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 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 replaceWithStack(self, roleIndex, laneIndex):
"""
The user clicked the "Import Stack Files" button.
"""
stackDlg = StackFileSelectionWidget(self)
stackDlg.exec_()
if stackDlg.result() != QDialog.Accepted:
return
files = stackDlg.selectedFiles
if len(files) == 0:
return
info = DatasetInfo()
info.filePath = "//".join(files)
prefix = os.path.commonprefix(files)
info.nickname = PathComponents(prefix).filenameBase
# Add an underscore for each wildcard digit
num_wildcards = len(files[-1]) - len(prefix) - len(
os.path.splitext(files[-1])[1])
info.nickname += "_" * num_wildcards
# Allow labels by default if this gui isn't being used for batch data.
info.allowLabels = (self.guiMode == GuiMode.Normal)
info.fromstack = True
originalNumLanes = len(self.topLevelOperator.DatasetGroup)
if laneIndex is None:
laneIndex = self._findFirstEmptyLane(roleIndex)
if len(self.topLevelOperator.DatasetGroup) < laneIndex + 1:
self.topLevelOperator.DatasetGroup.resize(laneIndex + 1)
def importStack():
self.guiControlSignal.emit(ControlCommand.DisableAll)
# Serializer will update the operator for us, which will propagate to the GUI.
try:
self.serializer.importStackAsLocalDataset(info)
try:
self.topLevelOperator.DatasetGroup[laneIndex][
roleIndex].setValue(info)
except DatasetConstraintError as ex:
# Give the user a chance to repair the problem.
filename = files[0] + "\n...\n" + files[-1]
return_val = [False]
self.handleDatasetConstraintError(info, filename, ex,
roleIndex, laneIndex,
return_val)
if not return_val[0]:
# Not successfully repaired. Roll back the changes and give up.
self.topLevelOperator.DatasetGroup.resize(
originalNumLanes)
finally:
self.guiControlSignal.emit(ControlCommand.Pop)
req = Request(importStack)
req.notify_failed(
partial(self.handleFailedStackLoad, files, originalNumLanes))
req.submit()
def addStack(self, roleIndex, laneIndex):
"""
The user clicked the "Import Stack Files" button.
"""
stackDlg = StackFileSelectionWidget(self)
stackDlg.exec_()
if stackDlg.result() != QDialog.Accepted :
return
files = stackDlg.selectedFiles
sequence_axis = stackDlg.sequence_axis
if len(files) == 0:
return
info = DatasetInfo()
info.filePath = os.path.pathsep.join( files )
prefix = os.path.commonprefix(files)
info.nickname = PathComponents(prefix).filenameBase
# Add an underscore for each wildcard digit
num_wildcards = len(files[-1]) - len(prefix) - len( os.path.splitext(files[-1])[1] )
info.nickname += "_"*num_wildcards
# Allow labels by default if this gui isn't being used for batch data.
info.allowLabels = ( self.guiMode == GuiMode.Normal )
info.fromstack = True
originalNumLanes = len(self.topLevelOperator.DatasetGroup)
if laneIndex is None or laneIndex == -1:
laneIndex = len(self.topLevelOperator.DatasetGroup)
if len(self.topLevelOperator.DatasetGroup) < laneIndex+1:
self.topLevelOperator.DatasetGroup.resize(laneIndex+1)
def importStack():
self.parentApplet.busy = True
self.parentApplet.appletStateUpdateRequested.emit()
# Serializer will update the operator for us, which will propagate to the GUI.
try:
self.serializer.importStackAsLocalDataset( info, sequence_axis )
try:
self.topLevelOperator.DatasetGroup[laneIndex][roleIndex].setValue(info)
except DatasetConstraintError as ex:
# Give the user a chance to repair the problem.
filename = files[0] + "\n...\n" + files[-1]
return_val = [False]
self.handleDatasetConstraintError( info, filename, ex, roleIndex, laneIndex, return_val )
if not return_val[0]:
# Not successfully repaired. Roll back the changes and give up.
self.topLevelOperator.DatasetGroup.resize(originalNumLanes)
finally:
self.parentApplet.busy = False
self.parentApplet.appletStateUpdateRequested.emit()
req = Request( importStack )
req.notify_finished( lambda result: self.showDataset(laneIndex, roleIndex) )
req.notify_failed( partial(self.handleFailedStackLoad, files, originalNumLanes ) )
req.submit()
def _createDatasetInfo(self, roleIndex, filePath, roi):
"""
Create a DatasetInfo object for the given filePath and roi.
roi may be None, in which case it is ignored.
"""
datasetInfo = DatasetInfo()
if roi is not None:
datasetInfo.subvolume_roi = roi
cwd = self.topLevelOperator.WorkingDirectory.value
absPath, relPath = getPathVariants(filePath, cwd)
# Relative by default, unless the file is in a totally different tree from the working directory.
if relPath is not None and len(os.path.commonprefix([cwd, absPath
])) > 1:
datasetInfo.filePath = relPath
else:
datasetInfo.filePath = absPath
datasetInfo.nickname = PathComponents(absPath).filenameBase
h5Exts = ['.ilp', '.h5', '.hdf5']
if os.path.splitext(datasetInfo.filePath)[1] in h5Exts:
datasetNames = self.getPossibleInternalPaths(absPath)
if len(datasetNames) == 0:
raise RuntimeError("HDF5 file %s has no image datasets" %
datasetInfo.filePath)
elif len(datasetNames) == 1:
datasetInfo.filePath += str(datasetNames[0])
else:
