Mercurial > repos > imgteam > imagej2_watershed_binary
diff imagej2_watershed_binary.xml @ 0:b143159845b4 draft
"planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/image_processing/imagej2 commit b08f0e6d1546caaf627b21f8c94044285d5d5b9c-dirty"
author | imgteam |
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date | Tue, 17 Sep 2019 17:02:55 -0400 |
parents | |
children | 5b154339fd90 |
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--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/imagej2_watershed_binary.xml Tue Sep 17 17:02:55 2019 -0400 @@ -0,0 +1,49 @@ +<?xml version='1.0' encoding='UTF-8'?> +<tool id="imagej2_watershed_binary" name="Watershed segmentation" version="@WRAPPER_VERSION@.0"> + <description>of binary image</description> + <macros> + <import>imagej2_macros.xml</import> + </macros> + <expand macro="fiji_requirements" /> + <command> +<![CDATA[ + python $__tool_directory__/imagej2_watershed_binary.py + --input "$input" + --input_datatype $input.ext + --black_background $black_background + --jython_script $__tool_directory__/imagej2_watershed_binary_jython_script.py + --output_datatype $output.ext + --output "$output" +]]> + </command> + <inputs> + <param format="bmp,eps,gif,jpg,pcx,pgm,png,psd,tiff" name="input" type="data" label="Select image"/> + <expand macro="black_background_param" /> + </inputs> + <outputs> + <data name="output" format_source="input" label="${tool.name} on ${on_string}" /> + </outputs> + <tests> + <test> + <param name="input" value="blobs.gif" /> + <output name="output" file="blobs_watershed_binary.gif" compare="sim_size" /> + </test> + </tests> + <help> +.. class:: warningmark + +@requires_binary_input@ + +**What it does** + +<![CDATA[ +Watershed segmentation is a way of automatically separating or cutting apart particles that touch. +It first calculates the Euclidian distance map (EDM) and finds the ultimate eroded points (UEPs). +It then dilates each of the UEPs (the peaks or local maxima of the EDM) as far as possible — either +until the edge of the particle is reached, or the edge touches a region of another (growing) UEP. +Watershed segmentation works best for smooth convex objects that don’t overlap too much. +]]> + + </help> + <expand macro="fiji_headless_citations" /> +</tool>