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planemo upload for repository https://github.com/bgruening/galaxytools/tree/recommendation_training/tools/bioimaging commit e08711c242a340a1671dfca35f52d3724086e968
author bgruening
date Wed, 26 Feb 2025 10:27:28 +0000
parents 0c0de5546fe1
children 2b61d8fcfa52
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2:0c0de5546fe1 3:bc28236f407b
1 <tool id="bioimage_inference" name="Process image using a BioImage.IO model" version="@TOOL_VERSION@+galaxy@VERSION_SUFFIX@" profile="23.0"> 1 <tool id="bioimage_inference" name="Process image using a BioImage.IO model" version="@TOOL_VERSION@+galaxy@VERSION_SUFFIX@" profile="23.0">
2 <description>with PyTorch</description> 2 <description>with PyTorch</description>
3 <macros> 3 <macros>
4 <token name="@TOOL_VERSION@">2.4.1</token> 4 <token name="@TOOL_VERSION@">2.4.1</token>
5 <token name="@VERSION_SUFFIX@">0</token> 5 <token name="@VERSION_SUFFIX@">1</token>
6 </macros> 6 </macros>
7 <creator> 7 <creator>
8 <organization name="European Galaxy Team" url="https://galaxyproject.org/eu/" /> 8 <organization name="European Galaxy Team" url="https://galaxyproject.org/eu/" />
9 <person givenName="Anup" familyName="Kumar" email="kumara@informatik.uni-freiburg.de" /> 9 <person givenName="Anup" familyName="Kumar" email="kumara@informatik.uni-freiburg.de" />
10 <person givenName="Beatriz" familyName="Serrano-Solano" email="beatriz.serrano.solano@eurobioimaging.eu" /> 10 <person givenName="Beatriz" familyName="Serrano-Solano" email="beatriz.serrano.solano@eurobioimaging.eu" />
28 <![CDATA[ 28 <![CDATA[
29 python '$__tool_directory__/main.py' 29 python '$__tool_directory__/main.py'
30 --imaging_model '$input_imaging_model' 30 --imaging_model '$input_imaging_model'
31 --image_file '$input_image_file' 31 --image_file '$input_image_file'
32 --image_size '$input_image_input_size' 32 --image_size '$input_image_input_size'
33 --image_axes '$input_image_input_axes'
33 ]]> 34 ]]>
34 </command> 35 </command>
35 <inputs> 36 <inputs>
36 <param name="input_imaging_model" type="data" format="zip" label="BioImage.IO model" help="Please upload a BioImage.IO model."/> 37 <param name="input_imaging_model" type="data" format="zip" label="BioImage.IO model" help="Please upload a BioImage.IO model."/>
37 <param name="input_image_file" type="data" format="tiff,png" label="Input image" help="Please provide an input image for the analysis."/> 38 <param name="input_image_file" type="data" format="tiff,png" label="Input image" help="Please provide an input image for the analysis."/>
38 <param name="input_image_input_size" type="text" label="Size of the input image" help="Provide the size of the input image. See the chosen model's RDF file to find the correct input size. For example: for the BioImage.IO model MitochondriaEMSegmentationBoundaryModel, the input size is 256 x 256 x 32 x 1. Enter the size as 256,256,32,1."/> 39 <param name="input_image_input_size" type="text" optional="false" label="Size of the input image" help="Provide the size of the input image. See the chosen model's RDF file to find the correct input size. For example: for the BioImage.IO model MitochondriaEMSegmentationBoundaryModel, the input size is 256 x 256 x 32 x 1. Enter the size as 256,256,32,1."/>
40 <param name="input_image_input_axes" type="select" label="Axes of the input image" optional="false" help="Provide the input axes of the input image. See the chosen model's RDF file to find the correct axes. For example: for the BioImage.IO model MitochondriaEMSegmentationBoundaryModel, the input axes is 'bczyx'">
41 <option value="bczyx">bczyx</option>
42 <option value="bcyx">bcyx</option>
43 <option value="byxc">byxc</option>
44 </param>
39 </inputs> 45 </inputs>
40 <outputs> 46 <outputs>
41 <data format="tif" name="output_predicted_image" from_work_dir="output_predicted_image.tif" label="Predicted image"></data> 47 <data format="tif" name="output_predicted_image" from_work_dir="output_predicted_image.tif" label="Predicted image"></data>
42 <data format="npy" name="output_predicted_image_matrix" from_work_dir="output_predicted_image_matrix.npy" label="Predicted image tensor"></data> 48 <data format="npy" name="output_predicted_image_matrix" from_work_dir="output_predicted_image_matrix.npy" label="Predicted image tensor"></data>
