Mercurial > repos > imgteam > 2d_auto_threshold
changeset 3:0c777d708acc draft
"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 | 3df9f0a4bf34 |
| files | auto_threshold.py auto_threshold.xml test-data/out.tif test-data/out2.tif |
| diffstat | 4 files changed, 64 insertions(+), 45 deletions(-) [+] |
line wrap: on
line diff
--- a/auto_threshold.py Wed Dec 18 05:00:41 2019 -0500 +++ b/auto_threshold.py Sat Feb 19 15:17:40 2022 +0000 @@ -1,35 +1,47 @@ +""" +Copyright 2017-2022 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 sys + +import skimage.filters import skimage.io -import skimage.filters import skimage.util +import tifffile -threshOptions = { - 'otsu': lambda img_raw: skimage.filters.threshold_otsu(img_raw), - 'gaussian_adaptive': lambda img_raw: skimage.filters.threshold_local(img_raw, 3, method='gaussian'), - 'mean_adaptive': lambda img_raw: skimage.filters.threshold_local(img_raw, 3, method='mean'), - 'isodata': lambda img_raw: skimage.filters.threshold_isodata(img_raw), - 'li': lambda img_raw: skimage.filters.threshold_li(img_raw), - 'yen': lambda img_raw: skimage.filters.threshold_yen(img_raw), +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), + + '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') } + +def auto_thresholding(in_fn, out_fn, th_method, block_size=5, dark_bg=True): + img = skimage.io.imread(in_fn) + th = thOptions[th_method](img, block_size) + if dark_bg: + res = img > th + else: + res = img <= th + tifffile.imwrite(out_fn, skimage.util.img_as_ubyte(res)) + + if __name__ == "__main__": - parser = argparse.ArgumentParser(description='Segment Foci') - parser.add_argument('input_file', type=argparse.FileType('r'), default=sys.stdin, help='input file') - parser.add_argument('out_file', type=argparse.FileType('w'), default=sys.stdin, help='out file (TIFF)') - parser.add_argument('thresh_type', choices=threshOptions.keys(), help='thresholding method') - parser.add_argument('dark_background', default=True, type=bool, help='True if background is dark') + 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('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') args = parser.parse_args() - img_in = skimage.io.imread(args.input_file.name) - img_in = np.reshape(img_in, [img_in.shape[0], img_in.shape[1]]) - thresh = threshOptions[args.thresh_type](img_in) - - if args.dark_background: - res = img_in > thresh - else: - res = img_in <= thresh - - res = skimage.util.img_as_uint(res) - skimage.io.imsave(args.out_file.name, res, plugin="tifffile") + auto_thresholding(args.im_in, args.im_out, args.th_method, args.block_size, args.dark_bg)
--- a/auto_threshold.xml Wed Dec 18 05:00:41 2019 -0500 +++ b/auto_threshold.xml Sat Feb 19 15:17:40 2022 +0000 @@ -1,47 +1,54 @@ -<tool id="ip_threshold" name="Auto Threshold" version="0.0.4"> - <description>applies a standard threshold algorithm to an image</description> +<tool id="ip_threshold" name="Auto Threshold" version="0.0.5" profile="20.05"> + <description>applies a standard thresholding algorithm to an image</description> <requirements> - <requirement type="package" version="0.14.2">scikit-image</requirement> - <requirement type="package" version="1.15.4">numpy</requirement> - <requirement type="package" version="5.3.0">pillow</requirement> - <requirement type="package" version="0.15.1">tifffile</requirement> + <requirement type="package" version="0.18.1">scikit-image</requirement> + <requirement type="package" version="2020.10.1">tifffile</requirement> </requirements> <command detect_errors="aggressive"> <![CDATA[ - python '$__tool_directory__/auto_threshold.py' '$input' '$output' $thresh_type $dark_background + python '$__tool_directory__/auto_threshold.py' + '$input' + ./out.tif + '$th_method' + '$block_size' + '$dark_bg' ]]> </command> <inputs> - <param name="input" type="data" format="tiff" label="Source file" /> - <param name="thresh_type" type="select" label="Threshold Algorithm"> + <param name="input" type="data" format="tiff,png" label="Input image" /> + <param name="th_method" type="select" label="Thresholding method"> <option value="otsu" selected="True">Otsu</option> - <option value="li">Li’s Minimum Cross Entropy</option> + <option value="li">Li's Minimum Cross Entropy</option> <option value="isodata">Isodata</option> - <option value="gaussian_adaptive">Adaptive (Gauss)</option> - <option value="mean_adaptive">Adaptive (Mean)</option> <option value="yen">Yen</option> + <option value="loc_gaussian">Adaptive (Gaussian)</option> + <option value="loc_median">Adaptive (Median)</option> + <option value="loc_mean">Adaptive (Mean)</option> </param> - <param name="dark_background" type="boolean" checked="true" truevalue="True" falsevalue="False" label="Dark Background" /> + <param name="block_size" type="integer" value="5" label="Odd size of pixel neighborhood for determining the threshold (only valid for adaptive thresholding methods)" /> + <param name="dark_bg" type="boolean" checked="true" truevalue="True" falsevalue="False" label="Dark Background" /> </inputs> <outputs> - <data format="tiff" name="output" /> + <data format="tiff" name="output" from_work_dir="out.tif" /> </outputs> <tests> <test> <param name="input" value="sample.tif"/> <output name="output" value="out.tif" ftype="tiff" compare="sim_size"/> - <param name="thresh_type" value="gaussian_adaptive"/> - <param name="dark_backgroud" value="True"/> + <param name="th_method" value="loc_gaussian"/> + <param name="block_size" value="3"/> + <param name="dark_bg" value="True"/> </test> <test> <param name="input" value="sample.tif"/> <output name="output" value="out2.tif" ftype="tiff" compare="sim_size"/> - <param name="thresh_type" value="otsu"/> - <param name="dark_backgroud" value="True"/> + <param name="th_method" value="otsu"/> + <param name="block_size" value="5"/> + <param name="dark_bg" value="True"/> </test> </tests> <help> - Applies a standard threshold algorithm to an image. + Applies a standard thresholding algorithm to an image. </help> <citations> <citation type="doi">10.1016/j.jbiotec.2017.07.019</citation>