comparison imagej2_noise_jython_script.py @ 1:29a4d422f32a draft default tip

"planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/image_processing/imagej2 commit 2afb24f3c81d625312186750a714d702363012b5"
author imgteam
date Mon, 28 Sep 2020 16:41:15 +0000
parents f1ba33cd9edf
children
comparison
equal deleted inserted replaced
0:f1ba33cd9edf 1:29a4d422f32a
1 import sys 1 import sys
2
2 from ij import IJ 3 from ij import IJ
3 from ij import ImagePlus
4 import jython_utils
5 4
6 # Fiji Jython interpreter implements Python 2.5 which does not 5 # Fiji Jython interpreter implements Python 2.5 which does not
7 # provide support for argparse. 6 # provide support for argparse.
8 error_log = sys.argv[ -19 ] 7 error_log = sys.argv[-19]
9 input = sys.argv[ -18 ] 8 input_file = sys.argv[-18]
10 image_datatype = sys.argv[ -17 ] 9 image_datatype = sys.argv[-17]
11 noise = sys.argv[ -16 ] 10 noise = sys.argv[-16]
12 standard_deviation = sys.argv[ -15 ] 11 standard_deviation = sys.argv[-15]
13 radius = sys.argv[ -14 ] 12 radius = sys.argv[-14]
14 threshold = sys.argv[ -13 ] 13 threshold = sys.argv[-13]
15 which_outliers = sys.argv[ -12 ] 14 which_outliers = sys.argv[-12]
16 randomj = sys.argv[ -11 ] 15 randomj = sys.argv[-11]
17 trials = sys.argv[ -10 ] 16 trials = sys.argv[-10]
18 probability = sys.argv[ -9 ] 17 probability = sys.argv[-9]
19 # Note the spelling - so things don't get confused due to Python lambda function. 18 # Note the spelling - so things don't get confused due to Python lambda function.
20 lammbda = sys.argv[ -8 ] 19 lammbda = sys.argv[-8]
21 order = sys.argv[ -7 ] 20 order = sys.argv[-7]
22 mean = sys.argv[ -6 ] 21 mean = sys.argv[-6]
23 sigma = sys.argv[ -5 ] 22 sigma = sys.argv[-5]
24 min = sys.argv[ -4 ] 23 min = sys.argv[-4]
25 max = sys.argv[ -3 ] 24 max = sys.argv[-3]
26 insertion = sys.argv[ -2 ] 25 insertion = sys.argv[-2]
27 tmp_output_path = sys.argv[ -1 ] 26 tmp_output_path = sys.argv[-1]
28
29 error = False
30 27
31 # Open the input image file. 28 # Open the input image file.
32 image_plus = IJ.openImage( input ) 29 image_plus = IJ.openImage(input_file)
33 bit_depth = image_plus.getBitDepth() 30 bit_depth = image_plus.getBitDepth()
34 image_type = image_plus.getType() 31 image_type = image_plus.getType()
35 # Create an ImagePlus object for the image. 32 # Create an ImagePlus object for the image.
36 image_plus_copy = image_plus.duplicate() 33 image_plus_copy = image_plus.duplicate()
37 # Make a copy of the image. 34 # Make a copy of the image.
38 image_processor_copy = image_plus_copy.getProcessor() 35 image_processor_copy = image_plus_copy.getProcessor()
39 36
40 # Perform the analysis on the ImagePlus object. 37 # Perform the analysis on the ImagePlus object.
