def setUp( self ):
self.GRAY1 = 60
self.ds1 = ConstantSource( self.GRAY1 )
self.GRAY2 = 120
self.ds2 = ConstantSource( self.GRAY2 )
self.GRAY3 = 190
self.ds3 = ConstantSource( self.GRAY3 )
self.layer1 = GrayscaleLayer( self.ds1, normalize = False )
self.layer1.visible = False
self.layer1.opacity = 0.1
self.ims1 = GrayscaleImageSource( self.ds1, self.layer1 )
self.layer2 = GrayscaleLayer( self.ds2, normalize = False )
self.layer2.visible = True
self.layer2.opacity = 0.3
self.ims2 = GrayscaleImageSource( self.ds2, self.layer2 )
self.layer3 = GrayscaleLayer( self.ds3, normalize = False )
self.layer3.visible = True
self.layer3.opacity = 1.0
self.ims3 = GrayscaleImageSource( self.ds3, self.layer3 )
lsm = LayerStackModel()
lsm.append(self.layer1)
lsm.append(self.layer2)
lsm.append(self.layer3)
self.lsm = lsm
sims = StackedImageSources( lsm )
sims.register( self.layer1, self.ims1 )
sims.register( self.layer2, self.ims2 )
sims.register( self.layer3, self.ims3 )
self.sims = sims
def _onReady(self, slot):
if slot is self.mainOperator.RawImage:
if slot.meta.shape and not self.rawsrc:
self.rawsrc = LazyflowSource(self.mainOperator.RawImage)
layerraw = GrayscaleLayer(self.rawsrc)
layerraw.name = "Raw"
self.layerstack.append(layerraw)
def setUp(self):
self.ds = ConstantSource()
self.layer1 = GrayscaleLayer(self.ds)
self.layer1.visible = False
self.layer1.opacity = 0.1
self.layer2 = GrayscaleLayer(self.ds)
self.layer2.visible = True
self.layer2.opacity = 0.3
self.layer3 = GrayscaleLayer(self.ds)
self.layer3.visible = True
self.layer3.opacity = 1.0
def setUp( self ):
dataShape = (1, 900, 400, 10, 1) # t,x,y,z,c
data = np.indices(dataShape)[3].astype(np.uint8) # Data is labeled according to z-index
self.ds1 = ArraySource( data )
self.CONSTANT = 13
self.ds2 = ConstantSource( self.CONSTANT )
self.layer1 = GrayscaleLayer( self.ds1, normalize=False )
self.layer1.visible = True
self.layer1.opacity = 1.0
self.layer2 = GrayscaleLayer( self.ds2, normalize=False )
self.lsm = LayerStackModel()
self.pump = ImagePump( self.lsm, SliceProjection(), sync_along=(0,1,2) )
def setUp(self):
super(GrayscaleImageSourceTest, self).setUp()
self.raw = numpy.load(
os.path.join(volumina._testing.__path__[0],
'lena.npy')).astype(numpy.uint32)
self.ars = _ArraySource2d(self.raw)
self.ims = GrayscaleImageSource(self.ars, GrayscaleLayer(self.ars))
def _onMetaChanged(self, slot):
if slot is self.mainOperator.LabelImage:
if slot.meta.shape:
self.editor.dataShape = slot.meta.shape
maxt = slot.meta.shape[0] - 1
self._setRanges()
self._drawer.from_time.setValue(0)
self._drawer.to_time.setValue(maxt)
if slot is self.mainOperator.RawImage:
if slot.meta.shape and not self.rawsrc:
self.rawsrc = LazyflowSource(self.mainOperator.RawImage)
layerraw = GrayscaleLayer(self.rawsrc)
layerraw.name = "Raw"
self.layerstack.append(layerraw)
def setUp(self):
self.layerstack = LayerStackModel()
self.sims = StackedImageSources(self.layerstack)
self.GRAY = 201
self.ds = ConstantSource(self.GRAY)
self.layer = GrayscaleLayer(self.ds)
self.layer.set_normalize(0, False)
self.layerstack.append(self.layer)
self.ims = self.layer.createImageSource([PlanarSliceSource(self.ds)])
self.sims.register(self.layer, self.ims)
self.scene = ImageScene2D(PositionModel(), (0, 3, 4), preemptive_fetch_number=0)
self.scene.stackedImageSources = self.sims
self.scene.dataShape = (310, 290)
def setupLayers(self):
layers = []
op = self.topLevelOperatorView
# Superpixels
if op.Superpixels.ready():
layer = ColortableLayer( LazyflowSource(op.Superpixels), self._sp_colortable )
layer.colortableIsRandom = True
layer.name = "Superpixels"
layer.visible = True
layer.opacity = 0.5
layers.append(layer)
del layer
# Debug layers
if op.debug_results:
for name, compressed_array in op.debug_results.items():
axiskeys = op.Superpixels.meta.getAxisKeys()[:-1] # debug images don't have a channel axis
permutation = map(lambda key: axiskeys.index(key) if key in axiskeys else None, 'txyzc')
arraysource = ArraySource( TransposedView(compressed_array, permutation) )
if compressed_array.dtype == np.uint32:
layer = ColortableLayer(arraysource, self._sp_colortable)
else:
layer = GrayscaleLayer(arraysource)
