def __init__(self, shell, headless, workflow_cmdline_args, project_creation_args, *args, **kwargs):
graph = kwargs["graph"] if "graph" in kwargs else Graph()
if "graph" in kwargs:
del kwargs["graph"]
super(CountingWorkflow, self).__init__(
shell, headless, workflow_cmdline_args, project_creation_args, graph=graph, *args, **kwargs
)
self.stored_classifier = None
# Parse workflow-specific command-line args
parser = argparse.ArgumentParser()
parser.add_argument(
"--csv-export-file",
help="Instead of exporting prediction density images, export total counts to the given csv path.",
)
self.parsed_counting_workflow_args, unused_args = parser.parse_known_args(workflow_cmdline_args)
######################
# Interactive workflow
######################
allowed_axis_orders = []
for space in itertools.permutations("xyz", 2):
allowed_axis_orders.append("".join(space) + "c")
self.dataSelectionApplet = DataSelectionApplet(
self, "Input Data", "Input Data", forceAxisOrder=allowed_axis_orders
)
opDataSelection = self.dataSelectionApplet.topLevelOperator
role_names = ["Raw Data"]
opDataSelection.DatasetRoles.setValue(role_names)
self.featureSelectionApplet = FeatureSelectionApplet(self, "Feature Selection", "FeatureSelections")
self.countingApplet = CountingApplet(workflow=self)
opCounting = self.countingApplet.topLevelOperator
opCounting.WorkingDirectory.connect(opDataSelection.WorkingDirectory)
self.dataExportApplet = CountingDataExportApplet(self, "Density Export", opCounting)
# Customization hooks
self.dataExportApplet.prepare_for_entire_export = self.prepare_for_entire_export
self.dataExportApplet.post_process_lane_export = self.post_process_lane_export
self.dataExportApplet.post_process_entire_export = self.post_process_entire_export
opDataExport = self.dataExportApplet.topLevelOperator
opDataExport.PmapColors.connect(opCounting.PmapColors)
opDataExport.LabelNames.connect(opCounting.LabelNames)
opDataExport.UpperBound.connect(opCounting.UpperBound)
opDataExport.WorkingDirectory.connect(opDataSelection.WorkingDirectory)
opDataExport.SelectionNames.setValue(["Probabilities"])
self._applets = []
self._applets.append(self.dataSelectionApplet)
self._applets.append(self.featureSelectionApplet)
self._applets.append(self.countingApplet)
self._applets.append(self.dataExportApplet)
self.batchProcessingApplet = BatchProcessingApplet(
self, "Batch Processing", self.dataSelectionApplet, self.dataExportApplet
)
self._applets.append(self.batchProcessingApplet)
if unused_args:
# We parse the export setting args first. All remaining args are considered input files by the input applet.
self._batch_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:
self._batch_input_args = None
self._batch_export_args = None
if unused_args:
logger.warning("Unused command-line args: {}".format(unused_args))
def _initBatchWorkflow(self):
"""
Connect the batch-mode top-level operators to the training workflow and to eachother.
