def __init__(self, workflow, title, is_batch=False, default_export_filename=''): self.export_op = None self._default_export_filename = default_export_filename self.__topLevelOperator = OpMultiLaneWrapper( OpTrackingBaseDataExport, parent=workflow, promotedSlotNames=set(['RawData', 'Inputs', 'RawDatasetInfo'])) extra_serial_slots = [ SerialSlot(self.topLevelOperator.SelectedPlugin), SerialSlot(self.topLevelOperator.SelectedExportSource), SerialDictSlot(self.topLevelOperator.AdditionalPluginArguments) ] self._serializers = [ DataExportSerializer(self.topLevelOperator, title, extra_serial_slots) ] super(TrackingBaseDataExportApplet, self).__init__(workflow, title, isBatch=is_batch)
def __init__( self, workflow, title, is_batch: bool = False, default_export_filename: str = "", pluginExportFunc: Optional[PluginExportCallable] = None, ): self.export_op = None self._default_export_filename = default_export_filename self.__topLevelOperator = OpMultiLaneWrapper( OpTrackingBaseDataExport, parent=workflow, promotedSlotNames=set(["RawData", "Inputs", "RawDatasetInfo"])) extra_serial_slots = [ SerialSlot(self.topLevelOperator.SelectedPlugin), SerialSlot(self.topLevelOperator.SelectedExportSource), SerialDictSlot(self.topLevelOperator.AdditionalPluginArguments), ] self._serializers = [ DataExportSerializer(self.topLevelOperator, title, extra_serial_slots) ] self._pluginExportFunc = pluginExportFunc super(TrackingBaseDataExportApplet, self).__init__(workflow, title, isBatch=is_batch)
def __init__(self, operator, projectFileGroupName): slots = [ SerialSlot(operator.Beta, selfdepends=True), SerialSlot(operator.SolverName, selfdepends=True) ] super(MulticutSerializer, self).__init__(projectFileGroupName, slots=slots)
def __init__(self, operator, projectFileGroupName): slots = [ SerialSlot(operator.MinValue, selfdepends=True), SerialSlot(operator.MaxValue, selfdepends=True) ] super(ThresholdMaskingSerializer, self).__init__(projectFileGroupName, slots=slots)
def __init__(self, operator, projectFileGroupName): slots = [ SerialSlot(operator.ScalingFactor, selfdepends=True), SerialSlot(operator.Offset, selfdepends=True) ] super(DeviationFromMeanSerializer, self).__init__(projectFileGroupName, slots=slots)
def __init__(self, operator, groupName): self.TestSerialSlot = SerialSlot(operator.TestSlot) self.TestMultiSerialSlot = SerialSlot(operator.TestMultiSlot) self.TestSerialListSlot = SerialListSlot(operator.TestListSlot, selfdepends=True) slots = (self.TestSerialSlot, self.TestMultiSerialSlot, self.TestSerialListSlot) super(OpMockSerializer, self).__init__(groupName, slots)
def __init__(self, topGroupName, topLevelOperator): slots = [ SerialSlot(topLevelOperator.PatchSize), SerialSlot(topLevelOperator.HaloSize) ] super(FillMissingSlicesSerializer, self).__init__(topGroupName, slots=slots) self._operator = topLevelOperator
def __init__(self, operator, projectFileGroupName): slots = [SerialListSlot(operator.InputChannelIndexes, selfdepends=True), SerialSlot(operator.WatershedPadding, selfdepends=True), SerialSlot(operator.CacheBlockShape, selfdepends=True), SerialSlot(operator.SeedThresholdValue, selfdepends=True), SerialSlot(operator.MinSeedSize, selfdepends=True) ] super(VigraWatershedViewerSerializer, self).__init__(projectFileGroupName, slots=slots)
