def setupLayers(self): layers = [self.createStandardLayerFromSlot(self.topLevelOperatorView.Input)] layers[0].opacity = 1.0 superVoxelBoundarySlot = self.topLevelOperatorView.BoundariesOutput if superVoxelBoundarySlot.ready(): layer = AlphaModulatedLayer( LazyflowSource(superVoxelBoundarySlot), tintColor=QColor(Qt.blue), range=(0.0, 1.0), normalize=(0.0, 1.0), ) layer.name = "Supervoxel Boundaries" layer.visible = True layer.opacity = 1.0 layers.insert(0, layer) superVoxelSlot = self.topLevelOperatorView.Output if superVoxelSlot.ready(): colortable = generateRandomColors(M=256, clamp={"v": 1.0, "s": 0.5}, zeroIsTransparent=False) layer = ColortableLayer(createDataSource(superVoxelSlot), colortable) layer.colortableIsRandom = True layer.name = "SLIC Superpixels" layer.visible = True layer.opacity = 1.0 layers.insert(0, layer) return layers
def _initPredictionLayers(self, predictionSlot): opLane = self.topLevelOperatorView if not opLane.LabelNames.ready() or not opLane.PmapColors.ready(): return [] layers = [] colors = opLane.PmapColors.value names = opLane.LabelNames.value # Use a slicer to provide a separate slot for each channel layer opSlicer = OpMultiArraySlicer2( parent=opLane.viewed_operator().parent ) opSlicer.Input.connect( predictionSlot ) opSlicer.AxisFlag.setValue('c') for channel, channelSlot in enumerate(opSlicer.Slices): if channelSlot.ready() and channel < len(colors) and channel < len(names): drange = channelSlot.meta.drange or (0.0, 1.0) predictsrc = LazyflowSource(channelSlot) predictLayer = AlphaModulatedLayer( predictsrc, tintColor=QColor(*colors[channel]), # FIXME: This is weird. Why are range and normalize both set to the same thing? range=drange, normalize=drange ) predictLayer.opacity = 0.25 predictLayer.visible = True predictLayer.name = names[channel] layers.append(predictLayer) return layers
def setupPredictionLayers(self, predictionChannels, name_suffix): """ Setup the layers for predicted class probabilities """ labels = self.labelListData layers = [] # Add each of the predictions for channel, predictionSlot in enumerate(predictionChannels): if predictionSlot.ready() and channel < len(labels): ref_label = labels[channel] predictsrc = LazyflowSource(predictionSlot) predictLayer = AlphaModulatedLayer( predictsrc, tintColor=ref_label.color, range=(0.0, 1.0), normalize=(0.0, 1.0) ) predictLayer.opacity = 0.25 predictLayer.visible = self._viewerControlUi.liveUpdateButton.isChecked() predictLayer.visibleChanged.connect(self.updateShowPredictionCheckbox) def setLayerColor(c): predictLayer.tintColor = c def setLayerName(n): newName = "Prediction for %s %s" % (ref_label.name, name_suffix) predictLayer.name = newName setLayerName(ref_label.name) ref_label.colorChanged.connect(setLayerColor) ref_label.nameChanged.connect(setLayerName) layers.append(predictLayer) return layers
def _initPredictionLayers(self, predictionSlot): opLane = self.topLevelOperatorView layers = [] # Use a slicer to provide a separate slot for each channel layer opSlicer = OpMultiArraySlicer2(parent=opLane.viewed_operator().parent) opSlicer.Input.connect(predictionSlot) opSlicer.AxisFlag.setValue('c') for channel, predictionSlot in enumerate(opSlicer.Slices): if predictionSlot.ready(): predictsrc = LazyflowSource(predictionSlot) predictLayer = AlphaModulatedLayer(predictsrc, range=(0.0, 1.0), normalize=(0.0, 1.0)) predictLayer.opacity = 0.25 predictLayer.visible = True def setPredLayerName(n, predictLayer_=predictLayer, initializing=False): """ function for setting the names for every Channel """ if not initializing and predictLayer_ not in self.layerstack: # This layer has been removed from the layerstack already. # Don't touch it. return newName = "Prediction for %s" % n predictLayer_.name = newName setPredLayerName(channel, initializing=True) layers.append(predictLayer) return layers
def _initPredictionLayers(self, predictionSlot): layers = [] opLane = self.topLevelOperatorView colors = opLane.PmapColors.value names = opLane.LabelNames.value # Use a slicer to provide a separate slot for each channel layer opSlicer = OpMultiArraySlicer2(parent=opLane.viewed_operator()) opSlicer.Input.connect(predictionSlot) opSlicer.AxisFlag.setValue('c') for channel, channelSlot in enumerate(opSlicer.Slices): if channelSlot.ready( ) and channel < len(colors) and channel < len(names): drange = channelSlot.meta.drange or (0.0, 1.0) predictsrc = LazyflowSource(channelSlot) predictLayer = AlphaModulatedLayer( predictsrc, tintColor=QColor(*colors[channel]), # FIXME: This is weird. Why are range and normalize both set to the same thing? range=drange, normalize=drange) predictLayer.opacity = 0.25 predictLayer.visible = True predictLayer.name = names[channel] layers.append(predictLayer) return layers
def _initPredictionLayers(self, predictionSlot): layers = [] opLane = self.topLevelOperatorView colors = opLane.PmapColors.value names = opLane.LabelNames.value # Use a slicer to provide a separate slot for each channel layer opSlicer = OpMultiArraySlicer2( parent=opLane.viewed_operator() ) opSlicer.Input.connect( predictionSlot ) opSlicer.AxisFlag.setValue('c') for channel, channelSlot in enumerate(opSlicer.Slices): if channelSlot.ready() and channel < len(colors) and channel < len(names): predictsrc = LazyflowSource(channelSlot) predictLayer = AlphaModulatedLayer( predictsrc, tintColor=QColor(*colors[channel]), range=(0.0, 1.0), normalize=(0.0, 1.0) ) predictLayer.opacity = 0.25 predictLayer.visible = True predictLayer.name = names[channel] layers.append(predictLayer) return layers
def _initPredictionLayers(self, predictionSlot): layers = [] opLane = self.topLevelOperatorView # Use a slicer to provide a separate slot for each channel layer opSlicer = OpMultiArraySlicer2(parent=opLane.viewed_operator().parent) opSlicer.Input.connect(predictionSlot) opSlicer.AxisFlag.setValue('c') for channel, channelSlot in enumerate(opSlicer.Slices): if channelSlot.ready(): drange = channelSlot.meta.drange or (0.0, 1.0) predictsrc = LazyflowSource(channelSlot) predictLayer = AlphaModulatedLayer( predictsrc, tintColor=QColor.fromRgba(self._colorTable16[channel + 1]), # FIXME: This is weird. Why are range and normalize both set to the same thing? range=drange, normalize=drange) predictLayer.opacity = 1.0 predictLayer.visible = True predictLayer.name = "Probability Channel #{}".format(channel + 1) layers.append(predictLayer) return layers
def _initPredictionLayers(self, predictionSlot): opLane = self.topLevelOperatorView if not opLane.LabelNames.ready() or not opLane.PmapColors.ready(): return [] layers = [] colors = opLane.PmapColors.value names = opLane.LabelNames.value if predictionSlot.ready(): num_channels = predictionSlot.meta.getTaggedShape()['c'] if num_channels != len(names) or num_channels != len(colors): names = map(lambda n: "Label {}".format(n), range(1, num_channels+1)) colors = self._createDefault16ColorColorTable()[:num_channels] # Use a slicer to provide a separate slot for each channel layer opSlicer = OpMultiArraySlicer2( parent=opLane.viewed_operator().parent ) opSlicer.Input.connect( predictionSlot ) opSlicer.AxisFlag.setValue('c') for channel, channelSlot in enumerate(opSlicer.Slices): if channelSlot.ready() and channel < len(colors) and channel < len(names): drange = channelSlot.meta.drange or (0.0, 1.0) predictsrc = LazyflowSource(channelSlot) predictLayer = AlphaModulatedLayer( predictsrc, tintColor=QColor(*colors[channel]), # FIXME: This is weird. Why are range and normalize both set to the same thing? range=drange, normalize=drange ) predictLayer.opacity = 0.25 predictLayer.visible = True predictLayer.name = names[channel] layers.append(predictLayer) return layers
def addPredictionLayer(self, icl, ref_label): selector=OpSingleChannelSelector(self.g) selector.inputs["Input"].connect(self.pCache.outputs['Output']) selector.inputs["Index"].setValue(icl) if self.checkInteractive.isChecked(): self.pCache.inputs["fixAtCurrent"].setValue(False) else: self.pCache.inputs["fixAtCurrent"].setValue(True) predictsrc = LazyflowSource(selector.outputs["Output"][0]) def srcName(newName): predictsrc.setObjectName("Prediction for %s" % ref_label.name) srcName("") predictLayer = AlphaModulatedLayer(predictsrc, tintColor=ref_label.color) predictLayer.nameChanged.connect(srcName) def setLayerColor(c): print "as the color of label '%s' has changed, setting layer's '%s' tint color to %r" % (ref_label.name, predictLayer.name, c) predictLayer.tintColor = c ref_label.colorChanged.connect(setLayerColor) def setLayerName(n): newName = "Prediction for %s" % ref_label.name print "as the name of label '%s' has changed, setting layer's '%s' name to '%s'" % (ref_label.name, predictLayer.name, newName) predictLayer.name = newName setLayerName(ref_label.name) ref_label.nameChanged.connect(setLayerName) predictLayer.ref_object = ref_label #make sure that labels (index = 0) stay on top! self.layerstack.insert(1, predictLayer ) self.fixableOperators.append(self.pCache)
def setupLayers(self): """ which layers will be shown in the layerviewergui. Triggers the prediciton by setting the layer on visible """ inputSlot = self.topLevelOperator.InputImage layers = [] for channel, predictionSlot in enumerate( self.topLevelOperator.PredictionProbabilityChannels): if predictionSlot.ready(): predictsrc = createDataSource(predictionSlot) predictionLayer = AlphaModulatedLayer(predictsrc, range=(0.0, 1.0), normalize=(0.0, 1.0)) predictionLayer.visible = self.drawer.liveUpdateButton.isChecked( ) predictionLayer.opacity = 0.25 predictionLayer.visibleChanged.connect( self.updateShowPredictionCheckbox) def setPredLayerName(n, predictLayer_=predictionLayer, initializing=False): """ function for setting the names for every Channel """ if not initializing and predictLayer_ not in self.layerstack: # This layer has been removed from the layerstack already. # Don't touch it. return newName = "Prediction for %s" % n predictLayer_.name = newName setPredLayerName(channel, initializing=True) layers.append(predictionLayer) # always as last layer if inputSlot.ready(): rawLayer = self.createStandardLayerFromSlot(inputSlot) rawLayer.visible = True rawLayer.opacity = 1.0 rawLayer.name = "Raw Data (display only)" layers.append(rawLayer) return layers
def setupPredictionLayers(self, predictionChannels, name_suffix): """ Setup the layers for predicted class probabilities """ labels = self.labelListData layers = [] # Add each of the predictions for channel, predictionSlot in enumerate(predictionChannels): if predictionSlot.ready() and channel < len(labels): ref_label = labels[channel] predictsrc = LazyflowSource(predictionSlot) predictLayer = AlphaModulatedLayer(predictsrc, tintColor=ref_label.color, range=(0.0, 1.0), normalize=(0.0, 1.0)) predictLayer.opacity = 0.25 predictLayer.visible = self._viewerControlUi.liveUpdateButton.isChecked( ) predictLayer.visibleChanged.connect( self.updateShowPredictionCheckbox) def setLayerColor(c, predictLayer_=predictLayer, initializing=False): if not initializing and predictLayer_ not in self.layerstack: # This layer has been removed from the layerstack already. # Don't touch it. return predictLayer_.tintColor = c def setLayerName(n, predictLayer_=predictLayer, initializing=False): if not initializing and predictLayer_ not in self.layerstack: # This layer has been removed from the layerstack already. # Don't touch it. return newName = "Prediction for %s" % n predictLayer_.name = newName setLayerName(ref_label.name, initializing=True) ref_label.colorChanged.connect(setLayerColor) ref_label.nameChanged.connect(setLayerName) layers.append(predictLayer) return layers