41 if noise == 'add_noise': 38 if noise == 'add_noise':
42 IJ.run( image_plus_copy, "Add Noise", "" ) 39 IJ.run(image_plus_copy, "Add Noise", "")
43 elif noise == 'add_specified_noise': 40 elif noise == 'add_specified_noise':
44 IJ.run( image_plus_copy, "Add Specified Noise", "standard=&standard_deviation" ) 41 IJ.run(image_plus_copy, "Add Specified Noise", "standard=&standard_deviation")
45 elif noise == 'salt_and_pepper': 42 elif noise == 'salt_and_pepper':
46 IJ.run( image_plus_copy, "Salt and Pepper", "" ) 43 IJ.run(image_plus_copy, "Salt and Pepper", "")
47 elif noise == 'despeckle': 44 elif noise == 'despeckle':
48 IJ.run( image_plus_copy, "Despeckle", "" ) 45 IJ.run(image_plus_copy, "Despeckle", "")
49 elif noise == 'remove_outliers': 46 elif noise == 'remove_outliers':
50 IJ.run( image_plus_copy, "Remove Outliers", "radius=&radius threshold=&threshold which=&which_outliers" ) 47 IJ.run(image_plus_copy, "Remove Outliers", "radius=&radius threshold=&threshold which=&which_outliers")
51 elif noise == 'remove_nans': 48 elif noise == 'remove_nans':
52 if bit_depth == 32: 49 IJ.run(image_plus_copy, "Remove NaNs", "")
53 IJ.run( image_plus_copy, "Remove NaNs", "" )
54 else:
55 # When Galaxy metadata for images is enhanced to include information like this,
56 # we'll be able to write tool validators rather than having to stop the job in
57 # an error state.
58 msg = "Remove NaNs requires a 32-bit image, the selected image is %d-bit" % bit_depth
59 jython_utils.handle_error( error_log, msg )
60 error = True
61 elif noise == 'rof_denoise': 50 elif noise == 'rof_denoise':
62 if image_type == ImagePlus.GRAY32: 51 IJ.run(image_plus_copy, "ROF Denoise", "")
63 IJ.run( image_plus_copy, "ROF Denoise", "" )
64 else:
65 msg = "ROF Denoise requires an image of type 32-bit grayscale, the selected image is %d-bit" % ( bit_depth )
66 jython_utils.handle_error( error_log, msg )
67 error = True
68 elif noise == 'randomj': 52 elif noise == 'randomj':
69 if randomj == 'randomj_binomial': 53 if randomj == 'randomj_binomial':
70 IJ.run( image_plus_copy, "RandomJ Binomial", "trials=&trials probability=&probability insertion=&insertion" ) 54 IJ.run(image_plus_copy, "RandomJ Binomial", "trials=&trials probability=&probability insertion=&insertion")
71 elif randomj == 'randomj_exponential': 55 elif randomj == 'randomj_exponential':
72 IJ.run( image_plus_copy, "RandomJ Exponential", "lambda=&lammbda insertion=&insertion" ) 56 IJ.run(image_plus_copy, "RandomJ Exponential", "lambda=&lammbda insertion=&insertion")
73 elif randomj == 'randomj_gamma': 57 elif randomj == 'randomj_gamma':
74 IJ.run( image_plus_copy, "RandomJ Gamma", "order=&order insertion=&insertion" ) 58 IJ.run(image_plus_copy, "RandomJ Gamma", "order=&order insertion=&insertion")
75 elif randomj == 'randomj_gaussian': 59 elif randomj == 'randomj_gaussian':
76 IJ.run( image_plus_copy, "RandomJ Gaussian", "mean=&mean sigma=&sigma insertion=&insertion" ) 60 IJ.run(image_plus_copy, "RandomJ Gaussian", "mean=&mean sigma=&sigma insertion=&insertion")
77 elif randomj == 'randomj_poisson': 61 elif randomj == 'randomj_poisson':
78 IJ.run( image_plus_copy, "RandomJ Poisson", "mean=&mean insertion=&insertion" ) 62 IJ.run(image_plus_copy, "RandomJ Poisson", "mean=&mean insertion=&insertion")
79 elif randomj == 'randomj_uniform': 63 elif randomj == 'randomj_uniform':
80 IJ.run( image_plus_copy, "RandomJ Uniform", "min=&min max=&max insertion=&insertion" ) 64 IJ.run(image_plus_copy, "RandomJ Uniform", "min=&min max=&max insertion=&insertion")
81 65
82 if not error: 66 # Save the ImagePlus object as a new image.
83 # Save the ImagePlus object as a new image. 67 IJ.saveAs(image_plus_copy, image_datatype, tmp_output_path)
84 IJ.saveAs( image_plus_copy, image_datatype, tmp_output_path )