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use available arguments

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This commit is contained in:
Lucille Delisle
2023-10-06 17:55:11 +02:00
parent 65529d132c
commit ada2dd8015
1 changed files with 46 additions and 7 deletions
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tools/interactive/interactivetool_ilastik.xml
+46 -7
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@@ -14,21 +14,58 @@
<command detect_errors="exit_code">
<![CDATA[
## export HOME=\$PWD &&
mkdir -p ./galaxy_outputs ./galaxy_inputs &&
#for input in $infile:
## Make a copy of the existing project if exists
#if str($input_type.existing) == "existing":
cp $input_type.project ./MyProject.ilp &&
#end if
mkdir -p ./galaxy_inputs &&
#for input in $infiles:
ln -s '$input' ./galaxy_inputs/'$input.name' &&
#end for
/init
ilastik
#if str($input_type.existing) == "new":
--new_project ./MyProject.ilp --workflow '$input_type.Workflow'
#else:
--project ./MyProject.ilp
#end if
]]>
</command>
<inputs>
<param name="infile" type="data" format="tiff" multiple="true" label="Input files in TIFF format"/>
<conditional name="input_type">
<param name="existing" type="select" label="Which project you want to work on?">
<option value="new">Start a new project</option>
<option value="existing">Modify an existing project</option>
</param>
<when value="new">
<param name="Workflow" type="select" label="Type of Workflow" >
<option value="PixelClassificationWorkflow">Pixel Classification</option>
<option value="AutocontextTwoStage">Autocontext (2-stage)</option>
<option value="ObjectClassificationWorkflowPixel">Pixel Classification + Object Classification</option>
<option value="ObjectClassificationWorkflowPrediction">Object Classification [Inputs: Raw Data, Pixel Prediction Map]</option>
<option value="ObjectClassificationWorkflowBinary">Object Classification [Inputs: Raw Data, Segmentation]</option>
<option value="ConservationTrackingWorkflowFromBinary">Tracking [Inputs: Raw Data, Segmentation Image]</option>
<option value="ConservationTrackingWorkflowFromPrediction">Tracking [Inputs: Raw Data, Pixel Prediction Map]</option>
<option value="AnimalConservationTrackingWorkflowFromBinary">Animal Tracking [Inputs: Raw Data, Segmentation Image]</option>
<option value="AnimalConservationTrackingWorkflowFromPrediction">Animal Tracking [Inputs: Raw Data, Pixel Prediction Map]</option>
<option value="StructuredTrackingWorkflowFromBinary">Tracking with Learning [Inputs: Raw Data, Segmentation Image]</option>
<option value="StructuredTrackingWorkflowFromPrediction">Tracking with Learning [Inputs: Raw Data, Pixel Prediction Map]</option>
<option value="EdgeTrainingWithMulticutWorkflow">Bounary-based Segmentation with Multicut</option>
<option value="CountingWorkflow">Cell Density Counting</option>
<option value="DataConversionWorkflow">Data Conversion</option>
<option value="neuralNetwork.RemoteWorkflow">Neural Network Classification (Remote)</option>
<option value="neuralNetwork.LocalWorkflow">Neural Network Classification (Local)</option>
</param>
</when>
<when value="existing">
<param argument="--project" type="data" format="h5" label="Existing ilastik project" />
</when>
</conditional>
<param name="infiles" type="data" format="tiff" multiple="true" label="Input files in TIFF format"/>
</inputs>
<outputs>
<data name="ilastik_project" format="h5" label="Ilastik project file">
<discover_datasets pattern=".*.ilp" directory="galaxy_outputs" visible="true" assign_primary_output="true"/>
</data>
<data name="ilastik_project" format="h5" label="Ilastik project file" from_work_dir="./MyProject.ilp"/>
</outputs>
<tests>
@@ -36,6 +73,8 @@
<help><![CDATA[
Leverage machine learning algorithms to easily segment, classify, track and count your cells or other experimental data. Most operations are interactive, even on large datasets: you just draw the labels and immediately see the result. No machine learning expertise required.
Please, check the documentation at https://www.ilastik.org/documentation/.
]]>
</help>
<citations>