def _initPredictionLayers(self, predictionSlot): opLane = self.topLevelOperatorView if not opLane.LabelNames.ready() or not opLane.PmapColors.ready(): return [] layers = [] colors = opLane.PmapColors.value names = opLane.LabelNames.value if predictionSlot.ready(): if 'c' in predictionSlot.meta.getAxisKeys(): num_channels = predictionSlot.meta.getTaggedShape()['c'] else: num_channels = 1 if num_channels != len(names) or num_channels != len(colors): names = [ "Label {}".format(n) for n in range(1, num_channels + 1) ] colors = num_channels * [ (0, 0, 0) ] # it doesn't matter, if the pmaps color is not known, # we are either initializing and it will be rewritten or # something is very wrong elsewhere # Use a slicer to provide a separate slot for each channel layer opSlicer = OpMultiArraySlicer2(parent=opLane.viewed_operator().parent) opSlicer.Input.connect(predictionSlot) opSlicer.AxisFlag.setValue('c') for channel, channelSlot in enumerate(opSlicer.Slices): if channelSlot.ready( ) and channel < len(colors) and channel < len(names): drange = channelSlot.meta.drange or (0.0, 1.0) predictsrc = LazyflowSource(channelSlot) predictLayer = AlphaModulatedLayer( predictsrc, tintColor=QColor(*colors[channel]), # FIXME: This is weird. Why are range and normalize both set to the same thing? range=drange, normalize=drange) predictLayer.opacity = 0.25 predictLayer.visible = True predictLayer.name = names[channel] layers.append(predictLayer) return layers
def setupLayers(self): """ which layers will be shown in the layerviewergui. Triggers the prediciton by setting the layer on visible """ inputSlot = self.topLevelOperator.InputImage layers = [] for channel, predictionSlot in enumerate(self.topLevelOperator.PredictionProbabilityChannels): if predictionSlot.ready(): predictsrc = LazyflowSource(predictionSlot) predictionLayer = AlphaModulatedLayer(predictsrc, range=(0.0, 1.0), normalize=(0.0, 1.0)) predictionLayer.visible = self.drawer.liveUpdateButton.isChecked() predictionLayer.opacity = 0.25 predictionLayer.visibleChanged.connect(self.updateShowPredictionCheckbox) def setPredLayerName(n, predictLayer_=predictionLayer, initializing=False): """ function for setting the names for every Channel """ if not initializing and predictLayer_ not in self.layerstack: # This layer has been removed from the layerstack already. # Don't touch it. return newName = "Prediction for %s" % n predictLayer_.name = newName setPredLayerName(channel, initializing=True) layers.append(predictionLayer) # always as last layer if inputSlot.ready(): rawLayer = self.createStandardLayerFromSlot(inputSlot) rawLayer.visible = True rawLayer.opacity = 1.0 rawLayer.name = "Raw Data (display only)" layers.append(rawLayer) return layers
def _initPredictionLayers(self, predictionSlot): layers = [] colors = [] names = [] opLane = self.topLevelOperatorView if opLane.PmapColors.ready(): colors = opLane.PmapColors.value if opLane.LabelNames.ready(): names = opLane.LabelNames.value # Use a slicer to provide a separate slot for each channel layer opSlicer = OpMultiArraySlicer2(parent=opLane.viewed_operator().parent) opSlicer.Input.connect(predictionSlot) opSlicer.AxisFlag.setValue("c") colors = [QColor(*c) for c in colors] for channel in range(len(colors), len(opSlicer.Slices)): colors.append(PredictionViewerGui.DefaultColors[channel]) for channel in range(len(names), len(opSlicer.Slices)): names.append("Class {}".format(channel + 1)) for channel, channelSlot in enumerate(opSlicer.Slices): if channelSlot.ready( ) and channel < len(colors) and channel < len(names): predictsrc = createDataSource(channelSlot) predictLayer = AlphaModulatedLayer(predictsrc, tintColor=colors[channel], range=(0.0, 1.0), normalize=(0.0, 1.0)) predictLayer.opacity = 0.25 predictLayer.visible = True predictLayer.name = names[channel] layers.append(predictLayer) return layers return colors
def _initPredictionLayers(self, predictionSlot): opLane = self.topLevelOperatorView if not opLane.LabelNames.ready() or not opLane.PmapColors.ready(): return [] layers = [] colors = opLane.PmapColors.value names = opLane.LabelNames.value if predictionSlot.ready(): if 'c' in predictionSlot.meta.getAxisKeys(): num_channels = predictionSlot.meta.getTaggedShape()['c'] else: num_channels = 1 if num_channels != len(names) or num_channels != len(colors): names = ["Label {}".format(n) for n in range(1, num_channels+1)] colors = num_channels * [(0, 0, 0)] # it doesn't matter, if the pmaps color is not known, # we are either initializing and it will be rewritten or # something is very wrong elsewhere # Use a slicer to provide a separate slot for each channel layer opSlicer = OpMultiArraySlicer2( parent=opLane.viewed_operator().parent ) opSlicer.Input.connect( predictionSlot ) opSlicer.AxisFlag.setValue('c') for channel, channelSlot in enumerate(opSlicer.Slices): if channelSlot.ready() and channel < len(colors) and channel < len(names): drange = channelSlot.meta.drange or (0.0, 1.0) predictsrc = LazyflowSource(channelSlot) predictLayer = AlphaModulatedLayer( predictsrc, tintColor=QColor(*colors[channel]), # FIXME: This is weird. Why are range and normalize both set to the same thing? range=drange, normalize=drange ) predictLayer.opacity = 0.25 predictLayer.visible = True predictLayer.name = names[channel] layers.append(predictLayer) return layers
def setupPredictionLayers(self, predictionChannels, name_suffix): """ Setup the layers for predicted class probabilities """ labels = self.labelListData layers = [] # Add each of the predictions for channel, predictionSlot in enumerate(predictionChannels): if predictionSlot.ready() and channel < len(labels): ref_label = labels[channel] predictsrc = LazyflowSource(predictionSlot) predictLayer = AlphaModulatedLayer( predictsrc, tintColor=ref_label.color, range=(0.0, 1.0), normalize=(0.0, 1.0) ) predictLayer.opacity = 0.25 predictLayer.visible = self._viewerControlUi.liveUpdateButton.isChecked() predictLayer.visibleChanged.connect(self.updateShowPredictionCheckbox) def setLayerColor(c, predictLayer_=predictLayer, initializing=False): if not initializing and predictLayer_ not in self.layerstack: # This layer has been removed from the layerstack already. # Don't touch it. return predictLayer_.tintColor = c def setLayerName(n, predictLayer_=predictLayer, initializing=False): if not initializing and predictLayer_ not in self.layerstack: # This layer has been removed from the layerstack already. # Don't touch it. return newName = "Prediction for %s" % n predictLayer_.name = newName setLayerName(ref_label.name, initializing=True) ref_label.colorChanged.connect(setLayerColor) ref_label.nameChanged.connect(setLayerName) layers.append(predictLayer) return layers
def _initSegmentationLayers(self, segmentationSlot): opLane = self.topLevelOperatorView layers = [] # Use a slicer to provide a separate slot for each channel layer opSlicer = OpMultiArraySlicer2(parent=opLane.viewed_operator().parent) opSlicer.Input.connect(segmentationSlot) opSlicer.AxisFlag.setValue("c") for channel, segmentationSlot in enumerate(opSlicer.Slices): if segmentationSlot.ready(): segmentationSrc = createDataSource(segmentationSlot) segmentationLayer = AlphaModulatedLayer(segmentationSrc, range=(0.0, 1.0), normalize=(0.0, 1.0)) segmentationLayer.visible = ( channel == 1) # only show the channel with the foreground segmentationLayer.opacity = 1 def setSegmentationLayerName( n, segmentationLayer_=segmentationLayer, initializing=False): """ function for setting the names for every Channel """ if not initializing and segmentationLayer_ not in self.layerstack: # This layer has been removed from the layerstack already. # Don't touch it. return newName = "Segmentation of %s" % n segmentationLayer_.name = newName setSegmentationLayerName(channel, initializing=True) layers.append(segmentationLayer) return layers
def _initPredictionLayers(self, predictionSlot): opLane = self.topLevelOperatorView if not opLane.LabelNames.ready() or not opLane.PmapColors.ready(): return [] layers = [] colors = opLane.PmapColors.value names = opLane.LabelNames.value if predictionSlot.ready(): num_channels = predictionSlot.meta.getTaggedShape()["c"] if num_channels != len(names) or num_channels != len(colors): names = map(lambda n: "Label {}".format(n), range(1, num_channels + 1)) colors = self._createDefault16ColorColorTable()[:num_channels] # Use a slicer to provide a separate slot for each channel layer opSlicer = OpMultiArraySlicer2(parent=opLane.viewed_operator().parent) opSlicer.Input.connect(predictionSlot) opSlicer.AxisFlag.setValue("c") for channel, channelSlot in enumerate(opSlicer.Slices): if channelSlot.ready() and channel < len(colors) and channel < len(names): drange = channelSlot.meta.drange or (0.0, 1.0) predictsrc = LazyflowSource(channelSlot) predictLayer = AlphaModulatedLayer( predictsrc, tintColor=QColor(*colors[channel]), # FIXME: This is weird. Why are range and normalize both set to the same thing? range=drange, normalize=drange, ) predictLayer.opacity = 0.25 predictLayer.visible = True predictLayer.name = names[channel] layers.append(predictLayer) return layers
def _initPredictionLayers(self, predictionSlot): layers = [] colors = [] names = [] opLane = self.topLevelOperatorView if opLane.PmapColors.ready(): colors = opLane.PmapColors.value if opLane.LabelNames.ready(): names = opLane.LabelNames.value # Use a slicer to provide a separate slot for each channel layer opSlicer = OpMultiArraySlicer2(parent=opLane.viewed_operator().parent) opSlicer.Input.connect(predictionSlot) opSlicer.AxisFlag.setValue("c") colors = map(lambda c: QColor(*c), colors) for channel in range(len(colors), len(opSlicer.Slices)): colors.append(PredictionViewerGui.DefaultColors[channel]) for channel in range(len(names), len(opSlicer.Slices)): names.append("Class {}".format(channel + 1)) for channel, channelSlot in enumerate(opSlicer.Slices): if channelSlot.ready() and channel < len(colors) and channel < len(names): predictsrc = LazyflowSource(channelSlot) predictLayer = AlphaModulatedLayer( predictsrc, tintColor=colors[channel], range=(0.0, 1.0), normalize=(0.0, 1.0) ) predictLayer.opacity = 0.25 predictLayer.visible = True predictLayer.name = names[channel] layers.append(predictLayer) return layers
def _initPredictionLayers(self, predictionSlot): layers = [] opLane = self.topLevelOperatorView # Use a slicer to provide a separate slot for each channel layer opSlicer = OpMultiArraySlicer2( parent=opLane.viewed_operator().parent ) opSlicer.Input.connect( predictionSlot ) opSlicer.AxisFlag.setValue('c') for channel, channelSlot in enumerate(opSlicer.Slices): if channelSlot.ready(): drange = channelSlot.meta.drange or (0.0, 1.0) predictsrc = LazyflowSource(channelSlot) predictLayer = AlphaModulatedLayer( predictsrc, tintColor=QColor.fromRgba(self._colorTable16[channel+1]), # FIXME: This is weird. Why are range and normalize both set to the same thing? range=drange, normalize=drange ) predictLayer.opacity = 1.0 predictLayer.visible = True predictLayer.name = "Probability Channel #{}".format( channel+1 ) layers.append(predictLayer) return layers
def setupLayers(self, currentImageIndex): """ Called by our base class when one of our data slots has changed. This function creates a layer for each slot we want displayed in the volume editor. """ # Base class provides the label layer. layers = super(PixelClassificationGui, self).setupLayers(currentImageIndex) labels = self.labelListData # Add the uncertainty estimate layer uncertaintySlot = self.pipeline.UncertaintyEstimate[currentImageIndex] if uncertaintySlot.ready(): uncertaintySrc = LazyflowSource(uncertaintySlot) uncertaintyLayer = AlphaModulatedLayer( uncertaintySrc, tintColor=QColor( Qt.cyan ), range=(0.0, 1.0), normalize=(0.0, 1.0) ) uncertaintyLayer.name = "Uncertainty" uncertaintyLayer.visible = False uncertaintyLayer.opacity = 1.0 uncertaintyLayer.shortcutRegistration = ( "Prediction Layers", "Show/Hide Uncertainty", QShortcut( QKeySequence("u"), self.viewerControlWidget(), uncertaintyLayer.toggleVisible ), uncertaintyLayer ) layers.append(uncertaintyLayer) # Add each of the predictions for channel, predictionSlot in enumerate(self.pipeline.PredictionProbabilityChannels[currentImageIndex]): if predictionSlot.ready() and channel < len(labels): ref_label = labels[channel] predictsrc = LazyflowSource(predictionSlot) predictLayer = AlphaModulatedLayer( predictsrc, tintColor=ref_label.color, range=(0.0, 1.0), normalize=(0.0, 1.0) ) predictLayer.opacity = 0.25 predictLayer.visible = self.labelingDrawerUi.checkInteractive.isChecked() predictLayer.visibleChanged.connect(self.updateShowPredictionCheckbox) def setLayerColor(c): predictLayer.tintColor = c def setLayerName(n): newName = "Prediction for %s" % ref_label.name predictLayer.name = newName setLayerName(ref_label.name) ref_label.colorChanged.connect(setLayerColor) ref_label.nameChanged.connect(setLayerName) layers.append(predictLayer) # Add each of the segementations for channel, segmentationSlot in enumerate(self.pipeline.SegmentationChannels[currentImageIndex]): if segmentationSlot.ready() and channel < len(labels): ref_label = labels[channel] segsrc = LazyflowSource(segmentationSlot) segLayer = AlphaModulatedLayer( segsrc, tintColor=ref_label.color, range=(0.0, 1.0), normalize=(0.0, 1.0) ) segLayer.opacity = 1 segLayer.visible = self.labelingDrawerUi.checkInteractive.isChecked() segLayer.visibleChanged.connect(self.updateShowSegmentationCheckbox) def setLayerColor(c): segLayer.tintColor = c def setLayerName(n): newName = "Segmentation (%s)" % ref_label.name segLayer.name = newName setLayerName(ref_label.name) ref_label.colorChanged.connect(setLayerColor) ref_label.nameChanged.connect(setLayerName) layers.append(segLayer) # Add the raw data last (on the bottom) inputDataSlot = self.pipeline.InputImages[currentImageIndex] if inputDataSlot.ready(): inputLayer = self.createStandardLayerFromSlot( inputDataSlot ) inputLayer.name = "Input Data" inputLayer.visible = True inputLayer.opacity = 1.0 def toggleTopToBottom(): index = self.layerstack.layerIndex( inputLayer ) self.layerstack.selectRow( index ) if index == 0: self.layerstack.moveSelectedToBottom() else: self.layerstack.moveSelectedToTop() inputLayer.shortcutRegistration = ( "Prediction Layers", "Bring Input To Top/Bottom", QShortcut( QKeySequence("i"), self.viewerControlWidget(), toggleTopToBottom), inputLayer ) layers.append(inputLayer) return layers
