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')
if predictionSlot.ready():
from volumina import colortables
predictLayer = ColortableLayer(LazyflowSource(predictionSlot),
colorTable=colortables.jet(),
normalize='auto')
#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 = "Prediction"
layers.append(predictLayer)
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(Counting3dGui, self).setupLayers()
# Add each of the predictions
labels = self.labelListData
slots = {'density' : self.op.Density}
for name, slot in slots.items():
if slot.ready():
from volumina import colortables
layer = ColortableLayer(LazyflowSource(slot), colorTable = colortables.jet(), normalize = 'auto')
layer.name = name
layers.append(layer)
boxlabelsrc = LazyflowSinkSource(self.op.BoxLabelImages,self.op.BoxLabelInputs )
boxlabellayer = ColortableLayer(boxlabelsrc, colorTable = self._colorTable16, direct = False)
boxlabellayer.name = "boxLabels"
boxlabellayer.opacity = 0.3
layers.append(boxlabellayer)
self.boxlabelsrc = boxlabelsrc
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
# Generate
# array[:] = np.random.randint(0,255,500*500).reshape(shape).astype(np.uint8)
a = np.zeros(500 * 500).reshape(500, 500).astype(np.uint8)
ii = np.random.randint(0, 500, 1)
jj = np.random.randint(0, 500, 1)
a[ii, jj] = 1
a = vigra.filters.discDilation(a, radius=20)
array[:] = a.reshape(shape).view(np.ndarray) * 255
op.Input.setDirty()
do()
cron.timeout.connect(do)
ds = createDataSource(op.Output)
layer = ColortableLayer(ds, jet())
mainwin = Viewer()
mainwin.layerstack.append(layer)
mainwin.dataShape = (1, h, w, 1, 1)
print(mainwin.centralWidget())
BoxContr = BoxController(mainwin.editor, op.Output, boxListModel)
BoxInt = BoxInterpreter(mainwin.editor.navInterpret, mainwin.editor.posModel, BoxContr, mainwin.centralWidget())
mainwin.editor.setNavigationInterpreter(BoxInt)
# boxListModel.boxRemoved.connect(BoxContr.deleteItem)
LV.show()
mainwin.show()
#Generate
#array[:] = np.random.randint(0,255,500*500).reshape(shape).astype(np.uint8)
a = np.zeros(500*500).reshape(500,500).astype(np.uint8)
ii=np.random.randint(0,500,1)
jj=np.random.randint(0,500,1)
a[ii,jj]=1
a=vigra.filters.discDilation(a,radius=20)
array[:]=a.reshape(shape).view(np.ndarray)*255
op.Input.setDirty()
do()
cron.connect(cron, SIGNAL('timeout()'), do)
ds = LazyflowSource( op.Output )
layer = ColortableLayer(ds,jet())
mainwin=Viewer()
mainwin.layerstack.append(layer)
mainwin.dataShape=(1,h,w,1,1)
print mainwin.centralWidget()
BoxContr=BoxController(mainwin.editor,op.Output,boxListModel)
BoxInt=BoxInterpreter(mainwin.editor.navInterpret,mainwin.editor.posModel,BoxContr,mainwin.centralWidget())
mainwin.editor.setNavigationInterpreter(BoxInt)
# boxListModel.boxRemoved.connect(BoxContr.deleteItem)
LV.show()
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')
if predictionSlot.ready() :
from volumina import colortables
predictLayer = ColortableLayer(LazyflowSource(predictionSlot), colorTable = colortables.jet(), normalize = 'auto')
#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 = "Prediction"
layers.append(predictLayer)
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
