def createLabelLayer(self, direct=False):
"""
Return a colortable layer that displays the label slot data, along with its associated label source.
direct: whether this layer is drawn synchronously by volumina
"""
labelOutput = self._labelingSlots.labelOutput
if not labelOutput.ready():
return (None, None)
else:
# Add the layer to draw the labels, but don't add any labels
labelsrc = LazyflowSinkSource(self._labelingSlots.labelOutput,
self._labelingSlots.labelInput)
labellayer = ColortableLayer(labelsrc,
colorTable=self._colorTable16,
direct=direct)
labellayer.name = "Labels"
labellayer.ref_object = None
labellayer.contexts.append(
("Import...",
partial(import_labeling_layer, labellayer,
self._labelingSlots, self)))
return labellayer, labelsrc
def createLabelLayer(self, direct=False):
"""
Return a colortable layer that displays the label slot data, along with its associated label source.
direct: whether this layer is drawn synchronously by volumina
"""
labelOutput = self._labelingSlots.labelOutput
if not labelOutput.ready():
return (None, None)
else:
# Add the layer to draw the labels, but don't add any labels
labelsrc = LazyflowSinkSource(self._labelingSlots.labelOutput,
self._labelingSlots.labelInput)
labellayer = ColortableLayer(labelsrc,
colorTable=self._colorTable16,
direct=direct)
labellayer.name = "Labels"
labellayer.ref_object = None
labellayer.contexts.append(
QAction("Import...",
None,
triggered=partial(import_labeling_layer, labellayer,
self._labelingSlots, self)))
labellayer.shortcutRegistration = ("0",
ShortcutManager.ActionInfo(
"Labeling",
"LabelVisibility",
"Show/Hide Labels",
labellayer.toggleVisible,
self.viewerControlWidget(),
labellayer))
return labellayer, labelsrc
def createLabelLayer(self, direct=False):
"""Return a colortable layer that displays the label slot
data, along with its associated label source.
direct: whether this layer is drawn synchronously by volumina
"""
labelInput = self._labelingSlots.labelInput
labelOutput = self._labelingSlots.labelOutput
if not labelOutput.ready():
return (None, None)
else:
self._colorTable16[15] = QColor(Qt.black).rgba() #for the objects with NaNs in features
labelsrc = LazyflowSinkSource(labelOutput,
labelInput)
labellayer = ColortableLayer(labelsrc,
colorTable=self._colorTable16,
direct=direct)
labellayer.segmentationImageSlot = self.op.SegmentationImagesOut
labellayer.name = "Labels"
labellayer.ref_object = None
labellayer.zeroIsTransparent = False
labellayer.colortableIsRandom = True
clickInt = ClickInterpreter(self.editor, labellayer,
self.onClick, right=False,
double=False)
self.editor.brushingInterpreter = clickInt
return labellayer, labelsrc
def createLabelLayer(self, direct=False):
"""Return a colortable layer that displays the label slot
data, along with its associated label source.
direct: whether this layer is drawn synchronously by volumina
"""
labelInput = self._labelingSlots.labelInput
labelOutput = self._labelingSlots.labelOutput
if not labelOutput.ready():
return (None, None)
else:
self._colorTable16[15] = QColor(
Qt.black).rgba() #for the objects with NaNs in features
labelsrc = LazyflowSinkSource(labelOutput, labelInput)
labellayer = ColortableLayer(labelsrc,
colorTable=self._colorTable16,
direct=direct)
labellayer.segmentationImageSlot = self.op.SegmentationImagesOut
labellayer.name = "Labels"
labellayer.ref_object = None
labellayer.zeroIsTransparent = False
labellayer.colortableIsRandom = True
clickInt = ClickInterpreter(self.editor,
labellayer,
self.onClick,
right=False,
double=False)
self.editor.brushingInterpreter = clickInt
return labellayer, labelsrc
def createLabelLayer(self, direct=False):
"""
Return a colortable layer that displays the label slot data, along with its associated label source.