def setupLayers(self): """ which layers will be shown in the layerviewergui. Triggers the prediction by setting the layer on visible """ inputSlot = self.topLevelOperator.InputImage layers = [] labels = self.drawer.labelListModel # Add the segmentations for channel, segmentationSlot in enumerate( self.topLevelOperatorView.SegmentationChannels): if segmentationSlot.ready(): ref_label = labels[channel] segsrc = createDataSource(segmentationSlot) segLayer = AlphaModulatedLayer(segsrc, tintColor=ref_label.pmapColor(), range=(0.0, 1.0), normalize=(0.0, 1.0)) segLayer.opacity = 1 segLayer.visible = False segLayer.visibleChanged.connect( self.updateShowSegmentationCheckbox) def setLayerColor(c, segLayer_=segLayer, initializing=False): if not initializing and segLayer_ not in self.layerstack: # This layer has been removed from the layerstack already. # Don't touch it. return segLayer_.tintColor = c def setSegLayerName(n, segLayer_=segLayer, initializing=False): if not initializing and segLayer_ not in self.layerstack: # This layer has been removed from the layerstack already. # Don't touch it. return newName = "Segmentation of %s" % n segLayer_.name = newName setSegLayerName(ref_label.name, initializing=True) ref_label.pmapColorChanged.connect(setLayerColor) ref_label.nameChanged.connect(setSegLayerName) layers.append(segLayer) # Add the prediction probabilities for channel, predictionSlot in enumerate( self.topLevelOperator.PredictionProbabilityChannels ): # CHECKME: would CachedPredictionProbabilities be better? if predictionSlot.ready(): ref_label = labels[channel] predictsrc = createDataSource(predictionSlot) predictionLayer = AlphaModulatedLayer( predictsrc, tintColor=ref_label.pmapColor(), range=(0.0, 1.0), normalize=(0.0, 1.0)) predictionLayer.opacity = 0.25 predictionLayer.visible = self.drawer.liveUpdateButton.isChecked( ) predictionLayer.visibleChanged.connect( self.updateShowPredictionCheckbox) def setLayerColor(c, predictLayer_=predictionLayer, initializing=False): if not initializing and predictLayer_ not in self.layerstack: # This layer has been removed from the layerstack already. # Don't touch it. return predictLayer_.tintColor = c def setPredLayerName(n, predictLayer_=predictionLayer, initializing=False): """ function for setting the names for every Channel """ if not initializing and predictLayer_ not in self.layerstack: # This layer has been removed from the layerstack already. # Don't touch it. return newName = "Probability of %s" % n predictLayer_.name = newName setPredLayerName(ref_label.name, initializing=True) ref_label.pmapColorChanged.connect(setLayerColor) ref_label.nameChanged.connect(setPredLayerName) layers.append(predictionLayer) # The raw input data, always as last layer if inputSlot.ready(): rawLayer = self.createStandardLayerFromSlot(inputSlot) rawLayer.visible = True rawLayer.opacity = 1.0 rawLayer.name = "Raw Data (display only)" layers.append(rawLayer) return layers
def setupLayers(self): layers = [] op = self.topLevelOperatorView ct = create_default_16bit() ct[0] = 0 # Show the cached output, since it goes through a blocked cache if op.CachedOutput.ready(): outputSrc = LazyflowSource(op.CachedOutput) outputLayer = ColortableLayer(outputSrc, ct) outputLayer.name = "Final output" outputLayer.visible = False outputLayer.opacity = 1.0 outputLayer.setToolTip("Results of thresholding and size filter") layers.append(outputLayer) if op.InputChannelColors.ready(): input_channel_colors = [ QColor(r_g_b1[0], r_g_b1[1], r_g_b1[2]) for r_g_b1 in op.InputChannelColors.value ] else: input_channel_colors = list( map(QColor, self._defaultInputChannelColors)) for channel, channelProvider in enumerate(self._channelProviders): slot_drange = channelProvider.Output.meta.drange if slot_drange is not None: drange = slot_drange else: drange = (0.0, 1.0) channelSrc = LazyflowSource(channelProvider.Output) inputChannelLayer = AlphaModulatedLayer( channelSrc, tintColor=input_channel_colors[channel], range=drange, normalize=drange) inputChannelLayer.opacity = 0.5 inputChannelLayer.visible = True inputChannelLayer.name = "Input Channel " + str(channel) inputChannelLayer.setToolTip("Select input channel " + str(channel) + \ " if this prediction image contains the objects of interest.") layers.append(inputChannelLayer) if self._showDebug: #FIXME: We have to do that, because lazyflow doesn't have a way to make an operator partially ready curIndex = op.CurOperator.value if curIndex in (1, 3): if op.FilteredSmallLabels.ready(): filteredSmallLabelsSrc = LazyflowSource( op.FilteredSmallLabels) #filteredSmallLabelsLayer = self.createStandardLayerFromSlot( op.FilteredSmallLabels ) filteredSmallLabelsLayer = ColortableLayer( filteredSmallLabelsSrc, DebugLayerCmap.BINARY_SHADE_1.value) filteredSmallLabelsLayer.name = "After high threshold and size filter" filteredSmallLabelsLayer.visible = False filteredSmallLabelsLayer.opacity = 1.0 filteredSmallLabelsLayer.setToolTip( "Results of thresholding with the high pixel value threshold,\ followed by the size filter" ) layers.append(filteredSmallLabelsLayer) if op.SmallRegions.ready(): highThresholdSrc = LazyflowSource(op.SmallRegions) highThresholdLayer = ColortableLayer( highThresholdSrc, DebugLayerCmap.BINARY_SHADE_0.value) highThresholdLayer.name = "After high threshold" highThresholdLayer.visible = False highThresholdLayer.opacity = 1.0 highThresholdLayer.setToolTip( "Results of thresholding with the high pixel value threshold" ) layers.append(highThresholdLayer) if op.BigRegions.ready(): lowThresholdSrc = LazyflowSource(op.BigRegions) lowThresholdLayer = ColortableLayer( lowThresholdSrc, DebugLayerCmap.BINARY_WHITE.value) lowThresholdLayer.name = "After low threshold" lowThresholdLayer.visible = False lowThresholdLayer.opacity = 1.0 lowThresholdLayer.setToolTip( "Results of thresholding with the low pixel value threshold" ) layers.append(lowThresholdLayer) elif curIndex == 0: if op.BeforeSizeFilter.ready(): thSrc = LazyflowSource(op.BeforeSizeFilter) thLayer = ColortableLayer( thSrc, DebugLayerCmap.BINARY_WHITE.value) thLayer.name = "Before size filter" thLayer.visible = False thLayer.opacity = 1.0 thLayer.setToolTip( "Results of thresholding before the size filter is applied" ) layers.append(thLayer) # Selected input channel, smoothed. if op.Smoothed.ready(): smoothedLayer = self.createStandardLayerFromSlot(op.Smoothed) smoothedLayer.name = "Smoothed input" smoothedLayer.visible = True smoothedLayer.opacity = 1.0 smoothedLayer.setToolTip( "Selected channel data, smoothed with a Gaussian with user-defined sigma" ) layers.append(smoothedLayer) # Show the raw input data rawSlot = self.topLevelOperatorView.RawInput if rawSlot.ready(): rawLayer = self.createStandardLayerFromSlot(rawSlot) rawLayer.name = "Raw data" rawLayer.visible = True rawLayer.opacity = 1.0 layers.append(rawLayer) return layers
def setupLayers(self): layers = [] op = self.topLevelOperatorView binct = [QColor(Qt.black), QColor(Qt.white)] ct = self._createDefault16ColorColorTable() ct[0]=0 # Show the cached output, since it goes through a blocked cache if op.CachedOutput.ready(): outputSrc = LazyflowSource(op.CachedOutput) outputLayer = ColortableLayer(outputSrc, binct) outputLayer.name = "Output (Cached)" outputLayer.visible = False outputLayer.opacity = 1.0 layers.append(outputLayer) #FIXME: We have to do that, because lazyflow doesn't have a way to make an operator partially ready curIndex = self._drawer.tabWidget.currentIndex() if curIndex==1: if op.BigRegions.ready(): lowThresholdSrc = LazyflowSource(op.BigRegions) lowThresholdLayer = ColortableLayer(lowThresholdSrc, binct) lowThresholdLayer.name = "Big Regions" lowThresholdLayer.visible = False lowThresholdLayer.opacity = 1.0 layers.append(lowThresholdLayer) if op.FilteredSmallLabels.ready(): filteredSmallLabelsLayer = self.createStandardLayerFromSlot( op.FilteredSmallLabels ) filteredSmallLabelsLayer.name = "Filtered Small Labels" filteredSmallLabelsLayer.visible = False filteredSmallLabelsLayer.opacity = 1.0 layers.append(filteredSmallLabelsLayer) if op.SmallRegions.ready(): highThresholdSrc = LazyflowSource(op.SmallRegions) highThresholdLayer = ColortableLayer(highThresholdSrc, binct) highThresholdLayer.name = "Small Regions" highThresholdLayer.visible = False highThresholdLayer.opacity = 1.0 layers.append(highThresholdLayer) elif curIndex==0: if op.BeforeSizeFilter.ready(): thSrc = LazyflowSource(op.BeforeSizeFilter) thLayer = ColortableLayer(thSrc, binct) thLayer.name = "Thresholded Labels" thLayer.visible = False thLayer.opacity = 1.0 layers.append(thLayer) # Selected input channel, smoothed. if op.Smoothed.ready(): smoothedLayer = self.createStandardLayerFromSlot( op.Smoothed ) smoothedLayer.name = "Smoothed Input" smoothedLayer.visible = True smoothedLayer.opacity = 1.0 layers.append(smoothedLayer) # Show the selected channel if op.InputChannel.ready(): drange = op.InputChannel.meta.drange if drange is None: drange = (0.0, 1.0) channelSrc = LazyflowSource(op.InputChannel) channelLayer = AlphaModulatedLayer( channelSrc, tintColor=QColor(self._channelColors[op.Channel.value]), range=drange, normalize=drange ) channelLayer.name = "Input Ch{}".format(op.Channel.value) channelLayer.opacity = 1.0 #channelLayer.visible = channelIndex == op.Channel.value # By default, only the selected input channel is visible. layers.append(channelLayer) # Show the raw input data rawSlot = self.topLevelOperatorView.RawInput if rawSlot.ready(): rawLayer = self.createStandardLayerFromSlot( rawSlot ) rawLayer.name = "Raw Data" rawLayer.visible = True rawLayer.opacity = 1.0 layers.append(rawLayer) return layers
