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)
def __init__(self, *args, **kwargs):
super(OpFeatureSelection, self).__init__(*args, **kwargs)
# Create the cache
self.opPixelFeatureCache = OpSlicedBlockedArrayCache(parent=self)
self.opPixelFeatureCache.name = "opPixelFeatureCache"
# Connect the cache to the feature output
self.opPixelFeatureCache.Input.connect(self.OutputImage)
self.opPixelFeatureCache.fixAtCurrent.setValue(False)
def setUp(self):
self.dataShape = (1,100,100,10,1)
self.data = (numpy.random.random(self.dataShape) * 100).astype(int)
self.data = self.data.view(vigra.VigraArray)
self.data.axistags = vigra.defaultAxistags('txyzc')
graph = Graph()
opProvider = OpArrayPiperWithAccessCount(graph=graph)
opProvider.Input.setValue(self.data)
self.opProvider = opProvider
opCache = OpSlicedBlockedArrayCache(graph=graph)
opCache.Input.connect(opProvider.Output)
opCache.innerBlockShape.setValue( ( (10,1,10,10,10), (10,10,1,10,10), (10,10,10,1,10) ) )
opCache.outerBlockShape.setValue( ( (20,2,20,20,20), (20,20,2,20,20), (20,20,20,2,20) ) )
opCache.fixAtCurrent.setValue(False)
self.opCache = opCache
def __init__(self, *args, **kwargs):
super(OpFeatureSelection, self).__init__(*args, **kwargs)
# Two internal operators: features and cache
self.opPixelFeatures = OpPixelFeaturesPresmoothed(parent=self)
self.opPixelFeatureCache = OpSlicedBlockedArrayCache(parent=self)
self.opPixelFeatureCache.name = "opPixelFeatureCache"
# Connect the cache to the feature output
self.opPixelFeatureCache.Input.connect(self.opPixelFeatures.Output)
self.opPixelFeatureCache.fixAtCurrent.setValue(False)
# Connect our internal operators to our external inputs
self.opPixelFeatures.Scales.connect( self.Scales )
self.opPixelFeatures.FeatureIds.connect( self.FeatureIds )
self.opPixelFeatures.Matrix.connect( self.SelectionMatrix )
self.opPixelFeatures.Input.connect( self.InputImage )
# Connect our external outputs to our internal operators
self.OutputImage.connect( self.opPixelFeatures.Output )
self.CachedOutputImage.connect( self.opPixelFeatureCache.Output )
self.FeatureLayers.connect( self.opPixelFeatures.Features )
def setupOperators(self, *args, **kwargs):
self.predictors = []
self.prediction_caches = []
#niter = len(self.Classifiers)
niter = self.AutocontextIterations.value
for i in range(niter):
#predict = OperatorWrapper(OpPredictRandomForest, parent=self, parent=self)
predict = OpPredictRandomForest(parent=self)
self.predictors.append(predict)
#prediction_cache = OperatorWrapper( OpSlicedBlockedArrayCache, parent=self, parent=self )
prediction_cache = OpSlicedBlockedArrayCache(parent=self)
self.prediction_caches.append(prediction_cache)
# Setup autocontext features
self.autocontextFeatures = []
self.autocontextFeaturesMulti = []
self.autocontext_caches = []
self.featureStackers = []
for i in range(niter - 1):
features = createAutocontextFeatureOperators(self, False)
self.autocontextFeatures.append(features)
opMulti = Op50ToMulti(parent=self)
self.autocontextFeaturesMulti.append(opMulti)
opStacker = OpMultiArrayStacker(parent=self)
opStacker.inputs["AxisFlag"].setValue("c")
opStacker.inputs["AxisIndex"].setValue(3)
self.featureStackers.append(opStacker)
autocontext_cache = OpSlicedBlockedArrayCache(parent=self)
self.autocontext_caches.append(autocontext_cache)
# connect the features to predictors
for i in range(niter - 1):
for ifeat, feat in enumerate(self.autocontextFeatures[i]):
feat.inputs['Input'].connect(self.prediction_caches[i].Output)
print "Multi: Connecting an output", "Input%.2d" % (ifeat)
self.autocontextFeaturesMulti[i].inputs[
"Input%.2d" % (ifeat)].connect(feat.outputs["Output"])
# connect the pixel features to the same multislot
print "Multi: Connecting an output", "Input%.2d" % (len(
self.autocontextFeatures[i]))
self.autocontextFeaturesMulti[i].inputs["Input%.2d" % (len(
self.autocontextFeatures[i]))].connect(self.FeatureImage)
