# HG changeset patch
# User imgteam
# Date 1645283812 0
# Node ID 5224cc463a9765515d93c81fc1830a2e584928e9
# Parent  f40e5d11eb47fe45e52e86f50b71508013e7ee7b
"planemo upload for repository https://github.com/BMCV/galaxy-image-analysis/tools/2d_auto_threshold/ commit b1b3c63ab021aa77875c3b04127f6836024812f9"

diff -r f40e5d11eb47 -r 5224cc463a97 auto_threshold.py
--- a/auto_threshold.py	Wed Dec 18 09:55:34 2019 +0000
+++ b/auto_threshold.py	Sat Feb 19 15:16:52 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)
diff -r f40e5d11eb47 -r 5224cc463a97 auto_threshold.xml
--- a/auto_threshold.xml	Wed Dec 18 09:55:34 2019 +0000
+++ b/auto_threshold.xml	Sat Feb 19 15:16:52 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>
diff -r f40e5d11eb47 -r 5224cc463a97 test-data/out.tif
Binary file test-data/out.tif has changed
diff -r f40e5d11eb47 -r 5224cc463a97 test-data/out2.tif
Binary file test-data/out2.tif has changed