def createPixelClassificationApplet(self):
"""
Can be overridden by subclasses, if they want to return their own type of PixelClassificationApplet.
NOTE: The applet returned here must have the same interface as the regular PixelClassificationApplet.
(If it looks like a duck...)
"""
return PixelClassificationApplet(self, "PixelClassification")
def setupInputs(self):
data_instructions = 'Use the "Raw Data" tab on the right to load your intensity image(s).'
self.dataSelectionApplet = DataSelectionApplet( self,
"Input Data",
"Input Data",
batchDataGui=False,
forceAxisOrder=None,
instructionText=data_instructions )
opData = self.dataSelectionApplet.topLevelOperator
opData.DatasetRoles.setValue(['Raw Data'])
self.featureSelectionApplet = FeatureSelectionApplet(
self,
"Feature Selection",
"FeatureSelections",
)
self.pcApplet = PixelClassificationApplet(
self, "PixelClassification")
self.thresholdingApplet = ThresholdTwoLevelsApplet(
self, "Thresholding", "ThresholdTwoLevels")
self._applets.append(self.dataSelectionApplet)
self._applets.append(self.featureSelectionApplet)
self._applets.append(self.pcApplet)
self._applets.append(self.thresholdingApplet)
if not self._headless:
self._shell.currentAppletChanged.connect( self.handle_applet_changed )
def setupInputs(self):
data_instructions = 'Use the "Raw Data" tab on the right to load your intensity image(s).'
self.dataSelectionApplet = DataSelectionApplet( self,
"Input Data",
"Input Data",
batchDataGui=False,
forceAxisOrder=None,
instructionText=data_instructions )
opData = self.dataSelectionApplet.topLevelOperator
opData.DatasetRoles.setValue(['Raw Data'])
self.featureSelectionApplet = FeatureSelectionApplet(
self,
"Feature Selection",
"FeatureSelections",
filter_implementation=self.filter_implementation
)
self.pcApplet = PixelClassificationApplet(
self, "PixelClassification")
self.thresholdingApplet = ThresholdTwoLevelsApplet(
self, "Thresholding", "ThresholdTwoLevels")
self._applets.append(self.dataSelectionApplet)
self._applets.append(self.featureSelectionApplet)
self._applets.append(self.pcApplet)
self._applets.append(self.thresholdingApplet)
def __init__(self, headless, workflow_cmdline_args, appendBatchOperators=True, *args, **kwargs):
# Create a graph to be shared by all operators
graph = Graph()
super( PixelClassificationWorkflow, self ).__init__( headless, graph=graph, *args, **kwargs )
self._applets = []
# Applets for training (interactive) workflow
self.projectMetadataApplet = ProjectMetadataApplet()
self.dataSelectionApplet = DataSelectionApplet(self, "Input Data", "Input Data", supportIlastik05Import=True, batchDataGui=False)
opDataSelection = self.dataSelectionApplet.topLevelOperator
opDataSelection.DatasetRoles.setValue( ['Raw Data'] )
self.featureSelectionApplet = FeatureSelectionApplet(self, "Feature Selection", "FeatureSelections")
self.pcApplet = PixelClassificationApplet(self, "PixelClassification")
# Expose for shell
self._applets.append(self.projectMetadataApplet)
self._applets.append(self.dataSelectionApplet)
self._applets.append(self.featureSelectionApplet)
self._applets.append(self.pcApplet)
if appendBatchOperators:
# Create applets for batch workflow
self.batchInputApplet = DataSelectionApplet(self, "Batch Prediction Input Selections", "BatchDataSelection", supportIlastik05Import=False, batchDataGui=True)
self.batchResultsApplet = PixelClassificationBatchResultsApplet(self, "Batch Prediction Output Locations")
# Expose in shell
self._applets.append(self.batchInputApplet)
self._applets.append(self.batchResultsApplet)
# Connect batch workflow (NOT lane-based)
self._initBatchWorkflow()
def __init__(
self,
shell,
headless,
workflow_cmdline_args,
project_creation_args,
hintoverlayFile=None,
pmapoverlayFile=None,
*args,
**kwargs,
):
if workflow_cmdline_args:
assert False, "Not using workflow cmdline args yet."
