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Add notebook for using Maxar Open Data (#198)
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‎README.md‎

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- [Batch segmentation with text prompts](https://samgeo.gishub.org/examples/text_prompts_batch)
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- [Using segment-geospatial with ArcGIS Pro](https://samgeo.gishub.org/examples/arcgis)
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- [Segmenting swimming pools with text prompts](https://samgeo.gishub.org/examples/swimming_pools)
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- [Segmenting satellite imagery from the Maxar Open Data Program](https://samgeo.gishub.org/examples/max_open_data)
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## Demos
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‎docs/index.md‎

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@@ -44,6 +44,7 @@ The **segment-geospatial** package draws its inspiration from [segment-anything-
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- [Batch segmentation with text prompts](https://samgeo.gishub.org/examples/text_prompts_batch)
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- [Using segment-geospatial with ArcGIS Pro](https://samgeo.gishub.org/examples/arcgis)
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- [Segmenting swimming pools with text prompts](https://samgeo.gishub.org/examples/swimming_pools)
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- [Segmenting satellite imagery from the Maxar Open Data Program](https://samgeo.gishub.org/examples/max_open_data)
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## Demos
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‎docs/maxar_open_data.ipynb‎

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{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Segmenting satellite imagery from the Maxar Open Data Program\n",
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"\n",
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"[![image](https://studiolab.sagemaker.aws/studiolab.svg)](https://studiolab.sagemaker.aws/import/github/opengeos/segment-geospatial/blob/main/docs/examples/maxar_open_data.ipynb)\n",
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"[![image](https://img.shields.io/badge/Open-Planetary%20Computer-black?style=flat&logo=microsoft)](https://pccompute.westeurope.cloudapp.azure.com/compute/hub/user-redirect/git-pull?repo=https://github.com/opengeos/segment-geospatial&urlpath=lab/tree/segment-geospatial/docs/examples/maxar_open_data.ipynb&branch=main)\n",
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"[![image](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/opengeos/segment-geospatial/blob/main/docs/examples/maxar_open_data.ipynb)\n",
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"\n",
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"This notebook shows how to segment satellite imagery from the [Maxar Open Data](https://github.com/opengeos/maxar-open-data) program for Libya floods. \n",
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"\n",
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"Make sure you use GPU runtime for this notebook. For Google Colab, go to `Runtime` -> `Change runtime type` and select `GPU` as the hardware accelerator. "
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Install dependencies\n",
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"\n",
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"Uncomment and run the following cell to install the required dependencies."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"# %pip install segment-geospatial"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"tags": []
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},
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"outputs": [],
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"source": [
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"import os\n",
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"import leafmap\n",
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"from samgeo import SamGeo, raster_to_vector, overlay_images"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Download sample data\n",
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"\n",
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"First, let's download a sample image of Derna, Libya from [here](https://drive.google.com/file/d/1jIIC5hvSPeJEC0fbDhtxVWk2XV9AxsQD/view?usp=sharing)."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"tags": []
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},
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"outputs": [],
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"source": [
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"url = 'https://drive.google.com/file/d/1jIIC5hvSPeJEC0fbDhtxVWk2XV9AxsQD/view?usp=sharing'"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"tags": []
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},
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"outputs": [],
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"source": [
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"leafmap.download_file(url, output='image.tif')"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Create an interactive map"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"tags": []
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},
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"outputs": [],
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"source": [
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"m = leafmap.Map(height=\"600px\")\n",
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"m.add_basemap(\"SATELLITE\")\n",
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"m.add_raster('image.tif', layer_name=\"Image\")\n",
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"m.add_layer_manager()\n",
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"m"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Initialize SAM class"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"There are several tunable parameters in automatic mask generation that control how densely points are sampled and what the thresholds are for removing low quality or duplicate masks. Additionally, generation can be automatically run on crops of the image to get improved performance on smaller objects, and post-processing can remove stray pixels and holes. Here is an example configuration that samples more masks:"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"tags": []
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},
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"outputs": [],
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"source": [
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"sam_kwargs = {\n",
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" \"points_per_side\": 32,\n",
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" \"pred_iou_thresh\": 0.86,\n",
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" \"stability_score_thresh\": 0.92,\n",
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" \"crop_n_layers\": 1,\n",
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" \"crop_n_points_downscale_factor\": 2,\n",
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" \"min_mask_region_area\": 80,\n",
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"}"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"tags": []
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},
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"outputs": [],
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"source": [
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"sam = SamGeo(\n",
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" model_type=\"vit_h\",\n",
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" sam_kwargs=sam_kwargs,\n",
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")"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Segment the image"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"tags": []
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},
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"outputs": [],
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"source": [
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"sam.generate('image.tif', output=\"mask.tif\", foreground=True)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Convert raster to vector"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"tags": []
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},
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"outputs": [],
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"source": [
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"raster_to_vector('mask.tif', output='mask.shp')"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Display the segmentation result\n",
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"\n",
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"First, let's show the result as a binary image."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"tags": []
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},
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"outputs": [],
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"source": [
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"sam.show_masks(cmap=\"binary_r\")"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Display the annotations (each mask with a random color)."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"tags": []
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},
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"outputs": [],
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"source": [
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"sam.show_anns(axis=\"off\", opacity=1, output=\"annotation.tif\")"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Compare images with a slider"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"tags": []
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},
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"outputs": [],
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"source": [
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"leafmap.image_comparison(\n",
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" 'image.tif',\n",
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" \"annotation.tif\",\n",
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" label1=\"Image\",\n",
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" label2=\"Segmentation\",\n",
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")"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Overlay the annotations on the image and use the slider to change the opacity interactively."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"tags": []
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},
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"outputs": [],
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"source": [
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"overlay_images('image.tif', \"annotation.tif\", backend=\"TkAgg\")"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Display images on an interactive map."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"m.add_raster('mask.tif', layer_name='Mask', nodata=0)\n",
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"m.add_raster('annotation.tif', layer_name='Annotation')\n",
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"m"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"tags": []
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},
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"outputs": [],
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"source": [
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"m.add_vector('mask.shp', layer_name='Vector', info_mode=None)"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3 (ipykernel)",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.9.16"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 4
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}

‎mkdocs.yml‎

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- examples/fast_sam.ipynb
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- examples/swimming_pools.ipynb
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- examples/arcgis.ipynb
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- examples/maxar_open_data.ipynb
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- API Reference:
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- common module: common.md
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- samgeo module: samgeo.md

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