def __init__(self, block_roi, halo_padding, *args, **kwargs): super(self.__class__, self).__init__(*args, **kwargs) self.block_roi = block_roi # In global coordinates self._halo_padding = halo_padding self._opBinarySubRegion = OpSubRegion(parent=self) self._opBinarySubRegion.Input.connect(self.BinaryImage) self._opRawSubRegion = OpSubRegion(parent=self) self._opRawSubRegion.Input.connect(self.RawImage) self._opExtract = OpObjectExtraction(parent=self) self._opExtract.BinaryImage.connect(self._opBinarySubRegion.Output) self._opExtract.RawImage.connect(self._opRawSubRegion.Output) self._opExtract.Features.connect(self.SelectedFeatures) self.BlockwiseRegionFeatures.connect( self._opExtract.BlockwiseRegionFeatures) self._opExtract._opRegFeats._opCache.name = "blockwise-regionfeats-cache" self._opPredict = OpObjectPredict(parent=self) self._opPredict.Features.connect(self._opExtract.RegionFeatures) self._opPredict.SelectedFeatures.connect(self.SelectedFeatures) self._opPredict.Classifier.connect(self.Classifier) self._opPredict.LabelsCount.connect(self.LabelsCount) self.ObjectwisePredictions.connect(self._opPredict.Predictions) self._opPredictionImage = OpRelabelSegmentation(parent=self) self._opPredictionImage.Image.connect(self._opExtract.LabelImage) self._opPredictionImage.Features.connect( self._opExtract.RegionFeatures) self._opPredictionImage.ObjectMap.connect(self._opPredict.Predictions) self._opPredictionCache = OpArrayCache(parent=self) self._opPredictionCache.Input.connect(self._opPredictionImage.Output) self._opProbabilityChannelsToImage = OpMultiRelabelSegmentation( parent=self) self._opProbabilityChannelsToImage.Image.connect( self._opExtract.LabelImage) self._opProbabilityChannelsToImage.ObjectMaps.connect( self._opPredict.ProbabilityChannels) self._opProbabilityChannelsToImage.Features.connect( self._opExtract.RegionFeatures) self._opProbabilityChannelStacker = OpMultiArrayStacker(parent=self) self._opProbabilityChannelStacker.Images.connect( self._opProbabilityChannelsToImage.Output) self._opProbabilityChannelStacker.AxisFlag.setValue('c') self._opProbabilityCache = OpArrayCache(parent=self) self._opProbabilityCache.Input.connect( self._opProbabilityChannelStacker.Output)
def __init__(self, *args, **kwargs): super(OpPreprocessing, self).__init__(*args, **kwargs) self._prepData = [None] self.applet = self.parent.parent.preprocessingApplet self._unsavedData = False # set to True if data is not yet saved self._dirty = False # set to True if any Input is dirty self.initialSigma = None # save settings of last preprocess self.initialFilter = None # applied to gui by pressing reset self._opFilter = OpFilter(parent=self) self._opFilter.Input.connect(self.InputData) self._opFilter.Sigma.connect(self.Sigma) self._opFilter.Filter.connect(self.Filter) self._opFilterNormalize = OpNormalize255(parent=self) self._opFilterNormalize.Input.connect(self._opFilter.Output) self._opFilterCache = OpArrayCache(parent=self) self._opWatershed = OpSimpleWatershed(parent=self) self._opWatershedCache = OpArrayCache(parent=self) self._opOverlayFilter = OpFilter(parent=self) self._opOverlayFilter.Input.connect(self.OverlayData) self._opOverlayFilter.Sigma.connect(self.Sigma) self._opOverlayNormalize = OpNormalize255(parent=self) self._opOverlayNormalize.Input.connect(self._opOverlayFilter.Output) self._opInputFilter = OpFilter(parent=self) self._opInputFilter.Input.connect(self.InputData) self._opInputFilter.Sigma.connect(self.Sigma) self._opInputNormalize = OpNormalize255(parent=self) self._opInputNormalize.Input.connect(self._opInputFilter.Output) self._opMstProvider = OpMstSegmentorProvider(self.applet, parent=self) self._opMstProvider.Image.connect(self._opFilterCache.Output) self._opMstProvider.LabelImage.connect(self._opWatershedCache.Output) self._opWatershedSourceCache = OpArrayCache(parent=self) #self.PreprocessedData.connect( self._opMstProvider.MST ) # Display slots self.FilteredImage.connect(self._opFilterCache.Output) self.WatershedImage.connect(self._opWatershedCache.Output) self.InputData.notifyReady(self._checkConstraints)
def __init__(self, *args, **kwargs): super(OpNansheGenerateDictionaryCached, self).__init__(*args, **kwargs) self.opDictionary = OpNansheGenerateDictionary(parent=self) self.opDictionary.K.connect(self.K) self.opDictionary.Gamma1.connect(self.Gamma1) self.opDictionary.Gamma2.connect(self.Gamma2) self.opDictionary.NumThreads.connect(self.NumThreads) self.opDictionary.Batchsize.connect(self.Batchsize) self.opDictionary.NumIter.connect(self.NumIter) self.opDictionary.Lambda1.connect(self.Lambda1) self.opDictionary.Lambda2.connect(self.Lambda2) self.opDictionary.PosAlpha.connect(self.PosAlpha) self.opDictionary.PosD.connect(self.PosD) self.opDictionary.Clean.connect(self.Clean) self.opDictionary.Mode.connect(self.Mode) self.opDictionary.ModeD.connect(self.ModeD) self.opCache = OpArrayCache(parent=self) self.opCache.fixAtCurrent.setValue(False) self.opDictionary.Input.connect(self.Input) self.opCache.Input.connect(self.opDictionary.Output) self.CleanBlocks.connect(self.opCache.CleanBlocks) self.Output.connect(self.opCache.Output)
def test(self): """ Test use-case from https://github.com/ilastik/lazyflow/issues/111 """ data = numpy.zeros((20, 20)) data = numpy.ma.masked_array(data, mask=numpy.ma.getmaskarray(data), fill_value=numpy.nan, shrink=False) data[...] = numpy.ma.masked op = OpArrayCache(graph=Graph()) op.Input.meta.axistags = vigra.defaultAxistags('xy') op.Input.meta.has_mask = True op.Input.setValue(data) result_before = op.Output[0:20, 0:20].wait() assert result_before.astype(bool).filled(True).all() assert (result_before.mask == True).all() assert numpy.isnan(result_before.fill_value) # Should not crash... new_data = numpy.ones((20, 20)) new_data = numpy.ma.masked_array(new_data, mask=numpy.ma.getmaskarray(new_data), fill_value=0, shrink=False) op.Input[0:20, 0:20] = new_data result_after = op.Output[0:20, 0:20].wait() assert (result_after == 1).all() assert (result_after.mask == False).all() assert (result_after.fill_value == 0).all()
def test(self): class SpecialNumber(object): def __init__(self, x): self.n = x data = numpy.ndarray(shape=(2, 3), dtype=object) data = numpy.ma.masked_array(data, mask=numpy.ma.getmaskarray(data), fill_value=None, shrink=False) for i in range(2): for j in range(3): data[i, j] = SpecialNumber(i * j) data[1, 1] = numpy.ma.masked graph = Graph() op = OpArrayCache(graph=graph) op.Input.meta.axistags = vigra.defaultAxistags('tc') op.Input.meta.has_mask = True op.Input.setValue(data) op.blockShape.setValue((1, 3)) assert op.Output.meta.shape == (2, 3) assert op.Output.meta.has_mask == True outputData = op.Output[...].wait() outputData2 = numpy.ma.masked_array(data, mask=numpy.ma.getmaskarray(data), fill_value=None, shrink=False) op.Output[...].writeInto(outputData2).wait() assert (outputData == data).all() assert (outputData.mask == data.mask).all() assert (outputData.fill_value == data.fill_value)