43 </outputs> 49 </outputs>
44 <tests> 50 <tests>
45 <test> 51 <test>
46 <param name="input_imaging_model" value="input_imaging_model.zip" location="https://zenodo.org/api/records/6647674/files/weights-torchscript.pt/content"/> 52 <param name="input_imaging_model" value="input_imaging_model.zip" location="https://zenodo.org/api/records/6647674/files/weights-torchscript.pt/content"/>
47 <param name="input_image_file" value="input_image_file.tif" location="https://zenodo.org/api/records/6647674/files/sample_input_0.tif/content"/> 53 <param name="input_image_file" value="input_image_file.tif" location="https://zenodo.org/api/records/6647674/files/sample_input_0.tif/content"/>
48 <param name="input_image_input_size" value="256,256,1,1"/> 54 <param name="input_image_input_size" value="256,256,1,1"/>
49 <output name="output_predicted_image" file="output_nucleisegboundarymodel.tif" compare="sim_size" delta="100" /> 55 <param name="input_image_input_axes" value="bcyx"/>
50 <output name="output_predicted_image_matrix" file="output_nucleisegboundarymodel_matrix.npy" compare="sim_size" delta="100" /> 56 <output name="output_predicted_image" ftype="tif">
57 <assert_contents>
58 <has_size size="524846" delta="110" />
59 </assert_contents>
60 </output>
61 <output name="output_predicted_image_matrix" ftype="npy">
62 <assert_contents>
63 <has_size size="524416" delta="110" />
64 </assert_contents>
65 </output>
51 </test> 66 </test>
52 <test> 67 <test>
53 <param name="input_imaging_model" value="input_imaging_model.zip" location="https://zenodo.org/api/records/6647674/files/weights-torchscript.pt/content"/> 68 <param name="input_imaging_model" value="input_imaging_model.zip" location="https://zenodo.org/api/records/6647674/files/weights-torchscript.pt/content"/>
54 <param name="input_image_file" value="input_nucleisegboundarymodel.png"/> 69 <param name="input_image_file" value="input_nucleisegboundarymodel.png"/>
55 <param name="input_image_input_size" value="256,256,1,1"/> 70 <param name="input_image_input_size" value="256,256,1,1"/>
56 <output name="output_predicted_image" file="output_nucleisegboundarymodel.tif" compare="sim_size" delta="100" /> 71 <param name="input_image_input_axes" value="bcyx"/>
57 <output name="output_predicted_image_matrix" file="output_nucleisegboundarymodel_matrix.npy" compare="sim_size" delta="100" /> 72 <output name="output_predicted_image" ftype="tif">
73 <assert_contents>
74 <has_size size="524846" delta="110" />
75 </assert_contents>
76 </output>
77 <output name="output_predicted_image_matrix" ftype="npy">
78 <assert_contents>
79 <has_size size="524416" delta="110" />
80 </assert_contents>
81 </output>
82 </test>
83 <test>
84 <param name="input_imaging_model" value="input_imaging_model.zip" location="https://uk1s3.embassy.ebi.ac.uk/public-datasets/bioimage.io/emotional-cricket/1.1/files/torchscript_tracing.pt"/>
85 <param name="input_image_file" value="input_image_file.tif" location="https://uk1s3.embassy.ebi.ac.uk/public-datasets/bioimage.io/emotional-cricket/1.1/files/sample_input_0.tif"/>
86 <param name="input_image_input_size" value="128,128,100,1"/>
87 <param name="input_image_input_axes" value="bczyx"/>
88 <output name="output_predicted_image" ftype="tif">
89 <assert_contents>
90 <has_size size="6572778" delta="100" />
91 </assert_contents>
92 </output>
93 <output name="output_predicted_image_matrix" ftype="npy">
94 <assert_contents>
95 <has_size size="6572778" delta="100" />
96 </assert_contents>
97 </output>
98 </test>
99 <test>
100 <param name="input_imaging_model" value="input_imaging_model.zip" location="https://uk1s3.embassy.ebi.ac.uk/public-datasets/bioimage.io/emotional-cricket/1.1/files/torchscript_tracing.pt"/>
101 <param name="input_image_file" value="input_3d-unet-arabidopsis-apical-stem-cells.png" location="https://uk1s3.embassy.ebi.ac.uk/public-datasets/bioimage.io/emotional-cricket/1.1/files/raw.png"/>
102 <param name="input_image_input_size" value="128,128,100,1"/>
103 <param name="input_image_input_axes" value="bczyx"/>
104 <output name="output_predicted_image" ftype="tif">
105 <assert_contents>
106 <has_size size="6572778" delta="100" />
107 </assert_contents>