# TODO: Normalize? Maybe the drange should be included with the debug image.
layer.name = name
layer.visible = False
layer.opacity = 1.0
layers.append(layer)
del layer
# Threshold
if op.ThresholdedInput.ready():
layer = ColortableLayer( LazyflowSource(op.ThresholdedInput), self._threshold_colortable )
layer.name = "Thresholded Input"
layer.visible = True
layer.opacity = 1.0
layers.append(layer)
del layer
# Raw Data (grayscale)
if op.Input.ready():
layer = self._create_grayscale_layer_from_slot( op.Input, op.Input.meta.getTaggedShape()['c'] )
layer.name = "Input"
layer.visible = False
layer.opacity = 1.0
layers.append(layer)
del layer
# Raw Data (grayscale)
if op.RawData.ready():
layer = self.createStandardLayerFromSlot( op.RawData )
layer.name = "Raw Data"
layer.visible = True
layer.opacity = 1.0
layers.append(layer)
del layer
return layers
def _add_grayscale_layer(self, data, name=None, visible=False):
'''
adds a grayscale layer to the layerstack
:param data: numpy array (2D) containing the data
:param name: name of layer
:param visible: bool determining whether this layer should be set to visible
:return:
'''
#assert len(data.shape) == 2
a, data_shape = createDataSource(data, True)
self.editor.dataShape = list(data_shape)
new_layer = GrayscaleLayer(a)
new_layer.visible = visible
if name is not None:
new_layer.name = name
self.layerstack.append(new_layer)
def setUp(self):
super(GrayscaleImageSourceTest, self).setUp()
self.raw = numpy.load(
os.path.join(volumina._testing.__path__[0],
"2d_cells_apoptotic_1channel.npy")).astype(
numpy.uint32)
self.ars = _ArraySource2d(self.raw)
self.ims = GrayscaleImageSource(self.ars, GrayscaleLayer(self.ars))
def initUic(self):
self.g = g = Graph()
#get the absolute path of the 'ilastik' module
uic.loadUi("designerElements/MainWindow.ui", self)
self.actionQuit.triggered.connect(qApp.quit)
def toggleDebugPatches(show):
self.editor.showDebugPatches = show
self.actionShowDebugPatches.toggled.connect(toggleDebugPatches)
self.layerstack = LayerStackModel()
readerNew = op.OpH5ReaderBigDataset(g)
readerNew.inputs["Filenames"].setValue([
"scripts/CB_compressed_XY.h5", "scripts/CB_compressed_XZ.h5",
"scripts/CB_compressed_YZ.h5"
])
readerNew.inputs["hdf5Path"].setValue("volume/data")
datasrc = LazyflowSource(readerNew.outputs["Output"])
layer1 = GrayscaleLayer(datasrc)
layer1.name = "Big Data"
self.layerstack.append(layer1)
shape = readerNew.outputs["Output"].meta.shape
print shape
self.editor = VolumeEditor(shape, self.layerstack)
#self.editor.setDrawingEnabled(False)
self.volumeEditorWidget.init(self.editor)
model = self.editor.layerStack
self.layerWidget.init(model)
self.UpButton.clicked.connect(model.moveSelectedUp)
model.canMoveSelectedUp.connect(self.UpButton.setEnabled)
self.DownButton.clicked.connect(model.moveSelectedDown)
model.canMoveSelectedDown.connect(self.DownButton.setEnabled)
self.DeleteButton.clicked.connect(model.deleteSelected)
model.canDeleteSelected.connect(self.DeleteButton.setEnabled)
def setUp(self):
self.ds = ConstantSource()
self.layer1 = GrayscaleLayer(self.ds)
self.layer1.visible = False
self.layer1.opacity = 0.1
self.ims1 = GrayscaleImageSource(PlanarSliceSource(self.ds),
self.layer1)
self.layer2 = GrayscaleLayer(self.ds)
self.layer2.visible = True
self.layer2.opacity = 0.3
self.ims2 = GrayscaleImageSource(PlanarSliceSource(self.ds),
self.layer2)
self.layer3 = GrayscaleLayer(self.ds)
self.layer3.visible = True
self.layer3.opacity = 1.0
self.ims3 = GrayscaleImageSource(PlanarSliceSource(self.ds),
self.layer3)
def setUp(self):
self.layerstack = LayerStackModel()
self.sims = StackedImageSources(self.layerstack)
self.g = Graph()
self.op = OpLazy(self.g)
self.ds = LazyflowSource(self.op.Output)
self.ss = PlanarSliceSource(self.ds, projectionAlongTZC)
self.layer = GrayscaleLayer(self.ds, normalize=False)
self.layerstack.append(self.layer)
self.ims = self.layer.createImageSource([self.ss])
self.sims.register(self.layer, self.ims)
self.scene = ImageScene2D(PositionModel(), (0, 0, 0),
preemptive_fetch_number=0)
self.scene.setCacheSize(1)
self.scene.stackedImageSources = self.sims
self.scene.dataShape = (30, 30)
def setUp(self):
super(GrayscaleImageSourceTest2, self).setUp()
self.raw = numpy.load(os.path.join(volumina._testing.__path__[0], "lena.npy")).astype(numpy.uint32)
self.raw = numpy.ma.masked_array(self.raw, numpy.zeros(self.raw.shape, dtype=bool), shrink=False)
self.raw[:10, :] = numpy.ma.masked
self.raw[-10:, :] = numpy.ma.masked
self.raw[:, :10] = numpy.ma.masked
self.raw[:, -10:] = numpy.ma.masked
self.ars = _ArraySource2d(self.raw)
self.ims = GrayscaleImageSource(self.ars, GrayscaleLayer(self.ars))
def createWidget(self, parent):
a = (numpy.random.random((1, 100, 200, 300, 1)) * 255).astype(numpy.uint8)
source = ArraySource(a)
layerstack = LayerStackModel()
layerstack.append(GrayscaleLayer(source))
editor = VolumeEditor(layerstack, labelsink=None, parent=self)
widget = VolumeEditorWidget(parent=parent)
if not _has_lazyflow:
widget.setEnabled(False)
widget.init(editor)
editor.dataShape = a.shape
return widget
def showStuff(raw_name,
pred_viewer1,
pred_viewer2,
cutout_name,
one_extra=None):
# display the raw and annotations for cremi challenge data
raw = vigra.impex.readHDF5(indir + datasets[raw_name], "data", order='C')
# raw_old = vigra.readHDF5(indir+datasets["raw_bad"], "data", order = 'C')
defect_prediction_128 = vigra.impex.readHDF5(indir +
datasets[pred_viewer2],
"data",
order='C')
defect_prediction_150 = vigra.impex.readHDF5(indir +
datasets[pred_viewer1],
"data",
order='C')