if hintoverlayFile is not None:
assert isinstance(hintoverlayFile, str), "hintoverlayFile should be a string, not '%s'" % type(
hintoverlayFile
)
if pmapoverlayFile is not None:
assert isinstance(pmapoverlayFile, str), "pmapoverlayFile should be a string, not '%s'" % type(
pmapoverlayFile
)
graph = Graph()
super(CarvingFromPixelPredictionsWorkflow, self).__init__(
shell, headless, workflow_cmdline_args, project_creation_args, *args, graph=graph, **kwargs
)
## Create applets
self.dataSelectionApplet = DataSelectionApplet(
self, "Input Data", "Input Data", supportIlastik05Import=True, batchDataGui=False
)
opDataSelection = self.dataSelectionApplet.topLevelOperator
opDataSelection.DatasetRoles.setValue(["Raw Data"])
self.featureSelectionApplet = FeatureSelectionApplet(self, "Feature Selection", "FeatureSelections")
self.pixelClassificationApplet = PixelClassificationApplet(self, "PixelClassification")
self.carvingApplet = CarvingApplet(
workflow=self,
projectFileGroupName="carving",
hintOverlayFile=hintoverlayFile,
pmapOverlayFile=pmapoverlayFile,
)
self.preprocessingApplet = PreprocessingApplet(
workflow=self, title="Preprocessing", projectFileGroupName="carving"
)
# self.carvingApplet.topLevelOperator.MST.connect(self.preprocessingApplet.topLevelOperator.PreprocessedData)
# Expose to shell
self._applets = []
self._applets.append(self.dataSelectionApplet)
self._applets.append(self.featureSelectionApplet)
self._applets.append(self.pixelClassificationApplet)
self._applets.append(self.preprocessingApplet)
self._applets.append(self.carvingApplet)
def createInputApplets(self):
super().createInputApplets()
self.featureSelectionApplet = FeatureSelectionApplet(self, "Feature Selection", "FeatureSelections")
self.pcApplet = PixelClassificationApplet(self, "PixelClassification")
self.thresholdingApplet = ThresholdTwoLevelsApplet(self, "Thresholding", "ThresholdTwoLevels")
self._applets.append(self.featureSelectionApplet)
self._applets.append(self.pcApplet)
self._applets.append(self.thresholdingApplet)
if not self._headless:
self._shell.currentAppletChanged.connect(self.handle_applet_changed)
def createPixelClassificationApplet(self, index=0):
"""
Can be overridden by subclasses, if they want to return their own type of PixelClassificationApplet.
NOTE: The applet returned here must have the same interface as the regular PixelClassificationApplet.
(If it looks like a duck...)
"""
# Make the first one compatible with the pixel classification workflow,
# in case the user uses "Import Project"
hdf5_group_name = 'PixelClassification'
if index > 0:
hdf5_group_name = "PixelClassification{index:02d}".format(index=index)
applet = PixelClassificationApplet( self, hdf5_group_name )
applet.topLevelOperator.name += '{}'.format(index)
return applet
class PixelClassificationWorkflow(Workflow):
workflowName = "Pixel Classification"
workflowDescription = "This is obviously self-explanoratory."