def setupLayers(self): layers = [] op = self.topLevelOperatorView binct = [QColor(Qt.black), QColor(Qt.white)] binct[0] = 0 ct = self._createDefault16ColorColorTable() ct[0]=0 # Show the cached output, since it goes through a blocked cache if op.CachedOutput.ready(): outputSrc = LazyflowSource(op.CachedOutput) outputLayer = ColortableLayer(outputSrc, binct) outputLayer.name = "Final output" outputLayer.visible = False outputLayer.opacity = 1.0 outputLayer.setToolTip("Results of thresholding and size filter") layers.append(outputLayer) if self._showDebug: #FIXME: We have to do that, because lazyflow doesn't have a way to make an operator partially ready curIndex = self._drawer.tabWidget.currentIndex() if curIndex==1: if op.BigRegions.ready(): lowThresholdSrc = LazyflowSource(op.BigRegions) lowThresholdLayer = ColortableLayer(lowThresholdSrc, binct) lowThresholdLayer.name = "After low threshold" lowThresholdLayer.visible = False lowThresholdLayer.opacity = 1.0 lowThresholdLayer.setToolTip("Results of thresholding with the low pixel value threshold") layers.append(lowThresholdLayer) if op.FilteredSmallLabels.ready(): filteredSmallLabelsLayer = self.createStandardLayerFromSlot( op.FilteredSmallLabels ) filteredSmallLabelsLayer.name = "After high threshold and size filter" filteredSmallLabelsLayer.visible = False filteredSmallLabelsLayer.opacity = 1.0 filteredSmallLabelsLayer.setToolTip("Results of thresholding with the high pixel value threshold,\ followed by the size filter") layers.append(filteredSmallLabelsLayer) if op.SmallRegions.ready(): highThresholdSrc = LazyflowSource(op.SmallRegions) highThresholdLayer = ColortableLayer(highThresholdSrc, binct) highThresholdLayer.name = "After high threshold" highThresholdLayer.visible = False highThresholdLayer.opacity = 1.0 highThresholdLayer.setToolTip("Results of thresholding with the high pixel value threshold") layers.append(highThresholdLayer) elif curIndex==0: if op.BeforeSizeFilter.ready(): thSrc = LazyflowSource(op.BeforeSizeFilter) thLayer = ColortableLayer(thSrc, ct) thLayer.name = "Before size filter" thLayer.visible = False thLayer.opacity = 1.0 thLayer.setToolTip("Results of thresholding before the size filter is applied") layers.append(thLayer) # Selected input channel, smoothed. if op.Smoothed.ready(): smoothedLayer = self.createStandardLayerFromSlot( op.Smoothed ) smoothedLayer.name = "Smoothed input" smoothedLayer.visible = True smoothedLayer.opacity = 1.0 smoothedLayer.setToolTip("Selected channel data, smoothed with a Gaussian with user-defined sigma") layers.append(smoothedLayer) # Show the selected channel if op.InputChannel.ready(): drange = op.InputChannel.meta.drange if drange is None: drange = (0.0, 1.0) channelSrc = LazyflowSource(op.InputChannel) #channelLayer = AlphaModulatedLayer( channelSrc, # tintColor=QColor(self._channelColors[op.Channel.value]), # range=drange, # normalize=drange ) #it used to be set to the label color, but people found it confusing channelLayer = AlphaModulatedLayer( channelSrc, tintColor = QColor(Qt.white), range = drange, normalize=drange) channelLayer.name = "Selected input channel" channelLayer.opacity = 1.0 channelLayer.setToolTip("The selected channel of the prediction images") #channelLayer.visible = channelIndex == op.Channel.value # By default, only the selected input channel is visible. layers.append(channelLayer) # Show the raw input data rawSlot = self.topLevelOperatorView.RawInput if rawSlot.ready(): rawLayer = self.createStandardLayerFromSlot( rawSlot ) rawLayer.name = "Raw data" rawLayer.visible = True rawLayer.opacity = 1.0 layers.append(rawLayer) return layers
def setupLayers(self): # Base class provides the label layer and the raw layer layers = super(ObjectClassificationGui, self).setupLayers() binarySlot = self.op.BinaryImages atlas_slot = self.op.Atlas segmentedSlot = self.op.SegmentationImages #This is just for colors labels = self.labelListData for channel, probSlot in enumerate( self.op.PredictionProbabilityChannels): if probSlot.ready() and channel < len(labels): ref_label = labels[channel] probsrc = LazyflowSource(probSlot) probLayer = AlphaModulatedLayer( probsrc, tintColor=ref_label.pmapColor(), range=(0.0, 1.0), normalize=(0.0, 1.0)) probLayer.opacity = 0.25 #probLayer.visible = self.labelingDrawerUi.checkInteractive.isChecked() #False, because it's much faster to draw predictions without these layers below probLayer.visible = False probLayer.setToolTip( "Probability that the object belongs to class {}".format( channel + 1)) def setLayerColor(c, predictLayer_=probLayer, ch=channel, initializing=False): if not initializing and predictLayer_ not in self.layerstack: # This layer has been removed from the layerstack already. # Don't touch it. return predictLayer_.tintColor = c def setLayerName(n, predictLayer_=probLayer, initializing=False): if not initializing and predictLayer_ not in self.layerstack: # This layer has been removed from the layerstack already. # Don't touch it. return newName = "Prediction for %s" % n predictLayer_.name = newName setLayerName(ref_label.name, initializing=True) ref_label.pmapColorChanged.connect(setLayerColor) ref_label.nameChanged.connect(setLayerName) layers.append(probLayer) predictionSlot = self.op.PredictionImages if predictionSlot.ready(): predictsrc = LazyflowSource(predictionSlot) self._colorTable16_forpmaps[0] = 0 predictLayer = ColortableLayer( predictsrc, colorTable=self._colorTable16_forpmaps) predictLayer.name = self.PREDICTION_LAYER_NAME predictLayer.ref_object = None predictLayer.opacity = 0.5 predictLayer.setToolTip( "Classification results, assigning a label to each object") # This weakref stuff is a little more fancy than strictly necessary. # The idea is to use the weakref's callback to determine when this layer instance is destroyed by the garbage collector, # and then we disconnect the signal that updates that layer. weak_predictLayer = weakref.ref(predictLayer) colortable_changed_callback = bind(self._setPredictionColorTable, weak_predictLayer) self._labelControlUi.labelListModel.dataChanged.connect( colortable_changed_callback) weak_predictLayer2 = weakref.ref( predictLayer, partial(self._disconnect_dataChange_callback, colortable_changed_callback)) # We have to make sure the weakref isn't destroyed because it is responsible for calling the callback. # Therefore, we retain it by adding it to a list. self._retained_weakrefs.append(weak_predictLayer2) # Ensure we're up-to-date (in case this is the first time the prediction layer is being added. for row in range(self._labelControlUi.labelListModel.rowCount()): self._setPredictionColorTableForRow(predictLayer, row) # put right after Labels, so that it is visible after hitting "live # predict". layers.insert(1, predictLayer) badObjectsSlot = self.op.BadObjectImages if badObjectsSlot.ready(): ct_black = [0, QColor(Qt.black).rgba()] badSrc = LazyflowSource(badObjectsSlot) badLayer = ColortableLayer(badSrc, colorTable=ct_black) badLayer.name = "Ambiguous objects" badLayer.setToolTip( "Objects with infinite or invalid values in features") badLayer.visible = False layers.append(badLayer) if segmentedSlot.ready(): ct = colortables.create_default_16bit() objectssrc = LazyflowSource(segmentedSlot) ct[0] = QColor(0, 0, 0, 0).rgba() # make 0 transparent objLayer = ColortableLayer(objectssrc, ct) objLayer.name = "Objects" objLayer.opacity = 0.5 objLayer.visible = False objLayer.setToolTip( "Segmented objects (labeled image/connected components)") layers.append(objLayer) uncertaintySlot = self.op.UncertaintyEstimateImage if uncertaintySlot.ready(): uncertaintySrc = LazyflowSource(uncertaintySlot) uncertaintyLayer = AlphaModulatedLayer(uncertaintySrc, tintColor=QColor(Qt.cyan), range=(0.0, 1.0), normalize=(0.0, 1.0)) uncertaintyLayer.name = "Uncertainty" uncertaintyLayer.visible = False uncertaintyLayer.opacity = 1.0 ActionInfo = ShortcutManager.ActionInfo uncertaintyLayer.shortcutRegistration = ( "u", ActionInfo("Uncertainty Layers", "Uncertainty", "Show/Hide Uncertainty", uncertaintyLayer.toggleVisible, self.viewerControlWidget(), uncertaintyLayer)) layers.append(uncertaintyLayer) if binarySlot.ready(): ct_binary = [0, QColor(255, 255, 255, 255).rgba()] # white foreground on transparent background, even for labeled images binct = [QColor(255, 255, 255, 255).rgba()] * 65536 binct[0] = 0 binaryimagesrc = LazyflowSource(binarySlot) binLayer = ColortableLayer(binaryimagesrc, binct) binLayer.name = "Binary image" binLayer.visible = True binLayer.opacity = 1.0 binLayer.setToolTip("Segmentation results as a binary mask") layers.append(binLayer) if atlas_slot.ready(): layers.append( self.createStandardLayerFromSlot(atlas_slot, name="Atlas", opacity=0.5)) # since we start with existing labels, it makes sense to start # with the first one selected. This would make more sense in # __init__(), but it does not take effect there. #self.selectLabel(0) return layers
def setupLayers(self): # Base class provides the label layer. layers = super(ObjectClassificationGui, self).setupLayers() binarySlot = self.op.BinaryImages segmentedSlot = self.op.SegmentationImages rawSlot = self.op.RawImages #This is just for colors labels = self.labelListData for channel, probSlot in enumerate(self.op.PredictionProbabilityChannels): if probSlot.ready() and channel < len(labels): ref_label = labels[channel] probsrc = LazyflowSource(probSlot) probLayer = AlphaModulatedLayer( probsrc, tintColor=ref_label.pmapColor(), range=(0.0, 1.0), normalize=(0.0, 1.0) ) probLayer.opacity = 0.25 #probLayer.visible = self.labelingDrawerUi.checkInteractive.isChecked() #False, because it's much faster to draw predictions without these layers below probLayer.visible = False probLayer.setToolTip("Probability that the object belongs to class {}".format(channel+1)) def setLayerColor(c, predictLayer_=probLayer, ch=channel, initializing=False): if not initializing and predictLayer_ not in self.layerstack: # This layer has been removed from the layerstack already. # Don't touch it. return predictLayer_.tintColor = c def setLayerName(n, predictLayer_=probLayer, initializing=False): if not initializing and predictLayer_ not in self.layerstack: # This layer has been removed from the layerstack already. # Don't touch it. return newName = "Prediction for %s" % n predictLayer_.name = newName setLayerName(ref_label.name, initializing=True) ref_label.pmapColorChanged.connect(setLayerColor) ref_label.nameChanged.connect(setLayerName) layers.append(probLayer) predictionSlot = self.op.PredictionImages if predictionSlot.ready(): predictsrc = LazyflowSource(predictionSlot) self._colorTable16_forpmaps[0] = 0 predictLayer = ColortableLayer(predictsrc, colorTable=self._colorTable16_forpmaps) predictLayer.name = self.PREDICTION_LAYER_NAME predictLayer.ref_object = None predictLayer.visible = self.labelingDrawerUi.checkInteractive.isChecked() predictLayer.opacity = 0.5 predictLayer.setToolTip("Classification results, assigning a label to each object") # This weakref stuff is a little more fancy than strictly necessary. # The idea is to use the weakref's callback to determine when this layer instance is destroyed by the garbage collector, # and then we disconnect the signal that updates that layer. weak_predictLayer = weakref.ref( predictLayer ) colortable_changed_callback = bind( self._setPredictionColorTable, weak_predictLayer ) self._labelControlUi.labelListModel.dataChanged.connect( colortable_changed_callback ) weak_predictLayer2 = weakref.ref( predictLayer, partial(self._disconnect_dataChange_callback, colortable_changed_callback) ) # We have to make sure the weakref isn't destroyed because it is responsible for calling the callback. # Therefore, we retain it by adding it to a list. self._retained_weakrefs.append( weak_predictLayer2 ) # Ensure we're up-to-date (in case this is the first time the prediction layer is being added. for row in range( self._labelControlUi.labelListModel.rowCount() ): self._setPredictionColorTableForRow( predictLayer, row ) # put right after Labels, so that it is visible after hitting "live # predict". layers.insert(1, predictLayer) badObjectsSlot = self.op.BadObjectImages if badObjectsSlot.ready(): ct_black = [0, QColor(Qt.black).rgba()] badSrc = LazyflowSource(badObjectsSlot) badLayer = ColortableLayer(badSrc, colorTable = ct_black) badLayer.name = "Ambiguous objects" badLayer.setToolTip("Objects with infinite or invalid values in features") badLayer.visible = False layers.append(badLayer) if segmentedSlot.ready(): ct = colortables.create_default_16bit() objectssrc = LazyflowSource(segmentedSlot) ct[0] = QColor(0, 0, 0, 0).rgba() # make 0 transparent objLayer = ColortableLayer(objectssrc, ct) objLayer.name = "Objects" objLayer.opacity = 0.5 objLayer.visible = False objLayer.setToolTip("Segmented objects (labeled image/connected components)") layers.append(objLayer) if binarySlot.ready(): ct_binary = [0, QColor(255, 255, 255, 255).rgba()] # white foreground on transparent background, even for labeled images binct = [QColor(255, 255, 255, 255).rgba()]*65536 binct[0] = 0 binaryimagesrc = LazyflowSource(binarySlot) binLayer = ColortableLayer(binaryimagesrc, binct) binLayer.name = "Binary image" binLayer.visible = True binLayer.opacity = 1.0 binLayer.setToolTip("Segmentation results as a binary mask") layers.append(binLayer) if rawSlot.ready(): rawLayer = self.createStandardLayerFromSlot(rawSlot) rawLayer.name = "Raw data" def toggleTopToBottom(): index = self.layerstack.layerIndex( rawLayer ) self.layerstack.selectRow( index ) if index == 0: self.layerstack.moveSelectedToBottom() else: self.layerstack.moveSelectedToTop() ActionInfo = ShortcutManager.ActionInfo rawLayer.shortcutRegistration = ( "i", ActionInfo( "Prediction Layers", "Bring Input To Top/Bottom", "Bring Input To Top/Bottom", toggleTopToBottom, self.viewerControlWidget(), rawLayer ) ) layers.append(rawLayer) # since we start with existing labels, it makes sense to start # with the first one selected. This would make more sense in # __init__(), but it does not take effect there. #self.selectLabel(0) return layers
