def __init__(self, shell, headless, workflow_cmdline_args,
project_creation_workflow, *args, **kwargs):
self.stored_classifier = None
# Create a graph to be shared by all operators
graph = Graph()
super(MulticutWorkflow, self).__init__(shell,
headless,
workflow_cmdline_args,
project_creation_workflow,
graph=graph,
*args,
**kwargs)
self._applets = []
# -- DataSelection applet
#
self.dataSelectionApplet = DataSelectionApplet(
self, "Input Data", "Input Data", forceAxisOrder=['zyxc', 'yxc'])
# Dataset inputs
opDataSelection = self.dataSelectionApplet.topLevelOperator
opDataSelection.DatasetRoles.setValue(self.ROLE_NAMES)
# -- Watershed applet
#
self.wsdtApplet = WsdtApplet(self, "DT Watershed", "DT Watershed")
# -- Edge training applet
#
self.edgeTrainingApplet = EdgeTrainingApplet(self, "Edge Training",
"Edge Training")
opEdgeTraining = self.edgeTrainingApplet.topLevelOperator
DEFAULT_FEATURES = {
self.ROLE_NAMES[self.DATA_ROLE_RAW]: ['standard_edge_mean']
}
opEdgeTraining.FeatureNames.setValue(DEFAULT_FEATURES)
# -- Multicut applet
#
self.multicutApplet = MulticutApplet(self, "Multicut Segmentation",
"Multicut Segmentation")
# -- DataExport applet
#
self.dataExportApplet = DataExportApplet(self, "Data Export")
self.dataExportApplet.prepare_for_entire_export = self.prepare_for_entire_export
self.dataExportApplet.post_process_entire_export = self.post_process_entire_export
# Configure global DataExport settings
opDataExport = self.dataExportApplet.topLevelOperator
opDataExport.WorkingDirectory.connect(opDataSelection.WorkingDirectory)
opDataExport.SelectionNames.setValue(self.EXPORT_NAMES)
# -- BatchProcessing applet
#
self.batchProcessingApplet = BatchProcessingApplet(
self, "Batch Processing", self.dataSelectionApplet,
self.dataExportApplet)
# -- Expose applets to shell
self._applets.append(self.dataSelectionApplet)
self._applets.append(self.wsdtApplet)
self._applets.append(self.edgeTrainingApplet)
self._applets.append(self.multicutApplet)
self._applets.append(self.dataExportApplet)
self._applets.append(self.batchProcessingApplet)
# -- Parse command-line arguments
# (Command-line args are applied in onProjectLoaded(), below.)
if workflow_cmdline_args:
self._data_export_args, unused_args = self.dataExportApplet.parse_known_cmdline_args(
workflow_cmdline_args)
self._batch_input_args, unused_args = self.batchProcessingApplet.parse_known_cmdline_args(
unused_args)
else:
unused_args = None
self._batch_input_args = None
self._data_export_args = None
if unused_args:
logger.warning("Unused command-line args: {}".format(unused_args))
if not self._headless:
shell.currentAppletChanged.connect(self.handle_applet_changed)
def __init__(self, shell, headless, workflow_cmdline_args,
project_creation_workflow, *args, **kwargs):
self.stored_classifier = None
# Create a graph to be shared by all operators
graph = Graph()
super(EdgeTrainingWithMulticutWorkflow,
self).__init__(shell,
headless,
workflow_cmdline_args,
project_creation_workflow,
graph=graph,
*args,
**kwargs)
self._applets = []
# -- DataSelection applet
#
self.dataSelectionApplet = DataSelectionApplet(self, "Input Data",
"Input Data")
# Dataset inputs
opDataSelection = self.dataSelectionApplet.topLevelOperator
opDataSelection.DatasetRoles.setValue(self.ROLE_NAMES)
# -- Watershed applet
#
self.wsdtApplet = WsdtApplet(self, "DT Watershed", "DT Watershed")
# -- Edge training AND Multicut applet
#
self.edgeTrainingWithMulticutApplet = EdgeTrainingWithMulticutApplet(
self, "Training and Multicut", "Training and Multicut")
opEdgeTrainingWithMulticut = self.edgeTrainingWithMulticutApplet.topLevelOperator
DEFAULT_FEATURES = {
