def handleImportLabelsAction():
# Find the directory of the most recently opened image file
mostRecentImageFile = PreferencesManager().get(
'DataSelection', 'recent image')
if mostRecentImageFile is not None:
defaultDirectory = os.path.split(mostRecentImageFile)[0]
else:
defaultDirectory = os.path.expanduser('~')
fileNames = DataSelectionGui.getImageFileNamesToOpen(
self, defaultDirectory)
fileNames = map(str, fileNames)
# For now, we require a single hdf5 file
if len(fileNames) > 1:
QMessageBox.critical(
self, "Too many files",
"Labels must be contained in a single hdf5 volume.")
return
if len(fileNames) == 0:
# user cancelled
return
file_path = fileNames[0]
internal_paths = DataSelectionGui.getPossibleInternalPaths(
file_path)
if len(internal_paths) == 0:
QMessageBox.critical(
self, "No volumes in file",
"Couldn't find a suitable dataset in your hdf5 file.")
return
if len(internal_paths) == 1:
internal_path = internal_paths[0]
else:
dlg = H5VolumeSelectionDlg(internal_paths, self)
if dlg.exec_() == QDialog.Rejected:
return
selected_index = dlg.combo.currentIndex()
internal_path = str(internal_paths[selected_index])
path_components = PathComponents(file_path)
path_components.internalPath = str(internal_path)
try:
top_op = self.topLevelOperatorView
opReader = OpInputDataReader(parent=top_op.parent)
opReader.FilePath.setValue(path_components.totalPath())
# Reorder the axes
op5 = OpReorderAxes(parent=top_op.parent)
op5.AxisOrder.setValue(
top_op.LabelInputs.meta.getAxisKeys())
op5.Input.connect(opReader.Output)
# Finally, import the labels
top_op.importLabels(top_op.current_view_index(),
op5.Output)
finally:
op5.cleanUp()
opReader.cleanUp()
def configure_operator_with_parsed_args(self, parsed_args):
"""
Helper function for headless workflows.
Configures this applet's top-level operator according to the settings provided in ``parsed_args``.
:param parsed_args: Must be an ``argparse.Namespace`` as returned by :py:meth:`parse_known_cmdline_args()`.
"""
# TODO: Support image stack inputs by checking for globstrings and converting to hdf5.
input_paths = parsed_args.input_files
input_infos = []
for p in input_paths:
info = DatasetInfo()
info.location = DatasetInfo.Location.FileSystem
# Convert all paths to absolute
# (otherwise they are relative to the project file, which probably isn't what the user meant)
comp = PathComponents(p)
comp.externalPath = os.path.abspath(comp.externalPath)
info.filePath = comp.totalPath()
info.nickname = comp.filenameBase
input_infos.append(info)
opDataSelection = self.topLevelOperator
opDataSelection.DatasetGroup.resize( len(input_infos) )
for lane_index, info in enumerate(input_infos):
# Only one dataset role in pixel classification
opDataSelection.DatasetGroup[lane_index][0].setValue( info )
def _applyInternalPathToTempOps(self, index):
if index == -1:
return
newInternalPath = str( self.internalDatasetNameComboBox.currentText() )
# Save a copy of our settings
oldInfos = {}
for laneIndex, op in self.tempOps.items():
oldInfos[laneIndex] = copy.copy( op.Dataset.value )
# Attempt to apply to all temp operators
try:
for laneIndex, op in self.tempOps.items():
info = copy.copy( op.Dataset.value )
pathComponents = PathComponents(info.filePath)
if pathComponents.internalPath != newInternalPath:
pathComponents.internalPath = newInternalPath
info.filePath = pathComponents.totalPath()
op.Dataset.setValue( info )
self._error_fields.discard('Internal Dataset Name')
return True
except Exception as e:
# Revert everything back to the previous state
for laneIndex, op in self.tempOps.items():
op.Dataset.setValue( oldInfos[laneIndex] )
msg = "Could not set new internal path settings due to an exception:\n"
msg += "{}".format( e )
log_exception( logger, msg )
QMessageBox.warning(self, "Error", msg)
self._error_fields.add('Internal Dataset Name')
return False
def configure_operator_with_parsed_args(self, parsed_args):
"""
Helper function for headless workflows.
Configures this applet's top-level operator according to the settings provided in ``parsed_args``.