cutout_from_150_pred = vigra.impex.readHDF5(indir + datasets[cutout_name],
"data",
order='C')
####################################################################################################################
# only used for fast testing stuff
#change_one = vigra.readHDF5(indir+datasets["segmentation_on_equalized_image"], "data", order = 'C')
#pdb.set_trace()
#defect_prediction_150[1,:,:] = change_one[0,:,:,0]
####################################################################################################################
# defect_prediction_150 = gt[..., 0]
cutout = numpy.asarray(cutout_from_150_pred)
rawdata = numpy.asarray(raw)
# rawdata_old = numpy.asarray(raw_old)
# op5ify
# shape5d = rawdata.shape
shape5d = (1, ) + rawdata.shape + (1, )
print shape5d, rawdata.shape, rawdata.dtype
app = QApplication([])
v = Viewer()
direct = False
# layer for raw data
rawdata = numpy.reshape(rawdata, shape5d)
rawsource = ArraySource(rawdata)
v.dataShape = shape5d
lraw = GrayscaleLayer(rawsource, direct=direct)
lraw.visible = True
lraw.name = "raw"
v.layerstack.append(lraw)
# layer for cutout regions from raw data
cutout = numpy.reshape(cutout, shape5d)
cutoutsource = ArraySource(cutout)
lcutout = GrayscaleLayer(cutoutsource, direct=direct)
lcutout.visible = False
lcutout.name = "cut_out"
v.layerstack.append(lcutout)
# layer for first prediction result
defect_prediction_128 = numpy.reshape(defect_prediction_128, shape5d)
synsource = ArraySource(defect_prediction_128)
ct = create_random_16bit()
ct[0] = 0
lsyn = ColortableLayer(synsource, ct)
lsyn.name = pred_viewer2
lsyn.visible = False
v.layerstack.append(lsyn)
# layer for second prediction result
segm = numpy.reshape(defect_prediction_150, shape5d)
segsource = ArraySource(segm)
ct = create_random_16bit()
ct[0] = 0
lseg = ColortableLayer(segsource, ct)
lseg.name = pred_viewer1
lseg.visible = False
v.layerstack.append(lseg)
if one_extra is None:
v.showMaximized()
app.exec_()
if one_extra is not None:
# layer for third prediction result
extra_prediction = vigra.readHDF5(indir + datasets[one_extra],
"data",
order='C')
extra_pred_reshaped = numpy.reshape(extra_prediction, shape5d)
segsource = ArraySource(extra_pred_reshaped)
ct = create_random_16bit()
ct[0] = 0
# ct = create_default_16bit()
lseg = ColortableLayer(segsource, ct)
lseg.name = one_extra
lseg.visible = False
v.layerstack.append(lseg)
v.showMaximized()
app.exec_()
from volumina.pixelpipeline.datasources import ArraySinkSource, ArraySource
from volumina.layer import ColortableLayer, GrayscaleLayer
from PyQt5.QtWidgets import QApplication
SHAPE = (1, 600, 800, 1, 1) # volumina expects 5d txyzc
data_arr = (255 * numpy.random.random(SHAPE)).astype(numpy.uint8)
label_arr = numpy.zeros(SHAPE, dtype=numpy.uint8)
##-----
app = QApplication(sys.argv)
v = Viewer()
data_src = ArraySource(data_arr)
data_layer = GrayscaleLayer(data_src)
data_layer.name = "Raw"
data_layer.numberOfChannels = 1
label_src = ArraySinkSource(label_arr)
label_layer = ColortableLayer(label_src,
colorTable=default16_new,
direct=False)
label_layer.name = "Labels"
label_layer.ref_object = None
assert SHAPE == label_arr.shape == data_arr.shape
v.dataShape = SHAPE
v.layerstack.append(data_layer)
v.layerstack.append(label_layer)
def setUp(self):
self.raw = numpy.load(
os.path.join(volumina._testing.__path__[0], 'lena.npy'))
self.ars = _ArraySource2d(self.raw)
self.ims = GrayscaleImageSource(self.ars, GrayscaleLayer(self.ars))
# self.editor.imageViews[self.editor._lastImageViewFocus]._cursorBackup = self.editor.imageViews[self.editor._lastImageViewFocus].cursor()
# self.editor.imageViews[self.editor._lastImageViewFocus].setCursor(Qt.CrossCursor)
# else:
# self.editor.imageViews[self.editor._lastImageViewFocus]._isRubberBandZoom = False
# self.editor.imageViews[self.editor._lastImageViewFocus].setCursor(self.editor.imageViews[self.editor._lastImageViewFocus]._cursorBackup)
# self._viewMenu.addAction( "RubberBand zoom" ).triggered.connect(rubberBandZoom)
# *******************************************************************************
# i f _ _ n a m e _ _ = = " _ _ m a i n _ _ " *
# *******************************************************************************
if __name__ == "__main__":
import sys
from .layerstack import LayerStackModel
from volumina.layer import GrayscaleLayer
array = numpy.random.rand(1, 100, 100, 100, 1)
array *= 255
array = array.astype("uint8")
layer = GrayscaleLayer(ArraySource(array))
app = QApplication(sys.argv)
layerStackModel = LayerStackModel()
layerStackModel.insert(0, layer)
volumeEditor = VolumeEditor(layerStackModel, parent=None)
volumeEditor.dataShape = array.shape
volumeEditorWidget = VolumeEditorWidget(editor=volumeEditor)
volumeEditorWidget.show()
app.exec_()
def setupLayers(self):
layers = []
if "MergerOutput" in self.topLevelOperatorView.outputs and self.topLevelOperatorView.MergerOutput.ready(
):
ct = colortables.create_default_8bit()
for i in range(7):
ct[i] = self.mergerColors[i].rgba()
self.mergersrc = LazyflowSource(
self.topLevelOperatorView.MergerOutput)
mergerLayer = ColortableLayer(self.mergersrc, ct)
mergerLayer.name = "Merger"
mergerLayer.visible = True
layers.append(mergerLayer)
ct = colortables.create_random_16bit()
ct[0] = QColor(0, 0, 0, 0).rgba() # make 0 transparent
ct[1] = QColor(128, 128, 128, 255).rgba(
) # misdetections have id 1 and will be indicated by grey
if self.topLevelOperatorView.CachedOutput.ready():
self.trackingsrc = LazyflowSource(
self.topLevelOperatorView.CachedOutput)
trackingLayer = ColortableLayer(self.trackingsrc, ct)