self.ROLE_NAMES[self.DATA_ROLE_RAW]: ['standard_edge_mean']
}
opEdgeTrainingWithMulticut.FeatureNames.setValue(DEFAULT_FEATURES)
# -- DataExport applet
#
self.dataExportApplet = DataExportApplet(self, "Data Export")
self.dataExportApplet.prepare_for_entire_export = self.prepare_for_entire_export
self.dataExportApplet.post_process_entire_export = self.post_process_entire_export
# Configure global DataExport settings
opDataExport = self.dataExportApplet.topLevelOperator
opDataExport.WorkingDirectory.connect(opDataSelection.WorkingDirectory)
opDataExport.SelectionNames.setValue(self.EXPORT_NAMES)
# -- BatchProcessing applet
#
self.batchProcessingApplet = BatchProcessingApplet(
self, "Batch Processing", self.dataSelectionApplet,
self.dataExportApplet)
# -- Expose applets to shell
self._applets.append(self.dataSelectionApplet)
self._applets.append(self.wsdtApplet)
self._applets.append(self.edgeTrainingWithMulticutApplet)
self._applets.append(self.dataExportApplet)
self._applets.append(self.batchProcessingApplet)
# -- Parse command-line arguments
# (Command-line args are applied in onProjectLoaded(), below.)
# Parse workflow-specific command-line args
parser = argparse.ArgumentParser()
parser.add_argument(
'--retrain',
help=
"Re-train the classifier based on labels stored in the project file, and re-save.",
action="store_true")
self.parsed_workflow_args, unused_args = parser.parse_known_args(
workflow_cmdline_args)
if unused_args:
# Parse batch export/input args.
self._data_export_args, unused_args = self.dataExportApplet.parse_known_cmdline_args(
unused_args)
self._batch_input_args, unused_args = self.batchProcessingApplet.parse_known_cmdline_args(
unused_args)
else:
unused_args = None
self._batch_input_args = None
self._data_export_args = None
if unused_args:
logger.warn("Unused command-line args: {}".format(unused_args))
if not self._headless:
shell.currentAppletChanged.connect(self.handle_applet_changed)
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(WsdtWorkflow, self).__init__(shell,
headless,
workflow_cmdline_args,
project_creation_workflow,
graph=graph,
*args,
**kwargs)
self._applets = []
# -- DataSelection applet
#
self.dataSelectionApplet = DataSelectionApplet(self, "Input Data",
"Input Data")
# Dataset inputs
opDataSelection = self.dataSelectionApplet.topLevelOperator
opDataSelection.DatasetRoles.setValue(self.ROLE_NAMES)
# -- Wsdt applet
#
self.wsdtApplet = WsdtApplet(self, "Watershed", "Wsdt Watershed")
# -- DataExport applet
#
self.dataExportApplet = DataExportApplet(self, "Data Export")
# Configure global DataExport settings
opDataExport = self.dataExportApplet.topLevelOperator
opDataExport.WorkingDirectory.connect(opDataSelection.WorkingDirectory)
opDataExport.SelectionNames.setValue(self.EXPORT_NAMES)
# -- BatchProcessing applet
#
self.batchProcessingApplet = BatchProcessingApplet(
self, "Batch Processing", self.dataSelectionApplet,
self.dataExportApplet)
# -- Expose applets to shell
self._applets.append(self.dataSelectionApplet)
self._applets.append(self.wsdtApplet)
self._applets.append(self.dataExportApplet)
self._applets.append(self.batchProcessingApplet)
# -- Parse command-line arguments
# (Command-line args are applied in onProjectLoaded(), below.)
if workflow_cmdline_args:
self._data_export_args, unused_args = self.dataExportApplet.parse_known_cmdline_args(
workflow_cmdline_args)
self._batch_input_args, unused_args = self.dataSelectionApplet.parse_known_cmdline_args(
unused_args, role_names)
else:
unused_args = None
self._batch_input_args = None
self._data_export_args = None
if unused_args:
logger.warning("Unused command-line args: {}".format(unused_args))