Mercurial > repos > imgteam > 2d_auto_threshold
diff auto_threshold.py @ 5:7db4fc31dbee draft
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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--- a/auto_threshold.py Fri Nov 10 14:23:07 2023 +0000 +++ b/auto_threshold.py Mon Mar 11 17:12:33 2024 +0000 @@ -1,37 +1,38 @@ """ -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 - """ import argparse +import numpy as np import skimage.filters import skimage.io import skimage.util import tifffile -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, - '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') + '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), + + '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') } -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)) @@ -39,9 +40,10 @@ 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() - 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)