108 </output>
109 <output name="output_predicted_image_matrix" ftype="npy">
110 <assert_contents>
111 <has_size size="6572778" delta="100" />
112 </assert_contents>
113 </output>
114 </test>
115 <test>
116 <param name="input_imaging_model" value="input_imaging_model.zip" location="https://uk1s3.embassy.ebi.ac.uk/public-datasets/bioimage.io/organized-badger/1/files/weights-torchscript.pt"/>
117 <param name="input_image_file" value="input_platynereisemnucleisegmentationboundarymodel.tif" location="https://uk1s3.embassy.ebi.ac.uk/public-datasets/bioimage.io/organized-badger/1/files/sample_input_0.tif"/>
118 <param name="input_image_input_size" value="256,256,32,1"/>
119 <param name="input_image_input_axes" value="bczyx"/>
120 <output name="output_predicted_image" ftype="tif">
121 <assert_contents>
122 <has_size size="16789714" delta="100" />
123 </assert_contents>
124 </output>
125 <output name="output_predicted_image_matrix" ftype="npy">
126 <assert_contents>
127 <has_size size="16777344" delta="100" />
128 </assert_contents>
129 </output>
130 </test>
131 <test>
132 <param name="input_imaging_model" value="input_imaging_model.zip" location="https://uk1s3.embassy.ebi.ac.uk/public-datasets/bioimage.io/thoughtful-turtle/1/files/torchscript_tracing.pt"/>
133 <param name="input_image_file" value="input_3d-unet-lateral-root-primordia-cells.tif" location="https://uk1s3.embassy.ebi.ac.uk/public-datasets/bioimage.io/thoughtful-turtle/1/files/sample_input_0.tif"/>
134 <param name="input_image_input_size" value="128,128,100,1"/>
135 <param name="input_image_input_axes" value="bczyx"/>
136 <output name="output_predicted_image" ftype="tif">
137 <assert_contents>
138 <has_size size="6572778" delta="100" />
139 </assert_contents>
140 </output>
141 <output name="output_predicted_image_matrix" ftype="npy">
142 <assert_contents>
143 <has_size size="6553728" delta="100" />
144 </assert_contents>
145 </output>
58 </test> 146 </test>
59 </tests> 147 </tests>
60 <help> 148 <help>
61 <![CDATA[ 149 <![CDATA[
62 **What it does** 150 **What it does**
63 151
64 The tool takes a BioImage.IO model and an image (as TIF or PNG) to be analyzed. The analysis is performed by the model. The model is used to obtain a prediction of the result of the analysis, and the predicted image becomes available as a TIF file in the Galaxy history. 152 The tool takes a BioImage.IO model and an image (as TIF or PNG) to be analyzed. The analysis is performed by the model. The model is used to obtain a prediction of the result of the analysis, and the predicted image becomes available as a TIF file in the Galaxy history.
65 153
66 **Input files** 154 **Input files**
67 - BioImage.IO model: Add one of the model from Galaxy file uploader by choosing a "remote" file at "ML Models/bioimaging-models" 155 - BioImage.IO model: Add one of the model from Galaxy file uploader by choosing a "remote" file at "ML Models/bioimaging-models"
68 - Image to be analyzed: Provide an image as TIF/PNG file 156 - Image to be analyzed: Provide an image as TIF/PNG file
69 - Provide the necessary input size for the model. This information can be found in the RDF file of each model (RDF file > config > test_information > inputs > size) 157 - Provide the necessary input size for the model. This information can be found in the RDF file of each model (RDF file > config > test_information > inputs > size)
158 - Provide axes of input image. This information can also be found in the RDF file of each model (RDF file > inputs > axes). An example value of axes is 'bczyx' for 3D U-Net Arabidopsis Lateral Root Primordia model
70 159
71 **Output files** 160 **Output files**
72 - Predicted image: Predicted image using the BioImage.IO model 161 - Predicted image: Predicted image using the BioImage.IO model
73 - Predicted image matrix: Predicted image matrix in original dimensions 162 - Predicted image matrix: Predicted image matrix in original dimensions
74 ]]> 163 ]]>
75 </help> 164 </help>
76 <citations> 165 <citations>
77 <citation type="doi">10.1145/3620665.3640366</citation> 166 <citation type="doi">10.1145/3620665.3640366</citation>
78 <citation type="doi">10.1101/2022.06.07.495102</citation> 167 <citation type="doi">10.1101/2022.06.07.495102</citation>