"""
# Access applet operators from the training workflow
opTrainingDataSelection = self.dataSelectionApplet.topLevelOperator
opTrainingFeatures = self.featureSelectionApplet.topLevelOperator
opClassify = self.countingApplet.topLevelOperator
opSelectFirstLane = OperatorWrapper( OpSelectSubslot, parent=self )
opSelectFirstLane.Inputs.connect( opTrainingDataSelection.ImageGroup )
opSelectFirstLane.SubslotIndex.setValue(0)
opSelectFirstRole = OpSelectSubslot( parent=self )
opSelectFirstRole.Inputs.connect( opSelectFirstLane.Output )
opSelectFirstRole.SubslotIndex.setValue(0)
## Create additional batch workflow operators
opBatchFeatures = OperatorWrapper( OpFeatureSelection, operator_kwargs={'filter_implementation':'Original'}, parent=self, promotedSlotNames=['InputImage'] )
opBatchPredictionPipeline = OperatorWrapper( OpPredictionPipeline, parent=self )
# Create applets for batch workflow
self.batchInputApplet = DataSelectionApplet(self, "Batch Prediction Input Selections", "BatchDataSelection", supportIlastik05Import=False, batchDataGui=True)
self.batchResultsApplet = CountingDataExportApplet(self, "Batch Prediction Output Locations", opBatchPredictionPipeline, isBatch=True)
# Expose in shell
self._applets.append(self.batchInputApplet)
self._applets.append(self.batchResultsApplet)
opBatchInputs = self.batchInputApplet.topLevelOperator
opBatchResults = self.batchResultsApplet.topLevelOperator
opBatchInputs.DatasetRoles.connect( opTrainingDataSelection.DatasetRoles )
opBatchResults.ConstraintDataset.connect( opSelectFirstRole.Output )
## Connect Operators ##
opTranspose = OpTransposeSlots( parent=self )
opTranspose.OutputLength.setValue(1)
opTranspose.Inputs.connect( opBatchInputs.DatasetGroup )
# Provide dataset paths from data selection applet to the batch export applet
opBatchResults.RawDatasetInfo.connect( opTranspose.Outputs[0] )
opBatchResults.WorkingDirectory.connect( opBatchInputs.WorkingDirectory )
# Connect (clone) the feature operator inputs from
# the interactive workflow's features operator (which gets them from the GUI)
opBatchFeatures.Scales.connect( opTrainingFeatures.Scales )
opBatchFeatures.FeatureIds.connect( opTrainingFeatures.FeatureIds )
opBatchFeatures.SelectionMatrix.connect( opTrainingFeatures.SelectionMatrix )
# Classifier and LabelsCount are provided by the interactive workflow
opBatchPredictionPipeline.Classifier.connect( opClassify.Classifier )
opBatchPredictionPipeline.MaxLabel.connect( opClassify.MaxLabelValue )
opBatchPredictionPipeline.FreezePredictions.setValue( False )
# Provide these for the gui
opBatchResults.RawData.connect( opBatchInputs.Image )
opBatchResults.PmapColors.connect( opClassify.PmapColors )
opBatchResults.LabelNames.connect( opClassify.LabelNames )
opBatchResults.UpperBound.connect( opClassify.UpperBound )
# Connect Image pathway:
# Input Image -> Features Op -> Prediction Op -> Export
opBatchFeatures.InputImage.connect( opBatchInputs.Image )
opBatchPredictionPipeline.FeatureImages.connect( opBatchFeatures.OutputImage )
opBatchResults.SelectionNames.setValue( ['Probabilities'] )
# opBatchResults.Inputs is indexed by [lane][selection],
# Use OpTranspose to allow connection.
opTransposeBatchInputs = OpTransposeSlots( parent=self )
opTransposeBatchInputs.OutputLength.setValue(0)
opTransposeBatchInputs.Inputs.resize(1)
opTransposeBatchInputs.Inputs[0].connect( opBatchPredictionPipeline.HeadlessPredictionProbabilities ) # selection 0
# Now opTransposeBatchInputs.Outputs is level-2 indexed by [lane][selection]
opBatchResults.Inputs.connect( opTransposeBatchInputs.Outputs )
# We don't actually need the cached path in the batch pipeline.
# Just connect the uncached features here to satisfy the operator.
opBatchPredictionPipeline.CachedFeatureImages.connect( opBatchFeatures.OutputImage )
self.opBatchPredictionPipeline = opBatchPredictionPipeline
def __init__(self, shell, headless, workflow_cmdline_args, project_creation_args, appendBatchOperators=True, *args, **kwargs):
graph = kwargs['graph'] if 'graph' in kwargs else Graph()
if 'graph' in kwargs: del kwargs['graph']
super( CountingWorkflow, self ).__init__( shell, headless, workflow_cmdline_args, project_creation_args, graph=graph, *args, **kwargs )
# Parse workflow-specific command-line args
parser = argparse.ArgumentParser()
parser.add_argument("--csv-export-file", help="Instead of exporting prediction density images, export total counts to the given csv path.")
