Mercurial > repos > imgteam > 2d_auto_threshold
comparison auto_threshold.py @ 4:7d80eb2411fb draft default tip
planemo upload for repository https://github.com/BMCV/galaxy-image-analysis/tree/master/tools/2d_auto_threshold/ commit 01343602708de3cc7fa4986af9000adc36dd0651
author | imgteam |
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date | Sat, 07 Jun 2025 18:38:16 +0000 |
parents | 5224cc463a97 |
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3:5224cc463a97 | 4:7d80eb2411fb |
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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 | |
13 import skimage.util | 12 import skimage.util |
14 import tifffile | 13 from giatools.image import Image |
15 | 14 |
16 thOptions = { | |
17 'otsu': lambda img_raw, bz: skimage.filters.threshold_otsu(img_raw), | |
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 | 15 |
22 'loc_gaussian': lambda img_raw, bz: skimage.filters.threshold_local(img_raw, bz, method='gaussian'), | 16 class DefaultThresholdingMethod: |
23 'loc_median': lambda img_raw, bz: skimage.filters.threshold_local(img_raw, bz, method='median'), | 17 |
24 'loc_mean': lambda img_raw, bz: skimage.filters.threshold_local(img_raw, bz, method='mean') | 18 def __init__(self, thres, accept: list[str] | None = None, **kwargs): |
19 self.thres = thres | |
20 self.accept = accept if accept else [] | |
21 self.kwargs = kwargs | |
22 | |
23 def __call__(self, image, *args, offset=0, **kwargs): | |
24 accepted_kwargs = self.kwargs.copy() | |
25 for key, val in kwargs.items(): | |
26 if key in self.accept: | |
27 accepted_kwargs[key] = val | |
28 thres = self.thres(image, *args, **accepted_kwargs) | |
29 return image > thres + offset | |
30 | |
31 | |
32 class ManualThresholding: | |
33 | |
34 def __call__(self, image, thres1: float, thres2: float | None, **kwargs): | |
35 if thres2 is None: | |
36 return image > thres1 | |
37 else: | |
38 thres1, thres2 = sorted((thres1, thres2)) | |
39 return skimage.filters.apply_hysteresis_threshold(image, thres1, thres2) | |
40 | |
41 | |
42 th_methods = { | |
43 'manual': ManualThresholding(), | |
44 | |
45 'otsu': DefaultThresholdingMethod(skimage.filters.threshold_otsu), | |
46 'li': DefaultThresholdingMethod(skimage.filters.threshold_li), | |
47 'yen': DefaultThresholdingMethod(skimage.filters.threshold_yen), | |
48 'isodata': DefaultThresholdingMethod(skimage.filters.threshold_isodata), | |
49 | |
50 'loc_gaussian': DefaultThresholdingMethod(skimage.filters.threshold_local, accept=['block_size'], method='gaussian'), | |
51 'loc_median': DefaultThresholdingMethod(skimage.filters.threshold_local, accept=['block_size'], method='median'), | |
52 'loc_mean': DefaultThresholdingMethod(skimage.filters.threshold_local, accept=['block_size'], method='mean'), | |
25 } | 53 } |
26 | 54 |
27 | 55 |
28 def auto_thresholding(in_fn, out_fn, th_method, block_size=5, dark_bg=True): | 56 def do_thresholding( |
29 img = skimage.io.imread(in_fn) | 57 input_filepath: str, |
30 th = thOptions[th_method](img, block_size) | 58 output_filepath: str, |
31 if dark_bg: | 59 th_method: str, |
32 res = img > th | 60 block_size: int, |
33 else: | 61 offset: float, |
34 res = img <= th | 62 threshold1: float, |
35 tifffile.imwrite(out_fn, skimage.util.img_as_ubyte(res)) | 63 threshold2: float | None, |
64 invert_output: bool, | |
65 ): | |
66 assert th_method in th_methods, f'Unknown method "{th_method}"' | |
67 | |
68 # Load image | |
69 img_in = Image.read(input_filepath) | |
70 | |
71 # Perform thresholding | |
72 result = th_methods[th_method]( | |
73 image=img_in.data, | |
74 block_size=block_size, | |
75 offset=offset, | |
76 thres1=threshold1, | |
77 thres2=threshold2, | |
78 ) | |
79 if invert_output: | |
80 result = np.logical_not(result) | |
81 | |
82 # Convert to canonical representation for binary images | |
83 result = (result * 255).astype(np.uint8) | |
84 | |
85 # Write result | |
86 Image( | |
87 data=skimage.util.img_as_ubyte(result), | |
88 axes=img_in.axes, | |
89 ).normalize_axes_like( | |
90 img_in.original_axes, | |
91 ).write( | |
92 output_filepath, | |
93 ) | |
36 | 94 |
37 | 95 |
38 if __name__ == "__main__": | 96 if __name__ == "__main__": |
39 parser = argparse.ArgumentParser(description='Automatic Image Thresholding') | 97 parser = argparse.ArgumentParser(description='Automatic image thresholding') |
40 parser.add_argument('im_in', help='Path to the input image') | 98 parser.add_argument('input', type=str, help='Path to the input image') |
41 parser.add_argument('im_out', help='Path to the output image (TIFF)') | 99 parser.add_argument('output', type=str, help='Path to the output image (uint8)') |
42 parser.add_argument('th_method', choices=thOptions.keys(), help='Thresholding method') | 100 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') | 101 parser.add_argument('block_size', type=int, 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') | 102 parser.add_argument('offset', type=float, help='Offset of automatically determined threshold value') |
103 parser.add_argument('threshold1', type=float, help='Manual threshold value') | |
104 parser.add_argument('--threshold2', type=float, help='Second manual threshold value (for hysteresis thresholding)') | |
105 parser.add_argument('--invert_output', default=False, action='store_true', help='Values below/above the threshold are labeled with 0/255 by default, and with 255/0 if this argument is used') | |
45 args = parser.parse_args() | 106 args = parser.parse_args() |
46 | 107 |
47 auto_thresholding(args.im_in, args.im_out, args.th_method, args.block_size, args.dark_bg) | 108 do_thresholding( |
109 args.input, | |
110 args.output, | |
111 args.th_method, | |
112 args.block_size, | |
113 args.offset, | |
114 args.threshold1, | |
115 args.threshold2, | |
116 args.invert_output, | |
117 ) |