# HG changeset patch
# User imgteam
# Date 1710023243 0
# Node ID 8b74843c136e00d8c5b3c6d830a458fac5d28793

planemo upload for repository https://github.com/BMCV/galaxy-image-analysis/tree/master/tools/voronoi_tesselation commit 43f036ed5341c36a1e076f9df9f47a3743ef4e6f

diff -r 000000000000 -r 8b74843c136e test-data/input1.tiff
Binary file test-data/input1.tiff has changed
diff -r 000000000000 -r 8b74843c136e test-data/input1_result.tiff
Binary file test-data/input1_result.tiff has changed
diff -r 000000000000 -r 8b74843c136e voronoi_tessellation.py
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/voronoi_tessellation.py	Sat Mar 09 22:27:23 2024 +0000
@@ -0,0 +1,33 @@
+import argparse
+
+import numpy as np
+import scipy.ndimage as ndi
+import skimage.io
+from skimage.segmentation import watershed
+
+
+if __name__ == '__main__':
+
+    parser = argparse.ArgumentParser()
+    parser.add_argument('input')
+    parser.add_argument('output')
+    args = parser.parse_args()
+
+    im = skimage.io.imread(args.input)
+    im = im.squeeze()
+    assert im.ndim == 2
+
+    distances = np.full(im.shape, np.inf)
+    for label in np.unique(im):
+        if label == 0:
+            continue
+
+        label_distances = ndi.distance_transform_edt(im != label)
+        distances = np.min((distances, label_distances), axis=0)
+
+    result = watershed(
+        image=distances,
+        markers=im,
+    )
+
+    skimage.io.imsave(args.output, result)
diff -r 000000000000 -r 8b74843c136e voronoi_tessellation.xml
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/voronoi_tessellation.xml	Sat Mar 09 22:27:23 2024 +0000
@@ -0,0 +1,53 @@
+<tool id="voronoi_tessellation" name="Compute Voronoi tessellation" version="@TOOL_VERSION@+galaxy@VERSION_SUFFIX@" profile="23.0">
+    <description>with scikit-image</description>
+    <macros>
+        <token name="@TOOL_VERSION@">0.22.0</token>
+        <token name="@VERSION_SUFFIX@">0</token>
+    </macros>
+    <edam_operations>
+        <edam_operation>operation_3443</edam_operation>
+    </edam_operations>
+    <xrefs>
+        <xref type="bio.tools">scikit-image</xref>
+        <xref type="biii">scikit-image</xref>
+    </xrefs>
+    <requirements>
+        <requirement type="package" version="1.26.4">numpy</requirement>
+        <requirement type="package" version="1.12.0">scipy</requirement>
+        <requirement type="package" version="0.22.0">scikit-image</requirement>
+    </requirements>
+    <command><![CDATA[
+    
+    ## Inputs
+    
+    python '$__tool_directory__/voronoi_tessellation.py' '$input'
+    
+    ## Outputs
+    
+    ./result.tiff
+    
+    ]]>
+    </command>
+    <inputs>
+        <param name="input" type="data" format="png,tiff" label="Labeled image" />
+    </inputs>
+    <outputs>
+       <data format="tiff" name="result" from_work_dir="result.tiff" />
+    </outputs>
+    <tests>
+        <test>
+            <param name="input" value="input1.tiff" />
+            <output name="result" value="input1_result.tiff" ftype="tiff" compare="sim_size" delta="0" />
+        </test>
+    </tests>
+    <help>
+
+        This tool computes Voronoi tessellations for labeled images.
+        Voronoi tessellations are also known as Vornoi diagrams, or Dirichlet tessellations.
+        Zero labels are treated as image background.
+
+    </help>
+    <citations>
+        <citation type="doi">10.7717/peerj.453</citation>
+    </citations>
+</tool>