERROR on", tile_id, ":", e) + + # runtime metrics for this folder + df = pd.DataFrame(rows) + metrics_csv = os.path.join(out_folder, "runtime_metrics.csv") + df.to_csv(metrics_csv, index=False) + print("Saved runtime metrics CSV:", metrics_csv) + + # ready table + ok = df[df["status"] == "ok"].copy() + n_ok = len(ok) + total_s = ok["t_total_s"].sum() + tiles_per_min = (n_ok / (total_s / 60.0)) if total_s > 0 else np.nan + + ready = pd.DataFrame({ + "metric": [ + "n_tiles_total", + "n_tiles_ok", + "n_tiles_skipped_nodata", + "n_tiles_error", + "total_runtime_min", + "tiles_per_min", + "total_s_per_tile (median)", + "unet_s (median)", + "label_s (median)", + "sam_s (median)", + "export_s (median)", + "prompts_used (median)", + "grains (median)", + ], + "value": [ + int(len(df)), + int(n_ok), + int((df["status"] == "skip_nodata").sum()), + int((df["status"] == "error").sum()), + float(total_s / 60.0) if n_ok else np.nan, + float(tiles_per_min) if n_ok else np.nan, + float(ok["t_total_s"].median()) if n_ok else np.nan, + float(ok["t_unet_s"].median()) if n_ok else np.nan, + float(ok["t_label_s"].median()) if n_ok else np.nan, + float(ok["t_sam_s"].median()) if n_ok else np.nan, + float(ok["t_export_s"].median()) if n_ok else np.nan, + float(ok["n_prompts_used"].median()) if n_ok else np.nan, + float(ok["n_grains"].median()) if n_ok else np.nan, + ] + }) + + ready_csv = os.path.join(out_folder, "runtime_summary_ready_table.csv") + ready.to_csv(ready_csv, index=False) + print("Saved ready table CSV:", ready_csv) + + return df, ready + + +import os, glob + +BASE = "/dss/dsstbyfs02/pr94no/pr94no-dss-0001/drylands/gravel_leonie_masterthesis/segmenteverygrain" +folders = sorted(glob.glob(os.path.join(BASE, "F*"))) + +MAX_TO_PROCESS = 50 +processed = 0 + +for folder in folders: + if processed >= MAX_TO_PROCESS: + print("Reached MAX_TO_PROCESS, stopping.") + break + + # 1) skip wenn schon Ergebnisse (GPKG) vorhanden + gpkg_files = glob.glob(os.path.join(folder, "ouputgpkg", "*.gpkg")) + if len(gpkg_files) > 0: + print("SKIP already has GPKG:", os.path.basename(folder), "n_gpkg:", len(gpkg_files)) + continue + + # 2) skip wenn noch keine tiles drin sind (inputtiles/ ODER folder-root) + tiles = glob.glob(os.path.join(folder, "inputtiles", "*.tif")) + if len(tiles) == 0: + tiles = glob.glob(os.path.join(folder, "*.tif")) + + if len(tiles) == 0: + print("SKIP no tiles yet:", os.path.basename(folder)) + continue + + print("PROCESS:", os.path.basename(folder), "tiles:", len(tiles)) + process_one_folder(folder) + processed += 1 \ No newline at end of file