trackingLayer.name = "Tracking"
trackingLayer.visible = True
trackingLayer.opacity = 1.0
layers.append(trackingLayer)
elif self.topLevelOperatorView.zeroProvider.Output.ready():
# provide zeros while waiting for the tracking result
self.trackingsrc = LazyflowSource(
self.topLevelOperatorView.zeroProvider.Output)
trackingLayer = ColortableLayer(self.trackingsrc, ct)
trackingLayer.name = "Tracking"
trackingLayer.visible = True
trackingLayer.opacity = 1.0
layers.append(trackingLayer)
if self.topLevelOperatorView.LabelImage.ready():
self.objectssrc = LazyflowSource(
self.topLevelOperatorView.LabelImage)
ct = colortables.create_random_16bit()
ct[0] = QColor(0, 0, 0, 0).rgba() # make 0 transparent
objLayer = ColortableLayer(self.objectssrc, ct)
objLayer.name = "Objects"
objLayer.opacity = 1.0
objLayer.visible = True
layers.append(objLayer)
if self.mainOperator.RawImage.ready():
self.rawsrc = None
self.rawsrc = LazyflowSource(self.mainOperator.RawImage)
rawLayer = GrayscaleLayer(self.rawsrc)
rawLayer.name = "Raw"
layers.insert(len(layers), rawLayer)
if self.topLevelOperatorView.LabelImage.meta.shape:
self.editor.dataShape = self.topLevelOperatorView.LabelImage.meta.shape
maxt = self.topLevelOperatorView.LabelImage.meta.shape[0] - 1
maxx = self.topLevelOperatorView.LabelImage.meta.shape[1] - 1
maxy = self.topLevelOperatorView.LabelImage.meta.shape[2] - 1
maxz = self.topLevelOperatorView.LabelImage.meta.shape[3] - 1
if not self.mainOperator.Parameters.ready():
raise Exception("Parameter slot is not ready")
parameters = self.mainOperator.Parameters.value
self._setRanges()
if 'size_range' in parameters:
self._drawer.to_size.setValue(parameters['size_range'][1] - 1)
self._drawer.from_size.setValue(parameters['size_range'][0])
else:
self._drawer.from_size.setValue(0)
self._drawer.to_size.setValue(10000)
if 'x_range' in parameters:
self._drawer.to_x.setValue(parameters['x_range'][1] - 1)
self._drawer.from_x.setValue(parameters['x_range'][0])
else:
self._drawer.from_x.setValue(0)
self._drawer.to_x.setValue(maxx)
if 'y_range' in parameters:
self._drawer.to_y.setValue(parameters['y_range'][1] - 1)
self._drawer.from_y.setValue(parameters['y_range'][0])
else:
self._drawer.from_y.setValue(0)
self._drawer.to_y.setValue(maxy)
if 'z_range' in parameters:
self._drawer.to_z.setValue(parameters['z_range'][1] - 1)
self._drawer.from_z.setValue(parameters['z_range'][0])
else:
self._drawer.from_z.setValue(0)
self._drawer.to_z.setValue(maxz)
if 'time_range' in parameters:
self._drawer.to_time.setValue(parameters['time_range'][1])
self._drawer.from_time.setValue(parameters['time_range'][0])
else:
self._drawer.from_time.setValue(0)
self._drawer.to_time.setValue(maxt)
if 'scales' in parameters:
self._drawer.x_scale.setValue(parameters['scales'][0])
self._drawer.y_scale.setValue(parameters['scales'][1])
self._drawer.z_scale.setValue(parameters['scales'][2])
else:
self._drawer.x_scale.setValue(1)
self._drawer.y_scale.setValue(1)
self._drawer.z_scale.setValue(1)
self.topLevelOperatorView.RawImage.notifyReady(self._onReady)
self.topLevelOperatorView.RawImage.notifyMetaChanged(
self._onMetaChanged)
return layers
def setupLayers( self, currentImageIndex ):
layers = []
def onButtonsEnabled(slot, roi):
currObj = self._carvingApplet.topLevelOperator.opCarving[currentImageIndex].CurrentObjectName.value
hasSeg = self._carvingApplet.topLevelOperator.opCarving[currentImageIndex].HasSegmentation.value
nzLB = self._carvingApplet.topLevelOperator.opLabeling.NonzeroLabelBlocks[currentImageIndex][:].wait()[0]
self.labelingDrawerUi.currentObjectLabel.setText("current object: %s" % currObj)
self.labelingDrawerUi.save.setEnabled(currObj != "" and hasSeg)
self.labelingDrawerUi.saveAs.setEnabled(currObj == "" and hasSeg)
#rethink this
#self.labelingDrawerUi.segment.setEnabled(len(nzLB) > 0)
#self.labelingDrawerUi.clear.setEnabled(len(nzLB) > 0)
self._carvingApplet.topLevelOperator.opCarving[currentImageIndex].CurrentObjectName.notifyDirty(onButtonsEnabled)
self._carvingApplet.topLevelOperator.opCarving[currentImageIndex].HasSegmentation.notifyDirty(onButtonsEnabled)
self._carvingApplet.topLevelOperator.opLabeling.NonzeroLabelBlocks[currentImageIndex].notifyDirty(onButtonsEnabled)
# Labels
labellayer, labelsrc = self.createLabelLayer(currentImageIndex, direct=True)
if labellayer is not None:
layers.append(labellayer)
# Tell the editor where to draw label data
self.editor.setLabelSink(labelsrc)
#segmentation
seg = self._carvingApplet.topLevelOperator.opCarving.Segmentation[currentImageIndex]
#seg = self._carvingApplet.topLevelOperator.opCarving[0]._mst.segmentation
#temp = self._done_lut[self._mst.regionVol[sl[1:4]]]
if seg.ready():
#source = RelabelingArraySource(seg)
#source.setRelabeling(numpy.arange(256, dtype=numpy.uint8))
colortable = [QColor(0,0,0,0).rgba(), QColor(0,0,0,0).rgba(), QColor(0,255,0).rgba()]
for i in range(256-len(colortable)):
r,g,b = numpy.random.randint(0,255), numpy.random.randint(0,255), numpy.random.randint(0,255)
colortable.append(QColor(r,g,b).rgba())
#layer = DirectColorTableLayer(seg, colortable, lazyflow=True)
layer = ColortableLayer(LazyflowSource(seg), colortable, direct=True)
layer.name = "segmentation"
layer.visible = True
layer.opacity = 0.3
layers.append(layer)
#done
done = self._carvingApplet.topLevelOperator.opCarving.DoneObjects[currentImageIndex]
if done.ready():
colortable = [QColor(0,0,0,0).rgba(), QColor(0,0,255).rgba()]
for i in range(254-len(colortable)):
r,g,b = numpy.random.randint(0,255), numpy.random.randint(0,255), numpy.random.randint(0,255)