self.parsed_counting_workflow_args, unused_args = parser.parse_known_args(workflow_cmdline_args)
######################
# Interactive workflow
######################
self.projectMetadataApplet = ProjectMetadataApplet()
self.dataSelectionApplet = DataSelectionApplet(self,
"Input Data",
"Input Data",
batchDataGui=False,
force5d=False
)
opDataSelection = self.dataSelectionApplet.topLevelOperator
role_names = ['Raw Data']
opDataSelection.DatasetRoles.setValue( role_names )
self.featureSelectionApplet = FeatureSelectionApplet(self,
"Feature Selection",
"FeatureSelections")
#self.pcApplet = PixelClassificationApplet(self, "PixelClassification")
self.countingApplet = CountingApplet(workflow=self)
opCounting = self.countingApplet.topLevelOperator
self.dataExportApplet = CountingDataExportApplet(self, "Density Export", opCounting)
opDataExport = self.dataExportApplet.topLevelOperator
opDataExport.PmapColors.connect(opCounting.PmapColors)
opDataExport.LabelNames.connect(opCounting.LabelNames)
opDataExport.UpperBound.connect(opCounting.UpperBound)
opDataExport.WorkingDirectory.connect(opDataSelection.WorkingDirectory)
opDataExport.SelectionNames.setValue( ['Probabilities'] )
self._applets = []
self._applets.append(self.projectMetadataApplet)
self._applets.append(self.dataSelectionApplet)
self._applets.append(self.featureSelectionApplet)
self._applets.append(self.countingApplet)
self._applets.append(self.dataExportApplet)
self._batch_input_args = None
self._batch_export_args = None
self.batchInputApplet = None
self.batchResultsApplet = None
if appendBatchOperators:
# Connect batch workflow (NOT lane-based)
self._initBatchWorkflow()
if unused_args:
# We parse the export setting args first.
# All remaining args are considered input files by the input applet.
self._batch_export_args, unused_args = self.batchResultsApplet.parse_known_cmdline_args( unused_args )
self._batch_input_args, unused_args = self.batchInputApplet.parse_known_cmdline_args( unused_args, role_names )
def __init__(self,
shell,
headless,
workflow_cmdline_args,
project_creation_args,
appendBatchOperators=True,
*args,
**kwargs):
graph = kwargs['graph'] if 'graph' in kwargs else Graph()
if 'graph' in kwargs: del kwargs['graph']
super(CountingWorkflow, self).__init__(shell,
headless,
workflow_cmdline_args,
project_creation_args,
graph=graph,
*args,
**kwargs)
######################
# Interactive workflow
######################
self.projectMetadataApplet = ProjectMetadataApplet()
self.dataSelectionApplet = DataSelectionApplet(self,
"Input Data",
"Input Data",
batchDataGui=False,
force5d=False)
opDataSelection = self.dataSelectionApplet.topLevelOperator
opDataSelection.DatasetRoles.setValue(['Raw Data'])
self.featureSelectionApplet = FeatureSelectionApplet(
self, "Feature Selection", "FeatureSelections")
#self.pcApplet = PixelClassificationApplet(self, "PixelClassification")
self.countingApplet = CountingApplet(workflow=self)
opCounting = self.countingApplet.topLevelOperator
self.dataExportApplet = CountingDataExportApplet(
self, "Density Export")
opDataExport = self.dataExportApplet.topLevelOperator
opDataExport.PmapColors.connect(opCounting.PmapColors)
opDataExport.LabelNames.connect(opCounting.LabelNames)
opDataExport.UpperBound.connect(opCounting.UpperBound)
opDataExport.WorkingDirectory.connect(opDataSelection.WorkingDirectory)
opDataExport.SelectionNames.setValue(['Probabilities'])
self._applets = []
self._applets.append(self.projectMetadataApplet)
self._applets.append(self.dataSelectionApplet)
self._applets.append(self.featureSelectionApplet)
self._applets.append(self.countingApplet)
self._applets.append(self.dataExportApplet)
if appendBatchOperators:
# Create applets for batch workflow
self.batchInputApplet = DataSelectionApplet(
self,
"Batch Prediction Input Selections",
"BatchDataSelection",
supportIlastik05Import=False,
batchDataGui=True)
self.batchResultsApplet = CountingDataExportApplet(
self, "Batch Prediction Output Locations", isBatch=True)
# Expose in shell
self._applets.append(self.batchInputApplet)
self._applets.append(self.batchResultsApplet)
# Connect batch workflow (NOT lane-based)
self._initBatchWorkflow()
