def __init__(self, shell, headless, workflow_cmdline_args,
project_creation_args, n_stages, *args, **kwargs):
"""
n_stages: How many iterations of feature selection and pixel classification should be inserted into the workflow.
All other params are just as in PixelClassificationWorkflow
"""
# Create a graph to be shared by all operators
graph = Graph()
super(NewAutocontextWorkflowBase, self).__init__(shell,
headless,
workflow_cmdline_args,
project_creation_args,
graph=graph,
*args,
**kwargs)
self.stored_classifers = []
self._applets = []
self._workflow_cmdline_args = workflow_cmdline_args
# Parse workflow-specific command-line args
parser = argparse.ArgumentParser()
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)
# Parse the cmdline args for the current session.
parsed_args, unused_args = parser.parse_known_args(
workflow_cmdline_args)
self.retrain = parsed_args.retrain
data_instructions = (
"Select your input data using the 'Raw Data' tab shown on the right.\n\n"
"Power users: Optionally use the 'Prediction Mask' tab to supply a binary image that tells ilastik where it should avoid computations you don't need."
)
self.dataSelectionApplet = self.createDataSelectionApplet()
opDataSelection = self.dataSelectionApplet.topLevelOperator
# see role constants, above
role_names = ["Raw Data", "Prediction Mask"]
opDataSelection.DatasetRoles.setValue(role_names)
self.featureSelectionApplets = []
self.pcApplets = []
for i in range(n_stages):
self.featureSelectionApplets.append(
self.createFeatureSelectionApplet(i))
self.pcApplets.append(self.createPixelClassificationApplet(i))
opFinalClassify = self.pcApplets[-1].topLevelOperator
# If *any* stage enters 'live update' mode, make sure they all enter live update mode.
def sync_freeze_predictions_settings(slot, *args):
freeze_predictions = slot.value
for pcApplet in self.pcApplets:
pcApplet.topLevelOperator.FreezePredictions.setValue(
freeze_predictions)
for pcApplet in self.pcApplets:
pcApplet.topLevelOperator.FreezePredictions.notifyDirty(
sync_freeze_predictions_settings)
self.dataExportApplet = PixelClassificationDataExportApplet(
self, "Prediction Export")
opDataExport = self.dataExportApplet.topLevelOperator
opDataExport.PmapColors.connect(opFinalClassify.PmapColors)
opDataExport.LabelNames.connect(opFinalClassify.LabelNames)
opDataExport.WorkingDirectory.connect(opDataSelection.WorkingDirectory)
self.EXPORT_NAMES = []
for stage_index in reversed(list(range(n_stages))):
self.EXPORT_NAMES += [
"{} Stage {}".format(name, stage_index + 1)
for name in self.EXPORT_NAMES_PER_STAGE
]
# And finally, one last item for *all* probabilities from all stages.
self.EXPORT_NAMES += ["Probabilities All Stages"]
opDataExport.SelectionNames.setValue(self.EXPORT_NAMES)
# Expose for shell
self._applets.append(self.dataSelectionApplet)
self._applets += itertools.chain(
*list(zip(self.featureSelectionApplets, self.pcApplets)))
self._applets.append(self.dataExportApplet)
self.dataExportApplet.prepare_for_entire_export = self.prepare_for_entire_export
self.dataExportApplet.post_process_entire_export = self.post_process_entire_export
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 __init__(self, shell, headless, workflow_cmdline_args,
project_creation_args, *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.stored_classifier = None
self._applets = []
self._workflow_cmdline_args = workflow_cmdline_args
# 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")
parser.add_argument('--tree-count',
help='Number of trees for Vigra RF classifier.',
type=int)
parser.add_argument('--variable-importance-path',
help='Location of variable-importance table.',
type=str)
parser.add_argument(
'--label-proportion',
help='Proportion of feature-pixels used to train the classifier.',
type=float)
# 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
self.tree_count = parsed_args.tree_count
self.variable_importance_path = parsed_args.variable_importance_path
self.label_proportion = parsed_args.label_proportion
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."
)
data_instructions = "Select your input data using the 'Raw Data' tab shown on the right.\n\n"\
"Power users: Optionally use the 'Prediction Mask' tab to supply a binary image that tells ilastik where it should avoid computations you don't need."
# Applets for training (interactive) workflow
self.dataSelectionApplet = self.createDataSelectionApplet()
opDataSelection = self.dataSelectionApplet.topLevelOperator
# see role constants, above
opDataSelection.DatasetRoles.setValue(
PixelClassificationWorkflow.ROLE_NAMES)
self.featureSelectionApplet = self.createFeatureSelectionApplet()
self.pcApplet = self.createPixelClassificationApplet()
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.dataSelectionApplet)
self._applets.append(self.featureSelectionApplet)
self._applets.append(self.pcApplet)
self._applets.append(self.dataExportApplet)
self.dataExportApplet.prepare_for_entire_export = self.prepare_for_entire_export
self.dataExportApplet.post_process_entire_export = self.post_process_entire_export
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.warn("Unused command-line args: {}".format(unused_args))
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))
