def _run_export_with_empty_batch_lane(self, role_input_paths, batch_lane_index, template_infos, progress_callback):
"""
Configure the fresh batch lane with the given input files, and export the results.
"""
assert role_input_paths[0], "At least one file must be provided for each dataset (the first role)."
opDataSelectionBatchLaneView = self.dataSelectionApplet.topLevelOperator.getLane( batch_lane_index )
# Apply new settings for each role
for role_index, path_for_role in enumerate(role_input_paths):
if not path_for_role:
continue
if template_infos[role_index]:
info = copy.copy(template_infos[role_index])
else:
info = DatasetInfo()
# Override the template settings with the current filepath.
default_info = DataSelectionApplet.create_default_headless_dataset_info(path_for_role)
info.filePath = default_info.filePath
info.location = default_info.location
info.nickname = default_info.nickname
# Apply to the data selection operator
opDataSelectionBatchLaneView.DatasetGroup[role_index].setValue(info)
# Make sure nothing went wrong
opDataExportBatchlaneView = self.dataExportApplet.topLevelOperator.getLane( batch_lane_index )
assert opDataExportBatchlaneView.ImageToExport.ready()
assert opDataExportBatchlaneView.ExportPath.ready()
# New lanes were added.
# Give the workflow a chance to restore anything that was unecessarily invalidated (e.g. classifiers)
self.workflow.handleNewLanesAdded()
# Call customization hook
self.dataExportApplet.prepare_lane_for_export(batch_lane_index)
# Finally, run the export
logger.info("Exporting to {}".format( opDataExportBatchlaneView.ExportPath.value ))
opDataExportBatchlaneView.progressSignal.subscribe(progress_callback)
opDataExportBatchlaneView.run_export()
# Call customization hook
self.dataExportApplet.post_process_lane_export(batch_lane_index)
def append_lane(workflow, input_filepath, axisorder=None):
# Sanity checks
assert isinstance(workflow, PixelClassificationWorkflow)
opPixelClassification = workflow.pcApplet.topLevelOperator
assert opPixelClassification.Classifier.ready()
# If the filepath is a globstring, convert the stack to h5
input_filepath = DataSelectionApplet.convertStacksToH5( [input_filepath], TMP_DIR )[0]
info = DatasetInfo()
info.location = DatasetInfo.Location.FileSystem
info.filePath = input_filepath
comp = PathComponents(input_filepath)
# Convert all (non-url) paths to absolute
# (otherwise they are relative to the project file, which probably isn't what the user meant)
if not isUrl(input_filepath):
comp.externalPath = os.path.abspath(comp.externalPath)
info.filePath = comp.totalPath()
info.nickname = comp.filenameBase
if axisorder:
info.axistags = vigra.defaultAxistags(axisorder)
logger.debug( "adding lane: {}".format( info ) )
opDataSelection = workflow.dataSelectionApplet.topLevelOperator
# Add a lane
num_lanes = len( opDataSelection.DatasetGroup )+1
logger.debug( "num_lanes: {}".format( num_lanes ) )
opDataSelection.DatasetGroup.resize( num_lanes )
# Configure it.
role_index = 0 # raw data
opDataSelection.DatasetGroup[-1][role_index].setValue( info )
# Sanity check
assert len(opPixelClassification.InputImages) == num_lanes
return opPixelClassification
def impl():
projFilePath = self.project_file
shell = self.shell
# New project
shell.createAndLoadNewProject(projFilePath, self.workflowClass())
workflow = shell.projectManager.workflow
# Add our input files:
opDataSelection = workflow.dataSelectionApplet.topLevelOperator
opDataSelection.DatasetGroup.resize(1)
info_raw = DatasetInfo()
info_raw.filePath = self.sample_data_raw
opDataSelection.DatasetGroup[0][0].setValue(info_raw)
info_prob = DatasetInfo()
info_prob.filePath = self.sample_data_prob
info_raw.nickname = 'test_data'
opDataSelection.DatasetGroup[0][1].setValue(info_prob)
# Save
shell.projectManager.saveProject()
def impl():
projFilePath = self.project_file
shell = self.shell
# New project
shell.createAndLoadNewProject(projFilePath, self.workflowClass())
workflow = shell.projectManager.workflow
# Add our input files:
opDataSelection = workflow.dataSelectionApplet.topLevelOperator
opDataSelection.DatasetGroup.resize(1)
info_raw = DatasetInfo()
info_raw.filePath = self.sample_data_raw
opDataSelection.DatasetGroup[0][0].setValue(info_raw)
info_prob = DatasetInfo()
info_prob.filePath = self.sample_data_prob
info_raw.nickname = 'test_data'
opDataSelection.DatasetGroup[0][1].setValue(info_prob)
# Save
shell.projectManager.saveProject()
def append_lane(workflow, input_filepath, axisorder=None):
"""
Add a lane to the project file for the given input file.
If axisorder is given, override the default axisorder for
the file and force the project to use the given one.
Globstrings are supported, in which case the files are converted to HDF5 first.
"""
# If the filepath is a globstring, convert the stack to h5
input_filepath = DataSelectionApplet.convertStacksToH5( [input_filepath], tempfile.mkdtemp() )[0]
info = DatasetInfo()
info.location = DatasetInfo.Location.FileSystem
info.filePath = input_filepath
comp = PathComponents(input_filepath)
# Convert all (non-url) paths to absolute
# (otherwise they are relative to the project file, which probably isn't what the user meant)
if not isUrl(input_filepath):
comp.externalPath = os.path.abspath(comp.externalPath)
info.filePath = comp.totalPath()
info.nickname = comp.filenameBase
if axisorder:
info.axistags = vigra.defaultAxistags(axisorder)
logger.debug( "adding lane: {}".format( info ) )
opDataSelection = workflow.dataSelectionApplet.topLevelOperator
# Add a lane
num_lanes = len( opDataSelection.DatasetGroup )+1
logger.debug( "num_lanes: {}".format( num_lanes ) )
opDataSelection.DatasetGroup.resize( num_lanes )
# Configure it.
role_index = 0 # raw data
opDataSelection.DatasetGroup[-1][role_index].setValue( info )