def __init__(self, operator, projectFileGroupName): self.topLevelOperator = operator slots = [ SerialSlot(operator.ExportDirectory, default=''), SerialSlot(operator.Format, default=ExportFormat.H5), SerialSlot(operator.Suffix, default='_results'), SerialDatasetPath(operator.DatasetPath, operator.Dirty, name='datasetInfos', subname='dataset{:>04}',), ] super(BatchIoSerializer, self).__init__(projectFileGroupName, slots=slots)
def __init__(self, operator, projectFileGroupName, extraSerialSlots=[]): self.topLevelOperator = operator SerialRoiSlot = partial(SerialListSlot, store_transform=lambda x: -1 if x is None else x, transform=lambda x: None if x == -1 else x, iterable=tuple) slots = [ SerialRoiSlot(operator.RegionStart), SerialRoiSlot(operator.RegionStop), SerialSlot(operator.InputMin), SerialSlot(operator.InputMax), SerialSlot(operator.ExportMin), SerialSlot(operator.ExportMax), SerialDtypeSlot(operator.ExportDtype), SerialSlot(operator.OutputAxisOrder), SerialSlot(operator.OutputFilenameFormat), SerialSlot(operator.OutputInternalPath), SerialSlot(operator.OutputFormat), ] slots += extraSerialSlots super(DataExportSerializer, self).__init__(projectFileGroupName, slots=slots)
def __init__(self, operator, projectFileGroupName): self.predictionSlot = SerialPredictionSlot( operator.PredictionProbabilities, operator, name='Predictions', subname='predictions{:04d}', ) slots = [ SerialListSlot( operator.LabelNames, ), SerialListSlot( operator.LabelColors, transform=lambda x: tuple(x.flat), ), SerialListSlot( operator.PmapColors, transform=lambda x: tuple(x.flat), ), SerialBlockSlot( operator.LabelImages, operator.LabelInputs, operator.NonzeroLabelBlocks, name='LabelSets', subname='labels{:0}', selfdepends=False, ), self.predictionSlot, SerialBoxSlot( operator.opTrain.BoxConstraintRois, operator.opTrain, name='Rois', subname='rois{:04d}', ), SerialBoxSlot( operator.opTrain.BoxConstraintValues, operator.opTrain, name='Values', subname='values{:04d}', ), SerialSlot( operator.opTrain.Sigma, name='Sigma', ), SerialBoxSlot( operator.boxViewer.rois, operator.boxViewer, name='ViewRois', subname='viewrois{:04d}', ), SerialCountingSlot( operator.Classifier, operator.classifier_cache, name='CountingWrappers', ), ] super(CountingSerializer, self).__init__(projectFileGroupName, slots=slots) self.predictionSlot.progressSignal.subscribe(self.progressSignal)
def __init__(self, operator, projectFileGroupName): slots = [ SerialListSlot(operator.ChannelSelections), SerialSlot(operator.Threshold), SerialSlot(operator.MinSize), SerialSlot(operator.Sigma), SerialSlot(operator.Alpha), SerialSlot(operator.PixelPitch), SerialBlockSlot( operator.Superpixels, operator.SuperpixelCacheInput, operator.CleanBlocks, name="Superpixels", subname="superpixels{:03d}", selfdepends=False, shrink_to_bb=False, compression_level=1, ), ] super(WsdtSerializer, self).__init__(projectFileGroupName, slots=slots, operator=operator)
def __init__(self, topLevelOperator, projectFileGroupName): slots = [ SerialDictSlot(topLevelOperator.Parameters, selfdepends=True), SerialHdf5BlockSlot(topLevelOperator.OutputHdf5, topLevelOperator.InputHdf5, topLevelOperator.CleanBlocks, name="CachedOutput"), SerialDictSlot(topLevelOperator.EventsVector, transform=str, selfdepends=True), SerialDictSlot(topLevelOperator.FilteredLabels, transform=str, selfdepends=True), SerialSlot(topLevelOperator.DivisionWeight), SerialSlot(topLevelOperator.DetectionWeight), SerialSlot(topLevelOperator.TransitionWeight), SerialSlot(topLevelOperator.AppearanceWeight), SerialSlot(topLevelOperator.DisappearanceWeight), SerialSlot(topLevelOperator.MaxNumObjOut) ] if 'MergerOutput' in topLevelOperator.outputs: slots.append( SerialHdf5BlockSlot(topLevelOperator.MergerOutputHdf5, topLevelOperator.MergerInputHdf5, topLevelOperator.MergerCleanBlocks, name="MergerCachedOutput"), ) if 'CoordinateMap' in topLevelOperator.outputs: slots.append( SerialPickleableSlot(topLevelOperator.CoordinateMap, 1, pgmlink.TimestepIdCoordinateMap())) super(StructuredTrackingSerializer, self).__init__(projectFileGroupName, slots=slots)