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(OpNanshePostprocessDataCached, self).__init__(*args, **kwargs) self.opPostprocessing = OpNanshePostprocessData(parent=self) self.opPostprocessing.SignificanceThreshold.connect( self.SignificanceThreshold) self.opPostprocessing.WaveletTransformScale.connect( self.WaveletTransformScale) self.opPostprocessing.NoiseThreshold.connect(self.NoiseThreshold) self.opPostprocessing.AcceptedRegionShapeConstraints_MajorAxisLength_Min.connect( self.AcceptedRegionShapeConstraints_MajorAxisLength_Min) self.opPostprocessing.AcceptedRegionShapeConstraints_MajorAxisLength_Min_Enabled.connect( self.AcceptedRegionShapeConstraints_MajorAxisLength_Min_Enabled) self.opPostprocessing.AcceptedRegionShapeConstraints_MajorAxisLength_Max.connect( self.AcceptedRegionShapeConstraints_MajorAxisLength_Max) self.opPostprocessing.AcceptedRegionShapeConstraints_MajorAxisLength_Max_Enabled.connect( self.AcceptedRegionShapeConstraints_MajorAxisLength_Max_Enabled) self.opPostprocessing.PercentagePixelsBelowMax.connect( self.PercentagePixelsBelowMax) self.opPostprocessing.MinLocalMaxDistance.connect( self.MinLocalMaxDistance) self.opPostprocessing.AcceptedNeuronShapeConstraints_Area_Min.connect( self.AcceptedNeuronShapeConstraints_Area_Min) self.opPostprocessing.AcceptedNeuronShapeConstraints_Area_Min_Enabled.connect( self.AcceptedNeuronShapeConstraints_Area_Min_Enabled) self.opPostprocessing.AcceptedNeuronShapeConstraints_Area_Max.connect( self.AcceptedNeuronShapeConstraints_Area_Max) self.opPostprocessing.AcceptedNeuronShapeConstraints_Area_Max_Enabled.connect( self.AcceptedNeuronShapeConstraints_Area_Max_Enabled) self.opPostprocessing.AcceptedNeuronShapeConstraints_Eccentricity_Min.connect( self.AcceptedNeuronShapeConstraints_Eccentricity_Min) self.opPostprocessing.AcceptedNeuronShapeConstraints_Eccentricity_Min_Enabled.connect( self.AcceptedNeuronShapeConstraints_Eccentricity_Min_Enabled) self.opPostprocessing.AcceptedNeuronShapeConstraints_Eccentricity_Max.connect( self.AcceptedNeuronShapeConstraints_Eccentricity_Max) self.opPostprocessing.AcceptedNeuronShapeConstraints_Eccentricity_Max_Enabled.connect( self.AcceptedNeuronShapeConstraints_Eccentricity_Max_Enabled) self.opPostprocessing.AlignmentMinThreshold.connect( self.AlignmentMinThreshold) self.opPostprocessing.OverlapMinThreshold.connect( self.OverlapMinThreshold) self.opPostprocessing.Fuse_FractionMeanNeuronMaxThreshold.connect( self.Fuse_FractionMeanNeuronMaxThreshold) self.opCache = OpArrayCache(parent=self) self.opCache.fixAtCurrent.setValue(False) self.CleanBlocks.connect(self.opCache.CleanBlocks) self.opPostprocessing.Input.connect(self.Input) self.opCache.Input.connect(self.opPostprocessing.Output) self.Output.connect(self.opCache.Output) self.opColorizeLabelImage = OpColorizeLabelImage(parent=self) self.opColorizeLabelImage.Input.connect(self.Output) self.ColorizedOutput.connect(self.opColorizeLabelImage.Output)
