def __init__(self):
super(VigraWatershedWorkflow, self).__init__()
self._applets = []
# Create a graph to be shared by all operators
graph = Graph()
self._graph = graph
# Create applets
self.dataSelectionApplet = DataSelectionApplet(
graph,
"Input Data",
"Input Data",
supportIlastik05Import=True,
batchDataGui=False)
self.watershedApplet = VigraWatershedViewerApplet(
graph, "Watershed", "Watershed")
# Connect top-level operators
self.watershedApplet.topLevelOperator.InputImage.connect(
self.dataSelectionApplet.topLevelOperator.Image)
self._applets.append(self.dataSelectionApplet)
self._applets.append(self.watershedApplet)
# The shell needs a slot from which he can read the list of image names to switch between.
# Use an OpAttributeSelector to create a slot containing just the filename from the OpDataSelection's DatasetInfo slot.
opSelectFilename = OperatorWrapper(OpAttributeSelector, graph=graph)
opSelectFilename.InputObject.connect(
self.dataSelectionApplet.topLevelOperator.Dataset)
opSelectFilename.AttributeName.setValue('filePath')
self._imageNameListSlot = opSelectFilename.Result
def __init__(self, shell, headless, workflow_cmdline_args,
project_creation_workflow, *args, **kwargs):
# Create a graph to be shared by all operators
graph = Graph()
super(VigraWatershedWorkflow, self).__init__(shell,
headless,
workflow_cmdline_args,
project_creation_workflow,
graph=graph,
*args,
**kwargs)
self._applets = []
# Create applets
self.dataSelectionApplet = DataSelectionApplet(
self,
"Input Data",
"Input Data",
supportIlastik05Import=True,
batchDataGui=False)
self.watershedApplet = VigraWatershedViewerApplet(
self, "Watershed", "Watershed")
# Dataset inputs
opDataSelection = self.dataSelectionApplet.topLevelOperator
opDataSelection.DatasetRoles.setValue(['Raw Data'])
# Expose to shell
self._applets.append(self.dataSelectionApplet)
self._applets.append(self.watershedApplet)
def __init__(self):
super(PixelClassificationWithVigraWatershedWorkflow, self).__init__()
self._pixelClassificationWorkflow = PixelClassificationWorkflow()
self.dataSelectionApplet = self._pixelClassificationWorkflow.dataSelectionApplet
self._graph = self._pixelClassificationWorkflow.graph
graph = self._graph
# Create applets
self.watershedApplet = VigraWatershedViewerApplet(
graph, "Watershed", "Watershed")
# Connect top-level operators
pixelClassificationApplet = self._pixelClassificationWorkflow.pcApplet
opPixelClassification = pixelClassificationApplet.topLevelOperator
opWatershedViewer = self.watershedApplet.topLevelOperator
opWatershedViewer.InputImage.connect(
opPixelClassification.CachedPredictionProbabilities)
opWatershedViewer.RawImage.connect(opPixelClassification.InputImages)
self._applets = []
self._applets += self._pixelClassificationWorkflow.applets[0:4]
self._applets.append(self.watershedApplet)
def __init__(self, headless, workflow_cmdline_args, *args, **kwargs):
super(PixelClassificationWithVigraWatershedWorkflow,
self).__init__(headless,
workflow_cmdline_args,
appendBatchOperators=False,
*args,
**kwargs)
# Create applets
self.watershedApplet = VigraWatershedViewerApplet(
self, "Watershed", "Watershed")
# Expose for shell
self._applets.append(self.watershedApplet)
def __init__(self, shell, headless, workflow_cmdline_args,
project_creation_args, *args, **kwargs):
super(PixelClassificationWithWatershedWorkflow,
self).__init__(shell, headless, workflow_cmdline_args,
project_creation_args, *args, **kwargs)
# Create applets
self.watershedApplet = VigraWatershedViewerApplet(
self, "Watershed", "Watershed")
opDataExport = self.dataExportApplet.topLevelOperator
opDataExport.SelectionNames.setValue(
self.EXPORT_NAMES + ["Watershed Seeds", "Watershed Labels"])
# Expose for shell (insert before last applet, which is the batch applet)
self._applets.insert(-2, self.watershedApplet)
def __init__(self, shell, headless, workflow_cmdline_args,
project_creation_args, *args, **kwargs):
super(PixelClassificationWithWatershedWorkflow,
self).__init__(shell,
headless,
workflow_cmdline_args,
project_creation_args,
appendBatchOperators=False,
supports_anisotropic_data=False,
*args,
**kwargs)
# Create applets
self.watershedApplet = VigraWatershedViewerApplet(
self, "Watershed", "Watershed")
# Expose for shell
self._applets.append(self.watershedApplet)
def __init__(self, headless, workflow_cmdline_args, *args, **kwargs):
# Create a graph to be shared by all operators
graph = Graph()
super(VigraWatershedWorkflow, self).__init__(headless,
graph=graph,
*args,
**kwargs)
self._applets = []
# Create applets
self.dataSelectionApplet = DataSelectionApplet(
self,
"Input Data",
"Input Data",
supportIlastik05Import=True,
batchDataGui=False)
self.watershedApplet = VigraWatershedViewerApplet(
self, "Watershed", "Watershed")
# Expose to shell
self._applets.append(self.dataSelectionApplet)
self._applets.append(self.watershedApplet)