def setupLayers(self): layers = [] op = self.topLevelOperatorView binct = [QColor(Qt.black), QColor(Qt.white)] binct[0] = 0 ct = create_default_16bit() ct[0]=0 # Show the cached output, since it goes through a blocked cache if op.CachedOutput.ready(): outputSrc = LazyflowSource(op.CachedOutput) outputLayer = ColortableLayer(outputSrc, ct) outputLayer.name = "Final output" outputLayer.visible = False outputLayer.opacity = 1.0 outputLayer.setToolTip("Results of thresholding and size filter") layers.append(outputLayer) if op.InputImage.ready(): numChannels = op.InputImage.meta.getTaggedShape()['c'] for channel in range(numChannels): channelProvider = OpSingleChannelSelector(parent=op.InputImage.getRealOperator().parent) channelProvider.Input.connect(op.InputImage) channelProvider.Index.setValue( channel ) channelSrc = LazyflowSource( channelProvider.Output ) inputChannelLayer = AlphaModulatedLayer( channelSrc, tintColor=QColor(self._channelColors[channel]), range=(0.0, 1.0), normalize=(0.0, 1.0) ) inputChannelLayer.opacity = 0.5 inputChannelLayer.visible = True inputChannelLayer.name = "Input Channel " + str(channel) inputChannelLayer.setToolTip("Select input channel " + str(channel) + \ " if this prediction image contains the objects of interest.") layers.append(inputChannelLayer) if self._showDebug: #FIXME: We have to do that, because lazyflow doesn't have a way to make an operator partially ready curIndex = self._drawer.tabWidget.currentIndex() if curIndex==1: if op.BigRegions.ready(): lowThresholdSrc = LazyflowSource(op.BigRegions) lowThresholdLayer = ColortableLayer(lowThresholdSrc, binct) lowThresholdLayer.name = "After low threshold" lowThresholdLayer.visible = False lowThresholdLayer.opacity = 1.0 lowThresholdLayer.setToolTip("Results of thresholding with the low pixel value threshold") layers.append(lowThresholdLayer) if op.FilteredSmallLabels.ready(): filteredSmallLabelsLayer = self.createStandardLayerFromSlot( op.FilteredSmallLabels ) filteredSmallLabelsLayer.name = "After high threshold and size filter" filteredSmallLabelsLayer.visible = False filteredSmallLabelsLayer.opacity = 1.0 filteredSmallLabelsLayer.setToolTip("Results of thresholding with the high pixel value threshold,\ followed by the size filter") layers.append(filteredSmallLabelsLayer) if op.SmallRegions.ready(): highThresholdSrc = LazyflowSource(op.SmallRegions) highThresholdLayer = ColortableLayer(highThresholdSrc, binct) highThresholdLayer.name = "After high threshold" highThresholdLayer.visible = False highThresholdLayer.opacity = 1.0 highThresholdLayer.setToolTip("Results of thresholding with the high pixel value threshold") layers.append(highThresholdLayer) elif curIndex==0: if op.BeforeSizeFilter.ready(): thSrc = LazyflowSource(op.BeforeSizeFilter) thLayer = ColortableLayer(thSrc, ct) thLayer.name = "Before size filter" thLayer.visible = False thLayer.opacity = 1.0 thLayer.setToolTip("Results of thresholding before the size filter is applied") layers.append(thLayer) # Selected input channel, smoothed. if op.Smoothed.ready(): smoothedLayer = self.createStandardLayerFromSlot( op.Smoothed ) smoothedLayer.name = "Smoothed input" smoothedLayer.visible = True smoothedLayer.opacity = 1.0 smoothedLayer.setToolTip("Selected channel data, smoothed with a Gaussian with user-defined sigma") layers.append(smoothedLayer) # Show the raw input data rawSlot = self.topLevelOperatorView.RawInput if rawSlot.ready(): rawLayer = self.createStandardLayerFromSlot( rawSlot ) rawLayer.name = "Raw data" rawLayer.visible = True rawLayer.opacity = 1.0 layers.append(rawLayer) return layers
def setupLayers(self): """ Called by our base class when one of our data slots has changed. This function creates a layer for each slot we want displayed in the volume editor. """ # Base class provides the label layer. layers = super(PixelClassificationGui, self).setupLayers() # Add the uncertainty estimate layer uncertaintySlot = self.topLevelOperatorView.UncertaintyEstimate if uncertaintySlot.ready(): uncertaintySrc = LazyflowSource(uncertaintySlot) uncertaintyLayer = AlphaModulatedLayer(uncertaintySrc, tintColor=QColor(Qt.cyan), range=(0.0, 1.0), normalize=(0.0, 1.0)) uncertaintyLayer.name = "Uncertainty" uncertaintyLayer.visible = False uncertaintyLayer.opacity = 1.0 uncertaintyLayer.shortcutRegistration = ( "Prediction Layers", "Show/Hide Uncertainty", QShortcut(QKeySequence("u"), self.viewerControlWidget(), uncertaintyLayer.toggleVisible), uncertaintyLayer) layers.append(uncertaintyLayer) labels = self.labelListData # Add each of the segmentations for channel, segmentationSlot in enumerate( self.topLevelOperatorView.SegmentationChannels): if segmentationSlot.ready() and channel < len(labels): ref_label = labels[channel] segsrc = LazyflowSource(segmentationSlot) segLayer = AlphaModulatedLayer(segsrc, tintColor=ref_label.pmapColor(), range=(0.0, 1.0), normalize=(0.0, 1.0)) segLayer.opacity = 1 segLayer.visible = False #self.labelingDrawerUi.liveUpdateButton.isChecked() segLayer.visibleChanged.connect( self.updateShowSegmentationCheckbox) def setLayerColor(c, segLayer=segLayer): segLayer.tintColor = c self._update_rendering() def setSegLayerName(n, segLayer=segLayer): oldname = segLayer.name newName = "Segmentation (%s)" % n segLayer.name = newName if not self.render: return if oldname in self._renderedLayers: label = self._renderedLayers.pop(oldname) self._renderedLayers[newName] = label setSegLayerName(ref_label.name) ref_label.pmapColorChanged.connect(setLayerColor) ref_label.nameChanged.connect(setSegLayerName) #check if layer is 3d before adding the "Toggle 3D" option #this check is done this way to match the VolumeRenderer, in #case different 3d-axistags should be rendered like t-x-y #_axiskeys = segmentationSlot.meta.getAxisKeys() if len(segmentationSlot.meta.shape) == 4: #the Renderer will cut out the last shape-dimension, so #we're checking for 4 dimensions self._setup_contexts(segLayer) layers.append(segLayer) # Add each of the predictions for channel, predictionSlot in enumerate( self.topLevelOperatorView.PredictionProbabilityChannels): if predictionSlot.ready() and channel < len(labels): ref_label = labels[channel] predictsrc = LazyflowSource(predictionSlot) predictLayer = AlphaModulatedLayer( predictsrc, tintColor=ref_label.pmapColor(), range=(0.0, 1.0), normalize=(0.0, 1.0)) predictLayer.opacity = 0.25 predictLayer.visible = self.labelingDrawerUi.liveUpdateButton.isChecked( ) predictLayer.visibleChanged.connect( self.updateShowPredictionCheckbox) def setLayerColor(c, predictLayer=predictLayer): predictLayer.tintColor = c def setPredLayerName(n, predictLayer=predictLayer): newName = "Prediction for %s" % n predictLayer.name = newName setPredLayerName(ref_label.name) ref_label.pmapColorChanged.connect(setLayerColor) ref_label.nameChanged.connect(setPredLayerName) layers.append(predictLayer) # Add the raw data last (on the bottom) inputDataSlot = self.topLevelOperatorView.InputImages if inputDataSlot.ready(): inputLayer = self.createStandardLayerFromSlot(inputDataSlot) inputLayer.name = "Input Data" inputLayer.visible = True inputLayer.opacity = 1.0 def toggleTopToBottom(): index = self.layerstack.layerIndex(inputLayer) self.layerstack.selectRow(index) if index == 0: self.layerstack.moveSelectedToBottom() else: self.layerstack.moveSelectedToTop() inputLayer.shortcutRegistration = ("Prediction Layers", "Bring Input To Top/Bottom", QShortcut( QKeySequence("i"), self.viewerControlWidget(), toggleTopToBottom), inputLayer) layers.append(inputLayer) self.handleLabelSelectionChange() return layers
def setupLayers(self): # Base class provides the label layer. layers = super(ObjectClassificationGui, self).setupLayers() labelOutput = self._labelingSlots.labelOutput binarySlot = self.op.BinaryImages segmentedSlot = self.op.SegmentationImages rawSlot = self.op.RawImages if segmentedSlot.ready(): ct = colortables.create_default_16bit() self.objectssrc = LazyflowSource(segmentedSlot) ct[0] = QColor(0, 0, 0, 0).rgba() # make 0 transparent layer = ColortableLayer(self.objectssrc, ct) layer.name = "Objects" layer.opacity = 0.5 layer.visible = True layers.append(layer) if binarySlot.ready(): ct_binary = [QColor(0, 0, 0, 0).rgba(), QColor(255, 255, 255, 255).rgba()] self.binaryimagesrc = LazyflowSource(binarySlot) layer = ColortableLayer(self.binaryimagesrc, ct_binary) layer.name = "Binary Image" layer.visible = False layers.append(layer) # This is just for colors labels = self.labelListData for channel, probSlot in enumerate(self.op.PredictionProbabilityChannels): if probSlot.ready() and channel < len(labels): ref_label = labels[channel] probsrc = LazyflowSource(probSlot) probLayer = AlphaModulatedLayer( probsrc, tintColor=ref_label.pmapColor(), range=(0.0, 1.0), normalize=(0.0, 1.0) ) probLayer.opacity = 0.25 probLayer.visible = self.labelingDrawerUi.checkInteractive.isChecked() def setLayerColor(c, predictLayer=probLayer): predictLayer.tintColor = c def setLayerName(n, predictLayer=probLayer): newName = "Prediction for %s" % n predictLayer.name = newName setLayerName(ref_label.name) ref_label.pmapColorChanged.connect(setLayerColor) ref_label.nameChanged.connect(setLayerName) layers.insert(0, probLayer) predictionSlot = self.op.PredictionImages if predictionSlot.ready(): self.predictsrc = LazyflowSource(predictionSlot) self.predictlayer = ColortableLayer(self.predictsrc, colorTable=self._colorTable16) self.predictlayer.name = "Prediction" self.predictlayer.ref_object = None self.predictlayer.visible = self.labelingDrawerUi.checkInteractive.isChecked() # put first, so that it is visible after hitting "live # predict". layers.insert(0, self.predictlayer) badObjectsSlot = self.op.BadObjectImages if badObjectsSlot.ready(): ct_black = [0, QColor(Qt.black).rgba()] self.badSrc = LazyflowSource(badObjectsSlot) self.badLayer = ColortableLayer(self.badSrc, colorTable=ct_black) self.badLayer.name = "Ambiguous objects" self.badLayer.visible = False layers.append(self.badLayer) if rawSlot.ready(): self.rawimagesrc = LazyflowSource(rawSlot) layer = self.createStandardLayerFromSlot(rawSlot) layer.name = "Raw data" layers.append(layer) # since we start with existing labels, it makes sense to start # with the first one selected. This would make more sense in # __init__(), but it does not take effect there. # self.selectLabel(0) return layers
