def __init__(self, shell, headless, workflow_cmdline_args,
project_creation_args, *args, **kwargs):
# Create a graph to be shared by all operators
graph = Graph()
super(LayerViewerWorkflow, self).__init__(shell,
headless,
workflow_cmdline_args,
project_creation_args,
graph=graph,
*args,
**kwargs)
self._applets = []
# Create applets
self.dataSelectionApplet = DataSelectionApplet(
self,
"Input Data",
"Input Data",
supportIlastik05Import=True,
forceAxisOrder='tzyxc')
self.viewerApplet = LayerViewerApplet(self)
self.dataExportApplet = DataExportApplet(self, "Data Export")
opDataExport = self.dataExportApplet.topLevelOperator
opDataExport.SelectionNames.setValue(['Raw Data', 'Other Data'])
opDataSelection = self.dataSelectionApplet.topLevelOperator
opDataSelection.DatasetRoles.setValue(["Raw Data", "Other Data"])
self._applets.append(self.dataSelectionApplet)
self._applets.append(self.viewerApplet)
self._applets.append(self.dataExportApplet)
self._workflow_cmdline_args = workflow_cmdline_args
def __init__(self, shell, headless, workflow_cmdline_args,
project_creation_args):
# Create a graph to be shared by all operators
graph = Graph()
super(ThresholdMaskingWorkflow, self).__init__(shell,
headless,
workflow_cmdline_args,
project_creation_args,
graph=graph)
self._applets = []
# Create applets
self.dataSelectionApplet = DataSelectionApplet(
self,
"Input Data",
"Input Data",
supportIlastik05Import=True,
batchDataGui=False)
self.thresholdMaskingApplet = ThresholdMaskingApplet(
self, "Thresholding", "Thresholding Stage 1")
opDataSelection = self.dataSelectionApplet.topLevelOperator
opDataSelection.DatasetRoles.setValue(self.ROLE_NAMES)
# Instantiate 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)
self._applets.append(self.dataSelectionApplet)
self._applets.append(self.thresholdMaskingApplet)
self._applets.append(self.dataExportApplet)
def __init__(self, headless, workflow_cmdline_args, *args, **kwargs):
# Create a graph to be shared by all operators
graph = Graph()
super(LayerViewerWorkflow, self).__init__(headless, graph=graph, *args, **kwargs)
self._applets = []
# Create applets
self.dataSelectionApplet = DataSelectionApplet(self,
"Input Data",
"Input Data",
supportIlastik05Import=True,
batchDataGui=False,
force5d=True)
self.viewerApplet = LayerViewerApplet(self)
self.dataExportApplet = DataExportApplet(self, "Data Export")
opDataSelection = self.dataSelectionApplet.topLevelOperator
opDataSelection.DatasetRoles.setValue( ["Raw Data", "Other Data"] )
self._applets.append( self.dataSelectionApplet )
self._applets.append( self.viewerApplet )
self._applets.append( self.dataExportApplet )
self._workflow_cmdline_args = workflow_cmdline_args
def __init__(self, shell, headless, workflow_cmdline_args,
project_creation_args, *args, **kwargs):
# Create a graph to be shared by all operators
graph = Graph()
super(LayerViewerWorkflow, self).__init__(shell,
headless,
workflow_cmdline_args,
project_creation_args,
graph=graph,
*args,
**kwargs)
self._applets = []
# Roles
# Create applets
self.dataSelectionApplet = DataSelectionApplet(self, "Input Data",
"Input Data")
self.viewerApplet = LayerViewerApplet(self)
self.dataExportApplet = DataExportApplet(self, "Data Export")
opDataExport = self.dataExportApplet.topLevelOperator
opDataExport.SelectionNames.setValue(ROLES)
opDataSelection = self.dataSelectionApplet.topLevelOperator
opDataSelection.DatasetRoles.setValue(ROLES)
self._applets.append(self.dataSelectionApplet)
self._applets.append(self.viewerApplet)
self._applets.append(self.dataExportApplet)
self._workflow_cmdline_args = workflow_cmdline_args
def __init__(self, headless, workflow_cmdline_args, *args, **kwargs):
# Create a graph to be shared by all operators
graph = Graph()
super(DataConversionWorkflow, self).__init__(headless,
graph=graph,
*args,
**kwargs)
self._applets = []
# Create applets
self.dataSelectionApplet = DataSelectionApplet(
self,
"Input Data",
