def __init__(self, *args, **kwargs):
super(OpDLClassification, self).__init__(*args, **kwargs)
self.predict = OpPixelwiseClassifierPredict(parent=self)
self.predict.name = "OpClassifierPredict"
self.predict.Image.connect(self.InputImage)
self.predict.Classifier.connect(self.Classifier)
self.predict.LabelsCount.connect(self.NumClasses)
self.PredictionProbabilities.connect(self.predict.PMaps)
self.prediction_cache = OpBlockedArrayCache(parent=self)
self.prediction_cache.name = "BlockedArrayCache"
self.prediction_cache.inputs["Input"].connect(self.predict.PMaps)
self.prediction_cache.BlockShape.connect(self.BlockShape)
self.prediction_cache.inputs["fixAtCurrent"].connect(self.FreezePredictions)
self.CachedPredictionProbabilities.connect(self.prediction_cache.Output)
self.opPredictionSlicer = OpMultiArraySlicer2(parent=self)
self.opPredictionSlicer.name = "opPredictionSlicer"
self.opPredictionSlicer.Input.connect(self.prediction_cache.Output)
self.opPredictionSlicer.AxisFlag.setValue("c")
self.PredictionProbabilityChannels.connect(self.opPredictionSlicer.Slices)
self.opSegmentor = OpMaxChannelIndicatorOperator(parent=self) # IMPROVEME: this "max channel" works for binary classification, but not when we have more than 2 classes
self.opSegmentor.Input.connect(self.prediction_cache.Output)
self.Segmentation.connect(self.opSegmentor.Output)
self.opSegmentationSlicer = OpMultiArraySlicer2(parent=self)
self.opSegmentationSlicer.name = "opSegmentationSlicer"
self.opSegmentationSlicer.Input.connect(self.opSegmentor.Output)
self.opSegmentationSlicer.AxisFlag.setValue("c")
self.SegmentationChannels.connect(self.opSegmentationSlicer.Slices)
def __init__(self, *args, **kwargs):
super(OpPredictionPipeline, self).__init__(*args, **kwargs)
# Random forest prediction using CACHED features.
self.predict = OpClassifierPredict(parent=self)
self.predict.name = "OpClassifierPredict"
self.predict.Classifier.connect(self.Classifier)
self.predict.Image.connect(self.CachedFeatureImages)
self.predict.PredictionMask.connect(self.PredictionMask)
self.predict.LabelsCount.connect(self.NumClasses)
self.PredictionProbabilities.connect(self.predict.PMaps)
# Alternate headless output: uint8 instead of float.
# Note that drange is automatically updated.
self.opConvertToUint8 = OpPixelOperator(parent=self)
self.opConvertToUint8.Input.connect(self.predict.PMaps)
self.opConvertToUint8.Function.setValue(lambda a:
(255 * a).astype(numpy.uint8))
self.PredictionProbabilitiesUint8.connect(self.opConvertToUint8.Output)
# Prediction cache for the GUI
self.prediction_cache_gui = OpSlicedBlockedArrayCache(parent=self)
self.prediction_cache_gui.name = "prediction_cache_gui"
self.prediction_cache_gui.inputs["fixAtCurrent"].connect(
self.FreezePredictions)
self.prediction_cache_gui.inputs["Input"].connect(self.predict.PMaps)
self.CachedPredictionProbabilities.connect(
self.prediction_cache_gui.Output)
# Also provide each prediction channel as a separate layer (for the GUI)
self.opPredictionSlicer = OpMultiArraySlicer2(parent=self)
self.opPredictionSlicer.name = "opPredictionSlicer"
self.opPredictionSlicer.Input.connect(self.prediction_cache_gui.Output)
self.opPredictionSlicer.AxisFlag.setValue('c')
self.PredictionProbabilityChannels.connect(
self.opPredictionSlicer.Slices)
self.opSegmentor = OpMaxChannelIndicatorOperator(parent=self)
self.opSegmentor.Input.connect(self.prediction_cache_gui.Output)
self.opSegmentationSlicer = OpMultiArraySlicer2(parent=self)
self.opSegmentationSlicer.name = "opSegmentationSlicer"
self.opSegmentationSlicer.Input.connect(self.opSegmentor.Output)
self.opSegmentationSlicer.AxisFlag.setValue('c')
self.SegmentationChannels.connect(self.opSegmentationSlicer.Slices)
# Create a layer for uncertainty estimate
self.opUncertaintyEstimator = OpEnsembleMargin(parent=self)
self.opUncertaintyEstimator.Input.connect(
self.prediction_cache_gui.Output)
# Cache the uncertainty so we get zeros for uncomputed points
self.opUncertaintyCache = OpSlicedBlockedArrayCache(parent=self)
self.opUncertaintyCache.name = "opUncertaintyCache"
self.opUncertaintyCache.Input.connect(
self.opUncertaintyEstimator.Output)
self.opUncertaintyCache.fixAtCurrent.connect(self.FreezePredictions)
self.UncertaintyEstimate.connect(self.opUncertaintyCache.Output)
def __init__(self, *args, **kwargs):
super(OpPredictionPipeline, self).__init__(*args, **kwargs)
