Mercurial > repos > imgteam > imagej2_bunwarpj_align
diff imagej2_bunwarpj_align.xml @ 0:ab54024c0a88 draft
"planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/image_processing/imagej2 commit b08f0e6d1546caaf627b21f8c94044285d5d5b9c-dirty"
| author | imgteam |
|---|---|
| date | Tue, 17 Sep 2019 17:02:18 -0400 |
| parents | |
| children | e9243cad1d67 |
line wrap: on
line diff
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/imagej2_bunwarpj_align.xml Tue Sep 17 17:02:18 2019 -0400 @@ -0,0 +1,247 @@ +<?xml version='1.0' encoding='UTF-8'?> +<tool id="imagej2_bunwarpj_align" name="Align two images" version="@WRAPPER_VERSION@.0"> + <description>with bUnwarpJ</description> + <macros> + <import>imagej2_macros.xml</import> + </macros> + <expand macro="fiji_requirements" /> + <command> +<![CDATA[ + python $__tool_directory__/imagej2_bunwarpj_align.py + --target_image "$target_image" + --target_image_format $target_image.ext + --source_image "$source_image" + --source_image_format $source_image.ext + #if $align_with_mask_cond.align_with_mask == 'yes': + --target_mask "$target_mask" + --target_mask_format $target_mask.ext + --source_mask "$source_mask" + --source_mask_format $source_mask.ext + #end if + --min_scale_def $min_scale_def + --max_scale_def $max_scale_def + --max_subsamp_fact $max_subsamp_fact + --divergence_weight $divergence_weight + --curl_weight $curl_weight + --image_weight $image_weight + --consistency_weight $consistency_weight + #if $advanced_options_cond.advanced_options == 'landmarks': + --landmarks_weight $advanced_options_cond.landmarks_weight + --landmarks_file "$advanced_options_cond.landmarks_file" + #else if $advanced_options_cond.advanced_options == 'affine': + --target_affine_file "$advanced_options_cond.target_affine_file" + --source_affine_file "$advanced_options_cond.source_affine_file" + #else if $advanced_options_cond.advanced_options == 'mono': + --mono "true" + #else if $advanced_options_cond.advanced_options == 'save_transformation': + --target_trans_out "$target_trans_out" + --source_trans_out "$source_trans_out" + #end if + --source_out "$source_out" + --source_out_datatype $source_out_datatype + #if $advanced_options_cond.advanced_options != 'mono': + --target_out "$target_out" + --target_out_datatype $target_out_datatype + #end if + --jython_script $__tool_directory__/imagej2_bunwarpj_align_jython_script.py +]]> + </command> + <inputs> + <param format="bmp,eps,gif,jpg,pcx,pgm,png,psd,tiff" name="target_image" type="data" label="Target image"/> + <param format="bmp,eps,gif,jpg,pcx,pgm,png,psd,tiff" name="source_image" type="data" label="Source image"/> + <conditional name="align_with_mask_cond"> + <param name="align_with_mask" type="select" label="Align with masks?" help="Source and target mask can be any image format."> + <option value="no" selected="True">No</option> + <option value="yes">Yes</option> + </param> + <when value="no" /> + <when value="yes"> + <param format="bmp,eps,gif,jpg,pcx,pgm,png,psd,tiff" name="target_mask" type="data" label="Target mask"/> + <param format="bmp,eps,gif,jpg,pcx,pgm,png,psd,tiff" name="source_mask" type="data" label="Source mask"/> + </when> + </conditional> + <param name="min_scale_def" type="select" label="Initial deformation"> + <option value="0" selected="True">Very Coarse</option> + <option value="1">Coarse</option> + <option value="2">Fine</option> + <option value="3">Very Fine</option> + </param> + <param name="max_scale_def" type="select" label="Final deformation"> + <option value="0">Very Coarse</option> + <option value="1">Coarse</option> + <option value="2" selected="True">Fine</option> + <option value="3">Very Fine</option> + </param> + <param name="max_subsamp_fact" type="integer" value="0" min="0" label="Image sub-sample factor" help="Power of 2: [0, 1, 2 .. 7]"/> + <param name="divergence_weight" type="float" value="0.0" label="Weight of the divergence term" help="Value is a floating point number"/> + <param name="curl_weight" type="float" label="Weight of the curl term" value="0.0" help="Value is a floating point number"/> + <param name="image_weight" type="float" label="Weight of the image term" value="1.0" help="Value is a floating point number"/> + <param name="consistency_weight" type="float" label="Weight of the deformation consistency" value="10.0" help="Value is a floating point number"/> + <param name="target_out_datatype" type="select" label="Save registered target image as format"> + <expand macro="image_datatypes" /> + </param> + <param name="source_out_datatype" type="select" label="Save registered source image as format"> + <expand macro="image_datatypes" /> + </param> + <!