def setupLayers(self): layers = [] op = self.topLevelOperatorView binct = [QColor(Qt.black), QColor(Qt.white)] binct[0] = 0 ct = create_default_16bit() ct[0] = 0 # Show the cached output, since it goes through a blocked cache if op.CachedOutput.ready(): outputSrc = LazyflowSource(op.CachedOutput) outputLayer = ColortableLayer(outputSrc, ct) outputLayer.name = "Final output" outputLayer.visible = False outputLayer.opacity = 1.0 outputLayer.setToolTip("Results of thresholding and size filter") layers.append(outputLayer) if op.InputChannelColors.ready(): input_channel_colors = [QColor(r_g_b1[0],r_g_b1[1],r_g_b1[2]) for r_g_b1 in op.InputChannelColors.value] else: input_channel_colors = list(map(QColor, self._defaultInputChannelColors)) for channel, channelProvider in enumerate(self._channelProviders): slot_drange = channelProvider.Output.meta.drange if slot_drange is not None: drange = slot_drange else: drange = (0.0, 1.0) channelSrc = LazyflowSource(channelProvider.Output) inputChannelLayer = AlphaModulatedLayer( channelSrc, tintColor=input_channel_colors[channel], range=drange, normalize=drange) inputChannelLayer.opacity = 0.5 inputChannelLayer.visible = True inputChannelLayer.name = "Input Channel " + str(channel) inputChannelLayer.setToolTip("Select input channel " + str(channel) + \ " if this prediction image contains the objects of interest.") layers.append(inputChannelLayer) if self._showDebug: #FIXME: We have to do that, because lazyflow doesn't have a way to make an operator partially ready curIndex = op.CurOperator.value if curIndex==1: if op.BigRegions.ready(): lowThresholdSrc = LazyflowSource(op.BigRegions) lowThresholdLayer = ColortableLayer(lowThresholdSrc, binct) lowThresholdLayer.name = "After low threshold" lowThresholdLayer.visible = False lowThresholdLayer.opacity = 1.0 lowThresholdLayer.setToolTip("Results of thresholding with the low pixel value threshold") layers.append(lowThresholdLayer) if op.FilteredSmallLabels.ready(): filteredSmallLabelsSrc = LazyflowSource(op.FilteredSmallLabels) #filteredSmallLabelsLayer = self.createStandardLayerFromSlot( op.FilteredSmallLabels ) filteredSmallLabelsLayer = ColortableLayer(filteredSmallLabelsSrc, binct) filteredSmallLabelsLayer.name = "After high threshold and size filter" filteredSmallLabelsLayer.visible = False filteredSmallLabelsLayer.opacity = 1.0 filteredSmallLabelsLayer.setToolTip("Results of thresholding with the high pixel value threshold,\ followed by the size filter") layers.append(filteredSmallLabelsLayer) if op.SmallRegions.ready(): highThresholdSrc = LazyflowSource(op.SmallRegions) highThresholdLayer = ColortableLayer(highThresholdSrc, binct) highThresholdLayer.name = "After high threshold" highThresholdLayer.visible = False highThresholdLayer.opacity = 1.0 highThresholdLayer.setToolTip("Results of thresholding with the high pixel value threshold") layers.append(highThresholdLayer) elif curIndex==0: if op.BeforeSizeFilter.ready(): thSrc = LazyflowSource(op.BeforeSizeFilter) thLayer = ColortableLayer(thSrc, ct) thLayer.name = "Before size filter" thLayer.visible = False thLayer.opacity = 1.0 thLayer.setToolTip("Results of thresholding before the size filter is applied") layers.append(thLayer) # Selected input channel, smoothed. if op.Smoothed.ready(): smoothedLayer = self.createStandardLayerFromSlot( op.Smoothed ) smoothedLayer.name = "Smoothed input" smoothedLayer.visible = True smoothedLayer.opacity = 1.0 smoothedLayer.setToolTip("Selected channel data, smoothed with a Gaussian with user-defined sigma") layers.append(smoothedLayer) # Show the raw input data rawSlot = self.topLevelOperatorView.RawInput if rawSlot.ready(): rawLayer = self.createStandardLayerFromSlot( rawSlot ) rawLayer.name = "Raw data" rawLayer.visible = True rawLayer.opacity = 1.0 layers.append(rawLayer) return layers
def setupLayers(self): layers = [] op = self.topLevelOperatorView # Show the cached output, since it goes through a blocked cache if op.CachedOutput.ready(): outputLayer = self.createStandardLayerFromSlot( op.CachedOutput ) outputLayer.name = "Output (Cached)" outputLayer.visible = False outputLayer.opacity = 1.0 layers.append(outputLayer) if op.BigRegions.ready(): lowThresholdLayer = self.createStandardLayerFromSlot( op.BigRegions ) lowThresholdLayer.name = "Big Regions" lowThresholdLayer.visible = False lowThresholdLayer.opacity = 1.0 layers.append(lowThresholdLayer) if op.FilteredSmallLabels.ready(): filteredSmallLabelsLayer = self.createStandardLayerFromSlot( op.FilteredSmallLabels, lastChannelIsAlpha=True ) filteredSmallLabelsLayer.name = "Filtered Small Labels" filteredSmallLabelsLayer.visible = False filteredSmallLabelsLayer.opacity = 1.0 layers.append(filteredSmallLabelsLayer) if op.SmallRegions.ready(): lowThresholdLayer = self.createStandardLayerFromSlot( op.SmallRegions ) lowThresholdLayer.name = "Small Regions" lowThresholdLayer.visible = False lowThresholdLayer.opacity = 1.0 layers.append(lowThresholdLayer) # Selected input channel, smoothed. if op.Smoothed.ready(): smoothedLayer = self.createStandardLayerFromSlot( op.Smoothed ) smoothedLayer.name = "Smoothed Input" smoothedLayer.visible = True smoothedLayer.opacity = 1.0 layers.append(smoothedLayer) # Show each input channel as a separate layer for channelIndex, channelSlot in enumerate(op.InputChannels): if op.InputChannels.ready(): drange = channelSlot.meta.drange if drange is None: drange = (0.0, 1.0) channelSrc = LazyflowSource(channelSlot) channelLayer = AlphaModulatedLayer( channelSrc, tintColor=QColor(self._channelColors[channelIndex]), range=drange, normalize=drange ) channelLayer.name = "Input Ch{}".format(channelIndex) channelLayer.opacity = 1.0 channelLayer.visible = channelIndex == op.Channel.value # By default, only the selected input channel is visible. layers.append(channelLayer) # Show the raw input data rawSlot = self.topLevelOperatorView.RawInput if rawSlot.ready(): rawLayer = self.createStandardLayerFromSlot( rawSlot ) rawLayer.name = "Raw Data" rawLayer.visible = True rawLayer.opacity = 1.0 layers.append(rawLayer) return layers
def setupLayers(self): layers = [] op = self.topLevelOperatorView binct = [QColor(Qt.black), QColor(Qt.white)] binct[0] = 0 ct = create_default_16bit() ct[0] = 0 # Show the cached output, since it goes through a blocked cache if op.CachedOutput.ready(): outputSrc = LazyflowSource(op.CachedOutput) outputLayer = ColortableLayer(outputSrc, ct) outputLayer.name = "Final output" outputLayer.visible = False outputLayer.opacity = 1.0 outputLayer.setToolTip("Results of thresholding and size filter") layers.append(outputLayer) for channel, channelProvider in enumerate(self._channelProviders): channelSrc = LazyflowSource(channelProvider.Output) inputChannelLayer = AlphaModulatedLayer( channelSrc, tintColor=QColor(self._channelColors[channel]), range=(0.0, 1.0), normalize=(0.0, 1.0)) inputChannelLayer.opacity = 0.5 inputChannelLayer.visible = True inputChannelLayer.name = "Input Channel " + str(channel) inputChannelLayer.setToolTip("Select input channel " + str(channel) + \ " if this prediction image contains the objects of interest.") layers.append(inputChannelLayer) if self._showDebug: #FIXME: We have to do that, because lazyflow doesn't have a way to make an operator partially ready curIndex = self._drawer.tabWidget.currentIndex() if curIndex==1: if op.BigRegions.ready(): lowThresholdSrc = LazyflowSource(op.BigRegions) lowThresholdLayer = ColortableLayer(lowThresholdSrc, binct) lowThresholdLayer.name = "After low threshold" lowThresholdLayer.visible = False lowThresholdLayer.opacity = 1.0 lowThresholdLayer.setToolTip("Results of thresholding with the low pixel value threshold") layers.append(lowThresholdLayer) if op.FilteredSmallLabels.ready(): filteredSmallLabelsSrc = LazyflowSource(op.FilteredSmallLabels) #filteredSmallLabelsLayer = self.createStandardLayerFromSlot( op.FilteredSmallLabels ) filteredSmallLabelsLayer = ColortableLayer(filteredSmallLabelsSrc, binct) filteredSmallLabelsLayer.name = "After high threshold and size filter" filteredSmallLabelsLayer.visible = False filteredSmallLabelsLayer.opacity = 1.0 filteredSmallLabelsLayer.setToolTip("Results of thresholding with the high pixel value threshold,\ followed by the size filter") layers.append(filteredSmallLabelsLayer) if op.SmallRegions.ready(): highThresholdSrc = LazyflowSource(op.SmallRegions) highThresholdLayer = ColortableLayer(highThresholdSrc, binct) highThresholdLayer.name = "After high threshold" highThresholdLayer.visible = False highThresholdLayer.opacity = 1.0 highThresholdLayer.setToolTip("Results of thresholding with the high pixel value threshold") layers.append(highThresholdLayer) elif curIndex==0: if op.BeforeSizeFilter.ready(): thSrc = LazyflowSource(op.BeforeSizeFilter) thLayer = ColortableLayer(thSrc, ct) thLayer.name = "Before size filter" thLayer.visible = False thLayer.opacity = 1.0 thLayer.setToolTip("Results of thresholding before the size filter is applied") layers.append(thLayer) # Selected input channel, smoothed. if op.Smoothed.ready(): smoothedLayer = self.createStandardLayerFromSlot( op.Smoothed ) smoothedLayer.name = "Smoothed input" smoothedLayer.visible = True smoothedLayer.opacity = 1.0 smoothedLayer.setToolTip("Selected channel data, smoothed with a Gaussian with user-defined sigma") layers.append(smoothedLayer) # Show the raw input data rawSlot = self.topLevelOperatorView.RawInput if rawSlot.ready(): rawLayer = self.createStandardLayerFromSlot( rawSlot ) rawLayer.name = "Raw data" rawLayer.visible = True rawLayer.opacity = 1.0 layers.append(rawLayer) return layers
def setupLayers(self): layers = [] opLane = self.topLevelOperatorView # This code depends on a specific order for the export slots. # If those change, update this function! selection_names = opLane.SelectionNames.value assert selection_names == [ 'Probabilities', 'Simple Segmentation', 'Uncertainty', 'Features' ] # see comment above selection = selection_names[opLane.InputSelection.value] if selection == 'Probabilities': exportedLayers = self._initPredictionLayers(opLane.ImageOnDisk) for layer in exportedLayers: layer.visible = True layer.name = layer.name + "- Exported" layers += exportedLayers previewLayers = self._initPredictionLayers(opLane.ImageToExport) for layer in previewLayers: layer.visible = False layer.name = layer.name + "- Preview" layers += previewLayers elif selection == "Simple Segmentation": exportedLayer = self._initSegmentationlayer(opLane.ImageOnDisk) if exportedLayer: exportedLayer.visible = True exportedLayer.name = exportedLayer.name + " - Exported" layers.append(exportedLayer) previewLayer = self._initSegmentationlayer(opLane.ImageToExport) if previewLayer: previewLayer.visible = False previewLayer.name = previewLayer.name + " - Preview" layers.append(previewLayer) elif selection == "Uncertainty": if opLane.ImageToExport.ready(): previewUncertaintySource = LazyflowSource(opLane.ImageToExport) previewLayer = AlphaModulatedLayer( previewUncertaintySource, tintColor=QColor(0, 255, 255), # cyan range=(0.0, 1.0), normalize=(0.0, 1.0)) previewLayer.opacity = 0.5 previewLayer.visible = False previewLayer.name = "Uncertainty - Preview" layers.append(previewLayer) if opLane.ImageOnDisk.ready(): exportedUncertaintySource = LazyflowSource(opLane.ImageOnDisk) exportedLayer = AlphaModulatedLayer( exportedUncertaintySource, tintColor=QColor(0, 255, 255), # cyan range=(0.0, 1.0), normalize=(0.0, 1.0)) exportedLayer.opacity = 0.5 exportedLayer.visible = True exportedLayer.name = "Uncertainty - Exported" layers.append(exportedLayer) elif selection == "Features": if opLane.ImageToExport.ready(): previewLayer = self.createStandardLayerFromSlot( opLane.ImageToExport) previewLayer.visible = False previewLayer.name = "Features - Preview" previewLayer.set_normalize(0, None) layers.append(previewLayer) if opLane.ImageOnDisk.ready(): exportedLayer = self.createStandardLayerFromSlot( opLane.ImageOnDisk) exportedLayer.visible = True exportedLayer.name = "Features - Exported" exportedLayer.set_normalize(0, None) layers.append(exportedLayer) # If available, also show the raw data layer rawSlot = opLane.FormattedRawData if rawSlot.ready(): rawLayer = self.createStandardLayerFromSlot(rawSlot) rawLayer.name = "Raw Data" rawLayer.visible = True rawLayer.opacity = 1.0 layers.append(rawLayer) return layers
