annotate imagej2_find_maxima_jython_script.py @ 2:056e74fbbf6c draft

planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/image_processing/imagej2 commit 57a0433defa3cbc37ab34fbb0ebcfaeb680db8d5
author imgteam
date Sun, 05 Nov 2023 14:25:00 +0000
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1 import sys
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3 from ij import IJ, ImagePlus
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4 from ij.plugin.filter import Analyzer, MaximumFinder
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5 from ij.process import ImageProcessor
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7 # Fiji Jython interpreter implements Python 2.5 which does not
edfc597fb180 "planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/image_processing/imagej2 commit b08f0e6d1546caaf627b21f8c94044285d5d5b9c-dirty"
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8 # provide support for argparse.
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9 error_log = sys.argv[-10]
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10 input_file = sys.argv[-9]
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11 scale_when_converting = sys.argv[-8] == "yes"
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12 weighted_rgb_conversions = sys.argv[-7] == "yes"
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13 noise_tolerance = int(sys.argv[-6])
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14 output_type = sys.argv[-5]
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15 exclude_edge_maxima = sys.argv[-4] == "yes"
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16 light_background = sys.argv[-3]
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17 tmp_output_path = sys.argv[-2]
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18 output_datatype = sys.argv[-1]
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20 # Open the input image file.
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21 input_image_plus = IJ.openImage(input_file)
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23 # Create a copy of the image.
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24 input_image_plus_copy = input_image_plus.duplicate()
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25 image_processor_copy = input_image_plus_copy.getProcessor()
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26 bit_depth = image_processor_copy.getBitDepth()
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27 analyzer = Analyzer(input_image_plus_copy)
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28
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29 # Set the conversion options.
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30 options = []
ef3de3e84817 "planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/image_processing/imagej2 commit 2afb24f3c81d625312186750a714d702363012b5"
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31 # The following 2 options are applicable only to RGB images.
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32 if bit_depth == 24:
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33 if scale_when_converting:
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34 options.append("scale")
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35 if weighted_rgb_conversions:
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36 options.append("weighted")
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37 # Perform conversion - must happen even if no options are set.
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38 IJ.run(input_image_plus_copy, "Conversions...", "%s" % " ".join(options))
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39 if output_type in ["List", "Count"]:
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40 # W're generating a tabular file for the output.
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41 # Set the Find Maxima options.
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42 options = ["noise=%d" % noise_tolerance]
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43 if output_type.find("_") > 0:
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44 output_type_str = "output=[%s]" % output_type.replace("_", " ")
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45 else:
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46 output_type_str = "output=%s" % output_type
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47 options.append(output_type_str)
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48 if exclude_edge_maxima:
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49 options.append("exclude")
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50 if light_background:
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51 options.append("light")
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52 # Run the command.
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53 IJ.run(input_image_plus_copy, "Find Maxima...", "%s" % " ".join(options))
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54 results_table = analyzer.getResultsTable()
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55 results_table.saveAs(tmp_output_path)
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56 else:
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57 # Find the maxima of an image (does not find minima).
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58 # LIMITATIONS: With output_type=Segmented_Particles
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59 # (watershed segmentation), some segmentation lines
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60 # may be improperly placed if local maxima are suppressed
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61 # by the tolerance.
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62 mf = MaximumFinder()
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63 if output_type == "Single_Points":
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64 output_type_param = mf.SINGLE_POINTS
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65 elif output_type == "Maxima_Within_Tolerance":
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66 output_type_param = mf.IN_TOLERANCE
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67 elif output_type == "Segmented_Particles":
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68 output_type_param = mf.SEGMENTED
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69 elif output_type == "List":
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70 output_type_param = mf.LIST
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71 elif output_type == "Count":
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72 output_type_param = mf.COUNT
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73 # Get a new byteProcessor with a normal (uninverted) LUT where
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74 # the marked points are set to 255 (Background 0). Pixels outside
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75 # of the roi of the input image_processor_copy are not set. No
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76 # output image is created for output types POINT_SELECTION, LIST
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77 # and COUNT. In these cases findMaxima returns null.
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78 byte_processor = mf.findMaxima(
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79 image_processor_copy,
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80 noise_tolerance,
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81 ImageProcessor.NO_THRESHOLD,
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82 output_type_param,
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83 exclude_edge_maxima,
056e74fbbf6c planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/image_processing/imagej2 commit 57a0433defa3cbc37ab34fbb0ebcfaeb680db8d5
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84 False,
056e74fbbf6c planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/image_processing/imagej2 commit 57a0433defa3cbc37ab34fbb0ebcfaeb680db8d5
imgteam
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85 )
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ef3de3e84817 "planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/image_processing/imagej2 commit 2afb24f3c81d625312186750a714d702363012b5"
imgteam
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86 # Invert the image or ROI.
ef3de3e84817 "planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/image_processing/imagej2 commit 2afb24f3c81d625312186750a714d702363012b5"
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87 byte_processor.invert()
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056e74fbbf6c planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/image_processing/imagej2 commit 57a0433defa3cbc37ab34fbb0ebcfaeb680db8d5
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88 if output_type == "Segmented_Particles" and not light_background:
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ef3de3e84817 "planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/image_processing/imagej2 commit 2afb24f3c81d625312186750a714d702363012b5"
imgteam
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89 # Invert the values in this image's LUT (indexed color model).
ef3de3e84817 "planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/image_processing/imagej2 commit 2afb24f3c81d625312186750a714d702363012b5"
imgteam
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90 byte_processor.invertLut()
ef3de3e84817 "planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/image_processing/imagej2 commit 2afb24f3c81d625312186750a714d702363012b5"
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91 image_plus = ImagePlus("output", byte_processor)
ef3de3e84817 "planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/image_processing/imagej2 commit 2afb24f3c81d625312186750a714d702363012b5"
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92 IJ.saveAs(image_plus, output_datatype, tmp_output_path)