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( MitochondriaSegmentationWorkflow, self ).__init__( shell, headless, workflow_cmdline_args, project_creation_args, graph=graph, *args, **kwargs )
self.stored_classifer = None
self._applets = []
self._workflow_cmdline_args = workflow_cmdline_args
# Parse workflow-specific command-line args # TODO : clean these 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.")
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.projectMetadataApplet = ProjectMetadataApplet()
self.dataSelectionApplet = self.createDataSelectionApplet()
opDataSelection = self.dataSelectionApplet.topLevelOperator
# see role constants, above
role_names = ['Raw Data', 'Prediction Mask']
opDataSelection.DatasetRoles.setValue( role_names )
self.featureSelectionApplet = self.createFeatureSelectionApplet()
self.msApplet = self.createMitochondriaSegmentationApplet() # TODO : factory to create applet
# self.msApplet = self.createMitochondriaSegmentationApplet() # TODO CHRIS : create applet + replace old one (*msApplet)
self.slicApplet = self.createSlicSegmentationApplet()
#opClassify = self.msApplet.topLevelOperator
self.dataExportApplet = MitochondriaSegmentationDataExportApplet(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.slicApplet)
self._applets.append(self.featureSelectionApplet)
# self._applets.append(self.msApplet)
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._batch_input_args = None
self._batch_export_args = None
if appendBatchOperators:
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 )
if unused_args:
logger.warn("Unused command-line args: {}".format( unused_args ))
class MitochondriaSegmentationWorkflow(Workflow):
workflowName = "Mitochondria Segmentation"
workflowDescription = "Segments mitochondria using contextual cues"
defaultAppletIndex = 1 # show DataSelection by default
DATA_ROLE_RAW = 0
DATA_ROLE_PREDICTION_MASK = 1
EXPORT_NAMES = ['Probabilities', 'Simple Segmentation', 'Uncertainty', 'Features'] # TODO : is Features really necessary or enough?
@property
def applets(self):
return self._applets
@property
def imageNameListSlot(self):
return self.dataSelectionApplet.topLevelOperator.ImageName
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( MitochondriaSegmentationWorkflow, self ).__init__( shell, headless, workflow_cmdline_args, project_creation_args, graph=graph, *args, **kwargs )
self.stored_classifer = None
self._applets = []
self._workflow_cmdline_args = workflow_cmdline_args
# Parse workflow-specific command-line args # TODO : clean these 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.")
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.projectMetadataApplet = ProjectMetadataApplet()
self.dataSelectionApplet = self.createDataSelectionApplet()
opDataSelection = self.dataSelectionApplet.topLevelOperator
# see role constants, above
role_names = ['Raw Data', 'Prediction Mask']
opDataSelection.DatasetRoles.setValue( role_names )
self.featureSelectionApplet = self.createFeatureSelectionApplet()
self.msApplet = self.createMitochondriaSegmentationApplet() # TODO : factory to create applet
# self.msApplet = self.createMitochondriaSegmentationApplet() # TODO CHRIS : create applet + replace old one (*msApplet)
self.slicApplet = self.createSlicSegmentationApplet()
#opClassify = self.msApplet.topLevelOperator
self.dataExportApplet = MitochondriaSegmentationDataExportApplet(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.slicApplet)
self._applets.append(self.featureSelectionApplet)
# self._applets.append(self.msApplet)
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._batch_input_args = None
self._batch_export_args = None
if appendBatchOperators:
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 )
if unused_args:
logger.warn("Unused command-line args: {}".format( unused_args ))
def createDataSelectionApplet(self):
"""
Can be overridden by subclasses, if they want to use
special parameters to initialize the DataSelectionApplet.
"""
data_instructions = "Select your input data using the 'Raw Data' tab shown on the right"
return DataSelectionApplet( self,
"Input Data",
"Input Data",
supportIlastik05Import=True,
instructionText=data_instructions )
def createFeatureSelectionApplet(self):
"""
Can be overridden by subclasses, if they want to return their own type of FeatureSelectionApplet.
NOTE: The applet returned here must have the same interface as the regular FeatureSelectionApplet.