:param parsed_args: Must be an ``argparse.Namespace`` as returned by :py:meth:`parse_known_cmdline_args()`.
"""
input_paths = parsed_args.input_files
# If the user doesn't want image stacks to be copied inte the project file,
# we generate hdf5 volumes in a temporary directory and use those files instead.
if parsed_args.preconvert_stacks:
import tempfile
input_paths = self.convertStacksToH5( input_paths, tempfile.gettempdir() )
input_infos = []
for p in input_paths:
info = DatasetInfo()
info.location = DatasetInfo.Location.FileSystem
# Convert all paths to absolute
# (otherwise they are relative to the project file, which probably isn't what the user meant)
comp = PathComponents(p)
comp.externalPath = os.path.abspath(comp.externalPath)
info.filePath = comp.totalPath()
info.nickname = comp.filenameBase
input_infos.append(info)
opDataSelection = self.topLevelOperator
opDataSelection.DatasetGroup.resize( len(input_infos) )
for lane_index, info in enumerate(input_infos):
# Only one dataset role in pixel classification
opDataSelection.DatasetGroup[lane_index][0].setValue( info )
def handleImportLabelsAction():
# Find the directory of the most recently opened image file
mostRecentImageFile = PreferencesManager().get( 'DataSelection', 'recent image' )
if mostRecentImageFile is not None:
defaultDirectory = os.path.split(mostRecentImageFile)[0]
else:
defaultDirectory = os.path.expanduser('~')
fileNames = DataSelectionGui.getImageFileNamesToOpen(self, defaultDirectory)
fileNames = list(map(str, fileNames))
# For now, we require a single hdf5 file
if len(fileNames) > 1:
QMessageBox.critical(self, "Too many files",
"Labels must be contained in a single hdf5 volume.")
return
if len(fileNames) == 0:
# user cancelled
return
file_path = fileNames[0]
internal_paths = DataSelectionGui.getPossibleInternalPaths(file_path)
if len(internal_paths) == 0:
QMessageBox.critical(self, "No volumes in file",
"Couldn't find a suitable dataset in your hdf5 file.")
return
if len(internal_paths) == 1:
internal_path = internal_paths[0]
else:
dlg = H5VolumeSelectionDlg(internal_paths, self)
if dlg.exec_() == QDialog.Rejected:
return
selected_index = dlg.combo.currentIndex()
internal_path = str(internal_paths[selected_index])
path_components = PathComponents(file_path)
path_components.internalPath = str(internal_path)
try:
top_op = self.topLevelOperatorView
opReader = OpInputDataReader(parent=top_op.parent)
opReader.FilePath.setValue( path_components.totalPath() )
# Reorder the axes
op5 = OpReorderAxes(parent=top_op.parent)
op5.AxisOrder.setValue( top_op.LabelInputs.meta.getAxisKeys() )
op5.Input.connect( opReader.Output )
# Finally, import the labels
top_op.importLabels( top_op.current_view_index(), op5.Output )
finally:
op5.cleanUp()
opReader.cleanUp()
def handleImportLabelsAction():
fileNames = ImageFileDialog(
self,
preferences_group="DataSelection",
preferences_setting="recent image").getSelectedPaths()
fileNames = list(map(str, fileNames))
# For now, we require a single hdf5 file
if len(fileNames) > 1:
QMessageBox.critical(
self, "Too many files",
"Labels must be contained in a single hdf5 volume.")
return
if len(fileNames) == 0:
# user cancelled
return
file_path = fileNames[0]
internal_paths = DatasetInfo.getPossibleInternalPathsFor(file_path)
if len(internal_paths) == 0:
QMessageBox.critical(
self, "No volumes in file",
"Couldn't find a suitable dataset in your hdf5 file.")
return
if len(internal_paths) == 1:
internal_path = internal_paths[0]
else:
dlg = SubvolumeSelectionDlg(internal_paths, self)
if dlg.exec_() == QDialog.Rejected:
return
selected_index = dlg.combo.currentIndex()
internal_path = str(internal_paths[selected_index])
path_components = PathComponents(file_path)
path_components.internalPath = str(internal_path)
try:
top_op = self.topLevelOperatorView
opReader = OpInputDataReader(parent=top_op.parent)
opReader.FilePath.setValue(path_components.totalPath())
# Reorder the axes
op5 = OpReorderAxes(parent=top_op.parent)
op5.AxisOrder.setValue(top_op.LabelInputs.meta.getAxisKeys())
op5.Input.connect(opReader.Output)
# Finally, import the labels
top_op.importLabels(top_op.current_view_index(), op5.Output)
finally:
op5.cleanUp()
opReader.cleanUp()
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 # todo: skip this?
