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Update "Threshold image" tool (#109)
Squashed commit of the following: commit bc594cf Author: Leonid Kostrykin <[email protected]> Date: Mon Mar 11 15:55:40 2024 +0000 Update tool version commit 92657e4 Author: Leonid Kostrykin <[email protected]> Date: Mon Mar 11 15:54:38 2024 +0000 Update help commit f8e732a Author: Leonid Kostrykin <[email protected]> Date: Mon Mar 11 15:51:41 2024 +0000 Add help commit 6124766 Author: Leonid Kostrykin <[email protected]> Date: Mon Mar 11 15:33:11 2024 +0000 Replace `dark_bg` by `invert_output` commit a41c52f Author: Leonid Kostrykin <[email protected]> Date: Mon Mar 11 12:42:16 2024 +0000 Update UI commit 044709d Author: Leonid Kostrykin <[email protected]> Date: Mon Mar 11 12:00:05 2024 +0000 Add help commit e5630a8 Author: Leonid Kostrykin <[email protected]> Date: Mon Mar 11 12:59:42 2024 +0100 Fix test output commit 108ab02 Author: Leonid Kostrykin <[email protected]> Date: Mon Mar 11 11:50:17 2024 +0000 Add test commit 6406061 Author: Leonid Kostrykin <[email protected]> Date: Mon Mar 11 11:46:15 2024 +0000 Add `manual` thresholding method commit 0a89784 Author: Leonid Kostrykin <[email protected]> Date: Mon Mar 11 11:36:42 2024 +0000 Fix issues commit 912977e Author: Leonid Kostrykin <[email protected]> Date: Mon Mar 11 11:27:51 2024 +0000 Refactor auto_threshold.xml
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -1,47 +1,49 @@ | ||
| """ | ||
| Copyright 2017-2022 Biomedical Computer Vision Group, Heidelberg University. | ||
| Copyright 2017-2024 Biomedical Computer Vision Group, Heidelberg University. | ||
| Distributed under the MIT license. | ||
| See file LICENSE for detail or copy at https://opensource.org/licenses/MIT | ||
| """ | ||
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| import argparse | ||
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| import numpy as np | ||
| import skimage.filters | ||
| import skimage.io | ||
| import skimage.util | ||
| import tifffile | ||
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| thOptions = { | ||
| 'otsu': lambda img_raw, bz: skimage.filters.threshold_otsu(img_raw), | ||
| 'li': lambda img_raw, bz: skimage.filters.threshold_li(img_raw), | ||
| 'yen': lambda img_raw, bz: skimage.filters.threshold_yen(img_raw), | ||
| 'isodata': lambda img_raw, bz: skimage.filters.threshold_isodata(img_raw), | ||
| th_methods = { | ||
| 'manual': lambda thres, **kwargs: thres, | ||
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| 'otsu': lambda img_raw, **kwargs: skimage.filters.threshold_otsu(img_raw), | ||
| 'li': lambda img_raw, **kwargs: skimage.filters.threshold_li(img_raw), | ||
| 'yen': lambda img_raw, **kwargs: skimage.filters.threshold_yen(img_raw), | ||
| 'isodata': lambda img_raw, **kwargs: skimage.filters.threshold_isodata(img_raw), | ||
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| 'loc_gaussian': lambda img_raw, bz: skimage.filters.threshold_local(img_raw, bz, method='gaussian'), | ||
| 'loc_median': lambda img_raw, bz: skimage.filters.threshold_local(img_raw, bz, method='median'), | ||
| 'loc_mean': lambda img_raw, bz: skimage.filters.threshold_local(img_raw, bz, method='mean') | ||
| 'loc_gaussian': lambda img_raw, bz, **kwargs: skimage.filters.threshold_local(img_raw, bz, method='gaussian'), | ||
| 'loc_median': lambda img_raw, bz, **kwargs: skimage.filters.threshold_local(img_raw, bz, method='median'), | ||
| 'loc_mean': lambda img_raw, bz, **kwargs: skimage.filters.threshold_local(img_raw, bz, method='mean') | ||
| } | ||
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| def auto_thresholding(in_fn, out_fn, th_method, block_size=5, dark_bg=True): | ||
| def do_thresholding(in_fn, out_fn, th_method, block_size=5, threshold=0, invert_output=False): | ||
| img = skimage.io.imread(in_fn) | ||
| th = thOptions[th_method](img, block_size) | ||
| if dark_bg: | ||
| res = img > th | ||
| else: | ||
| res = img <= th | ||
| th = th_methods[th_method](img_raw=img, bz=block_size, thres=threshold) | ||
| res = img > th | ||
| if invert_output: | ||
| res = np.logical_not(res) | ||
| tifffile.imwrite(out_fn, skimage.util.img_as_ubyte(res)) | ||
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| if __name__ == "__main__": | ||
| parser = argparse.ArgumentParser(description='Automatic Image Thresholding') | ||
| parser.add_argument('im_in', help='Path to the input image') | ||
| parser.add_argument('im_out', help='Path to the output image (TIFF)') | ||
| parser.add_argument('th_method', choices=thOptions.keys(), help='Thresholding method') | ||
| parser.add_argument('th_method', choices=th_methods.keys(), help='Thresholding method') | ||
| parser.add_argument('block_size', type=int, default=5, help='Odd size of pixel neighborhood for calculating the threshold') | ||
| parser.add_argument('dark_bg', default=True, type=bool, help='True if background is dark') | ||
| parser.add_argument('threshold', type=float, default=0, help='Manual thresholding value') | ||
| 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') | ||
| args = parser.parse_args() | ||
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| auto_thresholding(args.im_in, args.im_out, args.th_method, args.block_size, args.dark_bg) | ||
| do_thresholding(args.im_in, args.im_out, args.th_method, args.block_size, args.threshold, args.invert_output) |
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