(If it looks like a duck...)
"""
return FeatureSelectionApplet(self, "Feature Selection", "FeatureSelections", self.filter_implementation)
def createMitochondriaSegmentationApplet(self):
"""
Can be overridden by subclasses, if they want to return their own type of MitochondriaSegmentationApplet.
NOTE: The applet returned here must have the same interface as the regular MitochondriaSegmentationApplet.
(If it looks like a duck...)
"""
return MitochondriaSegmentationApplet( self, "MitochondriaSegmentation" )
def createSlicSegmentationApplet(self):
"""
SLIC applet, based on RK's C++ implementation
"""
print 'Creating SLIC segmentation applet'
return SlicApplet( self) # "SlicSegmentation" )
def prepareForNewLane(self, laneIndex):
"""
Overridden from Workflow base class.
Called immediately before a new lane is added to the workflow.
"""
# When the new lane is added, dirty notifications will propagate throughout the entire graph.
# This means the classifier will be marked 'dirty' even though it is still usable.
# Before that happens, let's store the classifier, so we can restore it at the end of connectLane(), below.
print '[MitochondriaSegmentation] prepareForNewLane - Function not ready yet, shouldnt be here '
opMitochondriaSegmentation = self.msApplet.topLevelOperator
if opMitochondriaSegmentation.classifier_cache.Output.ready() and \
not opMitochondriaSegmentation.classifier_cache._dirty:
self.stored_classifer = opMitochondriaSegmentation.classifier_cache.Output.value
else:
self.stored_classifer = None
def handleNewLanesAdded(self):
"""
Overridden from Workflow base class.
Called immediately after a new lane is added to the workflow and initialized.
"""
# Restore classifier we saved in prepareForNewLane() (if any)
if self.stored_classifer:
self.msApplet.topLevelOperator.classifier_cache.forceValue(self.stored_classifer)
# Release reference
self.stored_classifer = None
def connectLane(self, laneIndex):
print '[MitochondriaSegmentation] connectLane - Function not ready yet, shouldnt be here '
# Get a handle to each operator
opData = self.dataSelectionApplet.topLevelOperator.getLane(laneIndex)
opTrainingFeatures = self.featureSelectionApplet.topLevelOperator.getLane(laneIndex)
opClassify = self.msApplet.topLevelOperator.getLane(laneIndex)
opDataExport = self.dataExportApplet.topLevelOperator.getLane(laneIndex)
# Input Image -> Feature Op
# and -> Classification Op (for display)
opTrainingFeatures.InputImage.connect( opData.Image )
opClassify.InputImages.connect( opData.Image )
if ilastik_config.getboolean('ilastik', 'debug'):
opClassify.PredictionMasks.connect( opData.ImageGroup[self.DATA_ROLE_PREDICTION_MASK] )
# 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 )
# Data Export connections
opDataExport.RawData.connect( opData.ImageGroup[self.DATA_ROLE_RAW] )
opDataExport.RawDatasetInfo.connect( opData.DatasetGroup[self.DATA_ROLE_RAW] )
opDataExport.ConstraintDataset.connect( opData.ImageGroup[self.DATA_ROLE_RAW] )
opDataExport.Inputs.resize( len(self.EXPORT_NAMES) )
opDataExport.Inputs[0].connect( opClassify.HeadlessPredictionProbabilities )
opDataExport.Inputs[1].connect( opClassify.SimpleSegmentation )
opDataExport.Inputs[2].connect( opClassify.HeadlessUncertaintyEstimate )
opDataExport.Inputs[3].connect( opClassify.FeatureImages )
for slot in opDataExport.Inputs:
assert slot.partner is not None
def handleAppletStateUpdateRequested(self):
"""
Overridden from Workflow base class
Called when an applet has fired the :py:attr:`Applet.appletStateUpdateRequested`
"""