defaultAppletIndex = 1 # show DataSelection by default
@property
def applets(self):
return self._applets
@property
def imageNameListSlot(self):
return self.dataSelectionApplet.topLevelOperator.ImageName
def __init__(self, headless, workflow_cmdline_args, appendBatchOperators=True, *args, **kwargs):
# Create a graph to be shared by all operators
graph = Graph()
super( PixelClassificationWorkflow, self ).__init__( headless, graph=graph, *args, **kwargs )
self._applets = []
# Applets for training (interactive) workflow
self.projectMetadataApplet = ProjectMetadataApplet()
self.dataSelectionApplet = DataSelectionApplet(self, "Input Data", "Input Data", supportIlastik05Import=True, batchDataGui=False)
opDataSelection = self.dataSelectionApplet.topLevelOperator
opDataSelection.DatasetRoles.setValue( ['Raw Data'] )
self.featureSelectionApplet = FeatureSelectionApplet(self, "Feature Selection", "FeatureSelections")
self.pcApplet = PixelClassificationApplet(self, "PixelClassification")
# Expose for shell
self._applets.append(self.projectMetadataApplet)
self._applets.append(self.dataSelectionApplet)
self._applets.append(self.featureSelectionApplet)
self._applets.append(self.pcApplet)
if appendBatchOperators:
# Create applets for batch workflow
self.batchInputApplet = DataSelectionApplet(self, "Batch Prediction Input Selections", "BatchDataSelection", supportIlastik05Import=False, batchDataGui=True)
self.batchResultsApplet = PixelClassificationBatchResultsApplet(self, "Batch Prediction Output Locations")
# Expose in shell
self._applets.append(self.batchInputApplet)
self._applets.append(self.batchResultsApplet)
# Connect batch workflow (NOT lane-based)
self._initBatchWorkflow()
def connectLane(self, laneIndex):
# Get a handle to each operator
opData = self.dataSelectionApplet.topLevelOperator.getLane(laneIndex)
opTrainingFeatures = self.featureSelectionApplet.topLevelOperator.getLane(laneIndex)
opClassify = self.pcApplet.topLevelOperator.getLane(laneIndex)
if not self.pcApplet.enabled:
self.pcApplet.guiControlSignal.emit(ControlCommand.DisableSelf)
'''try:
self.batchResultsApplet.guiControlSignal.emit(ControlCommand.DisableSelf)
except NameError:
pass'''
# Input Image -> Feature Op
# and -> Classification Op (for display)
opTrainingFeatures.InputImage.connect( opData.Image )
opClassify.InputImages.connect( opData.Image )
# Feature Images -> Classification Op (for training, prediction)
opClassify.FeatureImages.connect( opTrainingFeatures.OutputImage )
opClassify.CachedFeatureImages.connect( opTrainingFeatures.CachedOutputImage )
# Training flags -> Classification Op (for GUI restrictions)
opClassify.LabelsAllowedFlags.connect( opData.AllowLabels )
def updateEnable(self,applet,en = True):
if applet == "training":
self.pcApplet.updateEnable(en)
elif applet == "features":
self.pcApplet.updateEnable(en)
elif applet == "batch":
self.batchResultsApplet.updateEnable(en)
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.pcApplet.topLevelOperator
# Access the batch operators
opBatchInputs = self.batchInputApplet.topLevelOperator
opBatchResults = self.batchResultsApplet.topLevelOperator
opBatchInputs.DatasetRoles.connect( opTrainingDataSelection.DatasetRoles )
## Create additional batch workflow operators
opBatchFeatures = OperatorWrapper( OpFeatureSelection, operator_kwargs={'filter_implementation':'Original'}, parent=self, promotedSlotNames=['InputImage'] )
opBatchPredictionPipeline = OperatorWrapper( OpPredictionPipeline, parent=self )
## Connect Operators ##
opTranspose = OpTransposeSlots( parent=self )
opTranspose.OutputLength.setValue(1)
opTranspose.Inputs.connect( opBatchInputs.DatasetGroup )
opFilePathProvider = OperatorWrapper(OpAttributeSelector, parent=self)
opFilePathProvider.InputObject.connect( opTranspose.Outputs[0] )
opFilePathProvider.AttributeName.setValue( 'filePath' )
# Provide dataset paths from data selection applet to the batch export applet
opBatchResults.DatasetPath.connect( opFilePathProvider.Result )
# 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.RawImage.connect( opBatchInputs.Image )
opBatchResults.PmapColors.connect( opClassify.PmapColors )
opBatchResults.LabelNames.connect( opClassify.LabelNames )
# Connect Image pathway:
# Input Image -> Features Op -> Prediction Op -> Export
opBatchFeatures.InputImage.connect( opBatchInputs.Image )
opBatchPredictionPipeline.FeatureImages.connect( opBatchFeatures.OutputImage )
opBatchResults.ImageToExport.connect( opBatchPredictionPipeline.HeadlessPredictionProbabilities )
# 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 getHeadlessOutputSlot(self, slotId):
# "Regular" (i.e. with the images that the user selected as input data)
if slotId == "Predictions":
return self.pcApplet.topLevelOperator.HeadlessPredictionProbabilities
elif slotId == "PredictionsUint8":