def __init__(self, operator, projectFileGroupName): slots = [ SerialDictSlot(operator.Crops), SerialSlot(operator.MinValueT, selfdepends=True), SerialSlot(operator.MaxValueT, selfdepends=True), SerialSlot(operator.MinValueX, selfdepends=True), SerialSlot(operator.MaxValueX, selfdepends=True), SerialSlot(operator.MinValueY, selfdepends=True), SerialSlot(operator.MaxValueY, selfdepends=True), SerialSlot(operator.MinValueZ, selfdepends=True), SerialSlot(operator.MaxValueZ, selfdepends=True), ] super(CropSelectionSerializer, self).__init__(projectFileGroupName, slots=slots)
def __init__(self, operator, projectFileGroupName): slots = [ SerialDictSlot(operator.FeatureNames), SerialEdgeLabelsDictSlot(operator.EdgeLabelsDict), SerialRagSlot(operator.Rag, operator.opRagCache, operator.Superpixels), SerialCachedDataFrameSlot(operator.opEdgeFeaturesCache.Output, operator.opEdgeFeaturesCache, name="EdgeFeatures"), SerialClassifierSlot(operator.opClassifierCache.Output, operator.opClassifierCache), SerialSlot(operator.TrainRandomForest), ] super().__init__(projectFileGroupName, slots=slots)
def __init__(self, topLevelOperator, projectFileGroupName): slots = [ SerialSlot(topLevelOperator.PatchWidth, selfdepends=True), SerialSlot(topLevelOperator.PatchHeight, selfdepends=True), SerialSlot(topLevelOperator.PatchOverlapVertical, selfdepends=True), SerialSlot(topLevelOperator.PatchOverlapHorizontal, selfdepends=True), SerialSlot(topLevelOperator.GridStartVertical, selfdepends=True), SerialSlot(topLevelOperator.GridStartHorizontal, selfdepends=True), SerialSlot(topLevelOperator.GridWidth, selfdepends=True), SerialSlot(topLevelOperator.GridHeight, selfdepends=True) ] super(PatchCreatorSerializer, self).__init__(projectFileGroupName, slots=slots) self.topLevelOperator = topLevelOperator
def __init__(self, operator, projectFileGroupName): slots = [ SerialSlot(operator.CurOperator, selfdepends=True), SerialSlot(operator.MinSize, selfdepends=True), SerialSlot(operator.MaxSize, selfdepends=True), SerialSlot(operator.HighThreshold, selfdepends=True), SerialSlot(operator.LowThreshold, selfdepends=True), SerialSlot(operator.SingleThreshold, selfdepends=True), SerialDictSlot(operator.SmootherSigma, selfdepends=True), SerialSlot(operator.Channel, selfdepends=True), SerialHdf5BlockSlot(operator.OutputHdf5, operator.InputHdf5, operator.CleanBlocks, name="CachedThresholdOutput") ] super(self.__class__, self).__init__(projectFileGroupName, slots=slots)
def __init__(self, workflow, title, opCounting, isBatch=False): # Our operator is a subclass of the generic data export operator self._topLevelOperator = OpMultiLaneWrapper( OpCountingDataExport, parent=workflow, promotedSlotNames=set(['RawData', 'Inputs', 'RawDatasetInfo'])) self._gui = None self._title = title self._serializers = [ DataExportSerializer( self._topLevelOperator, title, [SerialSlot(self._topLevelOperator.CsvFilepath)]) ] self.opCounting = opCounting # Base class init super(CountingDataExportApplet, self).__init__(workflow, title, isBatch)