tmp_dir = tempfile.mkdtemp()
input_filepath = DataSelectionApplet.convertStacksToH5([input_filepath],
tmp_dir)[0]
try:
os.rmdir(tmp_dir)
except OSError as e:
if e.errno == 39:
logger.warning(
'Temporary directory {} was populated: should be deleted')
else:
raise
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)
workflow.handleNewLanesAdded()
def generateBatchPredictions(workflow, batchInputPaths, batchExportDir, batchOutputSuffix, exportedDatasetName):
"""
Compute the predictions for each of the specified batch input files,
and export them to corresponding h5 files.
"""
batchInputPaths = convertStacksToH5(batchInputPaths)
batchInputInfos = []
for p in batchInputPaths:
info = DatasetInfo()
info.location = DatasetInfo.Location.FileSystem
# Convert all paths to absolute
# (otherwise they are relative to the project file, which probably isn't what the user meant)
comp = PathComponents(p)
comp.externalPath = os.path.abspath(comp.externalPath)
info.filePath = comp.totalPath()
batchInputInfos.append(info)
# Configure batch input operator
opBatchInputs = workflow.batchInputApplet.topLevelOperator
opBatchInputs.Dataset.setValues( batchInputInfos )
# Configure batch export operator
opBatchResults = workflow.batchResultsApplet.topLevelOperator
opBatchResults.ExportDirectory.setValue(batchExportDir)
opBatchResults.Format.setValue(ExportFormat.H5)
opBatchResults.Suffix.setValue(batchOutputSuffix)
opBatchResults.InternalPath.setValue(exportedDatasetName)
opBatchResults.SelectedSlices.setValue([30])
logger.info( "Exporting data to " + opBatchResults.OutputDataPath[0].value )
# Set up progress display handling (just logging for now)
currentProgress = [None]
def handleProgress(percentComplete):
if currentProgress[0] != percentComplete:
currentProgress[0] = percentComplete
logger.info("Batch job: {}% complete.".format(percentComplete))
progressSignal = opBatchResults.ProgressSignal[0].value
progressSignal.subscribe( handleProgress )
# Make it happen!
result = opBatchResults.ExportResult[0].value
return result
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 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 )
def create_default_headless_dataset_info(cls, filepath):
"""
filepath may be a globstring or a full hdf5 path+dataset
"""
comp = PathComponents(filepath)
nickname = comp.filenameBase
# Remove globstring syntax.
if '*' in nickname:
nickname = nickname.replace('*', '')
if os.path.pathsep in nickname:
nickname = PathComponents(nickname.split(os.path.pathsep)[0]).fileNameBase
info = DatasetInfo()
info.location = DatasetInfo.Location.FileSystem
info.nickname = nickname
info.filePath = 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(filepath):
comp.externalPath = os.path.abspath(comp.externalPath)
info.filePath = comp.totalPath()
return info
# Construct a parser with all the 'normal' export options, and add arg for prediction_image_paths.
parser = DataExportApplet.make_cmdline_parser( argparse.ArgumentParser() )
parser.add_argument("prediction_image_paths", nargs='+', help="Path(s) to your exported predictions.")
parsed_args = parser.parse_args()
parsed_args, unused_args = DataExportApplet.parse_known_cmdline_args( sys.argv[1:], parsed_args )