direct: whether this layer is drawn synchronously by volumina
"""
labelOutput = self._labelingSlots.labelOutput
if not labelOutput.ready():
return (None, None)
else:
# Add the layer to draw the labels, but don't add any labels
labelsrc = LazyflowSinkSource( self._labelingSlots.labelOutput,
self._labelingSlots.labelInput)
labellayer = ColortableLayer(labelsrc, colorTable = self._colorTable16, direct=direct)
labellayer.name = "Labels"
labellayer.ref_object = None
labellayer.contexts.append(QAction("Import...", None,
triggered=partial(import_labeling_layer, labellayer, self._labelingSlots, self)))
labellayer.shortcutRegistration = ("0", ShortcutManager.ActionInfo(
"Labeling",
"LabelVisibility",
"Show/Hide Labels",
labellayer.toggleVisible,
self.viewerControlWidget(),
labellayer))
return labellayer, labelsrc
def createLabelLayer(self, direct=False):
"""
Return a colortable layer that displays the label slot data, along with its associated label source.
direct: whether this layer is drawn synchronously by volumina
"""
labelOutput = self._labelingSlots.labelOutput
if not labelOutput.ready():
return (None, None)
else:
traceLogger.debug("Setting up labels")
# Add the layer to draw the labels, but don't add any labels
labelsrc = LazyflowSinkSource(self._labelingSlots.labelOutput, self._labelingSlots.labelInput)
labellayer = ColortableLayer(labelsrc, colorTable=self._colorTable16, direct=direct)
labellayer.name = "Labels"
labellayer.ref_object = None
return labellayer, labelsrc
def createCropLayer(self, direct=False):
"""
Return a colortable layer that displays the crop slot data, along with its associated crop source.
direct: whether this layer is drawn synchronously by volumina
"""
cropOutput = self._croppingSlots.cropOutput
if not cropOutput.ready():
return (None, None)
else:
# Add the layer to draw the crops, but don't add any crops
cropsrc = LazyflowSinkSource( self._croppingSlots.cropOutput,
self._croppingSlots.cropInput)
croplayer = ColortableLayer(cropsrc, colorTable = self._colorTable16, direct=direct )
croplayer.name = "Crops"
croplayer.ref_object = None
return croplayer, cropsrc
def createLabelLayer(self, direct=False):
"""
Return a colortable layer that displays the label slot data, along with its associated label source.
direct: whether this layer is drawn synchronously by volumina
"""
labelOutput = self._labelingSlots.labelOutput
if not labelOutput.ready():
return (None, None)
else:
# Add the layer to draw the labels, but don't add any labels
labelsrc = LazyflowSinkSource( self._labelingSlots.labelOutput,
self._labelingSlots.labelInput)
labellayer = ColortableLayer(labelsrc, colorTable = self._colorTable16, direct=direct )
labellayer.name = "Labels"
labellayer.ref_object = None
labellayer.contexts.append(("Import...",
partial( import_labeling_layer, labellayer, self._labelingSlots, self )))
return labellayer, labelsrc
def createLabelLayer(self, currentImageIndex, direct=False):
"""
Return a colortable layer that displays the label slot data, along with its associated label source.
direct: whether this layer is drawn synchronously by volumina
"""
labelOutput = self._labelingSlots.labelOutput[currentImageIndex]
if not labelOutput.ready():
return (None, None)
else:
traceLogger.debug("Setting up labels for image index={}".format(
currentImageIndex))
# Add the layer to draw the labels, but don't add any labels
labelsrc = LazyflowSinkSource(
self._labelingSlots.labelOutput[currentImageIndex],
self._labelingSlots.labelInput[currentImageIndex])
labellayer = ColortableLayer(labelsrc,
colorTable=self._colorTable16,
direct=direct)
labellayer.name = "Labels"
labellayer.ref_object = None
return labellayer, labelsrc
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):
# 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
