Mercurial > repos > imgteam > imagej2_bunwarpj_raw_transform
view imagej2_find_maxima_jython_script.py @ 0:95b7592729e2 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 17:03:42 -0400 |
parents | |
children | aa4a5a8c4cfd |
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import sys import jython_utils from ij import ImagePlus, IJ from ij.plugin.filter import Analyzer, MaximumFinder from ij.process import ImageProcessor from jarray import array # Fiji Jython interpreter implements Python 2.5 which does not # provide support for argparse. error_log = sys.argv[ -10 ] input = sys.argv[ -9 ] scale_when_converting = jython_utils.asbool( sys.argv[ -8 ] ) weighted_rgb_conversions = jython_utils.asbool( sys.argv[ -7 ] ) noise_tolerance = int( sys.argv[ -6 ] ) output_type = sys.argv[ -5 ] exclude_edge_maxima = jython_utils.asbool( sys.argv[ -4 ] ) light_background = jython_utils.asbool( sys.argv[ -3 ] ) tmp_output_path = sys.argv[ -2 ] output_datatype = sys.argv[ -1 ] # Open the input image file. input_image_plus = IJ.openImage( input ) # Create a copy of the image. input_image_plus_copy = input_image_plus.duplicate() image_processor_copy = input_image_plus_copy.getProcessor() bit_depth = image_processor_copy.getBitDepth() analyzer = Analyzer( input_image_plus_copy ) try: # Set the conversion options. options = [] # The following 2 options are applicable only to RGB images. if bit_depth == 24: if scale_when_converting: option.append( "scale" ) if weighted_rgb_conversions: options.append( "weighted" ) # Perform conversion - must happen even if no options are set. IJ.run( input_image_plus_copy, "Conversions...", "%s" % " ".join( options ) ) if output_type in [ 'List', 'Count' ]: # W're generating a tabular file for the output. # Set the Find Maxima options. options = [ 'noise=%d' % noise_tolerance ] if output_type.find( '_' ) > 0: output_type_str = 'output=[%s]' % output_type.replace( '_', ' ' ) else: output_type_str = 'output=%s' % output_type options.append( output_type_str ) if exclude_edge_maxima: options.append( 'exclude' ) if light_background: options.append( 'light' ) # Run the command. IJ.run( input_image_plus_copy, "Find Maxima...", "%s" % " ".join( options ) ) results_table = analyzer.getResultsTable() results_table.saveAs( tmp_output_path ) else: # Find the maxima of an image (does not find minima). # LIMITATIONS: With output_type=Segmented_Particles # (watershed segmentation), some segmentation lines # may be improperly placed if local maxima are suppressed # by the tolerance. mf = MaximumFinder() if output_type == 'Single_Points': output_type_param = mf.SINGLE_POINTS elif output_type == 'Maxima_Within_Tolerance': output_type_param = mf.IN_TOLERANCE elif output_type == 'Segmented_Particles': output_type_param = mf.SEGMENTED elif output_type == 'List': output_type_param = mf.LIST elif output_type == 'Count': output_type_param = mf.COUNT # Get a new byteProcessor with a normal (uninverted) LUT where # the marked points are set to 255 (Background 0). Pixels outside # of the roi of the input image_processor_copy are not set. No # output image is created for output types POINT_SELECTION, LIST # and COUNT. In these cases findMaxima returns null. byte_processor = mf.findMaxima( image_processor_copy, noise_tolerance, ImageProcessor.NO_THRESHOLD, output_type_param, exclude_edge_maxima, False ) # Invert the image or ROI. byte_processor.invert() if output_type == 'Segmented_Particles' and not light_background: # Invert the values in this image's LUT (indexed color model). byte_processor.invertLut() image_plus = ImagePlus( "output", byte_processor ) IJ.saveAs( image_plus, output_datatype, tmp_output_path ) except Exception, e: jython_utils.handle_error( error_log, str( e ) )