# If no data, nothing else is ready.
opDataSelection = self.dataSelectionApplet.topLevelOperator
input_ready = len(opDataSelection.ImageGroup) > 0 and not self.dataSelectionApplet.busy
opFeatureSelection = self.featureSelectionApplet.topLevelOperator
featureOutput = opFeatureSelection.OutputImage
features_ready = input_ready and \
len(featureOutput) > 0 and \
featureOutput[0].ready() and \
(TinyVector(featureOutput[0].meta.shape) > 0).all()
opDataExport = self.dataExportApplet.topLevelOperator
opMitochondriaSegmentation = self.msApplet.topLevelOperator # opMitochondriaSegmentation
invalid_classifier = opMitochondriaSegmentation.classifier_cache.fixAtCurrent.value and \
opMitochondriaSegmentation.classifier_cache.Output.ready() and\
opMitochondriaSegmentation.classifier_cache.Output.value is None
predictions_ready = features_ready and \
not invalid_classifier and \
len(opDataExport.Inputs) > 0 and \
opDataExport.Inputs[0][0].ready() and \
(TinyVector(opDataExport.Inputs[0][0].meta.shape) > 0).all()
# Problems can occur if the features or input data are changed during live update mode.
# Don't let the user do that.
live_update_active = not opMitochondriaSegmentation.FreezePredictions.value
# The user isn't allowed to touch anything while batch processing is running.
batch_processing_busy = self.batchProcessingApplet.busy
self._shell.setAppletEnabled(self.dataSelectionApplet, not live_update_active and not batch_processing_busy)
self._shell.setAppletEnabled(self.featureSelectionApplet, input_ready and not live_update_active and not batch_processing_busy)
self._shell.setAppletEnabled(self.msApplet, features_ready and not batch_processing_busy)
self._shell.setAppletEnabled(self.dataExportApplet, predictions_ready and not batch_processing_busy)
if self.batchProcessingApplet is not None:
self._shell.setAppletEnabled(self.batchProcessingApplet, predictions_ready and not batch_processing_busy)
# Lastly, check for certain "busy" conditions, during which we
# should prevent the shell from closing the project.
busy = False
busy |= self.dataSelectionApplet.busy
busy |= self.featureSelectionApplet.busy
busy |= self.dataExportApplet.busy
busy |= self.batchProcessingApplet.busy
self._shell.enableProjectChanges( not busy )
def onProjectLoaded(self, projectManager):
"""
Overridden from Workflow base class. Called by the Project Manager.
If the user provided command-line arguments, use them to configure
the workflow for batch mode and export all results.
(This workflow's headless mode supports only batch mode for now.)
"""
if self.generate_random_labels:
self._generate_random_labels(self.random_label_count, self.random_label_value)
logger.info("Saving project...")
self._shell.projectManager.saveProject()
logger.info("Done.")
if self.print_labels_by_slice:
self._print_labels_by_slice( self.label_search_value )
if self._headless:
# In headless mode, let's see the messages from the training operator.
logging.getLogger("lazyflow.operators.classifierOperators").setLevel(logging.DEBUG)
if self.retrain:
# Cause the classifier to be dirty so it is forced to retrain.
# (useful if the stored labels were changed outside ilastik)
self.msApplet.topLevelOperator.opTrain.ClassifierFactory.setDirty()
# Request the classifier, which forces training
self.msApplet.topLevelOperator.FreezePredictions.setValue(False)
_ = self.msApplet.topLevelOperator.Classifier.value
# store new classifier to project file
projectManager.saveProject(force_all_save=False)
# Configure the data export operator.
if self._batch_export_args:
self.dataExportApplet.configure_operator_with_parsed_args( self._batch_export_args )
if self._batch_input_args and self.msApplet.topLevelOperator.classifier_cache._dirty:
logger.warn("Your project file has no classifier. A new classifier will be trained for this run.")
if self._headless and self._batch_input_args and self._batch_export_args:
logger.info("Beginning Batch Processing")
self.batchProcessingApplet.run_export_from_parsed_args(self._batch_input_args)
logger.info("Completed Batch Processing")
def prepare_for_entire_export(self):
self.freeze_status = self.msApplet.topLevelOperator.FreezePredictions.value
self.msApplet.topLevelOperator.FreezePredictions.setValue(False)
def post_process_entire_export(self):
self.msApplet.topLevelOperator.FreezePredictions.setValue(self.freeze_status)
def _print_labels_by_slice(self, search_value):
"""
Iterate over each label image in the project and print the number of labels present on each Z-slice of the image.
