Mercurial > repos > imgteam > imagej2_bunwarpj_compare_elastic_raw
diff imagej2_noise_jython_script.py @ 2:3d5f59603e13 draft default tip
planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/image_processing/imagej2 commit 57a0433defa3cbc37ab34fbb0ebcfaeb680db8d5
author | imgteam |
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date | Sun, 05 Nov 2023 10:56:36 +0000 |
parents | 2be338fc671e |
children |
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--- a/imagej2_noise_jython_script.py Mon Sep 28 16:44:56 2020 +0000 +++ b/imagej2_noise_jython_script.py Sun Nov 05 10:56:36 2023 +0000 @@ -35,33 +35,51 @@ image_processor_copy = image_plus_copy.getProcessor() # Perform the analysis on the ImagePlus object. -if noise == 'add_noise': +if noise == "add_noise": IJ.run(image_plus_copy, "Add Noise", "") -elif noise == 'add_specified_noise': +elif noise == "add_specified_noise": IJ.run(image_plus_copy, "Add Specified Noise", "standard=&standard_deviation") -elif noise == 'salt_and_pepper': +elif noise == "salt_and_pepper": IJ.run(image_plus_copy, "Salt and Pepper", "") -elif noise == 'despeckle': +elif noise == "despeckle": IJ.run(image_plus_copy, "Despeckle", "") -elif noise == 'remove_outliers': - IJ.run(image_plus_copy, "Remove Outliers", "radius=&radius threshold=&threshold which=&which_outliers") -elif noise == 'remove_nans': +elif noise == "remove_outliers": + IJ.run( + image_plus_copy, + "Remove Outliers", + "radius=&radius threshold=&threshold which=&which_outliers", + ) +elif noise == "remove_nans": IJ.run(image_plus_copy, "Remove NaNs", "") -elif noise == 'rof_denoise': +elif noise == "rof_denoise": IJ.run(image_plus_copy, "ROF Denoise", "") -elif noise == 'randomj': - if randomj == 'randomj_binomial': - IJ.run(image_plus_copy, "RandomJ Binomial", "trials=&trials probability=&probability insertion=&insertion") - elif randomj == 'randomj_exponential': - IJ.run(image_plus_copy, "RandomJ Exponential", "lambda=&lammbda insertion=&insertion") - elif randomj == 'randomj_gamma': +elif noise == "randomj": + if randomj == "randomj_binomial": + IJ.run( + image_plus_copy, + "RandomJ Binomial", + "trials=&trials probability=&probability insertion=&insertion", + ) + elif randomj == "randomj_exponential": + IJ.run( + image_plus_copy, + "RandomJ Exponential", + "lambda=&lammbda insertion=&insertion", + ) + elif randomj == "randomj_gamma": IJ.run(image_plus_copy, "RandomJ Gamma", "order=&order insertion=&insertion") - elif randomj == 'randomj_gaussian': - IJ.run(image_plus_copy, "RandomJ Gaussian", "mean=&mean sigma=&sigma insertion=&insertion") - elif randomj == 'randomj_poisson': + elif randomj == "randomj_gaussian": + IJ.run( + image_plus_copy, + "RandomJ Gaussian", + "mean=&mean sigma=&sigma insertion=&insertion", + ) + elif randomj == "randomj_poisson": IJ.run(image_plus_copy, "RandomJ Poisson", "mean=&mean insertion=&insertion") - elif randomj == 'randomj_uniform': - IJ.run(image_plus_copy, "RandomJ Uniform", "min=&min max=&max insertion=&insertion") + elif randomj == "randomj_uniform": + IJ.run( + image_plus_copy, "RandomJ Uniform", "min=&min max=&max insertion=&insertion" + ) # Save the ImagePlus object as a new image. IJ.saveAs(image_plus_copy, image_datatype, tmp_output_path)