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Tools for the reproducibility of extraction of blocks and post-processing of SGNs
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‎flamingo_tools/extract_block.py‎

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import os
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from typing import Optional, List
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import imageio.v3 as imageio
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import numpy as np
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import zarr
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import flamingo_tools.s3_utils as s3_utils
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from flamingo_tools.file_utils import read_image_data
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def extract_block(
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input_path: str,
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coords: List[int],
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output_dir: Optional[str] = None,
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input_key: Optional[str] = None,
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output_key: Optional[str] = None,
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resolution: float = 0.38,
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roi_halo: List[int] = [128, 128, 64],
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tif: bool = False,
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s3: Optional[bool] = False,
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s3_credentials: Optional[str] = None,
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s3_bucket_name: Optional[str] = None,
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s3_service_endpoint: Optional[str] = None,
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):
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"""Extract block around coordinate from input data according to a given halo.
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Either from a local file or from an S3 bucket.
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Args:
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input_path: Input folder in n5 / ome-zarr format.
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coords: Center coordinates of extracted 3D volume.
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output_dir: Output directory for saving output as <basename>_crop.n5. Default: input directory.
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input_key: Input key for data in input file.
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output_key: Output key for data in n5 output or used as suffix for tif output.
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roi_halo: ROI halo of extracted 3D volume.
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tif: Flag for tif output
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s3: Flag for considering input_path for S3 bucket.
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s3_bucket_name: S3 bucket name.
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s3_service_endpoint: S3 service endpoint.
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s3_credentials: File path to credentials for S3 bucket.
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"""
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coords = [int(round(c)) for c in coords]
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coord_string = "-".join([str(c).zfill(4) for c in coords])
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# Dimensions are inversed to view in MoBIE (x y z) -> (z y x)
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coords.reverse()
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roi_halo.reverse()
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input_content = list(filter(None, input_path.split("/")))
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if s3:
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image_name = input_content[-1].split(".")[0]
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if len(image_name.split("_")) > 1:
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resized_suffix = "_resized"
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image_prefix = image_name.split("_")[0]
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else:
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resized_suffix = ""
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image_prefix = image_name
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basename = input_content[0] + resized_suffix
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else:
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basename = "".join(input_content[-1].split(".")[:-1])
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image_prefix = basename.split("_")[-1]
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input_dir = input_path.split(basename)[0]
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input_dir = os.path.abspath(input_dir)
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if output_dir == "":
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output_dir = input_dir
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if tif:
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if output_key is None:
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output_name = basename + "_crop_" + coord_string + "_" + image_prefix + ".tif"
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else:
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output_name = basename + "_" + image_prefix + "_crop_" + coord_string + "_" + output_key + ".tif"
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output_file = os.path.join(output_dir, output_name)
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else:
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output_key = "raw" if output_key is None else output_key
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output_file = os.path.join(output_dir, basename + "_crop_" + coord_string + ".n5")
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coords = np.array(coords)
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coords = coords / resolution
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coords = np.round(coords).astype(np.int32)
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roi = tuple(slice(co - rh, co + rh) for co, rh in zip(coords, roi_halo))
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if s3:
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input_path, fs = s3_utils.get_s3_path(input_path, bucket_name=s3_bucket_name,
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service_endpoint=s3_service_endpoint, credential_file=s3_credentials)
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data_ = read_image_data(input_path, input_key)
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data_roi = data_[roi]
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if tif:
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imageio.imwrite(output_file, data_roi, compression="zlib")
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else:
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with zarr.open(output_file, mode="w") as f_out:
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f_out.create_dataset(output_key, data=data_roi, compression="gzip")

