def __init__( self, workflow, projectFileGroupName ):
Applet.__init__( self, "Generic Labeling" )
self._topLevelOperator = OpLabeling(parent=workflow)
self._topLevelOperator.name = "Labeling Top-Level Operator"
self._serializableItems = [ LabelingSerializer( self._topLevelOperator, projectFileGroupName ) ]
self._gui = None
def __init__(self, graph, projectFileGroupName):
Applet.__init__(self, "Generic Labeling")
self._topLevelOperator = OpLabeling(graph=graph)
self._serializableItems = [
LabelingSerializer(self._topLevelOperator, projectFileGroupName)
]
self._gui = None
def __init__( self ):
Applet.__init__( self, "Project Metadata" )
self._projectMetadata = ProjectMetadata()
self._gui = None # Created on first acess
self._serializableItems = [ ProjectMetadataSerializer(self._projectMetadata, "ProjectMetadata"),
Ilastik05ProjectMetadataDeserializer(self._projectMetadata) ]
def __init__(self):
Applet.__init__(self, "Project Metadata")
self._projectMetadata = ProjectMetadata()
self._gui = None # Created on first acess
self._serializableItems = [
ProjectMetadataSerializer(self._projectMetadata,
"ProjectMetadata"),
Ilastik05ProjectMetadataDeserializer(self._projectMetadata)
]
def __init__( self, workflow, projectFileGroupName ):
Applet.__init__( self, "Pixel Classification" )
self._topLevelOperator = OpPixelClassification( parent=workflow )
# We provide two independent serializing objects:
# one for the current scheme and one for importing old projects.
self._serializableItems = [PixelClassificationSerializer(self._topLevelOperator, projectFileGroupName), # Default serializer for new projects
Ilastik05ImportDeserializer(self._topLevelOperator)] # Legacy (v0.5) importer
self._gui = None
# GUI needs access to the serializer to enable/disable prediction storage
self.predictionSerializer = self._serializableItems[0]
# FIXME: For now, we can directly connect the progress signal from the classifier training operator
# directly to the applet's overall progress signal, because it's the only thing we report progress for at the moment.
# If we start reporting progress for multiple tasks that might occur simulatneously,
# we'll need to aggregate the progress updates.
self._topLevelOperator.opTrain.progressSignal.subscribe(self.progressSignal.emit)
def __init__( self, graph, projectFileGroupName ):
Applet.__init__( self, "Pixel Classification" )
self._topLevelOperator = OpPixelClassification( graph=graph )
# We provide two independent serializing objects:
# one for the current scheme and one for importing old projects.
self._serializableItems = [PixelClassificationSerializer(self._topLevelOperator, projectFileGroupName), # Default serializer for new projects
Ilastik05ImportDeserializer(self._topLevelOperator)] # Legacy (v0.5) importer
self._gui = None
# GUI needs access to the serializer to enable/disable prediction storage
self.predictionSerializer = self._serializableItems[0]
# FIXME: For now, we can directly connect the progress signal from the classifier training operator
# directly to the applet's overall progress signal, because it's the only thing we report progress for at the moment.
# If we start reporting progress for multiple tasks that might occur simulatneously,
# we'll need to aggregate the progress updates.
self._topLevelOperator.opTrain.progressSignal.subscribe(self.progressSignal.emit)
def __init__( self, graph, projectFileGroupName ):
Applet.__init__( self, "Generic Labeling" )
self._topLevelOperator = OpLabeling(graph=graph)
self._serializableItems = [ LabelingSerializer( self._topLevelOperator, projectFileGroupName ) ]
self._gui = None
