diff 2d_split_binaryimage_by_watershed.py @ 6:364e235bf378 draft default tip

planemo upload for repository https://github.com/BMCV/galaxy-image-analysis/tree/master/tools/binary2labelimage/ commit f5a4de7535e433e3b0e96e0694e481b6643a54f8

--- a/2d_split_binaryimage_by_watershed.py	Mon May 12 08:15:44 2025 +0000
+++ /dev/null	Thu Jan 01 00:00:00 1970 +0000
@@ -1,33 +0,0 @@
-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")