diff imagej2_find_maxima_jython_script.py @ 0:018144807556 draft default tip

planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/image_processing/imagej2 commit 8f49f3c66b5a1de99ec15e65c2519a56792f1d56

--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/imagej2_find_maxima_jython_script.py	Tue Sep 24 17:12:52 2024 +0000
@@ -0,0 +1,91 @@
+import sys
+
+from ij import IJ, ImagePlus
+from ij.plugin.filter import Analyzer, MaximumFinder
+from ij.process import ImageProcessor
+
+# Fiji Jython interpreter implements Python 2.5 which does not
+# provide support for argparse.
+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_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)
+
+# 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:
+        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)