def test(self): """ Test use-case from https://github.com/ilastik/lazyflow/issues/111 """ data = numpy.zeros((20, 20)) data = vigra.taggedView(data, 'xy') op = OpArrayCache(graph=Graph()) op.Input.setValue(data) # Should not crash... op.Input[0:20, 0:20] = numpy.ones((20, 20))
def __init__(self, *args, **kwargs): super(OpMaskedWatershed, self).__init__(*args, **kwargs) # Use an internal operator to prepare the data, # for easy caching/parallelization. self._opPrepInput = _OpPrepWatershedInput(parent=self) self._opPrepInput.Input.connect(self.Input) self._opPrepInput.Mask.connect(self.Mask) self._opPreppedInputCache = OpArrayCache(parent=self) self._opPreppedInputCache.Input.connect(self._opPrepInput.Output)
def testCleanup(self): try: ArrayCacheMemoryMgr.instance.pause() op = OpArrayCache(graph=self.opProvider.graph) op.Input.connect(self.opProvider.Output) x = op.Output[...].wait() op.Input.disconnect() op.cleanUp() r = weakref.ref(op) del op gc.collect() assert r() is None, "OpArrayCache was not cleaned up correctly" finally: ArrayCacheMemoryMgr.instance.unpause()
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 = OpArrayCache(graph=graph) opCache.Input.connect(opProvider.Output) opCache.blockShape.setValue((10, 10, 10, 10, 10)) opCache.fixAtCurrent.setValue(False) self.opCache = opCache
def __init__(self, *args, **kwargs): super(OpCachedRegionFeatures, self).__init__(*args, **kwargs) # Hook up the labeler self._opRegionFeatures = OpRegionFeatures(parent=self) self._opRegionFeatures.RawImage.connect(self.RawImage) self._opRegionFeatures.LabelImage.connect(self.LabelImage) self._opRegionFeatures.Features.connect(self.Features) # Hook up the cache. self._opCache = OpArrayCache(parent=self) self._opCache.Input.connect(self._opRegionFeatures.Output) # Hook up our output slots self.Output.connect(self._opCache.Output) self.CleanBlocks.connect(self._opCache.CleanBlocks)
def __init__(self, *args, **kwargs): super(OpCachedDivisionFeatures, self).__init__(*args, **kwargs) # Hook up the labeler self._opDivisionFeatures = OpDivisionFeatures(parent=self) self._opDivisionFeatures.LabelVolume.connect(self.LabelImage) self._opDivisionFeatures.DivisionFeatureNames.connect(self.DivisionFeatureNames) self._opDivisionFeatures.RegionFeaturesVigra.connect(self.RegionFeaturesVigra) # Hook up the cache. self._opCache = OpArrayCache(parent=self) self._opCache.name = "OpCachedDivisionFeatures._opCache" self._opCache.Input.connect(self._opDivisionFeatures.BlockwiseDivisionFeatures) # Hook up our output slots self.Output.connect(self._opCache.Output) self.CleanBlocks.connect(self._opCache.CleanBlocks)
def __init__(self, *args, **kwargs): super(OpNanshePreprocessDataCached, self).__init__(*args, **kwargs) self.opConvertType = OpConvertTypeCached(parent=self) self.opConvertType.Dtype.setValue(numpy.float32) self.opNansheRemoveZeroedLines = OpNansheRemoveZeroedLinesCached( parent=self) self.opNansheRemoveZeroedLines.ErosionShape.connect(self.ErosionShape) self.opNansheRemoveZeroedLines.DilationShape.connect( self.DilationShape) self.opNansheExtractF0 = OpNansheExtractF0Cached(parent=self) self.opNansheExtractF0.HalfWindowSize.connect(self.HalfWindowSize) self.opNansheExtractF0.WhichQuantile.connect(self.WhichQuantile) self.opNansheExtractF0.TemporalSmoothingGaussianFilterStdev.connect( self.TemporalSmoothingGaussianFilterStdev) self.opNansheExtractF0.SpatialSmoothingGaussianFilterStdev.connect( self.SpatialSmoothingGaussianFilterStdev) self.opNansheExtractF0.TemporalSmoothingGaussianFilterWindowSize.connect( self.TemporalSmoothingGaussianFilterWindowSize) self.opNansheExtractF0.SpatialSmoothingGaussianFilterWindowSize.connect( self.SpatialSmoothingGaussianFilterWindowSize) self.opNansheExtractF0.BiasEnabled.connect(self.BiasEnabled) self.opNansheExtractF0.Bias.connect(self.Bias) self.opNansheWaveletTransform = OpNansheWaveletTransformCached( parent=self) self.opNansheWaveletTransform.Scale.connect(self.Scale) self.OpNansheRemoveZeroedLinesOutput.connect( self.opNansheRemoveZeroedLines.Output) self.OpNansheExtractF0_dF_F_Output.connect(self.opNansheExtractF0.dF_F) self.OpNansheExtractF0_F0_Output.connect(self.opNansheExtractF0.F0) self.OpNansheWaveletTransformOutput.connect( self.opNansheWaveletTransform.Output) self.opCache = OpArrayCache(parent=self) self.opCache.fixAtCurrent.setValue(False) self.CleanBlocks.connect(self.opCache.CleanBlocks) self.CacheOutput.connect(self.opCache.Output)
def test(self): class SpecialNumber(object): def __init__(self, x): self.n = x data = numpy.ndarray(shape=(2, 3), dtype=object) data = data.view(vigra.VigraArray) data.axistags = vigra.defaultAxistags('tc') for i in range(2): for j in range(3): data[i, j] = SpecialNumber(i * j) graph = Graph() op = OpArrayCache(graph=graph) op.Input.setValue(data) op.blockShape.setValue((1, 3)) assert op.Output.meta.shape == (2, 3) outputData = op.Output[:].wait() # Can't use (outputData == data).all() here because vigra doesn't do the right thing if dtype is object. for x, y in zip(outputData.flat, data.flat): assert x == y
def setUp(self): self.dataShape = (1, 100, 100, 10, 1) self.data = (numpy.random.random(self.dataShape) * 100).astype(int) self.data = numpy.ma.masked_array(self.data, mask=numpy.ma.getmaskarray( self.data), fill_value=numpy.iinfo(int).max, shrink=False) self.data[:, 0] = numpy.ma.masked graph = Graph() opProvider = OpArrayPiperWithAccessCount(graph=graph) opProvider.Input.meta.axistags = vigra.defaultAxistags('txyzc') opProvider.Input.meta.has_mask = True opProvider.Input.setValue(self.data) self.opProvider = opProvider opCache = OpArrayCache(graph=graph) opCache.Input.connect(opProvider.Output) opCache.blockShape.setValue((10, 10, 10, 10, 10)) opCache.fixAtCurrent.setValue(False) self.opCache = opCache
shape = tuple(self._slot.meta.shape) axes = "".join(self._slot.meta.getAxisKeys()) dtype = self._slot.meta.dtype.__name__ else: shape = axes = dtype = "" if not sip.isdeleted(self.shapeEdit): self.shapeEdit.setText(str(shape)) self.axisOrderEdit.setText(axes) self.dtypeEdit.setText(dtype) if __name__ == "__main__": import numpy import vigra from PyQt4.QtGui import QApplication from lazyflow.graph import Graph from lazyflow.operators import OpArrayCache data = numpy.zeros((10, 20, 30, 3), dtype=numpy.float32) data = vigra.taggedView(data, 'zyxc') op = OpArrayCache(graph=Graph()) op.Input.setValue(data) app = QApplication([]) w = SlotMetaInfoDisplayWidget(None) w.initSlot(op.Output) w.show() app.exec_()