def setupLayers(self, currentImageIndex): """ Called by our base class when one of our data slots has changed. This function creates a layer for each slot we want displayed in the volume editor. """ # Base class provides the label layer. layers = super(PixelClassificationGui, self).setupLayers(currentImageIndex) labels = self.labelListData # Add the uncertainty estimate layer uncertaintySlot = self.pipeline.UncertaintyEstimate[currentImageIndex] if uncertaintySlot.ready(): uncertaintySrc = LazyflowSource(uncertaintySlot) uncertaintyLayer = AlphaModulatedLayer(uncertaintySrc, tintColor=QColor(Qt.cyan), range=(0.0, 1.0), normalize=(0.0, 1.0)) uncertaintyLayer.name = "Uncertainty" uncertaintyLayer.visible = False uncertaintyLayer.opacity = 1.0 uncertaintyLayer.shortcutRegistration = ( "Prediction Layers", "Show/Hide Uncertainty", QShortcut(QKeySequence("u"), self.viewerControlWidget(), uncertaintyLayer.toggleVisible), uncertaintyLayer) layers.append(uncertaintyLayer) # Add each of the predictions for channel, predictionSlot in enumerate( self.pipeline.PredictionProbabilityChannels[currentImageIndex] ): if predictionSlot.ready() and channel < len(labels): ref_label = labels[channel] predictsrc = LazyflowSource(predictionSlot) predictLayer = AlphaModulatedLayer(predictsrc, tintColor=ref_label.color, range=(0.0, 1.0), normalize=(0.0, 1.0)) predictLayer.opacity = 0.25 predictLayer.visible = self.labelingDrawerUi.checkInteractive.isChecked( ) predictLayer.visibleChanged.connect( self.updateShowPredictionCheckbox) def setLayerColor(c): predictLayer.tintColor = c def setLayerName(n): newName = "Prediction for %s" % ref_label.name predictLayer.name = newName setLayerName(ref_label.name) ref_label.colorChanged.connect(setLayerColor) ref_label.nameChanged.connect(setLayerName) layers.append(predictLayer) # Add each of the segementations for channel, segmentationSlot in enumerate( self.pipeline.SegmentationChannels[currentImageIndex]): if segmentationSlot.ready() and channel < len(labels): ref_label = labels[channel] segsrc = LazyflowSource(segmentationSlot) segLayer = AlphaModulatedLayer(segsrc, tintColor=ref_label.color, range=(0.0, 1.0), normalize=(0.0, 1.0)) segLayer.opacity = 1 segLayer.visible = self.labelingDrawerUi.checkInteractive.isChecked( ) segLayer.visibleChanged.connect( self.updateShowSegmentationCheckbox) def setLayerColor(c): segLayer.tintColor = c def setLayerName(n): newName = "Segmentation (%s)" % ref_label.name segLayer.name = newName setLayerName(ref_label.name) ref_label.colorChanged.connect(setLayerColor) ref_label.nameChanged.connect(setLayerName) layers.append(segLayer) # Add the raw data last (on the bottom) inputDataSlot = self.pipeline.InputImages[currentImageIndex] if inputDataSlot.ready(): inputLayer = self.createStandardLayerFromSlot(inputDataSlot) inputLayer.name = "Input Data" inputLayer.visible = True inputLayer.opacity = 1.0 def toggleTopToBottom(): index = self.layerstack.layerIndex(inputLayer) self.layerstack.selectRow(index) if index == 0: self.layerstack.moveSelectedToBottom() else: self.layerstack.moveSelectedToTop() inputLayer.shortcutRegistration = ("Prediction Layers", "Bring Input To Top/Bottom", QShortcut( QKeySequence("i"), self.viewerControlWidget(), toggleTopToBottom), inputLayer) layers.append(inputLayer) return layers
def setupLayers(self): layers = [] opLane = self.topLevelOperatorView # This code depends on a specific order for the export slots. # If those change, update this function! selection_names = opLane.SelectionNames.value assert selection_names[0:4] == ['Probabilities', 'Simple Segmentation', 'Uncertainty', 'Features'] # see comment above selection = selection_names[ opLane.InputSelection.value ] if selection == 'Probabilities': exportedLayers = self._initPredictionLayers(opLane.ImageOnDisk) for layer in exportedLayers: layer.visible = True layer.name = layer.name + "- Exported" layers += exportedLayers previewLayers = self._initPredictionLayers(opLane.ImageToExport) for layer in previewLayers: layer.visible = False layer.name = layer.name + "- Preview" layers += previewLayers elif selection == "Simple Segmentation": exportedLayer = self._initSegmentationlayer(opLane.ImageOnDisk) if exportedLayer: exportedLayer.visible = True exportedLayer.name = exportedLayer.name + " - Exported" layers.append( exportedLayer ) previewLayer = self._initSegmentationlayer(opLane.ImageToExport) if previewLayer: previewLayer.visible = False previewLayer.name = previewLayer.name + " - Preview" layers.append( previewLayer ) elif selection == "Uncertainty": if opLane.ImageToExport.ready(): previewUncertaintySource = LazyflowSource(opLane.ImageToExport) previewLayer = AlphaModulatedLayer( previewUncertaintySource, tintColor=QColor(0,255,255), # cyan range=(0.0, 1.0), normalize=(0.0,1.0) ) previewLayer.opacity = 0.5 previewLayer.visible = False previewLayer.name = "Uncertainty - Preview" layers.append(previewLayer) if opLane.ImageOnDisk.ready(): exportedUncertaintySource = LazyflowSource(opLane.ImageOnDisk) exportedLayer = AlphaModulatedLayer( exportedUncertaintySource, tintColor=QColor(0,255,255), # cyan range=(0.0, 1.0), normalize=(0.0,1.0) ) exportedLayer.opacity = 0.5 exportedLayer.visible = True exportedLayer.name = "Uncertainty - Exported" layers.append(exportedLayer) else: # Features and all other layers. if selection != "Features": warnings.warn("Not sure how to display '{}' result. Showing with default layer settings." .format(selection)) if opLane.ImageToExport.ready(): previewLayer = self.createStandardLayerFromSlot( opLane.ImageToExport ) previewLayer.visible = False previewLayer.name = "{} - Preview".format( selection ) previewLayer.set_normalize( 0, None ) layers.append(previewLayer) if opLane.ImageOnDisk.ready(): exportedLayer = self.createStandardLayerFromSlot( opLane.ImageOnDisk ) exportedLayer.visible = True exportedLayer.name = "{} - Exported".format( selection ) exportedLayer.set_normalize( 0, None ) layers.append(exportedLayer) # If available, also show the raw data layer rawSlot = opLane.FormattedRawData if rawSlot.ready(): rawLayer = self.createStandardLayerFromSlot( rawSlot ) rawLayer.name = "Raw Data" rawLayer.visible = True rawLayer.opacity = 1.0 layers.append( rawLayer ) return layers
def setupLayers(self): layers = [] opLane = self.topLevelOperatorView # This code depends on a specific order for the export slots. # If those change, update this function! selection_names = opLane.SelectionNames.value # see comment above for name, expected in zip(selection_names[0:5], [ 'Probabilities', 'Simple Segmentation', 'Uncertainty', 'Features', 'Labels' ]): assert name.startswith( expected ), "The Selection Names don't match the expected selection names." selection = selection_names[opLane.InputSelection.value] if selection.startswith('Probabilities'): exportedLayers = self._initPredictionLayers(opLane.ImageOnDisk) for layer in exportedLayers: layer.visible = True layer.name = layer.name + "- Exported" layers += exportedLayers previewLayers = self._initPredictionLayers(opLane.ImageToExport) for layer in previewLayers: layer.visible = False layer.name = layer.name + "- Preview" layers += previewLayers elif selection.startswith( "Simple Segmentation") or selection.startswith("Labels"): exportedLayer = self._initColortablelayer(opLane.ImageOnDisk) if exportedLayer: exportedLayer.visible = True exportedLayer.name = selection + " - Exported" layers.append(exportedLayer) previewLayer = self._initColortablelayer(opLane.ImageToExport) if previewLayer: previewLayer.visible = False previewLayer.name = selection + " - Preview" layers.append(previewLayer) elif selection.startswith("Uncertainty"): if opLane.ImageToExport.ready(): previewUncertaintySource = LazyflowSource(opLane.ImageToExport) previewLayer = AlphaModulatedLayer( previewUncertaintySource, tintColor=QColor(0, 255, 255), # cyan range=(0.0, 1.0), normalize=(0.0, 1.0)) previewLayer.opacity = 0.5 previewLayer.visible = False previewLayer.name = "Uncertainty - Preview" layers.append(previewLayer) if opLane.ImageOnDisk.ready(): exportedUncertaintySource = LazyflowSource(opLane.ImageOnDisk) exportedLayer = AlphaModulatedLayer( exportedUncertaintySource, tintColor=QColor(0, 255, 255), # cyan range=(0.0, 1.0), normalize=(0.0, 1.0)) exportedLayer.opacity = 0.5 exportedLayer.visible = True exportedLayer.name = "Uncertainty - Exported" layers.append(exportedLayer) else: # Features and all other layers. if selection.startswith("Features"): warnings.warn( "Not sure how to display '{}' result. Showing with default layer settings." .format(selection)) if opLane.ImageToExport.ready(): previewLayer = self.createStandardLayerFromSlot( opLane.ImageToExport) previewLayer.visible = False previewLayer.name = "{} - Preview".format(selection) previewLayer.set_normalize(0, None) layers.append(previewLayer) if opLane.ImageOnDisk.ready(): exportedLayer = self.createStandardLayerFromSlot( opLane.ImageOnDisk) exportedLayer.visible = True exportedLayer.name = "{} - Exported".format(selection) exportedLayer.set_normalize(0, None) layers.append(exportedLayer) # If available, also show the raw data layer rawSlot = opLane.FormattedRawData if rawSlot.ready(): rawLayer = self.createStandardLayerFromSlot(rawSlot) rawLayer.name = "Raw Data" rawLayer.visible = True rawLayer.opacity = 1.0 layers.append(rawLayer) return layers
def setupLayers(self): """ Called by our base class when one of our data slots has changed. This function creates a layer for each slot we want displayed in the volume editor. """ # Base class provides the label layer. layers = super(PixelClassificationGui, self).setupLayers() # Add the uncertainty estimate layer uncertaintySlot = self.topLevelOperatorView.UncertaintyEstimate if uncertaintySlot.ready(): uncertaintySrc = LazyflowSource(uncertaintySlot) uncertaintyLayer = AlphaModulatedLayer( uncertaintySrc, tintColor=QColor( Qt.cyan ), range=(0.0, 1.0), normalize=(0.0, 1.0) ) uncertaintyLayer.name = "Uncertainty" uncertaintyLayer.visible = False uncertaintyLayer.opacity = 1.0 uncertaintyLayer.shortcutRegistration = ( "Prediction Layers", "Show/Hide Uncertainty", QShortcut( QKeySequence("u"), self.viewerControlWidget(), uncertaintyLayer.toggleVisible ), uncertaintyLayer ) layers.append(uncertaintyLayer) labels = self.labelListData # Add each of the segmentations for channel, segmentationSlot in enumerate(self.topLevelOperatorView.SegmentationChannels): if segmentationSlot.ready() and channel < len(labels): ref_label = labels[channel] segsrc = LazyflowSource(segmentationSlot) segLayer = AlphaModulatedLayer( segsrc, tintColor=ref_label.pmapColor(), range=(0.0, 1.0), normalize=(0.0, 1.0) ) segLayer.opacity = 1 segLayer.visible = False #self.labelingDrawerUi.liveUpdateButton.isChecked() segLayer.visibleChanged.connect(self.updateShowSegmentationCheckbox) def setLayerColor(c, segLayer=segLayer): segLayer.tintColor = c self._update_rendering() def setSegLayerName(n, segLayer=segLayer): oldname = segLayer.name newName = "Segmentation (%s)" % n segLayer.name = newName if not self.render: return if oldname in self._renderedLayers: label = self._renderedLayers.pop(oldname) self._renderedLayers[newName] = label setSegLayerName(ref_label.name) ref_label.pmapColorChanged.connect(setLayerColor) ref_label.nameChanged.connect(setSegLayerName) #check if layer is 3d before adding the "Toggle 3D" option #this check is done this way to match the VolumeRenderer, in #case different 3d-axistags should be rendered like t-x-y #_axiskeys = segmentationSlot.meta.getAxisKeys() if len(segmentationSlot.meta.shape) == 4: #the Renderer will cut out the last shape-dimension, so #we're checking for 4 dimensions self._setup_contexts(segLayer) layers.append(segLayer) # Add each of the predictions for channel, predictionSlot in enumerate(self.topLevelOperatorView.PredictionProbabilityChannels): if predictionSlot.ready() and channel < len(labels): ref_label = labels[channel] predictsrc = LazyflowSource(predictionSlot) predictLayer = AlphaModulatedLayer( predictsrc, tintColor=ref_label.pmapColor(), range=(0.0, 1.0), normalize=(0.0, 1.0) ) predictLayer.opacity = 0.25 predictLayer.visible = self.labelingDrawerUi.liveUpdateButton.isChecked() predictLayer.visibleChanged.connect(self.updateShowPredictionCheckbox) def setLayerColor(c, predictLayer=predictLayer): predictLayer.tintColor = c def setPredLayerName(n, predictLayer=predictLayer): newName = "Prediction for %s" % n predictLayer.name = newName setPredLayerName(ref_label.name) ref_label.pmapColorChanged.connect(setLayerColor) ref_label.nameChanged.connect(setPredLayerName) layers.append(predictLayer) # Add the raw data last (on the bottom) inputDataSlot = self.topLevelOperatorView.InputImages if inputDataSlot.ready(): inputLayer = self.createStandardLayerFromSlot( inputDataSlot ) inputLayer.name = "Input Data" inputLayer.visible = True inputLayer.opacity = 1.0 def toggleTopToBottom(): index = self.layerstack.layerIndex( inputLayer ) self.layerstack.selectRow( index ) if index == 0: self.layerstack.moveSelectedToBottom() else: self.layerstack.moveSelectedToTop() inputLayer.shortcutRegistration = ( "Prediction Layers", "Bring Input To Top/Bottom", QShortcut( QKeySequence("i"), self.viewerControlWidget(), toggleTopToBottom), inputLayer ) layers.append(inputLayer) self.handleLabelSelectionChange() return layers