-- + The following additional options can be added to the advanced_options select + list as soon as appropriate. Their use is supported in the imagej2_bunwarp_align.py + wrapper. + + I invested heavily in working with landmarks, but could never get them to + produce the same results from both the ImageJ GUI and the command line. I'm + convinced this is not a result of using Galaxy as I tested using ImageJ in headless + mode outside of Galaxy and the results differed from using the ImageJ GUI. Until + we know the reasons for these differences, we will eliminate this option. + <option value="landmarks">Apply landmarks</option> + + A separate tool will be needed to create an affine matrix transformation file, + so we'll eliminate this option until we have that tool. + <option value="affine">Apply affine matrix transformation</option> + + Similar to landmarks, I could not get the ImageJ mono option to produce the same + results from the ImageJ GUI and in headless mode from the command line. + <option value="mono">Unidirectional registration (source to target)</option> + --> + <conditional name="advanced_options_cond"> + <param name="advanced_options" type="select" label="Advanced options" help="These are mutually exclusive for each execution"> + <option value="none" selected="True">None</option> + <option value="save_transformation">Save calculated transformations</option> + </param> + <when value="none" /> + <!-- + <when value="landmarks"> + <param name="landmarks_weight" type="float" label="Landmark weight" value="0.0" help="Value is a floating point number"/> + <param format="txt" name="landmarks_file" type="data" label="Landmark file"/> + </when> + <when value="affine"> + <param format="txt" name="target_affine_file" type="data" label="Initial target affine matrix transformation"/> + <param format="txt" name="source_affine_file" type="data" label="Initial source affine matrix transformation"/> + </when> + <when value="mono"/> + --> + <when value="save_transformation"/> + </conditional> + </inputs> + <outputs> + <data name="source_out" format="png" label="${tool.name} on ${on_string}: Registered source image"> + <actions> + <action type="format"> + <option type="from_param" name="source_out_datatype" /> + </action> + </actions> + </data> + <data name="target_out" format="png" label="${tool.name} on ${on_string}: Registered target image"> + <filter>advanced_options_cond['advanced_options'] != "mono"</filter> + <actions> + <action type="format"> + <option type="from_param" name="target_out_datatype" /> + </action> + </actions> + </data> + <data format="txt" name="source_trans_out" label="${tool.name} on ${on_string}: Direct source transformation matrix"> + <filter>advanced_options_cond['advanced_options'] == "save_transformation"</filter> + </data> + <data format="txt" name="target_trans_out" label="${tool.name} on ${on_string}: Inverse target transformation matrix"> + <filter>advanced_options_cond['advanced_options'] == "save_transformation"</filter> + </data> + </outputs> + <tests> + <test> + <!-- Align two images without landmarks or mask and not saving transformation matrices --> + <param name="target_image" value="dotblot.jpg" /> + <param name="source_image" value="blobs.gif" /> + <param name="min_scale_def" value="0" /> + <param name="max_scale_def" value="2" /> + <param name="max_subsamp_fact" value="1" /> + <param name="divergence_weight" value="0.0" /> + <param name="curl_weight" value="0.0" /> + <param name="image_weight" value="1.0" /> + <param name="consistency_weight" value="10.0" /> + <param name="target_out_datatype" value="png" /> + <param name="source_out_datatype" value="png" /> + <output name="source_out" file="registered_source1.png" compare="sim_size" /> + <output name="target_out" file="registered_target1.png" compare="sim_size" /> + </test> + <test> + <!