diff imagej2_find_maxima_jython_script.py @ 0:301f24849032 draft

"planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/image_processing/imagej2 commit b08f0e6d1546caaf627b21f8c94044285d5d5b9c-dirty"

--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/imagej2_find_maxima_jython_script.py	Tue Sep 17 17:10:48 2019 -0400
@@ -0,0 +1,94 @@
+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 ) )