# As a convenience, auto-determine the internal dataset path if possible.
for index, input_path in enumerate(parsed_args.prediction_image_paths):
path_comp = PathComponents(input_path, os.getcwd())
if not parsed_args.output_internal_path:
parsed_args.output_internal_path = "segmentation"
if path_comp.extension in PathComponents.HDF5_EXTS and path_comp.internalDatasetName == "":
with h5py.File(path_comp.externalPath, 'r') as f:
all_internal_paths = all_dataset_internal_paths(f)
if len(all_internal_paths) == 1:
path_comp.internalPath = all_internal_paths[0]
parsed_args.prediction_image_paths[index] = path_comp.totalPath()
elif len(all_internal_paths) == 0:
sys.stderr.write("Could not find any datasets in your input file:\n"
"{}\n".format(input_path))
sys.exit(1)
else:
sys.stderr.write("Found more than one dataset in your input file:\n"
"{}\n".format(input_path) +
"Please specify the dataset name, e.g. /path/to/myfile.h5/internal/dataset_name\n")
sys.exit(1)
sys.exit( convert_predictions_to_segmentation( parsed_args.prediction_image_paths,
parsed_args ) )
def generateBatchPredictions(workflow, batchInputPaths, batchExportDir,
batchOutputSuffix, exportedDatasetName,
stackVolumeCacheDir):
"""
Compute the predictions for each of the specified batch input files,
and export them to corresponding h5 files.
"""
originalBatchInputPaths = list(batchInputPaths)
batchInputPaths = convertStacksToH5(batchInputPaths, stackVolumeCacheDir)
batchInputInfos = []
for p in batchInputPaths:
info = DatasetInfo()
info.location = DatasetInfo.Location.FileSystem
# Convert all paths to absolute
# (otherwise they are relative to the project file, which probably isn't what the user meant)
comp = PathComponents(p)
comp.externalPath = os.path.abspath(comp.externalPath)
info.filePath = comp.totalPath()
batchInputInfos.append(info)
# Also convert the export dir to absolute (for the same reason)
if batchExportDir != '':
batchExportDir = os.path.abspath(batchExportDir)
# Configure batch input operator
opBatchInputs = workflow.batchInputApplet.topLevelOperator
opBatchInputs.DatasetGroup.resize(len(batchInputInfos))
for info, multislot in zip(batchInputInfos, opBatchInputs.DatasetGroup):
# FIXME: This assumes that the workflow has exactly one dataset role.
multislot[0].setValue(info)
# Configure batch export operator
opBatchResults = workflow.batchResultsApplet.topLevelOperator
# By default, the output files from the batch export operator
# are named using the input file name.
# If we converted any stacks to hdf5, then the user won't recognize the input file name.
# Let's override the output file name using the *original* input file names.
outputFileNameBases = []
for origPath in originalBatchInputPaths:
outputFileNameBases.append(origPath.replace('*', 'STACKED'))
opBatchResults.OutputFileNameBase.setValues(outputFileNameBases)
opBatchResults.ExportDirectory.setValue(batchExportDir)
opBatchResults.Format.setValue(ExportFormat.H5)
opBatchResults.Suffix.setValue(batchOutputSuffix)
opBatchResults.InternalPath.setValue(exportedDatasetName)
logger.info("Exporting data to " + opBatchResults.OutputDataPath[0].value)
# Set up progress display handling (just logging for now)
currentProgress = [None]
def handleProgress(percentComplete):
if currentProgress[0] != percentComplete:
currentProgress[0] = percentComplete
logger.info("Batch job: {}% complete.".format(percentComplete))
progressSignal = opBatchResults.ProgressSignal[0].value
progressSignal.subscribe(handleProgress)
# Make it happen!
result = opBatchResults.ExportResult[0].value
return result
# Construct a parser with all the 'normal' export options, and add arg for prediction_image_paths.
parser = DataExportApplet.make_cmdline_parser( argparse.ArgumentParser() )
parser.add_argument("prediction_image_paths", nargs='+', help="Path(s) to your exported predictions.")
parsed_args = parser.parse_args()
parsed_args, unused_args = DataExportApplet.parse_known_cmdline_args( sys.argv[1:], parsed_args )
# As a convenience, auto-determine the internal dataset path if possible.
for index, input_path in enumerate(parsed_args.prediction_image_paths):
path_comp = PathComponents(input_path, os.getcwd())
if not parsed_args.output_internal_path:
parsed_args.output_internal_path = "segmentation"
if path_comp.extension in PathComponents.HDF5_EXTS and path_comp.internalDatasetName == "":
with h5py.File(path_comp.externalPath, 'r') as f:
all_internal_paths = all_dataset_internal_paths(f)
if len(all_internal_paths) == 1:
path_comp.internalPath = all_internal_paths[0]
parsed_args.prediction_image_paths[index] = path_comp.totalPath()
elif len(all_internal_paths) == 0:
sys.stderr.write("Could not find any datasets in your input file:\n"
"{}\n".format(input_path))
sys.exit(1)
else:
sys.stderr.write("Found more than one dataset in your input file:\n"
"{}\n".format(input_path) +
"Please specify the dataset name, e.g. /path/to/myfile.h5/internal/dataset_name\n")
sys.exit(1)
sys.exit( convert_predictions_to_segmentation( parsed_args.prediction_image_paths,
parsed_args ) )
parsed_args, unused_args = DataExportApplet.parse_known_cmdline_args(
sys.argv[1:], parsed_args)
