Mercurial > repos > imgteam > 2d_auto_threshold

comparison auto_threshold.py @ 3:0c777d708acc draft

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"planemo upload for repository https://github.com/BMCV/galaxy-image-analysis/tools/2d_auto_threshold/ commit b1b3c63ab021aa77875c3b04127f6836024812f9"
author imgteam
date Sat, 19 Feb 2022 15:17:40 +0000
parents 81f0cbca04a7
children 7db4fc31dbee
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2:81f0cbca04a7 3:0c777d708acc
1 """
2 Copyright 2017-2022 Biomedical Computer Vision Group, Heidelberg University.
3
4 Distributed under the MIT license.
5 See file LICENSE for detail or copy at https://opensource.org/licenses/MIT
6
7 """
8
1 import argparse 9 import argparse
2 import numpy as np 10
3 import sys 11 import skimage.filters
4 import skimage.io 12 import skimage.io
5 import skimage.filters
6 import skimage.util 13 import skimage.util
14 import tifffile
7 15
8 threshOptions = { 16 thOptions = {
9 'otsu': lambda img_raw: skimage.filters.threshold_otsu(img_raw), 17 'otsu': lambda img_raw, bz: skimage.filters.threshold_otsu(img_raw),
10 'gaussian_adaptive': lambda img_raw: skimage.filters.threshold_local(img_raw, 3, method='gaussian'), 18 'li': lambda img_raw, bz: skimage.filters.threshold_li(img_raw),
11 'mean_adaptive': lambda img_raw: skimage.filters.threshold_local(img_raw, 3, method='mean'), 19 'yen': lambda img_raw, bz: skimage.filters.threshold_yen(img_raw),
12 'isodata': lambda img_raw: skimage.filters.threshold_isodata(img_raw), 20 'isodata': lambda img_raw, bz: skimage.filters.threshold_isodata(img_raw),
13 'li': lambda img_raw: skimage.filters.threshold_li(img_raw), 21
14 'yen': lambda img_raw: skimage.filters.threshold_yen(img_raw), 22 'loc_gaussian': lambda img_raw, bz: skimage.filters.threshold_local(img_raw, bz, method='gaussian'),
23 'loc_median': lambda img_raw, bz: skimage.filters.threshold_local(img_raw, bz, method='median'),
24 'loc_mean': lambda img_raw, bz: skimage.filters.threshold_local(img_raw, bz, method='mean')
15 } 25 }
16 26
27
28 def auto_thresholding(in_fn, out_fn, th_method, block_size=5, dark_bg=True):
29 img = skimage.io.imread(in_fn)
30 th = thOptions[th_method](img, block_size)
31 if dark_bg:
32 res = img > th
33 else:
34 res = img <= th
35 tifffile.imwrite(out_fn, skimage.util.img_as_ubyte(res))
36
37
17 if __name__ == "__main__": 38 if __name__ == "__main__":
18 parser = argparse.ArgumentParser(description='Segment Foci') 39 parser = argparse.ArgumentParser(description='Automatic Image Thresholding')
19 parser.add_argument('input_file', type=argparse.FileType('r'), default=sys.stdin, help='input file') 40 parser.add_argument('im_in', help='Path to the input image')
20 parser.add_argument('out_file', type=argparse.FileType('w'), default=sys.stdin, help='out file (TIFF)') 41 parser.add_argument('im_out', help='Path to the output image (TIFF)')
21 parser.add_argument('thresh_type', choices=threshOptions.keys(), help='thresholding method') 42 parser.add_argument('th_method', choices=thOptions.keys(), help='Thresholding method')
22 parser.add_argument('dark_background', default=True, type=bool, help='True if background is dark') 43 parser.add_argument('block_size', type=int, default=5, help='Odd size of pixel neighborhood for calculating the threshold')
44 parser.add_argument('dark_bg', default=True, type=bool, help='True if background is dark')
23 args = parser.parse_args() 45 args = parser.parse_args()
24 46
25 img_in = skimage.io.imread(args.input_file.name) 47 auto_thresholding(args.im_in, args.im_out, args.th_method, args.block_size, args.dark_bg)
26 img_in = np.reshape(img_in, [img_in.shape[0], img_in.shape[1]])
27 thresh = threshOptions[args.thresh_type](img_in)
28
29 if args.dark_background:
30 res = img_in > thresh
31 else:
32 res = img_in <= thresh
33
34 res = skimage.util.img_as_uint(res)
35 skimage.io.imsave(args.out_file.name, res, plugin="tifffile")