colortable.append(QColor(r,g,b).rgba())
#have to use lazyflow because it provides dirty signals
#layer = DirectColorTableLayer(done, colortable, lazyflow=True)
layer = ColortableLayer(LazyflowSource(done), colortable, direct=True)
layer.name = "done"
layer.visible = False
layer.opacity = 0.5
layers.append(layer)
doneSeg = self._carvingApplet.topLevelOperator.opCarving.DoneSegmentation[currentImageIndex]
if doneSeg.ready():
layer = ColortableLayer(LazyflowSource(doneSeg), self._doneSegmentationColortable, direct=True)
layer.name = "done seg"
layer.visible = False
layer.opacity = 0.5
self._doneSegmentationLayer = layer
layers.append(layer)
#supervoxel
sv = self._carvingApplet.topLevelOperator.opCarving.Supervoxels[currentImageIndex]
if sv.ready():
for i in range(256):
r,g,b = numpy.random.randint(0,255), numpy.random.randint(0,255), numpy.random.randint(0,255)
colortable.append(QColor(r,g,b).rgba())
#layer = DirectColorTableLayer(sv, colortable, lazyflow=True)
layer = ColortableLayer(LazyflowSource(sv), colortable, direct=True)
layer.name = "supervoxels"
layer.visible = False
layer.opacity = 1.0
layers.append(layer)
#
# load additional layer: features / probability map
#
import h5py
f = h5py.File("pmap.h5")
pmap = f["data"].value
#
# here we load the actual raw data from an ArraySource rather than from a LazyflowSource for speed reasons
#
raw = self._carvingApplet.topLevelOperator.opCarving[0]._mst.raw
raw5D = numpy.zeros((1,)+raw.shape+(1,), dtype=raw.dtype)
raw5D[0,:,:,:,0] = raw[:,:,:]
#layer = DirectGrayscaleLayer(raw5D)
layer = GrayscaleLayer(ArraySource(raw5D), direct=True)
layer.name = "raw"
layer.visible = True
layer.opacity = 1.0
#layers.insert(1, layer)
layers.append(layer)
return layers
class ImageScene2D_RenderTest(ut.TestCase):
@classmethod
def setUpClass(cls):
cls.app = None
if QApplication.instance():
cls.app = QApplication.instance()
else:
cls.app = QApplication([])
@classmethod
def tearDownClass(cls):
del cls.app
def setUp(self):
self.layerstack = LayerStackModel()
self.sims = StackedImageSources(self.layerstack)
self.GRAY = 201
self.ds = ConstantSource(self.GRAY)
self.layer = GrayscaleLayer(self.ds)
self.layer.set_normalize(0, False)
self.layerstack.append(self.layer)
self.ims = self.layer.createImageSource([self.ds])
self.sims.register(self.layer, self.ims)
self.scene = ImageScene2D(PositionModel(), (0, 3, 4),
preemptive_fetch_number=0)
self.scene.stackedImageSources = self.sims
self.scene.dataShape = (310, 290)
def renderScene(self, s, exportFilename=None):
img = QImage(310, 290, QImage.Format_ARGB32_Premultiplied)
img.fill(0)
p = QPainter(img)
s.render(p)
s.joinRenderingAllTiles(viewport_only=False)
s.render(p)
p.end()
if exportFilename is not None:
img.save(exportFilename)
return byte_view(img)
def testBasicImageRenderingCapability(self):
aimg = self.renderScene(self.scene)
self.assertTrue(np.all(aimg[:, :, 0:3] == self.GRAY))
self.assertTrue(np.all(aimg[:, :, 3] == 255))
def testToggleVisibilityOfOneLayer(self):
aimg = self.renderScene(self.scene)
self.assertTrue(np.all(aimg[:, :, 0:3] == self.GRAY))
self.assertTrue(np.all(aimg[:, :, 3] == 255))
self.layer.visible = False
aimg = self.renderScene(self.scene)
self.assertTrue(np.all(aimg[:, :, 0:3] == 0)) # all white
self.assertTrue(np.all(aimg[:, :, 3] == 0))
self.layer.visible = True
aimg = self.renderScene(self.scene)
self.assertTrue(np.all(aimg[:, :, 0:3] == self.GRAY))
self.assertTrue(np.all(aimg[:, :, 3] == 255))
def setupLayers(self):
logger.debug("setupLayers")
layers = []
def onButtonsEnabled(slot, roi):
currObj = self.topLevelOperatorView.CurrentObjectName.value
hasSeg = self.topLevelOperatorView.HasSegmentation.value
self.labelingDrawerUi.currentObjectLabel.setText(currObj)
self.labelingDrawerUi.save.setEnabled(hasSeg)
self.topLevelOperatorView.CurrentObjectName.notifyDirty(
onButtonsEnabled)
self.topLevelOperatorView.HasSegmentation.notifyDirty(onButtonsEnabled)
self.topLevelOperatorView.opLabelArray.NonzeroBlocks.notifyDirty(
onButtonsEnabled)
# Labels
labellayer, labelsrc = self.createLabelLayer(direct=True)
if labellayer is not None:
labellayer._allowToggleVisible = False
layers.append(labellayer)
# Tell the editor where to draw label data
self.editor.setLabelSink(labelsrc)
# uncertainty
# if self._showUncertaintyLayer:
# uncert = self.topLevelOperatorView.Uncertainty
# if uncert.ready():
# colortable = []
# for i in range(256-len(colortable)):
# r,g,b,a = i,0,0,i
# colortable.append(QColor(r,g,b,a).rgba())
# layer = ColortableLayer(createDataSource(uncert), colortable, direct=True)
# layer.name = "Uncertainty"
# layer.visible = True
# layer.opacity = 0.3
# layers.append(layer)
# segmentation
seg = self.topLevelOperatorView.Segmentation
# seg = self.topLevelOperatorView.MST.value.segmentation
# temp = self._done_lut[self.MST.value.supervoxelUint32[sl[1:4]]]
if seg.ready():
# source = RelabelingArraySource(seg)
# source.setRelabeling(numpy.arange(256, dtype=numpy.uint8))
# assign to the object label color, 0 is transparent, 1 is background
colortable = [
QColor(0, 0, 0, 0).rgba(),
QColor(0, 0, 0, 0).rgba(), labellayer._colorTable[2]
]
for i in range(256 - len(colortable)):
r, g, b = numpy.random.randint(0, 255), numpy.random.randint(
0, 255), numpy.random.randint(0, 255)
colortable.append(QColor(r, g, b).rgba())
layer = ColortableLayer(createDataSource(seg),
colortable,
direct=True)
layer.name = "Segmentation"
layer.setToolTip(
"This layer displays the <i>current</i> segmentation. Simply add foreground and background "
"labels, then press <i>Segment</i>.")