# As a convenience, auto-determine the internal dataset path if possible.
for index, input_path in enumerate(parsed_args.prediction_image_paths):
path_comp = PathComponents(input_path, os.getcwd())
if not parsed_args.output_internal_path:
parsed_args.output_internal_path = "segmentation"
if path_comp.extension in PathComponents.HDF5_EXTS and path_comp.internalDatasetName == "":
with h5py.File(path_comp.externalPath, "r") as f:
all_internal_paths = all_dataset_internal_paths(f)
if len(all_internal_paths) == 1:
path_comp.internalPath = all_internal_paths[0]
parsed_args.prediction_image_paths[
index] = path_comp.totalPath()
elif len(all_internal_paths) == 0:
sys.stderr.write(
"Could not find any datasets in your input file:\n{}\n".
format(input_path))
sys.exit(1)
else:
sys.stderr.write(
"Found more than one dataset in your input file:\n"
"{}\n".format(input_path) +
"Please specify the dataset name, e.g. /path/to/myfile.h5/internal/dataset_name\n"
)
sys.exit(1)
sys.exit(
convert_predictions_to_segmentation(parsed_args.prediction_image_paths,
# Construct a parser with all the 'normal' export options, and add arg for input_path.
parser = DataExportApplet.make_cmdline_parser(argparse.ArgumentParser())
parser.add_argument("input_path",
help="Path to your exported predictions.")
parsed_args = parser.parse_args()
# As a convenience, auto-determine the internal dataset path if possible.
path_comp = PathComponents(parsed_args.input_path, os.getcwd())
if path_comp.extension in PathComponents.HDF5_EXTS and path_comp.internalDatasetName == "":
with h5py.File(path_comp.externalPath, "r") as f:
all_internal_paths = all_dataset_internal_paths(f)
if len(all_internal_paths) == 1:
path_comp.internalPath = all_internal_paths[0]
parsed_args.input_path = path_comp.totalPath()
elif len(all_internal_paths) == 0:
sys.stderr.write("Could not find any datasets in your input file.")
sys.exit(1)
else:
sys.stderr.write(
"Found more than one dataset in your input file.\n"
"Please specify the dataset name, e.g. /path/to/myfile.h5/internal/dataset_name"
)
sys.exit(1)
# As a convenience, if the user didn't explicitly specify an output file name, provide one for him.
if not parsed_args.output_filename_format:
output_path_comp = copy.copy(path_comp)
output_path_comp.filenameBase += "_Uncertainties"
parsed_args.output_filename_format = output_path_comp.externalPath
def generateBatchPredictions(workflow, batchInputPaths, batchExportDir, batchOutputSuffix, exportedDatasetName, stackVolumeCacheDir):
"""
Compute the predictions for each of the specified batch input files,
and export them to corresponding h5 files.
"""
originalBatchInputPaths = list(batchInputPaths)
batchInputPaths = convertStacksToH5(batchInputPaths, stackVolumeCacheDir)
batchInputInfos = []
for p in batchInputPaths:
info = DatasetInfo()
info.location = DatasetInfo.Location.FileSystem
# Convert all paths to absolute
# (otherwise they are relative to the project file, which probably isn't what the user meant)
comp = PathComponents(p)
comp.externalPath = os.path.abspath(comp.externalPath)
info.filePath = comp.totalPath()
batchInputInfos.append(info)
# Also convert the export dir to absolute (for the same reason)
if batchExportDir != '':
batchExportDir = os.path.abspath( batchExportDir )
# Configure batch input operator
opBatchInputs = workflow.batchInputApplet.topLevelOperator
opBatchInputs.DatasetGroup.resize( len(batchInputInfos) )
for info, multislot in zip(batchInputInfos, opBatchInputs.DatasetGroup):
# FIXME: This assumes that the workflow has exactly one dataset role.
multislot[0].setValue( info )