‎reproducibility/README.md‎

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# Scripts to ensure/facilitate the reproducbility of pre-/post-processing
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The scripts within these folders document steps involved in the pre-/post-processing or analysis of data.
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The folders contain a script for reproducibility as well as dictionaries in JSON format, which contain script parameters and additional information.
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## Extraction of blocks from a 3D volume
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The extraction of blocks from a 3D volume is required for the creation of annotations, empty crops, or others regions of interest.
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Usage:
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```
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python repro_block_extraction.py --input <JSON-file> --output <out-dir>
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```
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## Post-processing of SGN segmentation
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The post-processing of the SGN segmentation may involve the erosion of the segmentation to exclude artifacts, the variation of the minimal number of nodes within a component, or the minimal distance between nodes to consider them the same component.
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Usage:
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```
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python repro_postprocess_sgn_v1.py --input <JSON-file> --output <out-dir>
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```
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‎reproducibility/block_extraction/2025-05-SGN_annotated_crops.json‎

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[
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{
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"cochlea": "M_LR_000144_L",
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"image_channel": "PV_resized",
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"crop_centers": [
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[
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1040,
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700,
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1390
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],
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[
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1300,
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690,
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1191
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],
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[
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1455,
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814,
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1191
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]
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],
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"halo_size": [
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128,
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128,
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32
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]
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},
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{
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"cochlea": "M_LR_000145_L",
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"image_channel": "PV_resized",
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"crop_centers": [
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[
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972,
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862,
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610
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],
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[
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1301,
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812,
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740
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]
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],
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"halo_size": [
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128,
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128,
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32
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]
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},
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{
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"cochlea": "M_LR_000167_R",
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"image_channel": "PV",
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"crop_centers": [
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[
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909,
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813,
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810
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],
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[
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916,
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800,
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722
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],
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[
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951,
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742,
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652
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],
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[
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951,
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742,
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602
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],
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[
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1251,
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266,
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575
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],
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[
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1210,
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148,
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606
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],
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[
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1305,
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341,
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526
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],
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[
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1137,
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669,
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1044
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]
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],
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"halo_size": [
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128,
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128,
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32
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]
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},
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{
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"cochlea": "M_LR_000151_R",
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"image_channel": "PV_resized",
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"crop_centers": [
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[
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1275,
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806,
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578
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],
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[
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875,
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539,
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747
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],
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[
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1300,
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535,
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747
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]
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],
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"halo_size": [
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128,
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128,
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32
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]
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},
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{
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"cochlea": "M_LR_000184_L",
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"image_channel": "PV_resized",
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"crop_centers": [
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[
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1326,
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844,
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516
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],
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[
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885,
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913,
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1023
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]
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],
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"halo_size": [
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128,
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128,
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32
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]
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}
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]

‎reproducibility/block_extraction/2025-05-SGN_domain_crops.json‎

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[
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{
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"cochlea": "M_LR_000144_L",
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"image_channel": "PV_resized",
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"crop_centers": [
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[
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1490,
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600,
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735
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],
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[
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1540,
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930,
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865
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]
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],
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"halo_size": [
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256,
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256,
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128
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]
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},
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{
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"cochlea": "M_LR_000145_L",
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"image_channel": "PV_resized",
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"crop_centers": [
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[
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1300,
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700,
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700
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]
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],
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"halo_size": [
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256,
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256,
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128
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]
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},
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{
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"cochlea": "M_LR_000151_R",
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"image_channel": "PV_resized",
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"crop_centers": [
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[
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1350,
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690,
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1000
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],
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[
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1350,
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570,
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734
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]
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],
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"halo_size": [
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256,
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256,
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128
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]
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},
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{
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"cochlea": "M_LR_000155_L",
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"image_channel": "PV_resized",
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"crop_centers": [
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[
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900,
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1090,
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700
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],
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[
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1464,
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830,
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420
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]
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],
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"halo_size": [
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256,
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256,
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128
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]
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},
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{
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"cochlea": "M_LR_000167_R",
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"image_channel": "PV",
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"crop_centers": [
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[
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1360,
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1040,
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840
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],
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[
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900,
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780,
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780
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]
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],
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"halo_size": [
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256,
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256,
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128
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]
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}
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]

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