layer.visible = True
layer.opacity = 0.3
layers.append(layer)
# done
doneSeg = self.topLevelOperatorView.DoneSegmentation
if doneSeg.ready():
# FIXME: if the user segments more than 255 objects, those with indices that divide by 255 will be shown as transparent
# both here and in the _doneSegmentationColortable
colortable = 254 * [QColor(230, 25, 75).rgba()]
colortable.insert(0, QColor(0, 0, 0, 0).rgba())
# have to use lazyflow because it provides dirty signals
layer = ColortableLayer(createDataSource(doneSeg),
colortable,
direct=True)
layer.name = "Completed segments (unicolor)"
layer.setToolTip(
"In order to keep track of which objects you have already completed, this layer "
"shows <b>all completed object</b> in one color (<b>blue</b>). "
"The reason for only one color is that for finding out which "
"objects to label next, the identity of already completed objects is unimportant "
"and destracting.")
layer.visible = False
layer.opacity = 0.5
layers.append(layer)
layer = ColortableLayer(createDataSource(doneSeg),
self._doneSegmentationColortable,
direct=True)
layer.name = "Completed segments (one color per object)"
layer.setToolTip(
"<html>In order to keep track of which objects you have already completed, this layer "
"shows <b>all completed object</b>, each with a random color.</html>"
)
layer.visible = False
layer.opacity = 0.5
layer.colortableIsRandom = True
self._doneSegmentationLayer = layer
layers.append(layer)
# supervoxel
sv = self.topLevelOperatorView.Supervoxels
if sv.ready():
colortable = []
for i in range(256):
r, g, b = numpy.random.randint(0, 255), numpy.random.randint(
0, 255), numpy.random.randint(0, 255)
colortable.append(QColor(r, g, b).rgba())
layer = ColortableLayer(createDataSource(sv),
colortable,
direct=True)
layer.name = "Supervoxels"
layer.setToolTip(
"<html>This layer shows the partitioning of the input image into <b>supervoxels</b>. The carving "
"algorithm uses these tiny puzzle-piceces to piece together the segmentation of an "
"object. Sometimes, supervoxels are too large and straddle two distinct objects "
"(undersegmentation). In this case, it will be impossible to achieve the desired "
"segmentation. This layer helps you to understand these cases.</html>"
)
layer.visible = False
layer.colortableIsRandom = True
layer.opacity = 0.5
layers.append(layer)
# Visual overlay (just for easier labeling)
overlaySlot = self.topLevelOperatorView.OverlayData
if overlaySlot.ready():
overlay5D = self.topLevelOperatorView.OverlayData.value
layer = GrayscaleLayer(ArraySource(overlay5D), direct=True)
layer.visible = True
layer.name = "Overlay"
layer.opacity = 1.0
# if the flag window_leveling is set the contrast
# of the layer is adjustable
layer.window_leveling = True
self.labelingDrawerUi.thresToolButton.show()
layers.append(layer)
del layer
inputSlot = self.topLevelOperatorView.InputData
if inputSlot.ready():
layer = GrayscaleLayer(createDataSource(inputSlot), direct=True)
layer.name = "Input Data"
layer.setToolTip(
"<html>The data originally loaded into ilastik (unprocessed).</html>"
)
# layer.visible = not rawSlot.ready()
layer.visible = True
layer.opacity = 1.0
# Window leveling is already active on the Overlay,
# but if no overlay was provided, then activate window_leveling on the raw data instead.
if not overlaySlot.ready():
# if the flag window_leveling is set the contrast
# of the layer is adjustable
layer.window_leveling = True
self.labelingDrawerUi.thresToolButton.show()
layers.append(layer)
del layer
filteredSlot = self.topLevelOperatorView.FilteredInputData
if filteredSlot.ready():
layer = GrayscaleLayer(createDataSource(filteredSlot))
layer.name = "Filtered Input"
layer.visible = False
layer.opacity = 1.0
layers.append(layer)
return layers
def setupLayers(self):
layers = []
op = self.topLevelOperatorView
ravelerLabelsSlot = op.RavelerLabels
if ravelerLabelsSlot.ready():
colortable = []
for _ in range(256):
r,g,b = numpy.random.randint(0,255), numpy.random.randint(0,255), numpy.random.randint(0,255)
colortable.append(QColor(r,g,b).rgba())
ravelerLabelLayer = ColortableLayer(LazyflowSource(ravelerLabelsSlot), colortable, direct=True)
ravelerLabelLayer.name = "Raveler Labels"
ravelerLabelLayer.visible = False
ravelerLabelLayer.opacity = 0.4
layers.append(ravelerLabelLayer)
def addFragmentSegmentationLayers(mslot, name):
if mslot.ready():
for index, slot in enumerate(mslot):
if slot.ready():
raveler_label = slot.meta.selected_label
colortable = map(QColor.rgba, self._fragmentColors)
fragSegLayer = ColortableLayer(LazyflowSource(slot), colortable, direct=True)
fragSegLayer.name = "{} #{} ({})".format( name, index, raveler_label )
fragSegLayer.visible = False
fragSegLayer.opacity = 1.0
layers.append(fragSegLayer)
addFragmentSegmentationLayers( op.FragmentedBodies, "Saved Fragments" )
addFragmentSegmentationLayers( op.RelabeledFragments, "Relabeled Fragments" )
addFragmentSegmentationLayers( op.FilteredFragmentedBodies, "CC-Filtered Fragments" )
addFragmentSegmentationLayers( op.WatershedFilledBodies, "Watershed-filled Fragments" )
mslot = op.EditedRavelerBodies
for index, slot in enumerate(mslot):
if slot.ready():
raveler_label = slot.meta.selected_label
# 0=Black, 1=Transparent
colortable = [QColor(0, 0, 0).rgba(), QColor(0, 0, 0, 0).rgba()]
bodyMaskLayer = ColortableLayer(LazyflowSource(slot), colortable, direct=True)
bodyMaskLayer.name = "Raveler Body Mask #{} ({})".format( index, raveler_label )
bodyMaskLayer.visible = False
bodyMaskLayer.opacity = 1.0
layers.append(bodyMaskLayer)
finalSegSlot = op.FinalSegmentation
if finalSegSlot.ready():
colortable = []
for _ in range(256):
r,g,b = numpy.random.randint(0,255), numpy.random.randint(0,255), numpy.random.randint(0,255)
colortable.append(QColor(r,g,b).rgba())
finalLayer = ColortableLayer(LazyflowSource(finalSegSlot), colortable, direct=True)
finalLayer.name = "Final Segmentation"
finalLayer.visible = False
finalLayer.opacity = 0.4
layers.append(finalLayer)
inputSlot = op.InputData
if inputSlot.ready():
layer = GrayscaleLayer( LazyflowSource(inputSlot) )
layer.name = "WS Input"
layer.visible = False
layer.opacity = 1.0
layers.append(layer)
#raw data
rawSlot = self.topLevelOperatorView.RawData
rawLayer = None
if rawSlot.ready():
raw5D = self.topLevelOperatorView.RawData.value
rawLayer = GrayscaleLayer(ArraySource(raw5D), direct=True)
#rawLayer = GrayscaleLayer( LazyflowSource(rawSlot) )
rawLayer.name = "raw"
rawLayer.visible = True
rawLayer.opacity = 1.0
rawLayer.shortcutRegistration = ( "g", ShortcutManager.ActionInfo(
"Postprocessing",
"Raw Data to Top",
"Raw Data to Top",
partial(self._toggleRawDataPosition, rawLayer),
self.viewerControlWidget(),
rawLayer ) )
layers.append(rawLayer)
return layers
class ImageScene2D_LazyTest(ut.TestCase):
def setUp(self):
self.layerstack = LayerStackModel()
self.sims = StackedImageSources(self.layerstack)
self.g = Graph()
self.op = OpLazy(self.g)
self.ds = LazyflowSource(self.op.Output)
self.ss = PlanarSliceSource(self.ds, projectionAlongTZC)
self.layer = GrayscaleLayer(self.ds, normalize=False)
self.layerstack.append(self.layer)
self.ims = self.layer.createImageSource([self.ss])
self.sims.register(self.layer, self.ims)
self.scene = ImageScene2D(PositionModel(), (0, 0, 0),
preemptive_fetch_number=0)
self.scene.setCacheSize(1)
self.scene.stackedImageSources = self.sims
self.scene.dataShape = (30, 30)
def renderScene(self, s, exportFilename=None, joinRendering=True):
img = QImage(30, 30, QImage.Format_ARGB32_Premultiplied)
img.fill(Qt.white)
p = QPainter(img)
s.render(p) # trigger a rendering of the whole scene
if joinRendering:
# wait for all the data to arrive
s.joinRenderingAllTiles(
viewport_only=False) # There is no viewport!
