bd510336fef04cd3889c73c18483cd9d92b34b51,ilastik/applets/pixelClassification/opPixelClassification.py,OpPixelClassification,__init__,#OpPixelClassification#,51

Before Change

        // Create internal operators
        // Explicitly wrapped:
        self.opInputShapeReader = OperatorWrapper( OpShapeReader, parent=self, graph=self.graph )
        self.opLabelArray = OperatorWrapper( OpBlockedSparseLabelArray, parent=self, graph=self.graph )
        self.predict = OperatorWrapper( OpPredictRandomForest, parent=self, graph=self.graph )
        self.prediction_cache = OperatorWrapper( OpSlicedBlockedArrayCache, parent=self, graph=self.graph )
        assert len(self.prediction_cache.Input) == 0
        self.prediction_cache_gui = OperatorWrapper( OpSlicedBlockedArrayCache, parent=self, graph=self.graph )
        assert len(self.prediction_cache_gui.Input) == 0
        self.precomputed_predictions = OperatorWrapper( OpPrecomputedInput, parent=self, graph=self.graph )
        self.precomputed_predictions_gui = OperatorWrapper( OpPrecomputedInput, parent=self, graph=self.graph )

        // NOT wrapped
        self.opMaxLabel = OpMaxValue( parent=self, graph=self.graph)
        self.opTrain = OpTrainRandomForestBlocked( parent=self, graph=self.graph )

        // Set up label cache shape input
        self.opInputShapeReader.Input.connect( self.InputImages )
        self.opLabelArray.inputs["shape"].connect( self.opInputShapeReader.OutputShape )

        // Set up other label cache inputs
        self.LabelInputs.connect( self.InputImages )
        self.opLabelArray.inputs["Input"].connect( self.LabelInputs )
        self.opLabelArray.inputs["eraser"].setValue(100)
                
        // Initialize the delete input to -1, which means "no label".
        // Now changing this input to a positive value will cause label deletions.
        // (The deleteLabel input is monitored for changes.)
        self.opLabelArray.inputs["deleteLabel"].setValue(-1)
        
        // Find the highest label in all the label images
        self.opMaxLabel.Inputs.connect( self.opLabelArray.outputs["maxLabel"] )

        ////
        // training
        ////
        
        self.opTrain.inputs["Labels"].connect(self.opLabelArray.outputs["Output"])
        self.opTrain.inputs["Images"].connect(self.CachedFeatureImages)
        self.opTrain.inputs["nonzeroLabelBlocks"].connect(self.opLabelArray.outputs["nonzeroBlocks"])
        self.opTrain.inputs["fixClassifier"].setValue(False)

After Change

        
        // Create internal operators
        // Explicitly wrapped:
        self.opLabelPipeline = OpMultiLaneWrapper( OpLabelPipeline, parent=self )

        self.predict = OperatorWrapper( OpPredictRandomForest, parent=self, graph=self.graph )
        self.prediction_cache = OperatorWrapper( OpSlicedBlockedArrayCache, parent=self, graph=self.graph )
        assert len(self.prediction_cache.Input) == 0

Instances