def __init__(self, operator, projectFileGroupName): slots = [ SerialSlot(operator.ChannelSelection), SerialSlot(operator.Pmin), SerialSlot(operator.MinMembraneSize), SerialSlot(operator.MinSegmentSize), SerialSlot(operator.SigmaMinima), SerialSlot(operator.SigmaWeights), SerialSlot(operator.GroupSeeds), SerialSlot(operator.PreserveMembranePmaps), SerialBlockSlot(operator.Superpixels, operator.SuperpixelCacheInput, operator.CleanBlocks, name='Superpixels', subname='superpixels{:03d}', selfdepends=False, shrink_to_bb=False, compression_level=1) ] super(WsdtSerializer, self).__init__(projectFileGroupName, slots=slots, operator=operator)
def __init__(self, topGroupName, operator): serialSlots = [ SerialDictSlot(operator.SelectedFeatures, transform=str), SerialListSlot(operator.LabelNames, transform=str), SerialListSlot(operator.LabelColors, transform=lambda x: tuple(x.flat)), SerialListSlot(operator.PmapColors, transform=lambda x: tuple(x.flat)), SerialDictSlot(operator.LabelInputs, transform=int), SerialClassifierSlot(operator.Classifier, operator.classifier_cache, name="ClassifierForests"), SerialDictSlot(operator.CachedProbabilities, operator.InputProbabilities, transform=int), SerialSlot(operator.MaxNumObj), SerialPickledValueSlot(operator.ExportSettings) ] super(ObjectClassificationSerializer, self).__init__(topGroupName, slots=serialSlots, operator=operator)
def __init__(self, topGroupName, operator): self.VERSION = 1 # Make sure to bump the version in case you make any changes in the serialization serialSlots = [ SerialDictSlot(operator.SelectedFeatures), SerialListSlot(operator.LabelNames), SerialListSlot(operator.LabelColors, transform=lambda x: tuple(x.flat)), SerialListSlot(operator.PmapColors, transform=lambda x: tuple(x.flat)), SerialDictSlot(operator.LabelInputs, transform=int), SerialClassifierSlot(operator.Classifier, operator.classifier_cache, name="ClassifierForests"), SerialDictSlot(operator.CachedProbabilities, operator.InputProbabilities, transform=int), SerialSlot(operator.MaxNumObj), SerialPickleableSlot(operator.ExportSettings, self.VERSION, None) ] super(ObjectClassificationSerializer, self).__init__(topGroupName, slots=serialSlots, operator=operator)
def __init__(self, operator, projectFileGroupName): slots = [ SerialSlot(operator.CurOperator, selfdepends=True), SerialSlot(operator.MinSize, selfdepends=True), SerialSlot(operator.MaxSize, selfdepends=True), SerialSlot(operator.HighThreshold, selfdepends=True), SerialSlot(operator.LowThreshold, selfdepends=True), SerialDictSlot(operator.SmootherSigma, selfdepends=True), SerialSlot(operator.Channel, selfdepends=True), SerialSlot(operator.CoreChannel, selfdepends=True), SerialBlockSlot(operator.CachedOutput, operator.CacheInput, operator.CleanBlocks, name='CachedThresholdLabels', subname='threshold{:03d}', selfdepends=False, shrink_to_bb=False, compression_level=1) ] super(self.__class__, self).__init__(projectFileGroupName, slots, operator)
def __init__(self, operator, projectFileGroupName): super(NanshePostprocessingSerializer, self).