(This is a special feature requested by the FlyEM proofreaders.)
"""
opTopLevelClassify = self.msApplet.topLevelOperator
project_label_count = 0
for image_index, label_slot in enumerate(opTopLevelClassify.LabelImages):
tagged_shape = label_slot.meta.getTaggedShape()
if 'z' not in tagged_shape:
logger.error("Can't print label counts by Z-slices. Image #{} has no Z-dimension.".format(image_index))
else:
logger.info("Label counts in Z-slices of Image #{}:".format( image_index ))
slicing = [slice(None)] * len(tagged_shape)
blank_slices = []
image_label_count = 0
for z in range(tagged_shape['z']):
slicing[tagged_shape.keys().index('z')] = slice(z, z+1)
label_slice = label_slot[slicing].wait()
if search_value:
count = (label_slice == search_value).sum()
else:
count = (label_slice != 0).sum()
if count > 0:
logger.info("Z={}: {}".format( z, count ))
image_label_count += count
else:
blank_slices.append( z )
project_label_count += image_label_count
if len(blank_slices) > 20:
# Don't list the blank slices if there were a lot of them.
logger.info("Image #{} has {} blank slices.".format( image_index, len(blank_slices) ))
elif len(blank_slices) > 0:
logger.info( "Image #{} has {} blank slices: {}".format( image_index, len(blank_slices), blank_slices ) )
else:
logger.info( "Image #{} has no blank slices.".format( image_index ) )
logger.info( "Total labels for Image #{}: {}".format( image_index, image_label_count ) )
logger.info( "Total labels for project: {}".format( project_label_count ) )
def _generate_random_labels(self, labels_per_image, label_value):
"""
Inject random labels into the project file.
(This is a special feature requested by the FlyEM proofreaders.)
"""
logger.info( "Injecting {} labels of value {} into all images.".format( labels_per_image, label_value ) )
opTopLevelClassify = self.msApplet.topLevelOperator
label_names = copy.copy(opTopLevelClassify.LabelNames.value)
while len(label_names) < label_value:
label_names.append( "Label {}".format( len(label_names)+1 ) )
opTopLevelClassify.LabelNames.setValue( label_names )
for image_index in range(len(opTopLevelClassify.LabelImages)):
logger.info( "Injecting labels into image #{}".format( image_index ) )
# For reproducibility of label generation
SEED = 1
numpy.random.seed([SEED, image_index])
label_input_slot = opTopLevelClassify.LabelInputs[image_index]
label_output_slot = opTopLevelClassify.LabelImages[image_index]
shape = label_output_slot.meta.shape
random_labels = numpy.zeros( shape=shape, dtype=numpy.uint8 )
num_pixels = len(random_labels.flat)
current_progress = -1
for sample_index in range(labels_per_image):
flat_index = numpy.random.randint(0,num_pixels)
# Don't overwrite existing labels
# Keep looking until we find a blank pixel
while random_labels.flat[flat_index]:
flat_index = numpy.random.randint(0,num_pixels)
random_labels.flat[flat_index] = label_value
# Print progress every 10%
progress = float(sample_index) / labels_per_image
progress = 10 * (int(100*progress)/10)
if progress != current_progress:
current_progress = progress
sys.stdout.write( "{}% ".format( current_progress ) )
sys.stdout.flush()
sys.stdout.write( "100%\n" )
# Write into the operator
label_input_slot[fullSlicing(shape)] = random_labels
logger.info( "Done injecting labels" )
def getHeadlessOutputSlot(self, slotId):
"""
Not used by the regular app.
Only used for special cluster scripts.
"""
# "Regular" (i.e. with the images that the user selected as input data)
if slotId == "Predictions":
return self.msApplet.topLevelOperator.HeadlessPredictionProbabilities
elif slotId == "PredictionsUint8":
return self.msApplet.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")