![]() ![]() backgroundLabel(0, ColorTools.WHITE) // Specify background label (usually 0 or 255). getName ()) def pathOutput = buildFilePath ( PROJECT_BASE_DIR, 'tiles', name ) mkdirs ( pathOutput ) // Define output resolution double requestedPixelSize = 10.0 // Convert to downsample double downsample = requestedPixelSize / imageData.getServer().getPixelCalibration().getAveragedPixelSize() // Create an ImageServer where the pixels are derived from annotations def labelServer = new LabeledImageServer.Builder(imageData). Import .LabeledImageServer def imageData = getCurrentImageData () // Define output path (relative to project) def name = GeneralTools. ![]() build () // Write the image writeImage ( labelServer, path ) multichannelOutput ( false ) // If true, each label refers to the channel of a multichannel binary image (required for multiclass probability). addLabel ( 'Tumor', 1 ) // Choose output labels (the order matters!). downsample ( downsample ) // Choose server resolution this should match the resolution at which tiles are exported. WHITE ) // Specify background label (usually 0 or 255). ![]() getName ()) def path = buildFilePath ( outputDir, name + "-labels.png" ) // Define how much to downsample during export (may be required for large images) double downsample = 8 // Create an ImageServer where the pixels are derived from annotations def labelServer = new LabeledImageServer. ignore the corresponding annotations).ĭef imageData = getCurrentImageData () // Define output path (relative to project) def outputDir = buildFilePath ( PROJECT_BASE_DIR, 'export' ) mkdirs ( outputDir ) def name = GeneralTools. The multichannelOutput option controls whether the image will be binary (if true) or labeled (if false).įinally, the builder makes it possible to assign distinct classifications within the image to have the same label in the output, and also to skip particular classifications (i.e. The builder also makes if possible to define the background label for unannotated pixels (here, 0) and even specify that the boundaries of annotations are assigned a different class to the ‘filled’ areas – in addition to how thick those boundaries should be. Of note, the labels provided to the builder correspond to QuPath classifications and the integer value in the output image. Not all options need to be provided, in which case defaults will be used. The builder pattern used to create the LabeledImageServer makes it possible to tune the output. build() / / Export each region int i = 0 for ( annotation in getAnnotationObjects ()) multichannelOutput(false) // If true, each label refers to the channel of a multichannel binary image (required for multiclass probability). setBoundaryLabel('Boundary*', 4) // Define annotation boundary label. lineThickness(2) // Optionally export annotation boundaries with another label. ![]() addLabel('Tumor', 1) // Choose output labels (the order matters!). downsample(downsample) // Choose server resolution this should match the resolution at which tiles are exported. getName ()) def pathOutput = buildFilePath ( PROJECT_BASE_DIR, 'export', name ) mkdirs ( pathOutput ) // Define output resolution double requestedPixelSize = 2.0 // Convert to downsample double downsample = requestedPixelSize / imageData.getServer().getPixelCalibration().getAveragedPixelSize() // Create an ImageServer where the pixels are derived from annotations def labelServer = new LabeledImageServer.Builder(imageData). ![]()
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