# Random forest prediction using CACHED features.
self.predict = OpClassifierPredict(parent=self)
self.predict.name = "OpClassifierPredict"
self.predict.Classifier.connect(self.Classifier)
self.predict.Image.connect(self.CachedFeatureImages)
self.predict.PredictionMask.connect(self.PredictionMask)
self.predict.LabelsCount.connect(self.NumClasses)
self.PredictionProbabilities.connect(self.predict.PMaps)
# Prepare operator for Autocontext
self.opConvertPMapsToInputPixelType = OpPixelOperator(parent=self)
self.opConvertPMapsToInputPixelType.Input.connect(self.predict.PMaps)
self.PredictionProbabilitiesAutocontext.connect(
self.opConvertPMapsToInputPixelType.Output)
# Prediction cache for the GUI
self.prediction_cache_gui = OpSlicedBlockedArrayCache(parent=self)
self.prediction_cache_gui.name = "prediction_cache_gui"
self.prediction_cache_gui.inputs["fixAtCurrent"].connect(
self.FreezePredictions)
self.prediction_cache_gui.inputs["Input"].connect(self.predict.PMaps)
self.CachedPredictionProbabilities.connect(
self.prediction_cache_gui.Output)
# Also provide each prediction channel as a separate layer (for the GUI)
self.opPredictionSlicer = OpMultiArraySlicer2(parent=self)
self.opPredictionSlicer.name = "opPredictionSlicer"
self.opPredictionSlicer.Input.connect(self.prediction_cache_gui.Output)
self.opPredictionSlicer.AxisFlag.setValue("c")
self.PredictionProbabilityChannels.connect(
self.opPredictionSlicer.Slices)
self.opSegmentor = OpMaxChannelIndicatorOperator(parent=self)
self.opSegmentor.Input.connect(self.prediction_cache_gui.Output)
self.opSegmentationSlicer = OpMultiArraySlicer2(parent=self)
self.opSegmentationSlicer.name = "opSegmentationSlicer"
self.opSegmentationSlicer.Input.connect(self.opSegmentor.Output)
self.opSegmentationSlicer.AxisFlag.setValue("c")
self.SegmentationChannels.connect(self.opSegmentationSlicer.Slices)
# Create a layer for uncertainty estimate
self.opUncertaintyEstimator = OpEnsembleMargin(parent=self)
self.opUncertaintyEstimator.Input.connect(
self.prediction_cache_gui.Output)
# Cache the uncertainty so we get zeros for uncomputed points
self.opUncertaintyCache = OpSlicedBlockedArrayCache(parent=self)
self.opUncertaintyCache.name = "opUncertaintyCache"
self.opUncertaintyCache.Input.connect(
self.opUncertaintyEstimator.Output)
self.opUncertaintyCache.fixAtCurrent.connect(self.FreezePredictions)
self.UncertaintyEstimate.connect(self.opUncertaintyCache.Output)