Mercurial > repos > imgteam > imagej2_bunwarpj_compose_raw_elastic
view imagej2_noise_jython_script.py @ 1:8d357579e407 draft
"planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/image_processing/imagej2 commit 2afb24f3c81d625312186750a714d702363012b5"
author | imgteam |
---|---|
date | Mon, 28 Sep 2020 16:56:53 +0000 |
parents | 1bf7e70e67f9 |
children | 4deffc9540a3 |
line wrap: on
line source
import sys from ij import IJ # Fiji Jython interpreter implements Python 2.5 which does not # provide support for argparse. error_log = sys.argv[-19] input_file = sys.argv[-18] image_datatype = sys.argv[-17] noise = sys.argv[-16] standard_deviation = sys.argv[-15] radius = sys.argv[-14] threshold = sys.argv[-13] which_outliers = sys.argv[-12] randomj = sys.argv[-11] trials = sys.argv[-10] probability = sys.argv[-9] # Note the spelling - so things don't get confused due to Python lambda function. lammbda = sys.argv[-8] order = sys.argv[-7] mean = sys.argv[-6] sigma = sys.argv[-5] min = sys.argv[-4] max = sys.argv[-3] insertion = sys.argv[-2] tmp_output_path = sys.argv[-1] # Open the input image file. image_plus = IJ.openImage(input_file) bit_depth = image_plus.getBitDepth() image_type = image_plus.getType() # Create an ImagePlus object for the image. image_plus_copy = image_plus.duplicate() # Make a copy of the image. image_processor_copy = image_plus_copy.getProcessor() # Perform the analysis on the ImagePlus object. if noise == 'add_noise': IJ.run(image_plus_copy, "Add Noise", "") elif noise == 'add_specified_noise': IJ.run(image_plus_copy, "Add Specified Noise", "standard=&standard_deviation") elif noise == 'salt_and_pepper': IJ.run(image_plus_copy, "Salt and Pepper", "") 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': IJ.run(image_plus_copy, "Remove NaNs", "") 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': 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': 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") # Save the ImagePlus object as a new image. IJ.saveAs(image_plus_copy, image_datatype, tmp_output_path)