# Configure batch export operator
opBatchResults = workflow.batchResultsApplet.topLevelOperator
# By default, the output files from the batch export operator
# are named using the input file name.
# If we converted any stacks to hdf5, then the user won't recognize the input file name.
# Let's override the output file name using the *original* input file names.
outputFileNameBases = []
for origPath in originalBatchInputPaths:
outputFileNameBases.append( origPath.replace('*', 'STACKED') )
opBatchResults.OutputFileNameBase.setValues( outputFileNameBases )
opBatchResults.ExportDirectory.setValue(batchExportDir)
opBatchResults.Format.setValue(ExportFormat.H5)
opBatchResults.Suffix.setValue(batchOutputSuffix)
opBatchResults.InternalPath.setValue(exportedDatasetName)
logger.info( "Exporting data to " + opBatchResults.OutputDataPath[0].value )
# Set up progress display handling (just logging for now)
currentProgress = [None]
def handleProgress(percentComplete):
if currentProgress[0] != percentComplete:
currentProgress[0] = percentComplete
logger.info("Batch job: {}% complete.".format(percentComplete))
progressSignal = opBatchResults.ProgressSignal[0].value
progressSignal.subscribe( handleProgress )
# Make it happen!
result = opBatchResults.ExportResult[0].value
return result
# Construct a parser with all the 'normal' export options, and add arg for input_path.
parser = DataExportApplet.make_cmdline_parser( argparse.ArgumentParser() )
parser.add_argument("input_path", help="Path to your exported predictions.")
parsed_args = parser.parse_args()
# As a convenience, auto-determine the internal dataset path if possible.
path_comp = PathComponents(parsed_args.input_path, os.getcwd())
if path_comp.extension in PathComponents.HDF5_EXTS and path_comp.internalDatasetName == "":
with h5py.File(path_comp.externalPath, 'r') as f:
all_internal_paths = all_dataset_internal_paths(f)
if len(all_internal_paths) == 1:
path_comp.internalPath = all_internal_paths[0]
parsed_args.input_path = path_comp.totalPath()
elif len(all_internal_paths) == 0:
sys.stderr.write("Could not find any datasets in your input file.")
sys.exit(1)
else:
sys.stderr.write("Found more than one dataset in your input file.\n"
"Please specify the dataset name, e.g. /path/to/myfile.h5/internal/dataset_name")
sys.exit(1)
# As a convenience, if the user didn't explicitly specify an output file name, provide one for him.
if not parsed_args.output_filename_format:
output_path_comp = copy.copy(path_comp)
output_path_comp.filenameBase += "_Uncertainties"
parsed_args.output_filename_format = output_path_comp.externalPath
if path_comp.extension in PathComponents.HDF5_EXTS and not parsed_args.output_internal_path:
parsed_args.output_internal_path = "uncertainties"
def configure_operator_with_parsed_args(self, parsed_args):
"""
Helper function for headless workflows.
Configures this applet's top-level operator according to the settings provided in ``parsed_args``.
:param parsed_args: Must be an ``argparse.Namespace`` as returned by :py:meth:`parse_known_cmdline_args()`.
"""
role_names = self.topLevelOperator.DatasetRoles.value
role_paths = collections.OrderedDict()
if role_names:
for role_index, role_name in enumerate(role_names):
arg_name = self._role_name_to_arg_name(role_name)
input_paths = getattr(parsed_args, arg_name)
role_paths[role_index] = input_paths
if parsed_args.input_files:
# We allow the file list to go to the 'default' role, but only if no other roles were explicitly configured.
for role_index, input_paths in role_paths.items():
if input_paths:
# FIXME: This error message could be more helpful.
role_args = map(self._role_name_to_arg_name, role_names)
role_args = map(lambda s: '--' + s, role_args)
role_args_str = ", ".join(role_args)
raise Exception(
"Invalid command line arguments: All roles must be configured explicitly.\n"
"Use the following flags to specify which files are matched with which inputs:\n"
+ role_args_str)
role_paths = {0: parsed_args.input_files}
for role_index, input_paths in role_paths.items():
