Mercurial > repos > imgteam > imagej2_bunwarpj_align
comparison imagej2_find_maxima_jython_script.py @ 0:ab54024c0a88 draft
"planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/image_processing/imagej2 commit b08f0e6d1546caaf627b21f8c94044285d5d5b9c-dirty"
author | imgteam |
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date | Tue, 17 Sep 2019 17:02:18 -0400 |
parents | |
children | e9243cad1d67 |
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-1:000000000000 | 0:ab54024c0a88 |
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1 import sys | |
2 import jython_utils | |
3 from ij import ImagePlus, IJ | |
4 from ij.plugin.filter import Analyzer, MaximumFinder | |
5 from ij.process import ImageProcessor | |
6 from jarray import array | |
7 | |
8 # Fiji Jython interpreter implements Python 2.5 which does not | |
9 # provide support for argparse. | |
10 error_log = sys.argv[ -10 ] | |
11 input = sys.argv[ -9 ] | |
12 scale_when_converting = jython_utils.asbool( sys.argv[ -8 ] ) | |
13 weighted_rgb_conversions = jython_utils.asbool( sys.argv[ -7 ] ) | |
14 noise_tolerance = int( sys.argv[ -6 ] ) | |
15 output_type = sys.argv[ -5 ] | |
16 exclude_edge_maxima = jython_utils.asbool( sys.argv[ -4 ] ) | |
17 light_background = jython_utils.asbool( sys.argv[ -3 ] ) | |
18 tmp_output_path = sys.argv[ -2 ] | |
19 output_datatype = sys.argv[ -1 ] | |
20 | |
21 # Open the input image file. | |
22 input_image_plus = IJ.openImage( input ) | |
23 | |
24 # Create a copy of the image. | |
25 input_image_plus_copy = input_image_plus.duplicate() | |
26 image_processor_copy = input_image_plus_copy.getProcessor() | |
27 bit_depth = image_processor_copy.getBitDepth() | |
28 analyzer = Analyzer( input_image_plus_copy ) | |
29 | |
30 try: | |
31 # Set the conversion options. | |
32 options = [] | |
33 # The following 2 options are applicable only to RGB images. | |
34 if bit_depth == 24: | |
35 if scale_when_converting: | |
36 option.append( "scale" ) | |
37 if weighted_rgb_conversions: | |
38 options.append( "weighted" ) | |
39 # Perform conversion - must happen even if no options are set. | |
40 IJ.run( input_image_plus_copy, "Conversions...", "%s" % " ".join( options ) ) | |
41 if output_type in [ 'List', 'Count' ]: | |
42 # W're generating a tabular file for the output. | |
43 # Set the Find Maxima options. | |
44 options = [ 'noise=%d' % noise_tolerance ] | |
45 if output_type.find( '_' ) > 0: | |
46 output_type_str = 'output=[%s]' % output_type.replace( '_', ' ' ) | |
47 else: | |
48 output_type_str = 'output=%s' % output_type | |
49 options.append( output_type_str ) | |
50 if exclude_edge_maxima: | |
51 options.append( 'exclude' ) | |
52 if light_background: | |
53 options.append( 'light' ) | |
54 # Run the command. | |
55 IJ.run( input_image_plus_copy, "Find Maxima...", "%s" % " ".join( options ) ) | |
56 results_table = analyzer.getResultsTable() | |
57 results_table.saveAs( tmp_output_path ) | |
58 else: | |
59 # Find the maxima of an image (does not find minima). | |
60 # LIMITATIONS: With output_type=Segmented_Particles | |
61 # (watershed segmentation), some segmentation lines | |
62 # may be improperly placed if local maxima are suppressed | |
63 # by the tolerance. | |
64 mf = MaximumFinder() | |
65 if output_type == 'Single_Points': | |
66 output_type_param = mf.SINGLE_POINTS | |
67 elif output_type == 'Maxima_Within_Tolerance': | |
68 output_type_param = mf.IN_TOLERANCE | |
69 elif output_type == 'Segmented_Particles': | |
70 output_type_param = mf.SEGMENTED | |
71 elif output_type == 'List': | |
72 output_type_param = mf.LIST | |
73 elif output_type == 'Count': | |
74 output_type_param = mf.COUNT | |
75 # Get a new byteProcessor with a normal (uninverted) LUT where | |
76 # the marked points are set to 255 (Background 0). Pixels outside | |
77 # of the roi of the input image_processor_copy are not set. No | |
78 # output image is created for output types POINT_SELECTION, LIST | |
79 # and COUNT. In these cases findMaxima returns null. | |
80 byte_processor = mf.findMaxima( image_processor_copy, | |
81 noise_tolerance, | |
82 ImageProcessor.NO_THRESHOLD, | |
83 output_type_param, | |
84 exclude_edge_maxima, | |
85 False ) | |
86 # Invert the image or ROI. | |
87 byte_processor.invert() | |
88 if output_type == 'Segmented_Particles' and not light_background: | |
89 # Invert the values in this image's LUT (indexed color model). | |
90 byte_processor.invertLut() | |
91 image_plus = ImagePlus( "output", byte_processor ) | |
92 IJ.saveAs( image_plus, output_datatype, tmp_output_path ) | |
93 except Exception, e: | |
94 jython_utils.handle_error( error_log, str( e ) ) |