__init__(projectFileGroupName, slots=[SerialSlot(operator.SignificanceThreshold, selfdepends=True), SerialListSlot(operator.WaveletTransformScale, selfdepends=True), SerialSlot(operator.NoiseThreshold, selfdepends=True), SerialSlot(operator.AcceptedRegionShapeConstraints_MajorAxisLength_Min, selfdepends=True), SerialSlot(operator.AcceptedRegionShapeConstraints_MajorAxisLength_Min_Enabled, selfdepends=True), SerialSlot(operator.AcceptedRegionShapeConstraints_MajorAxisLength_Max, selfdepends=True), SerialSlot(operator.AcceptedRegionShapeConstraints_MajorAxisLength_Max_Enabled, selfdepends=True), SerialSlot(operator.MinLocalMaxDistance, selfdepends=True), SerialSlot(operator.AcceptedNeuronShapeConstraints_Area_Min, selfdepends=True), SerialSlot(operator.AcceptedNeuronShapeConstraints_Area_Min_Enabled, selfdepends=True), SerialSlot(operator.AcceptedNeuronShapeConstraints_Area_Max, selfdepends=True), SerialSlot(operator.AcceptedNeuronShapeConstraints_Area_Max_Enabled, selfdepends=True), SerialSlot(operator.AcceptedNeuronShapeConstraints_Eccentricity_Min, selfdepends=True), SerialSlot(operator.AcceptedNeuronShapeConstraints_Eccentricity_Min_Enabled, selfdepends=True), SerialSlot(operator.AcceptedNeuronShapeConstraints_Eccentricity_Max, selfdepends=True), SerialSlot(operator.AcceptedNeuronShapeConstraints_Eccentricity_Max_Enabled, selfdepends=True), SerialSlot(operator.AlignmentMinThreshold, selfdepends=True), SerialSlot(operator.OverlapMinThreshold, selfdepends=True), SerialSlot(operator.Fuse_FractionMeanNeuronMaxThreshold, selfdepends=True), SerialBlockSlot(operator.Output, operator.CacheInput, operator.CleanBlocks, selfdepends=True)])
def __init__(self, topLevelOperator, projectFileGroupName): self.VERSION = 1 # Make sure to bump the version in case you make any changes in the serialization try: slots = [ SerialDictSlot(topLevelOperator.Parameters, selfdepends=True), SerialDictSlot(topLevelOperator.FilteredLabels, transform=str, selfdepends=True), SerialPickleableSlot(topLevelOperator.ExportSettings, version=self.VERSION), SerialPickleableSlot(topLevelOperator.HypothesesGraph, version=self.VERSION), SerialPickleableSlot(topLevelOperator.LearningHypothesesGraph, version=self.VERSION), SerialPickleableSlot(topLevelOperator.ResolvedMergers, version=self.VERSION), SerialSlot(topLevelOperator.DivisionWeight), SerialSlot(topLevelOperator.DetectionWeight), SerialSlot(topLevelOperator.TransitionWeight), SerialSlot(topLevelOperator.AppearanceWeight), SerialSlot(topLevelOperator.DisappearanceWeight), SerialSlot(topLevelOperator.MaxNumObjOut), ] except: slots = [ SerialDictSlot(topLevelOperator.Parameters, selfdepends=True), SerialDictSlot(topLevelOperator.FilteredLabels, transform=str, selfdepends=True), SerialPickleableSlot(topLevelOperator.ExportSettings, version=self.VERSION), SerialPickleableSlot(topLevelOperator.ResolvedMergers, version=self.VERSION), SerialSlot(topLevelOperator.DivisionWeight), SerialSlot(topLevelOperator.DetectionWeight), SerialSlot(topLevelOperator.TransitionWeight), SerialSlot(topLevelOperator.AppearanceWeight), SerialSlot(topLevelOperator.DisappearanceWeight), SerialSlot(topLevelOperator.MaxNumObjOut), ] super(StructuredTrackingSerializer, self).__init__(projectFileGroupName, slots=slots, operator=topLevelOperator)
def __init__(self, projectFileGroupName, operator): super().__init__(projectFileGroupName, [SerialSlot(operator.ServerId)])
def __init__(self, topLevelOperator, projectFileGroupName): if WITH_HYTRA: slots = [ SerialDictSlot(topLevelOperator.Parameters, selfdepends=True), SerialDictSlot(topLevelOperator.EventsVector, transform=str, selfdepends=True), SerialDictSlot(topLevelOperator.FilteredLabels, transform=str, selfdepends=True), SerialPickledValueSlot(topLevelOperator.ExportSettings), SerialPickledValueSlot(topLevelOperator.HypothesesGraph), SerialPickledValueSlot(topLevelOperator.ResolvedMergers), SerialSlot(topLevelOperator.DivisionWeight), SerialSlot(topLevelOperator.DetectionWeight), SerialSlot(topLevelOperator.TransitionWeight), SerialSlot(topLevelOperator.AppearanceWeight), SerialSlot(topLevelOperator.DisappearanceWeight), SerialSlot(topLevelOperator.MaxNumObjOut) ] else: try: import pgmlink except: import pgmlinkNoIlpSolver as pgmlink slots = [ SerialDictSlot(topLevelOperator.Parameters, selfdepends=True), SerialHdf5BlockSlot(topLevelOperator.OutputHdf5, topLevelOperator.InputHdf5, topLevelOperator.CleanBlocks, name="CachedOutput"), SerialDictSlot(topLevelOperator.EventsVector, transform=str, selfdepends=True), SerialDictSlot(topLevelOperator.FilteredLabels, transform=str, selfdepends=True), SerialSlot(topLevelOperator.DivisionWeight), SerialSlot(topLevelOperator.DetectionWeight), SerialSlot(topLevelOperator.TransitionWeight), SerialSlot(topLevelOperator.AppearanceWeight), SerialSlot(topLevelOperator.DisappearanceWeight), SerialSlot(topLevelOperator.MaxNumObjOut) ] if 'MergerOutput' in topLevelOperator.outputs: slots.append(SerialHdf5BlockSlot(topLevelOperator.MergerOutputHdf5, topLevelOperator.MergerInputHdf5, topLevelOperator.MergerCleanBlocks, name="MergerCachedOutput"), ) if 'CoordinateMap' in topLevelOperator.outputs: slots.append(SerialPickleableSlot(topLevelOperator.CoordinateMap, 1, pgmlink.TimestepIdCoordinateMap())) super(StructuredTrackingSerializer, self ).__init__(projectFileGroupName, slots=slots, operator=topLevelOperator)
def __init__(self, operator: "OpCarving", groupName): super().__init__(groupName, slots=[SerialSlot(operator.ObjectPrefix)]) self._o = operator
def __init__(self, operator, projectFileGroupName): super(NansheDictionaryLearningSerializer, self).__init__(projectFileGroupName, slots=[ SerialSlot(operator.Ord, selfdepends=True), SerialSlot(operator.K, selfdepends=True), SerialSlot(operator.Gamma1, selfdepends=True), SerialSlot(operator.Gamma2, selfdepends=True), SerialSlot(operator.NumThreads, selfdepends=True), SerialSlot(operator.Batchsize, selfdepends=True), SerialSlot(operator.NumIter, selfdepends=True), SerialSlot(operator.Lambda1, selfdepends=True), SerialSlot(operator.Lambda2, selfdepends=True), SerialSlot(operator.PosAlpha, selfdepends=True), SerialSlot(operator.PosD, selfdepends=True), SerialSlot(operator.Clean, selfdepends=True), SerialSlot(operator.Mode, selfdepends=True), SerialSlot(operator.ModeD, selfdepends=True), SerialBlockSlot(operator.Output, operator.CacheInput, operator.CleanBlocks, selfdepends=True) ])
def __init__(self, operator, projectFileGroupName): super(NanshePreprocessingSerializer, self).__init__( projectFileGroupName, slots=[ SerialSlot(operator.ToRemoveZeroedLines, selfdepends=True), SerialListSlot(operator.ErosionShape, selfdepends=True), SerialListSlot(operator.DilationShape, selfdepends=True), SerialSlot(operator.ToExtractF0, selfdepends=True), SerialSlot(operator.HalfWindowSize, selfdepends=True), SerialSlot(operator.WhichQuantile, selfdepends=True), SerialSlot(operator.TemporalSmoothingGaussianFilterStdev, selfdepends=True), SerialSlot(operator.SpatialSmoothingGaussianFilterStdev, selfdepends=True), SerialSlot(operator.TemporalSmoothingGaussianFilterWindowSize, selfdepends=True), SerialSlot(operator.SpatialSmoothingGaussianFilterWindowSize, selfdepends=True), SerialSlot(operator.BiasEnabled, selfdepends=True), SerialSlot(operator.Bias, selfdepends=True), SerialSlot(operator.ToWaveletTransform, selfdepends=True), SerialListSlot(operator.Scale, selfdepends=True), SerialBlockSlot(operator.CacheOutput, operator.CacheInput, operator.CleanBlocks, selfdepends=True) ])