def setupLayers(self): """ Called by our base class when one of our data slots has changed. This function creates a layer for each slot we want displayed in the volume editor. """ # Base class provides the label layer. layers = super(PixelClassificationGui, self).setupLayers() ActionInfo = ShortcutManager.ActionInfo if ilastik_config.getboolean('ilastik', 'debug'): # Add the label projection layer. labelProjectionSlot = self.topLevelOperatorView.opLabelPipeline.opLabelArray.Projection2D if labelProjectionSlot.ready(): projectionSrc = LazyflowSource(labelProjectionSlot) try: # This colortable requires matplotlib from volumina.colortables import jet projectionLayer = ColortableLayer( projectionSrc, colorTable=[QColor(0,0,0,128).rgba()]+jet(N=255), normalize=(0.0, 1.0) ) except (ImportError, RuntimeError): pass else: projectionLayer.name = "Label Projection" projectionLayer.visible = False projectionLayer.opacity = 1.0 layers.append(projectionLayer) # Show the mask over everything except labels maskSlot = self.topLevelOperatorView.PredictionMasks if maskSlot.ready(): maskLayer = self._create_binary_mask_layer_from_slot( maskSlot ) maskLayer.name = "Mask" maskLayer.visible = True maskLayer.opacity = 1.0 layers.append( maskLayer ) # Add the uncertainty estimate layer uncertaintySlot = self.topLevelOperatorView.UncertaintyEstimate if uncertaintySlot.ready(): uncertaintySrc = LazyflowSource(uncertaintySlot) uncertaintyLayer = AlphaModulatedLayer( uncertaintySrc, tintColor=QColor( Qt.cyan ), range=(0.0, 1.0), normalize=(0.0, 1.0) ) uncertaintyLayer.name = "Uncertainty" uncertaintyLayer.visible = False uncertaintyLayer.opacity = 1.0 uncertaintyLayer.shortcutRegistration = ( "u", ActionInfo( "Prediction Layers", "Uncertainty", "Show/Hide Uncertainty", uncertaintyLayer.toggleVisible, self.viewerControlWidget(), uncertaintyLayer ) ) layers.append(uncertaintyLayer) labels = self.labelListData # Add each of the segmentations for channel, segmentationSlot in enumerate(self.topLevelOperatorView.SegmentationChannels): if segmentationSlot.ready() and channel < len(labels): ref_label = labels[channel] segsrc = LazyflowSource(segmentationSlot) segLayer = AlphaModulatedLayer( segsrc, tintColor=ref_label.pmapColor(), range=(0.0, 1.0), normalize=(0.0, 1.0) ) segLayer.opacity = 1 segLayer.visible = False #self.labelingDrawerUi.liveUpdateButton.isChecked() segLayer.visibleChanged.connect(self.updateShowSegmentationCheckbox) def setLayerColor(c, segLayer_=segLayer, initializing=False): if not initializing and segLayer_ not in self.layerstack: # This layer has been removed from the layerstack already. # Don't touch it. return segLayer_.tintColor = c self._update_rendering() def setSegLayerName(n, segLayer_=segLayer, initializing=False): if not initializing and segLayer_ not in self.layerstack: # This layer has been removed from the layerstack already. # Don't touch it. return oldname = segLayer_.name newName = "Segmentation (%s)" % n segLayer_.name = newName if not self.render: return if oldname in self._renderedLayers: label = self._renderedLayers.pop(oldname) self._renderedLayers[newName] = label setSegLayerName(ref_label.name, initializing=True) ref_label.pmapColorChanged.connect(setLayerColor) ref_label.nameChanged.connect(setSegLayerName) #check if layer is 3d before adding the "Toggle 3D" option #this check is done this way to match the VolumeRenderer, in #case different 3d-axistags should be rendered like t-x-y #_axiskeys = segmentationSlot.meta.getAxisKeys() if len(segmentationSlot.meta.shape) == 4: #the Renderer will cut out the last shape-dimension, so #we're checking for 4 dimensions self._setup_contexts(segLayer) layers.append(segLayer) # Add each of the predictions for channel, predictionSlot in enumerate(self.topLevelOperatorView.PredictionProbabilityChannels): if predictionSlot.ready() and channel < len(labels): ref_label = labels[channel] predictsrc = LazyflowSource(predictionSlot) predictLayer = AlphaModulatedLayer( predictsrc, tintColor=ref_label.pmapColor(), range=(0.0, 1.0), normalize=(0.0, 1.0) ) predictLayer.opacity = 0.25 predictLayer.visible = self.labelingDrawerUi.liveUpdateButton.isChecked() predictLayer.visibleChanged.connect(self.updateShowPredictionCheckbox) def setLayerColor(c, predictLayer_=predictLayer, initializing=False): if not initializing and predictLayer_ not in self.layerstack: # This layer has been removed from the layerstack already. # Don't touch it. return predictLayer_.tintColor = c def setPredLayerName(n, predictLayer_=predictLayer, initializing=False): if not initializing and predictLayer_ not in self.layerstack: # This layer has been removed from the layerstack already. # Don't touch it. return newName = "Prediction for %s" % n predictLayer_.name = newName setPredLayerName(ref_label.name, initializing=True) ref_label.pmapColorChanged.connect(setLayerColor) ref_label.nameChanged.connect(setPredLayerName) layers.append(predictLayer) # Add the raw data last (on the bottom) inputDataSlot = self.topLevelOperatorView.InputImages if inputDataSlot.ready(): inputLayer = self.createStandardLayerFromSlot( inputDataSlot ) inputLayer.name = "Input Data" inputLayer.visible = True inputLayer.opacity = 1.0 # the flag window_leveling is used to determine if the contrast # of the layer is adjustable if isinstance( inputLayer, GrayscaleLayer ): inputLayer.window_leveling = True else: inputLayer.window_leveling = False def toggleTopToBottom(): index = self.layerstack.layerIndex( inputLayer ) self.layerstack.selectRow( index ) if index == 0: self.layerstack.moveSelectedToBottom() else: self.layerstack.moveSelectedToTop() inputLayer.shortcutRegistration = ( "i", ActionInfo( "Prediction Layers", "Bring Input To Top/Bottom", "Bring Input To Top/Bottom", toggleTopToBottom, self.viewerControlWidget(), inputLayer ) ) layers.append(inputLayer) # The thresholding button can only be used if the data is displayed as grayscale. if inputLayer.window_leveling: self.labelingDrawerUi.thresToolButton.show() else: self.labelingDrawerUi.thresToolButton.hide() self.handleLabelSelectionChange() return layers
def setupLayers(self): layers = [] op = self.topLevelOperatorView binct = [QColor(Qt.black), QColor(Qt.white)] binct[0] = 0 ct = self._createDefault16ColorColorTable() ct[0] = 0 # Show the cached output, since it goes through a blocked cache if op.CachedOutput.ready(): outputSrc = LazyflowSource(op.CachedOutput) outputLayer = ColortableLayer(outputSrc, binct) outputLayer.name = "Final output" outputLayer.visible = False outputLayer.opacity = 1.0 outputLayer.setToolTip("Results of thresholding and size filter") layers.append(outputLayer) if self._showDebug: #FIXME: We have to do that, because lazyflow doesn't have a way to make an operator partially ready curIndex = self._drawer.tabWidget.currentIndex() if curIndex == 1: if op.BigRegions.ready(): lowThresholdSrc = LazyflowSource(op.BigRegions) lowThresholdLayer = ColortableLayer(lowThresholdSrc, binct) lowThresholdLayer.name = "After low threshold" lowThresholdLayer.visible = False lowThresholdLayer.opacity = 1.0 lowThresholdLayer.setToolTip( "Results of thresholding with the low pixel value threshold" ) layers.append(lowThresholdLayer) if op.FilteredSmallLabels.ready(): filteredSmallLabelsLayer = self.createStandardLayerFromSlot( op.FilteredSmallLabels) filteredSmallLabelsLayer.name = "After high threshold and size filter" filteredSmallLabelsLayer.visible = False filteredSmallLabelsLayer.opacity = 1.0 filteredSmallLabelsLayer.setToolTip( "Results of thresholding with the high pixel value threshold,\ followed by the size filter" ) layers.append(filteredSmallLabelsLayer) if op.SmallRegions.ready(): highThresholdSrc = LazyflowSource(op.SmallRegions) highThresholdLayer = ColortableLayer( highThresholdSrc, binct) highThresholdLayer.name = "After high threshold" highThresholdLayer.visible = False highThresholdLayer.opacity = 1.0 highThresholdLayer.setToolTip( "Results of thresholding with the high pixel value threshold" ) layers.append(highThresholdLayer) elif curIndex == 0: if op.BeforeSizeFilter.ready(): thSrc = LazyflowSource(op.BeforeSizeFilter) thLayer = ColortableLayer(thSrc, ct) thLayer.name = "Before size filter" thLayer.visible = False thLayer.opacity = 1.0 thLayer.setToolTip( "Results of thresholding before the size filter is applied" ) layers.append(thLayer) # Selected input channel, smoothed. if op.Smoothed.ready(): smoothedLayer = self.createStandardLayerFromSlot(op.Smoothed) smoothedLayer.name = "Smoothed input" smoothedLayer.visible = True smoothedLayer.opacity = 1.0 smoothedLayer.setToolTip( "Selected channel data, smoothed with a Gaussian with user-defined sigma" ) layers.append(smoothedLayer) # Show the selected channel if op.InputChannel.ready(): drange = op.InputChannel.meta.drange if drange is None: drange = (0.0, 1.0) channelSrc = LazyflowSource(op.InputChannel) #channelLayer = AlphaModulatedLayer( channelSrc, # tintColor=QColor(self._channelColors[op.Channel.value]), # range=drange, # normalize=drange ) #it used to be set to the label color, but people found it confusing channelLayer = AlphaModulatedLayer(channelSrc, tintColor=QColor(Qt.white), range=drange, normalize=drange) channelLayer.name = "Selected input channel" channelLayer.opacity = 1.0 channelLayer.setToolTip( "The selected channel of the prediction images") #channelLayer.visible = channelIndex == op.Channel.value # By default, only the selected input channel is visible. layers.append(channelLayer) # Show the raw input data rawSlot = self.topLevelOperatorView.RawInput if rawSlot.ready(): rawLayer = self.createStandardLayerFromSlot(rawSlot) rawLayer.name = "Raw data" rawLayer.visible = True rawLayer.opacity = 1.0 layers.append(rawLayer) return layers
def setupLayers(self): """ which layers will be shown in the layerviewergui. Triggers the prediction by setting the layer on visible """ layers = super(NNClassGui, self).setupLayers() labels = self.labelListData # validationlayer = AlphaModulatedLayer() for channel, predictionSlot in enumerate(self.topLevelOperatorView.PredictionProbabilityChannels): logger.info(f"prediction_slot: {predictionSlot}") if predictionSlot.ready() and channel < len(labels): ref_label = labels[channel] predictsrc = LazyflowSource(predictionSlot) predictionLayer = AlphaModulatedLayer(predictsrc, tintColor=ref_label.pmapColor(), normalize=(0.0, 1.0)) predictionLayer.visible = self.labelingDrawerUi.livePrediction.isChecked() predictionLayer.opacity = 0.5 predictionLayer.visibleChanged.connect(self.updateShowPredictionCheckbox) def setLayerColor(c, predictLayer_=predictionLayer, initializing=False): if not initializing and predictLayer_ not in self.layerstack: # This layer has been removed from the layerstack already. # Don't touch it. return predictLayer_.tintColor = c def setPredLayerName(n, predictLayer_=predictionLayer, initializing=False): """ function for setting the names for every Channel """ if not initializing and predictLayer_ not in self.layerstack: # This layer has been removed from the layerstack already. # Don't touch it. return newName = "Prediction for %s" % n predictLayer_.name = newName setPredLayerName(channel, initializing=True) setPredLayerName(ref_label.name, initializing=True) ref_label.pmapColorChanged.connect(setLayerColor) ref_label.nameChanged.connect(setPredLayerName) layers.append(predictionLayer) # Add the raw data last (on the bottom) inputDataSlot = self.topLevelOperatorView.InputImages if inputDataSlot.ready(): inputLayer = self.createStandardLayerFromSlot(inputDataSlot) inputLayer.name = "Input Data" inputLayer.visible = True inputLayer.opacity = 1.0 # the flag window_leveling is used to determine if the contrast # of the layer is adjustable if isinstance(inputLayer, GrayscaleLayer): inputLayer.window_leveling = True else: inputLayer.window_leveling = False def toggleTopToBottom(): index = self.layerstack.layerIndex(inputLayer) self.layerstack.selectRow(index) if index == 0: self.layerstack.moveSelectedToBottom() else: self.layerstack.moveSelectedToTop() layers.append(inputLayer) # The thresholding button can only be used if the data is displayed as grayscale. if inputLayer.window_leveling: self.labelingDrawerUi.thresToolButton.show() else: self.labelingDrawerUi.thresToolButton.hide() self.handleLabelSelectionChange() return layers
def _create_alpha_modulated_layer_from_slot(cls, slot): layer = AlphaModulatedLayer(LazyflowSource(slot), tintColor=QColor(Qt.cyan), range=(0.0, 1.0), normalize=(0.0, 1.0)) return layer