Mercurial > repos > iuc > imagej2_noise

comparison jython_script.py @ 0:da9a450c85b7 draft

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planemo upload commit 7195716d447effc5bc32154279de4b84369eccdb-dirty
author iuc
date Fri, 19 Jun 2015 16:59:22 -0400
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children c8fc3f34cbff
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-1:000000000000 0:da9a450c85b7
1 import sys
2 from ij import IJ
3 from ij import ImagePlus
4
5 def handle_error( error_log, msg ):
6 elh = open( error_log, 'wb' )
7 elh.write( msg )
8 elh.close()
9
10 # Fiji Jython interpreter implements Python 2.5 which does not
11 # provide support for argparse.
12 error_log = sys.argv[ -19 ]
13 input = sys.argv[ -18 ]
14 image_datatype = sys.argv[ -17 ]
15 noise = sys.argv[ -16 ]
16 standard_deviation = sys.argv[ -15 ]
17 radius = sys.argv[ -14 ]
18 threshold = sys.argv[ -13 ]
19 which_outliers = sys.argv[ -12 ]
20 randomj = sys.argv[ -11 ]
21 trials = sys.argv[ -10 ]
22 probability = sys.argv[ -9 ]
23 # Note the spelling - so things don't get confused due to Python lambda function.
24 lammbda = sys.argv[ -8 ]
25 order = sys.argv[ -7 ]
26 mean = sys.argv[ -6 ]
27 sigma = sys.argv[ -5 ]
28 min = sys.argv[ -4 ]
29 max = sys.argv[ -3 ]
30 insertion = sys.argv[ -2 ]
31 tmp_output_path = sys.argv[ -1 ]
32
33 error = False
34
35 # Open the input image file.
36 image_plus = IJ.openImage( input )
37 bit_depth = image_plus.getBitDepth()
38 image_type = image_plus.getType()
39 # Create an ImagePlus object for the image.
40 image_plus_copy = image_plus.createImagePlus()
41 # Make a copy of the image.
42 image_processor_copy = image_plus.getProcessor().duplicate()
43 # Set the ImageProcessor on the duplicate ImagePlus object.
44 image_plus_copy.setProcessor( "image copy", image_processor_copy )
45
46 # Perform the analysis on the ImagePlus object.
47 if noise == 'add_noise':
48 IJ.run( image_plus_copy, "Add Noise", "" )
49 elif noise == 'add_specified_noise':
50 IJ.run( image_plus_copy, "Add Specified Noise", "standard=&standard_deviation" )
51 elif noise == 'salt_and_pepper':
52 IJ.run( image_plus_copy, "Salt and Pepper", "" )
53 elif noise == 'despeckle':
54 IJ.run( image_plus_copy, "Despeckle", "" )
55 elif noise == 'remove_outliers':
56 IJ.run( image_plus_copy, "Remove Outliers", "radius=&radius threshold=&threshold which=&which_outliers" )
57 elif noise == 'remove_nans':
58 if bit_depth == 32:
59 IJ.run( image_plus_copy, "Remove NaNs", "" )
60 else:
61 # When Galaxy metadata for images is enhanced to include information like this,
62 # we'll be able to write tool validators rather than having to stop the job in
63 # an error state.
64 msg = "Remove NaNs requires a 32-bit image, the selected image is %d-bit" % bit_depth
65 handle_error( error_log, msg )
66 error = True
67 elif noise == 'rof_denoise':
68 if image_type == ImagePlus.GRAY32:
69 IJ.run( image_plus_copy, "ROF Denoise", "" )
70 else:
71 msg = "ROF Denoise requires an image of type 32-bit grayscale, the selected image is %d-bit" % ( bit_depth )
72 handle_error( error_log, msg )
73 error = True
74 elif noise == 'randomj':
75 if randomj == 'randomj_binomial':
76 IJ.run( image_plus_copy, "RandomJ Binomial", "trials=&trials probability=&probability insertion=&insertion" )
77 elif randomj == 'randomj_exponential':
78 IJ.run( image_plus_copy, "RandomJ Exponential", "lambda=&lammbda insertion=&insertion" )
79 elif randomj == 'randomj_gamma':
80 IJ.run( image_plus_copy, "RandomJ Gamma", "order=&order insertion=&insertion" )
81 elif randomj == 'randomj_gaussian':
82 IJ.run( image_plus_copy, "RandomJ Gaussian", "mean=&mean sigma=&sigma insertion=&insertion" )
83 elif randomj == 'randomj_poisson':
84 IJ.run( image_plus_copy, "RandomJ Poisson", "mean=&mean insertion=&insertion" )
85 elif randomj == 'randomj_uniform':
86 IJ.run( image_plus_copy, "RandomJ Uniform", "min=&min max=&max insertion=&insertion" )
87
88 if not error:
89 # Save the ImagePlus object as a new image.
90 IJ.saveAs( image_plus_copy, image_datatype, tmp_output_path )