-- Align two images without landmarks or mask, but saving transformation matrices --> + <param name="target_image" value="dotblot.jpg" /> + <param name="source_image" value="blobs.gif" /> + <param name="min_scale_def" value="0" /> + <param name="max_scale_def" value="2" /> + <param name="max_subsamp_fact" value="1" /> + <param name="divergence_weight" value="0.0" /> + <param name="curl_weight" value="0.0" /> + <param name="image_weight" value="1.0" /> + <param name="consistency_weight" value="10.0" /> + <param name="target_out_datatype" value="png" /> + <param name="source_out_datatype" value="png" /> + <param name="advanced_options" value="save_transformation" /> + <output name="source_out" file="registered_source1.png" compare="sim_size" /> + <output name="target_out" file="registered_target1.png" compare="sim_size" /> + <output name="source_trans_out" file="source_elastic_transformation.txt" /> + <output name="target_trans_out" file="target_elastic_transformation.txt" /> + </test> + <test> + <!-- Align two images without landmarks but with mask, not saving transformation matrices --> + <param name="target_image" value="dotblot.jpg" /> + <param name="source_image" value="blobs.gif" /> + <param name="target_mask" value="mask_white.png" /> + <param name="source_mask" value="mask_ramp.gif" /> + <param name="min_scale_def" value="0" /> + <param name="max_scale_def" value="2" /> + <param name="max_subsamp_fact" value="1" /> + <param name="divergence_weight" value="0.0" /> + <param name="curl_weight" value="0.0" /> + <param name="image_weight" value="1.0" /> + <param name="consistency_weight" value="10.0" /> + <param name="target_out_datatype" value="png" /> + <param name="source_out_datatype" value="png" /> + <output name="source_out" file="registered_source2.png" compare="sim_size" /> + <output name="target_out" file="registered_target2.png" compare="sim_size" /> + </test> + </tests> + <help> +**What it does** + +<![CDATA[ +Performs a simultaneous registration of two images, A and B. Image A is elastically deformed +in order to look as similar as possible to image B, and, at the same time, the "inverse" +transformation (from B to A) is also calculated so a pseudo-invertibility of the final deformation +could be guaranteed. RGB Color images will be converted to grayscale during the registration +process but the resulting transformations will be applied to the original color images. + +Two images are produced: the deformed versions of A and B images. + +.. image:: ./static/images/bunwarpj_scheme.png + +Both selected images will work simultaneously as source and target, their tags are there only for the sake +of clarification. The registration mode is currently restricted to "Fast" ("Mono" has been eliminated for now). +The registration mode "Mono" perfoms only unidirectional registration, i.e. from source to target. The +registration mode "Fast" involve performing bidirectional registration and affects the stopping criteria +internally used by the bUnwarpJ program. + +Using the **Align with masks** otion, masks are introduced together with the input images. In this mode, the +input images is treated as the first slice of the mask pair and the mask is treated as the second slice. In this +way, the mask can have any shape. + +The **Initial deformation** and **Final deformation** options allow you to select the coarsest and finest scale +of the spline deformation field. "Very coarse" corresponds to 4 splines (one in each corner of the image). As you +increase the deformation level, the number of splines is doubled in each direction (horizontal and vertical). + +The value of the **Image sub-sample factor** will enable the registration to be calculated using the subsampled +versions of the images but the results will be applied to the original ones. The image subsampling parameter can +be set from 0 and 7, i.e. the image dimensions can be reduced by a factor of 20 = 1 to 27 = 128. This is very +useful when registering large images. + +The different weights of the goal function control the relative weight of each one of the terms. These weights +are not restricted to be between 0 and 1, and they may take any value as long as it is non-negative=2E. + +If the **Advanced option - Save transformation** option is selected, two files are produced in addition to +the registerd source and target images: the direct transformation matrix of A and the inverse transformation +matrix of B. +]]> + + </help> + <expand macro="bunwarpj_citations" /> +</tool>