# If the user doesn't want image stacks to be copied into the project file,
# we generate hdf5 volumes in a temporary directory and use those files instead.
if parsed_args.preconvert_stacks:
import tempfile
input_paths = self.convertStacksToH5(input_paths,
tempfile.gettempdir())
input_infos = []
for p in input_paths:
info = DatasetInfo()
info.location = DatasetInfo.Location.FileSystem
info.filePath = p
comp = PathComponents(p)
# 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(p):
comp.externalPath = os.path.abspath(comp.externalPath)
info.filePath = comp.totalPath()
info.nickname = comp.filenameBase
# Remove globstring syntax.
if '*' in info.nickname:
info.nickname = info.nickname.replace('*', '')
if os.path.pathsep in info.nickname:
info.nickname = PathComponents(
info.nickname.split(os.path.pathsep)[0]).fileNameBase
input_infos.append(info)
opDataSelection = self.topLevelOperator
existing_lanes = len(opDataSelection.DatasetGroup)
opDataSelection.DatasetGroup.resize(
max(len(input_infos), existing_lanes))
for lane_index, info in enumerate(input_infos):
opDataSelection.DatasetGroup[lane_index][role_index].setValue(
info)
need_warning = False
for lane_index in range(len(input_infos)):
output_slot = opDataSelection.ImageGroup[lane_index][
role_index]
if output_slot.meta.prefer_2d:
need_warning = True
break
if need_warning:
logger.warn(
"*******************************************************************************************"
)
logger.warn(
"Some of your input data is stored in a format that is not efficient for 3D access patterns."
)
logger.warn(
"Performance may suffer as a result. For best performance, use a chunked HDF5 volume."
)
logger.warn(
"*******************************************************************************************"
)
def configure_operator_with_parsed_args(self, parsed_args):
"""
Helper function for headless workflows.
Configures this applet's top-level operator according to the settings provided in ``parsed_args``.
:param parsed_args: Must be an ``argparse.Namespace`` as returned by :py:meth:`parse_known_cmdline_args()`.
"""
role_names = self.topLevelOperator.DatasetRoles.value
role_paths = collections.OrderedDict()
if role_names:
for role_index, role_name in enumerate(role_names):
arg_name = self._role_name_to_arg_name(role_name)
input_paths = getattr(parsed_args, arg_name)
role_paths[role_index] = input_paths
if parsed_args.input_files:
# We allow the file list to go to the 'default' role, but only if no other roles were explicitly configured.
for role_index, input_paths in role_paths.items():
if input_paths:
# FIXME: This error message could be more helpful.
role_args = map( self._role_name_to_arg_name, role_names )
role_args = map( lambda s: '--' + s, role_args )
role_args_str = ", ".join( role_args )
raise Exception("Invalid command line arguments: All roles must be configured explicitly.\n"
"Use the following flags to specify which files are matched with which inputs:\n"
+ role_args_str )
role_paths = { 0 : parsed_args.input_files }
for role_index, input_paths in role_paths.items():
# If the user doesn't want image stacks to be copied into the project file,
# we generate hdf5 volumes in a temporary directory and use those files instead.
if parsed_args.preconvert_stacks:
import tempfile
input_paths = self.convertStacksToH5( input_paths, tempfile.gettempdir() )
input_infos = []
for p in input_paths:
info = DatasetInfo()
info.location = DatasetInfo.Location.FileSystem
info.filePath = p
comp = PathComponents(p)
# 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(p):
comp.externalPath = os.path.abspath(comp.externalPath)
info.filePath = comp.totalPath()
info.nickname = comp.filenameBase
# Remove globstring syntax.
if '*' in info.nickname:
info.nickname = info.nickname.replace('*', '')
if os.path.pathsep in info.nickname:
info.nickname = PathComponents(info.nickname.split(os.path.pathsep)[0]).fileNameBase
input_infos.append(info)
opDataSelection = self.topLevelOperator
existing_lanes = len(opDataSelection.DatasetGroup)
opDataSelection.DatasetGroup.resize( max(len(input_infos), existing_lanes) )
for lane_index, info in enumerate(input_infos):
opDataSelection.DatasetGroup[lane_index][role_index].setValue( info )
need_warning = False
for lane_index in range(len(input_infos)):
output_slot = opDataSelection.ImageGroup[lane_index][role_index]
if output_slot.meta.prefer_2d:
need_warning = True
break
if need_warning:
logger.warn("*******************************************************************************************")
logger.warn("Some of your input data is stored in a format that is not efficient for 3D access patterns.")
logger.warn("Performance may suffer as a result. For best performance, use a chunked HDF5 volume.")
logger.warn("*******************************************************************************************")
