def runWorkflow(parsed_args):
args = parsed_args
# Make sure project file exists.
if not os.path.exists(args.project):
raise RuntimeError("Project file '" + args.project + "' does not exist.")
# Make sure batch inputs exist.
for p in args.batch_inputs:
error = False
p = PathComponents(p).externalPath
if not os.path.exists(p):
logger.error("Batch input file does not exist: " + p)
error = True
if error:
raise RuntimeError("Could not find one or more batch inputs. See logged errors.")
if not args.generate_project_predictions and len(args.batch_inputs) == 0:
logger.error("Command-line arguments didn't specify a workload.")
return
# Instantiate 'shell'
shell, workflow = startShellHeadless( PixelClassificationWorkflow )
if args.assume_old_ilp_axes:
# Special hack for Janelia:
# In some old versions of 0.5, the data was stored in tyxzc order.
# We have no way of inspecting the data to determine this, so we allow
# users to specify that their ilp is very old using the
# assume_old_ilp_axes command-line flag
ilastik.utility.globals.ImportOptions.default_axis_order = 'tyxzc'
# Load project (auto-import it if necessary)
logger.info("Opening project: '" + args.project + "'")
shell.openProjectPath(args.project)
# Predictions for project input datasets
if args.generate_project_predictions:
generateProjectPredictions(shell, workflow)
# Predictions for other datasets ('batch datasets')
result = True
if len(args.batch_inputs) > 0:
result = generateBatchPredictions(workflow,
args.batch_inputs,
args.batch_export_dir,
args.batch_output_suffix,
args.batch_output_dataset_name)
logger.info("Closing project...")
shell.projectManager.closeCurrentProject()
assert result
logger.info("FINISHED.")
def create_new_tst_project(cls):
# Instantiate 'shell'
shell, workflow = startShellHeadless(PixelClassificationWorkflow)
# Create a blank project file and load it.
newProjectFilePath = cls.PROJECT_FILE
newProjectFile = shell.projectManager.createBlankProjectFile(
newProjectFilePath)
shell.projectManager.loadProject(newProjectFile, newProjectFilePath,
False)
# Add a file
from ilastik.applets.dataSelection.opDataSelection import DatasetInfo
info = DatasetInfo()
info.filePath = cls.SAMPLE_DATA
opDataSelection = workflow.dataSelectionApplet.topLevelOperator
opDataSelection.Dataset.resize(1)
opDataSelection.Dataset[0].setValue(info)
# Set some features
ScalesList = [0.3, 0.7, 1, 1.6, 3.5, 5.0, 10.0]
FeatureIds = [
'GaussianSmoothing', 'LaplacianOfGaussian',
'StructureTensorEigenvalues', 'HessianOfGaussianEigenvalues',
'GaussianGradientMagnitude', 'DifferenceOfGaussians'
]
opFeatures = workflow.featureSelectionApplet.topLevelOperator
opFeatures.Scales.setValue(ScalesList)
opFeatures.FeatureIds.setValue(FeatureIds)
# sigma: 0.3 0.7 1.0 1.6 3.5 5.0 10.0
selections = numpy.array(
[[True, False, False, False, False, False, False],
[True, False, False, False, False, False, False],
[True, False, False, False, False, False, False],
[False, False, False, False, False, False, False],
[False, False, False, False, False, False, False],
[False, False, False, False, False, False, False]])
opFeatures.SelectionMatrix.setValue(selections)
# Add some labels directly to the operator
opPixelClass = workflow.pcApplet.topLevelOperator
slicing1 = sl[0:1, 0:10, 0:10, 0:1, 0:1]
labels1 = 1 * numpy.ones(slicing2shape(slicing1), dtype=numpy.uint8)
opPixelClass.LabelInputs[0][slicing1] = labels1
slicing2 = sl[0:1, 0:10, 10:20, 0:1, 0:1]
labels2 = 2 * numpy.ones(slicing2shape(slicing2), dtype=numpy.uint8)
opPixelClass.LabelInputs[0][slicing2] = labels2
# Save and close
shell.projectManager.saveProject()
shell.projectManager.closeCurrentProject()
def create_new_tst_project(cls):
# Instantiate 'shell'
shell, workflow = startShellHeadless( PixelClassificationWorkflow )
