def __init__(self, *args, **kwargs):
super(OpPredictionPipelineNoCache, self).__init__(*args, **kwargs)
# Random forest prediction using the raw feature image slot (not the cached features)
# This would be bad for interactive labeling, but it's good for headless flows
# because it avoids the overhead of cache.
self.cacheless_predict = OpPredictCounter(parent=self)
self.cacheless_predict.name = "OpPredictCounter (Cacheless Path)"
self.cacheless_predict.inputs['Classifier'].connect(self.Classifier)
self.cacheless_predict.inputs['Image'].connect(
self.FeatureImages) # <--- Not from cache
self.cacheless_predict.inputs['LabelsCount'].connect(self.MaxLabel)
self.meaner = OpMean(parent=self)
self.meaner.Input.connect(self.cacheless_predict.PMaps)
self.HeadlessPredictionProbabilities.connect(self.meaner.Output)
def __init__(self, *args, **kwargs):
super(OpPredictionPipeline, self).__init__(*args, **kwargs)
# Random forest prediction using CACHED features.
self.predict = OpPredictCounter(parent=self)
self.predict.name = "OpPredictCounter"
self.predict.inputs['Classifier'].connect(self.Classifier)
self.predict.inputs['Image'].connect(self.CachedFeatureImages)
self.predict.inputs['LabelsCount'].connect(self.MaxLabel)
self.PredictionProbabilities.connect(self.predict.PMaps)
# Prediction cache for the GUI
self.prediction_cache_gui = OpArrayCache(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.prediction_cache_gui.blockShape.setValue(128)
## Also provide each prediction channel as a separate layer (for the GUI)
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 = OpArrayCache(parent=self)
self.opUncertaintyCache.name = "opUncertaintyCache"
self.opUncertaintyCache.blockShape.setValue(128)
self.opUncertaintyCache.Input.connect(
self.opUncertaintyEstimator.Output)
self.opUncertaintyCache.fixAtCurrent.connect(self.FreezePredictions)
self.UncertaintyEstimate.connect(self.opUncertaintyCache.Output)
self.meaner = OpMean(parent=self)
self.meaner.Input.connect(self.prediction_cache_gui.Output)
self.precomputed_predictions_gui = OpPrecomputedInput(
ignore_dirty_input=False, parent=self)
self.precomputed_predictions_gui.name = "precomputed_predictions_gui"
self.precomputed_predictions_gui.SlowInput.connect(self.meaner.Output)
self.precomputed_predictions_gui.PrecomputedInput.connect(
self.PredictionsFromDisk)
self.CachedPredictionProbabilities.connect(
self.precomputed_predictions_gui.Output)
def __init__(self, *args, **kwargs):
super(OpPredictionPipelineNoCache, self).__init__(*args, **kwargs)
# Random forest prediction using the raw feature image slot (not the cached features)
# This would be bad for interactive labeling, but it's good for headless flows
# because it avoids the overhead of cache.
self.cacheless_predict = OpPredictCounter(parent=self)
self.cacheless_predict.name = "OpPredictCounter (Cacheless Path)"
self.cacheless_predict.inputs['Classifier'].connect(self.Classifier)
self.cacheless_predict.inputs['Image'].connect(
self.FeatureImages) # <--- Not from cache
self.cacheless_predict.inputs['LabelsCount'].connect(self.MaxLabel)
self.HeadlessPredictionProbabilities.connect(
self.cacheless_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.cacheless_predict.PMaps)
self.opConvertToUint8.Function.setValue(lambda a:
(255 * a).astype(numpy.uint8))
self.HeadlessUint8PredictionProbabilities.connect(
self.opConvertToUint8.Output)