"Input Data",
supportIlastik05Import=True,
batchDataGui=False,
force5d=False)
opDataSelection = self.dataSelectionApplet.topLevelOperator
opDataSelection.DatasetRoles.setValue(["Input Data"])
self.dataExportApplet = DataExportApplet(self, "Data Export")
opDataExport = self.dataExportApplet.topLevelOperator
opDataExport.WorkingDirectory.connect(opDataSelection.WorkingDirectory)
self._applets.append(self.dataSelectionApplet)
self._applets.append(self.dataExportApplet)
self._workflow_cmdline_args = workflow_cmdline_args
def __init__(self, shell, headless, workflow_cmdline_args, project_creation_args, *args, **kwargs):
# Create a graph to be shared by all operators
graph = Graph()
super(DataConversionWorkflow, self).__init__(
shell, headless, workflow_cmdline_args, project_creation_args, graph=graph, *args, **kwargs
)
self._applets = []
# Instantiate DataSelection applet
self.dataSelectionApplet = DataSelectionApplet(
self, "Input Data", "Input Data", supportIlastik05Import=True, forceAxisOrder=None
)
# Configure global DataSelection settings
role_names = ["Input Data"]
opDataSelection = self.dataSelectionApplet.topLevelOperator
opDataSelection.DatasetRoles.setValue(role_names)
# Instantiate DataExport applet
self.dataExportApplet = DataExportApplet(self, "Data Export")
# Configure global DataExport settings
opDataExport = self.dataExportApplet.topLevelOperator
opDataExport.WorkingDirectory.connect(opDataSelection.WorkingDirectory)
opDataExport.SelectionNames.setValue(["Input"])
# No special data pre/post processing necessary in this workflow,
# but this is where we'd hook it up if we needed it.
#
# self.dataExportApplet.prepare_for_entire_export = self.prepare_for_entire_export
# self.dataExportApplet.prepare_lane_for_export = self.prepare_lane_for_export
# self.dataExportApplet.post_process_lane_export = self.post_process_lane_export
# self.dataExportApplet.post_process_entire_export = self.post_process_entire_export
# Instantiate BatchProcessing applet
self.batchProcessingApplet = BatchProcessingApplet(
self, "Batch Processing", self.dataSelectionApplet, self.dataExportApplet
)
# Expose our applets in a list (for the shell to use)
self._applets.append(self.dataSelectionApplet)
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))
def __init__(self, shell, headless, workflow_cmdline_args,
project_creation_args, *args, **kwargs):
# Create a graph to be shared by all operators
graph = Graph()
super(DataConversionWorkflow, self).__init__(shell,
headless,
workflow_cmdline_args,
project_creation_args,
graph=graph,
*args,
**kwargs)
self._applets = []
# Create applets
self.dataSelectionApplet = DataSelectionApplet(
self,
"Input Data",
"Input Data",
supportIlastik05Import=True,
batchDataGui=False,
force5d=False)
opDataSelection = self.dataSelectionApplet.topLevelOperator
role_names = ["Input Data"]
opDataSelection.DatasetRoles.setValue(role_names)
self.dataExportApplet = DataExportApplet(self, "Data Export")
opDataExport = self.dataExportApplet.topLevelOperator
opDataExport.WorkingDirectory.connect(opDataSelection.WorkingDirectory)
opDataExport.SelectionNames.setValue(["Input"])
self._applets.append(self.dataSelectionApplet)
self._applets.append(self.dataExportApplet)
# Parse command-line arguments
# Command-line args are applied in onProjectLoaded(), below.
self._workflow_cmdline_args = workflow_cmdline_args
self._data_input_args = None
self._data_export_args = None
if workflow_cmdline_args:
self._data_export_args, unused_args = self.dataExportApplet.parse_known_cmdline_args(
unused_args)
self._data_input_args, unused_args = self.dataSelectionApplet.parse_known_cmdline_args(
workflow_cmdline_args, role_names)
if unused_args:
logger.warn("Unused command-line args: {}".format(unused_args))
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):
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):
# 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))
