Mercurial > repos > perssond > basic_illumination

annotate imagej_basic_ashlar.py @ 0:fd8dfd64f25e draft

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"planemo upload for repository https://github.com/ohsu-comp-bio/basic-illumination commit a8d2367c8c66eecfc2586a593acc8547a7f8611c-dirty"
author perssond
date Fri, 12 Mar 2021 00:13:46 +0000
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1 # @File(label="Select a slide to process") filename
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2 # @File(label="Select the output location", style="directory") output_dir
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3 # @String(label="Experiment name (base name for output files)") experiment_name
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4 # @Float(label="Flat field smoothing parameter (0 for automatic)", value=0.1) lambda_flat
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5 # @Float(label="Dark field smoothing parameter (0 for automatic)", value=0.01) lambda_dark
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7 # Takes a slide (or other multi-series BioFormats-compatible file set) and
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8 # generates flat- and dark-field correction profile images with BaSiC. The
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9 # output format is two multi-series TIFF files (one for flat and one for dark)
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10 # which is the input format used by Ashlar.
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12 # Invocation for running from the commandline:
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13 #
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14 # ImageJ --ij2 --headless --run imagej_basic_ashlar.py "filename='input.ext',output_dir='output',experiment_name='my_experiment'"
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16 import sys
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17 from ij import IJ, WindowManager, Prefs
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18 from ij.macro import Interpreter
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19 from loci.plugins import BF
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20 from loci.plugins.in import ImporterOptions
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21 from loci.formats import ImageReader
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22 from loci.formats.in import DynamicMetadataOptions
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23 import BaSiC_ as Basic
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25 import pdb
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28 def main():
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30 Interpreter.batchMode = True
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31
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32 if (lambda_flat == 0) ^ (lambda_dark == 0):
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33 print ("ERROR: Both of lambda_flat and lambda_dark must be zero,"
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34 " or both non-zero.")
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35 return
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36 lambda_estimate = "Automatic" if lambda_flat == 0 else "Manual"
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37
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38 print "Loading images..."
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39
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40 # For multi-scene .CZI files, we need raw tiles instead of the
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41 # auto-stitched mosaic and we don't want labels or overview images. This
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42 # only affects BF.openImagePlus, not direct use of the BioFormats reader
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43 # classes which we also do (see below)
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44 Prefs.set("bioformats.zeissczi.allow.autostitch", "false")
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45 Prefs.set("bioformats.zeissczi.include.attachments", "false")
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46
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47 # Use BioFormats reader directly to determine dataset dimensions without
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48 # reading every single image. The series count (num_images) is the one value
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49 # we can't easily get any other way, but we might as well grab the others
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50 # while we have the reader available.
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51 dyn_options = DynamicMetadataOptions()
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52 # Directly calling a BioFormats reader will not use the IJ Prefs settings
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53 # so we need to pass these options explicitly.
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54 dyn_options.setBoolean("zeissczi.autostitch", False)
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55 dyn_options.setBoolean("zeissczi.attachments", False)
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56 bfreader = ImageReader()
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57 bfreader.setMetadataOptions(dyn_options)
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58 bfreader.id = str(filename)
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59 num_images = bfreader.seriesCount
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60 num_channels = bfreader.sizeC
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61 width = bfreader.sizeX
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62 height = bfreader.sizeY
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63 bfreader.close()
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64
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65 # The internal initialization of the BaSiC code fails when we invoke it via
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66 # scripting, unless we explicitly set a the private 'noOfSlices' field.
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67 # Since it's private, we need to use Java reflection to access it.
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68 Basic_noOfSlices = Basic.getDeclaredField('noOfSlices')
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69 Basic_noOfSlices.setAccessible(True)
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70 basic = Basic()
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71 Basic_noOfSlices.setInt(basic, num_images)
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72
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73 # Pre-allocate the output profile images, since we have all the dimensions.
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74 ff_image = IJ.createImage("Flat-field", width, height, num_channels, 32);
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75 df_image = IJ.createImage("Dark-field", width, height, num_channels, 32);
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76
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77 print("\n\n")
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78
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79 # BaSiC works on one channel at a time, so we only read the images from one
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80 # channel at a time to limit memory usage.
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81 for channel in range(num_channels):
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82 print "Processing channel %d/%d..." % (channel + 1, num_channels)
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83 print "==========================="
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84
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85 options = ImporterOptions()
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86 options.id = str(filename)
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87 options.setOpenAllSeries(True)
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88 # concatenate=True gives us a single stack rather than a list of
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89 # separate images.
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90 options.setConcatenate(True)
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91 # Limit the reader to the channel we're currently working on. This loop
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92 # is mainly why we need to know num_images before opening anything.
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93 for i in range(num_images):
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94 options.setCBegin(i, channel)
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95 options.setCEnd(i, channel)
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96 # openImagePlus returns a list of images, but we expect just one (a
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97 # stack).
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98 input_image = BF.openImagePlus(options)[0]
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99
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100 # BaSiC seems to require the input image is actually the ImageJ
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101 # "current" image, otherwise it prints an error and aborts.
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102 WindowManager.setTempCurrentImage(input_image)
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103 basic.exec(
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104 input_image, None, None,
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105 "Estimate shading profiles", "Estimate both flat-field and dark-field",
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106 lambda_estimate, lambda_flat, lambda_dark,
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107 "Ignore", "Compute shading only"
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108 )
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109 input_image.close()
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110
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111 # Copy the pixels from the BaSiC-generated profile images to the
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112 # corresponding channel of our output images.
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113 ff_channel = WindowManager.getImage("Flat-field:%s" % input_image.title)
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114 ff_image.slice = channel + 1
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115 ff_image.getProcessor().insert(ff_channel.getProcessor(), 0, 0)
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116 ff_channel.close()
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117 df_channel = WindowManager.getImage("Dark-field:%s" % input_image.title)
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118 df_image.slice = channel + 1
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119 df_image.getProcessor().insert(df_channel.getProcessor(), 0, 0)
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120 df_channel.close()
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121
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122 print("\n\n")
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123
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124 template = '%s/%s-%%s.tif' % (output_dir, experiment_name)
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125 ff_filename = template % 'ffp'
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126 IJ.saveAsTiff(ff_image, ff_filename)
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127 ff_image.close()
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128 df_filename = template % 'dfp'
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129 IJ.saveAsTiff(df_image, df_filename)
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130 df_image.close()
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131
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132 print "Done!"
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133
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134
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135 main()