--- a/imagej2_find_maxima_jython_script.py	Tue Sep 17 17:08:47 2019 -0400
+++ b/imagej2_find_maxima_jython_script.py	Mon Sep 28 16:47:44 2020 +0000
@@ -1,94 +1,90 @@
 import sys
-import jython_utils
-from ij import ImagePlus, IJ
+
+from ij import IJ, ImagePlus
 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 ]
+error_log = sys.argv[-10]
+input_file = sys.argv[-9]
+scale_when_converting = sys.argv[-8] == 'yes'
+weighted_rgb_conversions = sys.argv[-7] == 'yes'
+noise_tolerance = int(sys.argv[-6])
+output_type = sys.argv[-5]
+exclude_edge_maxima = sys.argv[-4] == 'yes'
+light_background = 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 )
+input_image_plus = IJ.openImage(input_file)
 
 # 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 )
+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 )
+# Set the conversion options.
+options = []
+# The following 2 options are applicable only to RGB images.
+if bit_depth == 24:
+    if scale_when_converting:
+        options.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:
-        # 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 ) )
+        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)