--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/2d_split_binaryimage_by_watershed.py	Mon May 12 08:15:32 2025 +0000
@@ -0,0 +1,33 @@
+import argparse
+import sys
+
+import numpy as np
+import skimage.io
+import skimage.util
+from scipy import ndimage as ndi
+from skimage.feature import peak_local_max
+from skimage.segmentation import watershed
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser(description='Split binaryimage by watershed')
+    parser.add_argument('input_file', type=argparse.FileType('r'), default=sys.stdin, help='input file')
+    parser.add_argument('out_file', type=argparse.FileType('w'), default=sys.stdin, help='out file (TIFF)')
+    parser.add_argument('min_distance', type=int, default=100, help='Minimum distance to next object')
+    args = parser.parse_args()
+
+    img_in = skimage.io.imread(args.input_file.name)
+    distance = ndi.distance_transform_edt(img_in)
+
+    local_max_indices = peak_local_max(
+        distance,
+        min_distance=args.min_distance,
+        labels=img_in,
+    )
+    local_max_mask = np.zeros(img_in.shape, dtype=bool)
+    local_max_mask[tuple(local_max_indices.T)] = True
+    markers = ndi.label(local_max_mask)[0]
+    res = watershed(-distance, markers, mask=img_in)
+
+    res = skimage.util.img_as_uint(res)
+    skimage.io.imsave(args.out_file.name, res, plugin="tifffile")