class PixelClassificationWorkflow(Workflow):
workflowName = "Pixel Classification"
workflowDescription = "This is obviously self-explanoratory."
defaultAppletIndex = 1 # show DataSelection by default
@property
def applets(self):
return self._applets
@property
def imageNameListSlot(self):
return self.dataSelectionApplet.topLevelOperator.ImageName
def __init__(self, headless, workflow_cmdline_args, appendBatchOperators=True, *args, **kwargs):
# Create a graph to be shared by all operators
graph = Graph()
super( PixelClassificationWorkflow, self ).__init__( headless, graph=graph, *args, **kwargs )
self._applets = []
# Applets for training (interactive) workflow
self.projectMetadataApplet = ProjectMetadataApplet()
self.dataSelectionApplet = DataSelectionApplet(self, "Input Data", "Input Data", supportIlastik05Import=True, batchDataGui=False)
opDataSelection = self.dataSelectionApplet.topLevelOperator
opDataSelection.DatasetRoles.setValue( ['Raw Data'] )
self.featureSelectionApplet = FeatureSelectionApplet(self, "Feature Selection", "FeatureSelections")
self.pcApplet = PixelClassificationApplet(self, "PixelClassification")
# Expose for shell
self._applets.append(self.projectMetadataApplet)
self._applets.append(self.dataSelectionApplet)
self._applets.append(self.featureSelectionApplet)
self._applets.append(self.pcApplet)
if appendBatchOperators:
# Create applets for batch workflow
self.batchInputApplet = DataSelectionApplet(self, "Batch Prediction Input Selections", "BatchDataSelection", supportIlastik05Import=False, batchDataGui=True)
self.batchResultsApplet = PixelClassificationBatchResultsApplet(self, "Batch Prediction Output Locations")
# Expose in shell
self._applets.append(self.batchInputApplet)
self._applets.append(self.batchResultsApplet)
# Connect batch workflow (NOT lane-based)
self._initBatchWorkflow()
def connectLane(self, laneIndex):
# Get a handle to each operator
opData = self.dataSelectionApplet.topLevelOperator.getLane(laneIndex)
opTrainingFeatures = self.featureSelectionApplet.topLevelOperator.getLane(laneIndex)
opClassify = self.pcApplet.topLevelOperator.getLane(laneIndex)
if not self.pcApplet.enabled:
self.pcApplet.guiControlSignal.emit(ControlCommand.DisableSelf)
'''try:
self.batchResultsApplet.guiControlSignal.emit(ControlCommand.DisableSelf)
except NameError:
pass'''
# Input Image -> Feature Op
# and -> Classification Op (for display)
opTrainingFeatures.InputImage.connect( opData.Image )
opClassify.InputImages.connect( opData.Image )
# Feature Images -> Classification Op (for training, prediction)
opClassify.FeatureImages.connect( opTrainingFeatures.OutputImage )
opClassify.CachedFeatureImages.connect( opTrainingFeatures.CachedOutputImage )
# Training flags -> Classification Op (for GUI restrictions)
opClassify.LabelsAllowedFlags.connect( opData.AllowLabels )
def updateEnable(self,applet,en = True):
if applet == "training":
self.pcApplet.updateEnable(en)
elif applet == "features":
self.pcApplet.updateEnable(en)
elif applet == "batch":
self.batchResultsApplet.updateEnable(en)
def _initBatchWorkflow(self):
"""
Connect the batch-mode top-level operators to the training workflow and to eachother.
"""
# Access applet operators from the training workflow
opTrainingDataSelection = self.dataSelectionApplet.topLevelOperator
opTrainingFeatures = self.featureSelectionApplet.topLevelOperator
opClassify = self.pcApplet.topLevelOperator
# Access the batch operators
opBatchInputs = self.batchInputApplet.topLevelOperator
opBatchResults = self.batchResultsApplet.topLevelOperator
opBatchInputs.DatasetRoles.connect( opTrainingDataSelection.DatasetRoles )
## Create additional batch workflow operators
opBatchFeatures = OperatorWrapper( OpFeatureSelection, operator_kwargs={'filter_implementation':'Original'}, parent=self, promotedSlotNames=['InputImage'] )
opBatchPredictionPipeline = OperatorWrapper( OpPredictionPipeline, parent=self )
## Connect Operators ##
opTranspose = OpTransposeSlots( parent=self )
opTranspose.OutputLength.setValue(1)
opTranspose.Inputs.connect( opBatchInputs.DatasetGroup )
opFilePathProvider = OperatorWrapper(OpAttributeSelector, parent=self)
opFilePathProvider.InputObject.connect( opTranspose.Outputs[0] )
opFilePathProvider.AttributeName.setValue( 'filePath' )
# Provide dataset paths from data selection applet to the batch export applet
opBatchResults.DatasetPath.connect( opFilePathProvider.Result )
# Connect (clone) the feature operator inputs from
# the interactive workflow's features operator (which gets them from the GUI)
opBatchFeatures.Scales.connect( opTrainingFeatures.Scales )
opBatchFeatures.FeatureIds.connect( opTrainingFeatures.FeatureIds )
opBatchFeatures.SelectionMatrix.connect( opTrainingFeatures.SelectionMatrix )
# Classifier and LabelsCount are provided by the interactive workflow
opBatchPredictionPipeline.Classifier.connect( opClassify.Classifier )
opBatchPredictionPipeline.MaxLabel.connect( opClassify.MaxLabelValue )
opBatchPredictionPipeline.FreezePredictions.setValue( False )
# Provide these for the gui
opBatchResults.RawImage.connect( opBatchInputs.Image )
opBatchResults.PmapColors.connect( opClassify.PmapColors )
opBatchResults.LabelNames.connect( opClassify.LabelNames )
# Connect Image pathway:
# Input Image -> Features Op -> Prediction Op -> Export
opBatchFeatures.InputImage.connect( opBatchInputs.Image )
opBatchPredictionPipeline.FeatureImages.connect( opBatchFeatures.OutputImage )
opBatchResults.ImageToExport.connect( opBatchPredictionPipeline.HeadlessPredictionProbabilities )
# We don't actually need the cached path in the batch pipeline.
# Just connect the uncached features here to satisfy the operator.
opBatchPredictionPipeline.CachedFeatureImages.connect( opBatchFeatures.OutputImage )
self.opBatchPredictionPipeline = opBatchPredictionPipeline
def getHeadlessOutputSlot(self, slotId):
# "Regular" (i.e. with the images that the user selected as input data)
if slotId == "Predictions":
return self.pcApplet.topLevelOperator.HeadlessPredictionProbabilities
elif slotId == "PredictionsUint8":
return self.pcApplet.topLevelOperator.HeadlessUint8PredictionProbabilities
# "Batch" (i.e. with the images that the user selected as batch inputs).
elif slotId == "BatchPredictions":
return self.opBatchPredictionPipeline.HeadlessPredictionProbabilities
if slotId == "BatchPredictionsUint8":
return self.opBatchPredictionPipeline.HeadlessUint8PredictionProbabilities
raise Exception("Unknown headless output slot")