return self.pcApplet.topLevelOperator.HeadlessUint8PredictionProbabilities
# "Batch" (i.e. with the images that the user selected as batch inputs).
elif slotId == "BatchPredictions":
return self.opBatchPredictionPipeline.HeadlessPredictionProbabilities
if slotId == "BatchPredictionsUint8":
return self.opBatchPredictionPipeline.HeadlessUint8PredictionProbabilities
raise Exception("Unknown headless output slot")
def __init__(self,
shell,
headless,
workflow_cmdline_args,
project_creation_args,
appendBatchOperators=True,
*args,
**kwargs):
# Create a graph to be shared by all operators
graph = Graph()
super(PixelClassificationWorkflow,
self).__init__(shell,
headless,
workflow_cmdline_args,
project_creation_args,
graph=graph,
*args,
**kwargs)
self._applets = []
self._workflow_cmdline_args = workflow_cmdline_args
data_instructions = "Select your input data using the 'Raw Data' tab shown on the right"
# Parse workflow-specific command-line args
parser = argparse.ArgumentParser()
parser.add_argument('--filter',
help="pixel feature filter implementation.",
choices=['Original', 'Refactored', 'Interpolated'],
default='Original')
parser.add_argument(
'--print-labels-by-slice',
help="Print the number of labels for each Z-slice of each image.",
action="store_true")
parser.add_argument(
'--label-search-value',
help=
"If provided, only this value is considered when using --print-labels-by-slice",
default=0,
type=int)
parser.add_argument('--generate-random-labels',
help="Add random labels to the project file.",
action="store_true")
parser.add_argument(
'--random-label-value',
help="The label value to use injecting random labels",
default=1,
type=int)
parser.add_argument(
'--random-label-count',
help=
"The number of random labels to inject via --generate-random-labels",
default=2000,
type=int)
parser.add_argument(
'--retrain',
help=
"Re-train the classifier based on labels stored in project file, and re-save.",
action="store_true")
# Parse the creation args: These were saved to the project file when this project was first created.
parsed_creation_args, unused_args = parser.parse_known_args(
project_creation_args)
self.filter_implementation = parsed_creation_args.filter
# Parse the cmdline args for the current session.
parsed_args, unused_args = parser.parse_known_args(
workflow_cmdline_args)
self.print_labels_by_slice = parsed_args.print_labels_by_slice
self.label_search_value = parsed_args.label_search_value
self.generate_random_labels = parsed_args.generate_random_labels
self.random_label_value = parsed_args.random_label_value
self.random_label_count = parsed_args.random_label_count
self.retrain = parsed_args.retrain
if parsed_args.filter and parsed_args.filter != parsed_creation_args.filter:
logger.error(
"Ignoring new --filter setting. Filter implementation cannot be changed after initial project creation."
)
# Applets for training (interactive) workflow
self.projectMetadataApplet = ProjectMetadataApplet()
self.dataSelectionApplet = DataSelectionApplet(
self,
"Input Data",
"Input Data",
supportIlastik05Import=True,
batchDataGui=False,
instructionText=data_instructions)
opDataSelection = self.dataSelectionApplet.topLevelOperator
if ilastik_config.getboolean('ilastik', 'debug'):
# see role constants, above
role_names = ['Raw Data', 'Prediction Mask']
opDataSelection.DatasetRoles.setValue(role_names)
else:
role_names = ['Raw Data']
opDataSelection.DatasetRoles.setValue(role_names)
self.featureSelectionApplet = FeatureSelectionApplet(
self, "Feature Selection", "FeatureSelections",
self.filter_implementation)
self.pcApplet = PixelClassificationApplet(self, "PixelClassification")
opClassify = self.pcApplet.topLevelOperator
self.dataExportApplet = PixelClassificationDataExportApplet(
self, "Prediction Export")
opDataExport = self.dataExportApplet.topLevelOperator
opDataExport.PmapColors.connect(opClassify.PmapColors)
opDataExport.LabelNames.connect(opClassify.LabelNames)
opDataExport.WorkingDirectory.connect(opDataSelection.WorkingDirectory)
opDataExport.SelectionNames.setValue(self.EXPORT_NAMES)
# Expose for shell
self._applets.append(self.projectMetadataApplet)
self._applets.append(self.dataSelectionApplet)
