Mercurial > repos > imgteam > 2d_auto_threshold

comparison auto_threshold.py @ 5:7db4fc31dbee draft

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planemo upload for repository https://github.com/BMCV/galaxy-image-analysis/tree/master/tools/2d_auto_threshold/ commit 8b9f24cbfaf54f140705f0bf4b6732269bd401da
author imgteam
date Mon, 11 Mar 2024 17:12:33 +0000
parents 0c777d708acc
children 8bccb36e055a
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4:3df9f0a4bf34 5:7db4fc31dbee
1 """ 1 """
2 Copyright 2017-2022 Biomedical Computer Vision Group, Heidelberg University. 2 Copyright 2017-2024 Biomedical Computer Vision Group, Heidelberg University.
3 3
4 Distributed under the MIT license. 4 Distributed under the MIT license.
5 See file LICENSE for detail or copy at https://opensource.org/licenses/MIT 5 See file LICENSE for detail or copy at https://opensource.org/licenses/MIT
6
7 """ 6 """
8 7
9 import argparse 8 import argparse
10 9
10 import numpy as np
11 import skimage.filters 11 import skimage.filters
12 import skimage.io 12 import skimage.io
13 import skimage.util 13 import skimage.util
14 import tifffile 14 import tifffile
15 15
16 thOptions = { 16 th_methods = {
17 'otsu': lambda img_raw, bz: skimage.filters.threshold_otsu(img_raw), 17 'manual': lambda thres, **kwargs: thres,
18 'li': lambda img_raw, bz: skimage.filters.threshold_li(img_raw),
19 'yen': lambda img_raw, bz: skimage.filters.threshold_yen(img_raw),
20 'isodata': lambda img_raw, bz: skimage.filters.threshold_isodata(img_raw),
21 18
22 'loc_gaussian': lambda img_raw, bz: skimage.filters.threshold_local(img_raw, bz, method='gaussian'), 19 'otsu': lambda img_raw, **kwargs: skimage.filters.threshold_otsu(img_raw),
23 'loc_median': lambda img_raw, bz: skimage.filters.threshold_local(img_raw, bz, method='median'), 20 'li': lambda img_raw, **kwargs: skimage.filters.threshold_li(img_raw),
24 'loc_mean': lambda img_raw, bz: skimage.filters.threshold_local(img_raw, bz, method='mean') 21 'yen': lambda img_raw, **kwargs: skimage.filters.threshold_yen(img_raw),
22 'isodata': lambda img_raw, **kwargs: skimage.filters.threshold_isodata(img_raw),
23
24 'loc_gaussian': lambda img_raw, bz, **kwargs: skimage.filters.threshold_local(img_raw, bz, method='gaussian'),
25 'loc_median': lambda img_raw, bz, **kwargs: skimage.filters.threshold_local(img_raw, bz, method='median'),
26 'loc_mean': lambda img_raw, bz, **kwargs: skimage.filters.threshold_local(img_raw, bz, method='mean')
25 } 27 }
26 28
27 29
28 def auto_thresholding(in_fn, out_fn, th_method, block_size=5, dark_bg=True): 30 def do_thresholding(in_fn, out_fn, th_method, block_size=5, threshold=0, invert_output=False):
29 img = skimage.io.imread(in_fn) 31 img = skimage.io.imread(in_fn)
30 th = thOptions[th_method](img, block_size) 32 th = th_methods[th_method](img_raw=img, bz=block_size, thres=threshold)
31 if dark_bg: 33 res = img > th
32 res = img > th 34 if invert_output:
33 else: 35 res = np.logical_not(res)
34 res = img <= th
35 tifffile.imwrite(out_fn, skimage.util.img_as_ubyte(res)) 36 tifffile.imwrite(out_fn, skimage.util.img_as_ubyte(res))
36 37
37 38
38 if __name__ == "__main__": 39 if __name__ == "__main__":
39 parser = argparse.ArgumentParser(description='Automatic Image Thresholding') 40 parser = argparse.ArgumentParser(description='Automatic Image Thresholding')
40 parser.add_argument('im_in', help='Path to the input image') 41 parser.add_argument('im_in', help='Path to the input image')
41 parser.add_argument('im_out', help='Path to the output image (TIFF)') 42 parser.add_argument('im_out', help='Path to the output image (TIFF)')
42 parser.add_argument('th_method', choices=thOptions.keys(), help='Thresholding method') 43 parser.add_argument('th_method', choices=th_methods.keys(), help='Thresholding method')
43 parser.add_argument('block_size', type=int, default=5, help='Odd size of pixel neighborhood for calculating the threshold') 44 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') 45 parser.add_argument('threshold', type=float, default=0, help='Manual thresholding value')
46 parser.add_argument('invert_output', default=False, type=bool, help='Values below/above the threshold are labeled with 0/255 if False, and with 255/0 otherwise')
45 args = parser.parse_args() 47 args = parser.parse_args()
46 48
47 auto_thresholding(args.im_in, args.im_out, args.th_method, args.block_size, args.dark_bg) 49 do_thresholding(args.im_in, args.im_out, args.th_method, args.block_size, args.threshold, args.invert_output)