# finally, render everything
s.render(p)
p.end()
if exportFilename is not None:
img.save(exportFilename)
return byte_view(img)
def testLazy(self):
for i in range(3):
self.op.setConstant(i)
aimg = self.renderScene(self.scene, "/tmp/a_%03d.png" % i)
assert numpy.all(
aimg[:, :,
0] == i), "!= %d, [0,0,0]=%d" % (i, aimg[0, 0, 0])
self.op.setConstant(42)
self.op.setDelay(1)
aimg = self.renderScene(self.scene,
joinRendering=False,
exportFilename="/tmp/x_%03d.png" % i)
# this should be "i", not 255 (the default background for the imagescene)
assert numpy.all(
aimg[:, :,
0] == i), "!= %d, [0,0,0]=%d" % (i, aimg[0, 0, 0])
# Now give the scene time to update before we change it again...
self.scene.joinRenderingAllTiles(viewport_only=False)
def setupLayers(self):
layers = []
op = self.topLevelOperatorView
ravelerLabelsSlot = op.RavelerLabels
if ravelerLabelsSlot.ready():
colortable = []
for _ in range(256):
r, g, b = numpy.random.randint(0, 255), numpy.random.randint(
0, 255), numpy.random.randint(0, 255)
colortable.append(QColor(r, g, b).rgba())
ravelerLabelLayer = ColortableLayer(
LazyflowSource(ravelerLabelsSlot), colortable, direct=True)
ravelerLabelLayer.name = "Raveler Labels"
ravelerLabelLayer.visible = False
ravelerLabelLayer.opacity = 0.4
layers.append(ravelerLabelLayer)
supervoxelsSlot = op.Supervoxels
if supervoxelsSlot.ready():
colortable = []
for i in range(256):
r, g, b = numpy.random.randint(0, 255), numpy.random.randint(
0, 255), numpy.random.randint(0, 255)
colortable.append(QColor(r, g, b).rgba())
supervoxelsLayer = ColortableLayer(LazyflowSource(supervoxelsSlot),
colortable)
supervoxelsLayer.name = "Input Supervoxels"
supervoxelsLayer.visible = False
supervoxelsLayer.opacity = 1.0
layers.append(supervoxelsLayer)
def addFragmentSegmentationLayers(mslot, name):
if mslot.ready():
for index, slot in enumerate(mslot):
if slot.ready():
raveler_label = slot.meta.selected_label
colortable = []
for i in range(256):
r, g, b = numpy.random.randint(
0, 255), numpy.random.randint(
0, 255), numpy.random.randint(0, 255)
colortable.append(QColor(r, g, b).rgba())
colortable[0] = QColor(0, 0, 0, 0).rgba()
fragSegLayer = ColortableLayer(LazyflowSource(slot),
colortable,
direct=True)
fragSegLayer.name = "{} #{} ({})".format(
name, index, raveler_label)
fragSegLayer.visible = False
fragSegLayer.opacity = 1.0
layers.append(fragSegLayer)
addFragmentSegmentationLayers(op.MaskedSupervoxels,
"Masked Supervoxels")
addFragmentSegmentationLayers(op.FilteredMaskedSupervoxels,
"Filtered Masked Supervoxels")
addFragmentSegmentationLayers(op.HoleFilledSupervoxels,
"Hole Filled Supervoxels")
addFragmentSegmentationLayers(op.RelabeledSupervoxels,
"Relabeled Supervoxels")
mslot = op.EditedRavelerBodies
for index, slot in enumerate(mslot):
if slot.ready():
raveler_label = slot.meta.selected_label
# 0=Black, 1=Transparent
colortable = [
QColor(0, 0, 0).rgba(),
QColor(0, 0, 0, 0).rgba()
]
bodyMaskLayer = ColortableLayer(LazyflowSource(slot),
colortable,
direct=True)
bodyMaskLayer.name = "Raveler Body Mask #{} ({})".format(
index, raveler_label)
bodyMaskLayer.visible = False
bodyMaskLayer.opacity = 1.0
layers.append(bodyMaskLayer)
finalSegSlot = op.FinalSupervoxels
if finalSegSlot.ready():
colortable = []
for _ in range(256):
r, g, b = numpy.random.randint(0, 255), numpy.random.randint(
0, 255), numpy.random.randint(0, 255)
colortable.append(QColor(r, g, b).rgba())
finalLayer = ColortableLayer(LazyflowSource(finalSegSlot),
colortable)
finalLayer.name = "Final Supervoxels"
finalLayer.visible = False
finalLayer.opacity = 0.4
layers.append(finalLayer)
inputSlot = op.InputData
if inputSlot.ready():
layer = GrayscaleLayer(LazyflowSource(inputSlot))
layer.name = "WS Input"
layer.visible = False
layer.opacity = 1.0
layers.append(layer)
#raw data
rawSlot = self.topLevelOperatorView.RawData
if rawSlot.ready():
raw5D = self.topLevelOperatorView.RawData.value
layer = GrayscaleLayer(ArraySource(raw5D), direct=True)
#layer = GrayscaleLayer( LazyflowSource(rawSlot) )
layer.name = "raw"
layer.visible = True
layer.opacity = 1.0
layers.append(layer)
return layers
def setupLayers( self ):
logger.debug( "setupLayers" )
layers = []
def onButtonsEnabled(slot, roi):
currObj = self.topLevelOperatorView.CurrentObjectName.value
hasSeg = self.topLevelOperatorView.HasSegmentation.value
self.labelingDrawerUi.currentObjectLabel.setText(currObj)
self.labelingDrawerUi.save.setEnabled(hasSeg)
self.topLevelOperatorView.CurrentObjectName.notifyDirty(onButtonsEnabled)
self.topLevelOperatorView.HasSegmentation.notifyDirty(onButtonsEnabled)
self.topLevelOperatorView.opLabelArray.NonzeroBlocks.notifyDirty(onButtonsEnabled)