self._applets.append(self.featureSelectionApplet)
self._applets.append(self.pcApplet)
self._applets.append(self.dataExportApplet)
self._batch_input_args = None
self._batch_export_args = None
self.batchInputApplet = None
self.batchResultsApplet = None
if appendBatchOperators:
# Create applets for batch workflow
self.batchInputApplet = DataSelectionApplet(
self,
"Batch Prediction Input Selections",
"Batch Inputs",
supportIlastik05Import=False,
batchDataGui=True)
self.batchResultsApplet = PixelClassificationDataExportApplet(
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()
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)
if unused_args:
logger.warn("Unused command-line args: {}".format(unused_args))
def __init__(self):
super(PixelClassificationWorkflow, self).__init__()
self._applets = []
# Create a graph to be shared by all operators
graph = Graph()
self._graph = graph
######################
# Interactive workflow
######################
## Create applets
self.projectMetadataApplet = ProjectMetadataApplet()
self.dataSelectionApplet = DataSelectionApplet(
graph,
"Input Data",
"Input Data",
supportIlastik05Import=True,
batchDataGui=False)
self.featureSelectionApplet = FeatureSelectionApplet(
graph, "Feature Selection", "FeatureSelections")
self.pcApplet = PixelClassificationApplet(graph, "PixelClassification")
## Access applet operators
opData = self.dataSelectionApplet.topLevelOperator
opTrainingFeatures = self.featureSelectionApplet.topLevelOperator
opClassify = self.pcApplet.topLevelOperator
## Connect operators ##
# Input Image -> Feature Op
# and -> Classification Op (for display)
opTrainingFeatures.InputImage.connect(opData.Image)
opClassify.InputImages.connect(opData.Image)
# Feature Images -> Classification Op (for training, prediction)
opClassify.FeatureImages.connect(opTrainingFeatures.OutputImage)
opClassify.CachedFeatureImages.connect(
opTrainingFeatures.CachedOutputImage)
# Training flags -> Classification Op (for GUI restrictions)
opClassify.LabelsAllowedFlags.connect(opData.AllowLabels)
######################
# Batch workflow
######################
## Create applets
self.batchInputApplet = DataSelectionApplet(
graph,
"Batch Inputs",
"BatchDataSelection",
supportIlastik05Import=False,
batchDataGui=True)
self.batchResultsApplet = BatchIoApplet(graph, "Batch Results")
## Access applet operators
opBatchInputs = self.batchInputApplet.topLevelOperator
opBatchInputs.name = 'opBatchInputs'
opBatchResults = self.batchResultsApplet.topLevelOperator
## Create additional batch workflow operators
opBatchFeatures = OperatorWrapper(OpFeatureSelection,
graph=graph,
promotedSlotNames=['InputImage'])
opBatchFeatures.name = "opBatchFeatures"
opBatchPredictor = OperatorWrapper(OpPredictRandomForest,
graph=graph,
promotedSlotNames=['Image'])
opBatchPredictor.name = "opBatchPredictor"
opSelectBatchDatasetPath = OperatorWrapper(OpAttributeSelector,
graph=graph)
## Connect Operators ##
# Provide dataset paths from data selection applet to the batch export applet via an attribute selector
opSelectBatchDatasetPath.InputObject.connect(opBatchInputs.Dataset)
opSelectBatchDatasetPath.AttributeName.setValue('filePath')
opBatchResults.DatasetPath.connect(opSelectBatchDatasetPath.Result)
# 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
opBatchPredictor.Classifier.connect(opClassify.Classifier)
opBatchPredictor.LabelsCount.connect(opClassify.MaxLabelValue)
# Connect Image pathway:
# Input Image -> Features Op -> Prediction Op -> Export
opBatchFeatures.InputImage.connect(opBatchInputs.Image)
opBatchPredictor.Image.connect(opBatchFeatures.OutputImage)
opBatchResults.ImageToExport.connect(opBatchPredictor.PMaps)
self._applets.append(self.projectMetadataApplet)
self._applets.append(self.dataSelectionApplet)
self._applets.append(self.featureSelectionApplet)
self._applets.append(self.pcApplet)
self._applets.append(self.batchInputApplet)
self._applets.append(self.batchResultsApplet)
# 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(opData.Dataset)
opSelectFilename.AttributeName.setValue('filePath')
self._imageNameListSlot = opSelectFilename.Result