# stack the autocontext features with pixel features
self.featureStackers[i].inputs["Images"].connect(
self.autocontextFeaturesMulti[i].outputs["Outputs"])
# cache the stacks
self.autocontext_caches[i].inputs["Input"].connect(
self.featureStackers[i].outputs["Output"])
self.autocontext_caches[i].inputs["fixAtCurrent"].setValue(False)
for i in range(niter):
self.predictors[i].inputs['Classifier'].connect(
self.Classifiers[i])
self.predictors[i].inputs['LabelsCount'].connect(
self.MaxLabelValue)
self.prediction_caches[i].inputs["fixAtCurrent"].setValue(False)
self.prediction_caches[i].inputs["Input"].connect(
self.predictors[i].PMaps)
self.predictors[0].inputs['Image'].connect(self.FeatureImage)
for i in range(1, niter):
self.predictors[i].inputs['Image'].connect(
self.autocontext_caches[i - 1].outputs["Output"])
#self.PixelOnlyPredictions.connect(self.predictors[-1].PMaps)
self.PredictionProbabilities.connect(self.predictors[0].PMaps)
def __init__(self, *args, **kwargs):
super(OpVigraWatershedViewer, self).__init__(*args, **kwargs)
self._seedThreshold = None
# Overview Schematic
# Example here uses input channels 0,2,5
# InputChannelIndexes=[0,2,5] ----
# \
# InputImage --> opChannelSlicer .Slices[0] ---\
# .Slices[1] ----> opAverage -------------------------------------------------> opWatershed --> opWatershedCache --> opColorizer --> GUI
# .Slices[2] ---/ \ /
# \ MinSeedSize /
# \ \ /
# SeedThresholdValue ----------> opThreshold --> opSeedLabeler --> opSeedFilter --> opSeedCache --> opSeedColorizer --> GUI
# Create operators
self.opChannelSlicer = OpMultiArraySlicer2(parent=self)
self.opAverage = OpMultiArrayMerger(parent=self)
self.opWatershed = OpVigraWatershed(parent=self)
self.opWatershedCache = OpSlicedBlockedArrayCache(parent=self)
self.opColorizer = OpColorizeLabels(parent=self)
self.opThreshold = OpPixelOperator(parent=self)
self.opSeedLabeler = OpVigraLabelVolume(parent=self)
self.opSeedFilter = OpFilterLabels(parent=self)
self.opSeedCache = OpSlicedBlockedArrayCache(parent=self)
self.opSeedColorizer = OpColorizeLabels(parent=self)
# Select specific input channels
self.opChannelSlicer.Input.connect(self.InputImage)
self.opChannelSlicer.SliceIndexes.connect(self.InputChannelIndexes)
self.opChannelSlicer.AxisFlag.setValue('c')
# Average selected channels
def average(arrays):
if len(arrays) == 0:
return 0
else:
return sum(arrays) / float(len(arrays))
self.opAverage.MergingFunction.setValue(average)
self.opAverage.Inputs.connect(self.opChannelSlicer.Slices)
# Threshold for seeds
self.opThreshold.Input.connect(self.opAverage.Output)
# Label seeds
self.opSeedLabeler.Input.connect(self.opThreshold.Output)
# Filter seeds
self.opSeedFilter.MinLabelSize.connect(self.MinSeedSize)
self.opSeedFilter.Input.connect(self.opSeedLabeler.Output)
# Cache seeds
self.opSeedCache.fixAtCurrent.connect(self.FreezeCache)
self.opSeedCache.Input.connect(self.opSeedFilter.Output)
# Color seeds for RBG display
self.opSeedColorizer.Input.connect(self.opSeedCache.Output)
self.opSeedColorizer.OverrideColors.setValue({0: (0, 0, 0, 0)})
# Compute watershed labels (possibly with seeds, see setupOutputs)
self.opWatershed.InputImage.connect(self.opAverage.Output)
self.opWatershed.PaddingWidth.connect(self.WatershedPadding)
# Cache the watershed output
self.opWatershedCache.fixAtCurrent.connect(self.FreezeCache)
self.opWatershedCache.Input.connect(self.opWatershed.Output)
# Colorize the watershed labels for RGB display
self.opColorizer.Input.connect(self.opWatershedCache.Output)
self.opColorizer.OverrideColors.connect(self.OverrideLabels)
# Connnect external outputs the operators that provide them
self.Seeds.connect(self.opSeedCache.Output)
self.ColoredPixels.connect(self.opColorizer.Output)
self.SelectedInputChannels.connect(self.opChannelSlicer.Slices)
self.SummedInput.connect(self.opAverage.Output)
self.ColoredSeeds.connect(self.opSeedColorizer.Output)
