comparison 2d_split_binaryimage_by_watershed.py @ 5:7f8102bdbfa1 draft default tip

planemo upload for repository https://github.com/BMCV/galaxy-image-analysis/tree/master/tools/binary2labelimage/ commit 48df7d9c58fb88e472caeb4d4a1e14170d79b643

4:984358e43242

import argparse
import sys

import skimage.io
import skimage.util
from scipy import ndimage as ndi
from skimage.feature import peak_local_max
from skimage.morphology 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('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_maxi = peak_local_max(distance,
                                indices=False,
                                min_distance=args.min_distance,
                                labels=img_in)
    markers = ndi.label(local_maxi)[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")

5:7f8102bdbfa1

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('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")