Mercurial > repos > iuc > imagej2_noise

annotate jython_script.py @ 0:da9a450c85b7 draft

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planemo upload commit 7195716d447effc5bc32154279de4b84369eccdb-dirty
author iuc
date Fri, 19 Jun 2015 16:59:22 -0400
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children c8fc3f34cbff
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1 import sys
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2 from ij import IJ
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3 from ij import ImagePlus
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4
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5 def handle_error( error_log, msg ):
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6 elh = open( error_log, 'wb' )
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7 elh.write( msg )
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8 elh.close()
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9
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10 # Fiji Jython interpreter implements Python 2.5 which does not
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11 # provide support for argparse.
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12 error_log = sys.argv[ -19 ]
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13 input = sys.argv[ -18 ]
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14 image_datatype = sys.argv[ -17 ]
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15 noise = sys.argv[ -16 ]
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16 standard_deviation = sys.argv[ -15 ]
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17 radius = sys.argv[ -14 ]
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18 threshold = sys.argv[ -13 ]
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19 which_outliers = sys.argv[ -12 ]
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20 randomj = sys.argv[ -11 ]
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21 trials = sys.argv[ -10 ]
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22 probability = sys.argv[ -9 ]
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23 # Note the spelling - so things don't get confused due to Python lambda function.
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24 lammbda = sys.argv[ -8 ]
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25 order = sys.argv[ -7 ]
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26 mean = sys.argv[ -6 ]
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27 sigma = sys.argv[ -5 ]
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28 min = sys.argv[ -4 ]
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29 max = sys.argv[ -3 ]
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30 insertion = sys.argv[ -2 ]
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31 tmp_output_path = sys.argv[ -1 ]
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32
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33 error = False
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34
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35 # Open the input image file.
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36 image_plus = IJ.openImage( input )
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37 bit_depth = image_plus.getBitDepth()
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38 image_type = image_plus.getType()
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39 # Create an ImagePlus object for the image.
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40 image_plus_copy = image_plus.createImagePlus()
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41 # Make a copy of the image.
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42 image_processor_copy = image_plus.getProcessor().duplicate()
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43 # Set the ImageProcessor on the duplicate ImagePlus object.
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44 image_plus_copy.setProcessor( "image copy", image_processor_copy )
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45
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46 # Perform the analysis on the ImagePlus object.
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47 if noise == 'add_noise':
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48 IJ.run( image_plus_copy, "Add Noise", "" )
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49 elif noise == 'add_specified_noise':
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50 IJ.run( image_plus_copy, "Add Specified Noise", "standard=&standard_deviation" )
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51 elif noise == 'salt_and_pepper':
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52 IJ.run( image_plus_copy, "Salt and Pepper", "" )
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53 elif noise == 'despeckle':
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54 IJ.run( image_plus_copy, "Despeckle", "" )
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55 elif noise == 'remove_outliers':
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56 IJ.run( image_plus_copy, "Remove Outliers", "radius=&radius threshold=&threshold which=&which_outliers" )
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57 elif noise == 'remove_nans':
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58 if bit_depth == 32:
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59 IJ.run( image_plus_copy, "Remove NaNs", "" )
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60 else:
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61 # When Galaxy metadata for images is enhanced to include information like this,
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62 # we'll be able to write tool validators rather than having to stop the job in
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63 # an error state.
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64 msg = "Remove NaNs requires a 32-bit image, the selected image is %d-bit" % bit_depth
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65 handle_error( error_log, msg )
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66 error = True
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67 elif noise == 'rof_denoise':
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68 if image_type == ImagePlus.GRAY32:
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69 IJ.run( image_plus_copy, "ROF Denoise", "" )
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70 else:
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71 msg = "ROF Denoise requires an image of type 32-bit grayscale, the selected image is %d-bit" % ( bit_depth )
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72 handle_error( error_log, msg )
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73 error = True
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74 elif noise == 'randomj':
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75 if randomj == 'randomj_binomial':
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76 IJ.run( image_plus_copy, "RandomJ Binomial", "trials=&trials probability=&probability insertion=&insertion" )
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77 elif randomj == 'randomj_exponential':
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78 IJ.run( image_plus_copy, "RandomJ Exponential", "lambda=&lammbda insertion=&insertion" )
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79 elif randomj == 'randomj_gamma':
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80 IJ.run( image_plus_copy, "RandomJ Gamma", "order=&order insertion=&insertion" )
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81 elif randomj == 'randomj_gaussian':
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82 IJ.run( image_plus_copy, "RandomJ Gaussian", "mean=&mean sigma=&sigma insertion=&insertion" )
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83 elif randomj == 'randomj_poisson':
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84 IJ.run( image_plus_copy, "RandomJ Poisson", "mean=&mean insertion=&insertion" )
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85 elif randomj == 'randomj_uniform':
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86 IJ.run( image_plus_copy, "RandomJ Uniform", "min=&min max=&max insertion=&insertion" )
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87
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88 if not error:
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89 # Save the ImagePlus object as a new image.
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90 IJ.saveAs( image_plus_copy, image_datatype, tmp_output_path )