# HG changeset patch
# User imgteam
# Date 1712244392 0
# Node ID 4512751bccca851fa649b1444dfe0fe41a127179
# Parent  b6e88e7bf2faff4165d5239503db7ba6b9d5a005
planemo upload for repository https://github.com/BMCV/galaxy-image-analysis/tree/master/tools/segmetrics/ commit c045f067a57e8308308cf6329060c7ccd3fc372f

diff -r b6e88e7bf2fa -r 4512751bccca creators.xml
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/creators.xml	Thu Apr 04 15:26:32 2024 +0000
@@ -0,0 +1,23 @@
+<macros>
+
+    <xml name="creators/bmcv">
+        <organization name="Biomedical Computer Vision Group, Heidelberg Universtiy" alternateName="BMCV" url="http://www.bioquant.uni-heidelberg.de/research/groups/biomedical_computer_vision.html" />
+        <yield />
+    </xml>
+
+    <xml name="creators/alliecreason">
+        <person givenName="Allison" familyName="Creason"/>
+        <yield/>
+    </xml>
+
+    <xml name="creators/bugraoezdemir">
+        <person givenName="Bugra" familyName="Oezdemir"/>
+        <yield/>
+    </xml>
+
+    <xml name="creators/thawn">
+        <person givenName="Till" familyName="Korten"/>
+        <yield/>
+    </xml>
+    
+</macros>
diff -r b6e88e7bf2fa -r 4512751bccca segmetrics.xml
--- a/segmetrics.xml	Thu Nov 16 12:29:35 2023 +0000
+++ b/segmetrics.xml	Thu Apr 04 15:26:32 2024 +0000
@@ -1,9 +1,13 @@
 <tool id="ip_segmetrics" name="Compute image segmentation and object detection performance measures" version="@TOOL_VERSION@+galaxy@VERSION_SUFFIX@" profile="20.05">
     <description>with SegMetrics</description>
     <macros>
+        <import>creators.xml</import>
         <token name="@TOOL_VERSION@">1.4</token>
-        <token name="@VERSION_SUFFIX@">4</token>
+        <token name="@VERSION_SUFFIX@">5</token>
     </macros>
+    <creator>
+        <expand macro="creators/bmcv"/>
+    </creator>
     <edam_operations>
         <edam_operation>operation_3443</edam_operation>
     </edam_operations>
@@ -50,8 +54,8 @@
     </command>
     <inputs>
 
-        <param name="input_seg" type="data" format="tiff,png,zip" label="Segmented images" />
-        <param name="input_gt"  type="data" format="tiff,png,zip" label="Ground truth images" />
+        <param name="input_seg" type="data" format="tiff,png,zip" label="Segmented image(s)" />
+        <param name="input_gt"  type="data" format="tiff,png,zip" label="Ground truth image(s)" />
 
         <param name="unzip" type="boolean" checked="false" truevalue="-unzip" falsevalue="" label="Unzip" />
 
@@ -315,11 +319,13 @@
         </test>
     </tests>
     <help>
-        This tool permits the computation of image segmentation and object detection performance measures for 2-D image data.
+
+        **Computates image segmentation and object detection performance measures for 2-D image data.**
 
         You can either use a pair of individual input images (a segmented and a ground truth image), or a pair of ZIP archives which contain the segmented and the correspondiong ground truth images. When using a pair of individual images, remember to turn off the "Unzip" option. When using ZIP archives, turn it on instead and make sure that the directory structure is the same for the segmented and the ground truth images. Correspondences are estbalished based on the file names, which should be *identical* for a pair of corresponding images.
 
         If all objects within your segmented images are *uniquely* labeled, you can turn on the "Segmentation is uniquely labeled" switch to speed up the computations. Leave it off otherwise, or if you are unsure. The same accounts for the "Ground truth is uniquely labeled" switch and your ground turth image data.
+
     </help>
     <citations>
         <citation type="doi">10.1093/bioinformatics/btu080</citation>