# Labels
labellayer, labelsrc = self.createLabelLayer(direct=True)
if labellayer is not None:
labellayer._allowToggleVisible = False
layers.append(labellayer)
# Tell the editor where to draw label data
self.editor.setLabelSink(labelsrc)
#uncertainty
if self._showUncertaintyLayer:
uncert = self.topLevelOperatorView.Uncertainty
if uncert.ready():
colortable = []
for i in range(256-len(colortable)):
r,g,b,a = i,0,0,i
colortable.append(QColor(r,g,b,a).rgba())
layer = ColortableLayer(LazyflowSource(uncert), colortable, direct=True)
layer.name = "Uncertainty"
layer.visible = True
layer.opacity = 0.3
layers.append(layer)
#segmentation
seg = self.topLevelOperatorView.Segmentation
#seg = self.topLevelOperatorView.MST.value.segmentation
#temp = self._done_lut[self.MST.value.regionVol[sl[1:4]]]
if seg.ready():
#source = RelabelingArraySource(seg)
#source.setRelabeling(numpy.arange(256, dtype=numpy.uint8))
colortable = [QColor(0,0,0,0).rgba(), QColor(0,0,0,0).rgba(), QColor(0,255,0).rgba()]
for i in range(256-len(colortable)):
r,g,b = numpy.random.randint(0,255), numpy.random.randint(0,255), numpy.random.randint(0,255)
colortable.append(QColor(r,g,b).rgba())
layer = ColortableLayer(LazyflowSource(seg), colortable, direct=True)
layer.name = "Segmentation"
layer.setToolTip("This layer displays the <i>current</i> segmentation. Simply add foreground and background " \
"labels, then press <i>Segment</i>.")
layer.visible = True
layer.opacity = 0.3
layers.append(layer)
#done
done = self.topLevelOperatorView.DoneObjects
if done.ready():
colortable = [QColor(0,0,0,0).rgba(), QColor(0,0,255).rgba()]
#have to use lazyflow because it provides dirty signals
layer = ColortableLayer(LazyflowSource(done), colortable, direct=True)
layer.name = "Completed segments (unicolor)"
layer.setToolTip("In order to keep track of which objects you have already completed, this layer " \
"shows <b>all completed object</b> in one color (<b>blue</b>). " \
"The reason for only one color is that for finding out which " \
"objects to label next, the identity of already completed objects is unimportant " \
"and destracting.")
layer.visible = False
layer.opacity = 0.5
layers.append(layer)
#done seg
doneSeg = self.topLevelOperatorView.DoneSegmentation
if doneSeg.ready():
layer = ColortableLayer(LazyflowSource(doneSeg), self._doneSegmentationColortable, direct=True)
layer.name = "Completed segments (one color per object)"
layer.setToolTip("<html>In order to keep track of which objects you have already completed, this layer " \
"shows <b>all completed object</b>, each with a random color.</html>")
layer.visible = False
layer.opacity = 0.5
self._doneSegmentationLayer = layer
layers.append(layer)
#supervoxel
sv = self.topLevelOperatorView.Supervoxels
if sv.ready():
colortable = []
for i in range(256):
r,g,b = numpy.random.randint(0,255), numpy.random.randint(0,255), numpy.random.randint(0,255)
colortable.append(QColor(r,g,b).rgba())
layer = ColortableLayer(LazyflowSource(sv), colortable, direct=True)
layer.name = "Supervoxels"
layer.setToolTip("<html>This layer shows the partitioning of the input image into <b>supervoxels</b>. The carving " \
"algorithm uses these tiny puzzle-piceces to piece together the segmentation of an " \
"object. Sometimes, supervoxels are too large and straddle two distinct objects " \
"(undersegmentation). In this case, it will be impossible to achieve the desired " \
"segmentation. This layer helps you to understand these cases.</html>")
layer.visible = False
layer.opacity = 1.0
layers.append(layer)
#raw data
rawSlot = self.topLevelOperatorView.RawData
if rawSlot.ready():
raw5D = self.topLevelOperatorView.RawData.value
layer = GrayscaleLayer(ArraySource(raw5D), direct=True)
#layer = GrayscaleLayer( LazyflowSource(rawSlot) )
layer.visible = True
layer.name = 'Raw Data'
layer.opacity = 1.0
layers.append(layer)
inputSlot = self.topLevelOperatorView.InputData
if inputSlot.ready():
layer = GrayscaleLayer( LazyflowSource(inputSlot), direct=True )
layer.name = "Input Data"
layer.setToolTip("<html>The data originally loaded into ilastik (unprocessed).</html>")
#layer.visible = not rawSlot.ready()
layer.visible = True
layer.opacity = 1.0
# if the flag window_leveling is set the contrast
# of the layer is adjustable
layer.window_leveling = True
layers.append(layer)
if layer.window_leveling:
self.labelingDrawerUi.thresToolButton.show()
else:
self.labelingDrawerUi.thresToolButton.hide()
filteredSlot = self.topLevelOperatorView.FilteredInputData
if filteredSlot.ready():
layer = GrayscaleLayer( LazyflowSource(filteredSlot) )
layer.name = "Filtered Input"
layer.visible = False
layer.opacity = 1.0
layers.append(layer)
return layers