# Create a blank project file and load it.
newProjectFilePath = cls.PROJECT_FILE
newProjectFile = shell.projectManager.createBlankProjectFile(newProjectFilePath)
shell.projectManager.loadProject(newProjectFile, newProjectFilePath, False)
# Add a file
from ilastik.applets.dataSelection.opDataSelection import DatasetInfo
info = DatasetInfo()
info.filePath = cls.SAMPLE_DATA
opDataSelection = workflow.dataSelectionApplet.topLevelOperator
opDataSelection.Dataset.resize(1)
opDataSelection.Dataset[0].setValue(info)
# Set some features
ScalesList = [0.3, 0.7, 1, 1.6, 3.5, 5.0, 10.0]
FeatureIds = [ 'GaussianSmoothing',
'LaplacianOfGaussian',
'StructureTensorEigenvalues',
'HessianOfGaussianEigenvalues',
'GaussianGradientMagnitude',
'DifferenceOfGaussians' ]
opFeatures = workflow.featureSelectionApplet.topLevelOperator
opFeatures.Scales.setValue( ScalesList )
opFeatures.FeatureIds.setValue( FeatureIds )
# sigma: 0.3 0.7 1.0 1.6 3.5 5.0 10.0
selections = numpy.array( [[True, False, False, False, False, False, False],
[True, False, False, False, False, False, False],
[True, False, False, False, False, False, False],
[False, False, False, False, False, False, False],
[False, False, False, False, False, False, False],
[False, False, False, False, False, False, False]] )
opFeatures.SelectionMatrix.setValue(selections)
# Add some labels directly to the operator
opPixelClass = workflow.pcApplet.topLevelOperator
slicing1 = sl[0:1,0:10,0:10,0:1,0:1]
labels1 = 1 * numpy.ones(slicing2shape(slicing1), dtype=numpy.uint8)
opPixelClass.LabelInputs[0][slicing1] = labels1
slicing2 = sl[0:1,0:10,10:20,0:1,0:1]
labels2 = 2 * numpy.ones(slicing2shape(slicing2), dtype=numpy.uint8)
opPixelClass.LabelInputs[0][slicing2] = labels2
# Save and close
shell.projectManager.saveProject()
shell.projectManager.closeCurrentProject()
def runWorkflow(parsed_args):
args = parsed_args
# Make sure project file exists.
if not os.path.exists(args.project):
raise RuntimeError("Project file '" + args.project + "' does not exist.")
# Make sure batch inputs exist.
for p in args.batch_inputs:
print p
error = False
p = PathComponents(p).externalPath
if not os.path.exists(p):
logger.error("Batch input file does not exist: " + p)
error = True
if error:
raise RuntimeError("Could not find one or more batch inputs. See logged errors.")
if not args.generate_project_predictions and len(args.batch_inputs) == 0:
logger.error("Command-line arguments didn't specify a workload.")
return
# Instantiate 'shell'
shell, workflow = startShellHeadless( AutocontextClassificationWorkflow )
# Load project (auto-import it if necessary)
logger.info("Opening project: '" + args.project + "'")
shell.openProjectPath(args.project)
# Predictions for project input datasets
if args.generate_project_predictions:
generateProjectPredictions(shell, workflow)
# Predictions for other datasets ('batch datasets')
result = True
if len(args.batch_inputs) > 0:
result = generateBatchPredictions(workflow,
args.batch_inputs,
args.batch_export_dir,
args.batch_output_suffix,
args.batch_output_dataset_name)
logger.info("Closing project...")
shell.projectManager.closeCurrentProject()
assert result
logger.info("FINISHED.")
def runWorkflow(parsed_args):
args = parsed_args
# Make sure project file exists.
if not os.path.exists(args.project):
raise RuntimeError("Project file '" + args.project + "' does not exist.")
# Make sure batch inputs exist.
for p in args.batch_inputs:
print p
error = False
p = PathComponents(p).externalPath
if not os.path.exists(p):
logger.error("Batch input file does not exist: " + p)
error = True
if error:
raise RuntimeError("Could not find one or more batch inputs. See logged errors.")
if not args.generate_project_predictions and len(args.batch_inputs) == 0:
logger.error("Command-line arguments didn't specify a workload.")
return
# Instantiate 'shell'
shell, workflow = startShellHeadless( AutocontextClassificationWorkflow )
# Load project (auto-import it if necessary)
logger.info("Opening project: '" + args.project + "'")
shell.openProjectPath(args.project)
# Predictions for project input datasets
if args.generate_project_predictions:
generateProjectPredictions(shell, workflow)
# Predictions for other datasets ('batch datasets')
result = True
if len(args.batch_inputs) > 0:
result = generateBatchPredictions(workflow,
args.batch_inputs,
args.batch_export_dir,
args.batch_output_suffix,
args.batch_output_dataset_name)
logger.info("Closing project...")
shell.projectManager.closeCurrentProject()
assert result
logger.info("FINISHED.")
