Python AlphaModulatedLayer.setToolTip方法代码示例

# 需要导入模块: from volumina.api import AlphaModulatedLayer [as 别名]
# 或者: from volumina.api.AlphaModulatedLayer import setToolTip [as 别名]
    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():
#.........这里部分代码省略.........

# 需要导入模块: from volumina.api import AlphaModulatedLayer [as 别名]
# 或者: from volumina.api.AlphaModulatedLayer import setToolTip [as 别名]
    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

# 需要导入模块: from volumina.api import AlphaModulatedLayer [as 别名]
# 或者: from volumina.api.AlphaModulatedLayer import setToolTip [as 别名]
    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

# 需要导入模块: from volumina.api import AlphaModulatedLayer [as 别名]
# 或者: from volumina.api.AlphaModulatedLayer import setToolTip [as 别名]
    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)

#.........这里部分代码省略.........