# If exactly one of the file's datasets matches a user's previous choice, use it.
if roleIndex not in self._default_h5_volumes:
self._default_h5_volumes[roleIndex] = set()
previous_selections = self._default_h5_volumes[roleIndex]
possible_auto_selections = previous_selections.intersection(
datasetNames)
if len(possible_auto_selections) == 1:
datasetInfo.filePath += str(
list(possible_auto_selections)[0])
else:
# Ask the user which dataset to choose
dlg = H5VolumeSelectionDlg(datasetNames, self)
if dlg.exec_() == QDialog.Accepted:
selected_index = dlg.combo.currentIndex()
selected_dataset = str(datasetNames[selected_index])
datasetInfo.filePath += selected_dataset
self._default_h5_volumes[roleIndex].add(
selected_dataset)
else:
raise DataSelectionGui.UserCancelledError()
# Allow labels by default if this gui isn't being used for batch data.
datasetInfo.allowLabels = (self.guiMode == GuiMode.Normal)
return datasetInfo
def deserializeFromHdf5(self, hdf5File, projectFilePath, headless=False):
# Check the overall file version
ilastikVersion = hdf5File["ilastikVersion"].value
# This is the v0.5 import deserializer. Don't work with 0.6 projects (or anything else).
if ilastikVersion != 0.5:
return
# The 'working directory' for the purpose of constructing absolute
# paths from relative paths is the project file's directory.
projectDir = os.path.split(projectFilePath)[0]
self.topLevelOperator.WorkingDirectory.setValue(projectDir)
# Access the top group and the info group
try:
#dataset = hdf5File["DataSets"]["dataItem00"]["data"]
dataDir = hdf5File["DataSets"]
except KeyError:
# If our group (or subgroup) doesn't exist, then make sure the operator is empty
self.topLevelOperator.DatasetGroup.resize(0)
return
self.topLevelOperator.DatasetGroup.resize(len(dataDir))
for index, (datasetDirName,
datasetDir) in enumerate(sorted(dataDir.items())):
datasetInfo = DatasetInfo()
# We'll set up the link to the dataset in the old project file,
# but we'll set the location to ProjectInternal so that it will
# be copied to the new file when the project is saved.
datasetInfo.location = DatasetInfo.Location.ProjectInternal
# Some older versions of ilastik 0.5 stored the data in tzyxc order.
# Some power-users can enable a command-line flag that tells us to
# transpose the data back to txyzc order when we import the old project.
default_axis_order = ilastik.utility.globals.ImportOptions.default_axis_order
if default_axis_order is not None:
import warnings
warnings.warn(
"Using a strange axis order to import ilastik 0.5 projects: {}"
.format(default_axis_order))
datasetInfo.axistags = vigra.defaultAxistags(
default_axis_order)
# Write to the 'private' members to avoid resetting the dataset id
totalDatasetPath = str(projectFilePath + '/DataSets/' +
datasetDirName + '/data')
datasetInfo._filePath = totalDatasetPath
datasetInfo._datasetId = datasetDirName # Use the old dataset name as the new dataset id
datasetInfo.nickname = "{} (imported from v0.5)".format(
datasetDirName)
# Give the new info to the operator
self.topLevelOperator.DatasetGroup[index][0].setValue(datasetInfo)
def _createDatasetInfo(self, roleIndex, filePath, roi):
"""
Create a DatasetInfo object for the given filePath and roi.
roi may be None, in which case it is ignored.
"""
datasetInfo = DatasetInfo()
if roi is not None:
datasetInfo.subvolume_roi = roi
cwd = self.topLevelOperator.WorkingDirectory.value
absPath, relPath = getPathVariants(filePath, cwd)
# Relative by default, unless the file is in a totally different tree from the working directory.
if relPath is not None and len(os.path.commonprefix([cwd, absPath])) > 1:
datasetInfo.filePath = relPath
else:
datasetInfo.filePath = absPath
datasetInfo.nickname = PathComponents(absPath).filenameBase
h5Exts = ['.ilp', '.h5', '.hdf5']
if os.path.splitext(datasetInfo.filePath)[1] in h5Exts:
datasetNames = self.getPossibleInternalPaths( absPath )
if len(datasetNames) == 0:
raise RuntimeError("HDF5 file %s has no image datasets" % datasetInfo.filePath)
elif len(datasetNames) == 1:
datasetInfo.filePath += str(datasetNames[0])
else:
# If exactly one of the file's datasets matches a user's previous choice, use it.
if roleIndex not in self._default_h5_volumes:
self._default_h5_volumes[roleIndex] = set()
previous_selections = self._default_h5_volumes[roleIndex]
possible_auto_selections = previous_selections.intersection(datasetNames)
if len(possible_auto_selections) == 1:
datasetInfo.filePath += str(list(possible_auto_selections)[0])
else:
# Ask the user which dataset to choose
dlg = H5VolumeSelectionDlg(datasetNames, self)
if dlg.exec_() == QDialog.Accepted:
selected_index = dlg.combo.currentIndex()
selected_dataset = str(datasetNames[selected_index])
datasetInfo.filePath += selected_dataset
self._default_h5_volumes[roleIndex].add( selected_dataset )
else:
raise DataSelectionGui.UserCancelledError()
# Allow labels by default if this gui isn't being used for batch data.
datasetInfo.allowLabels = ( self.guiMode == GuiMode.Normal )
return datasetInfo
def deserializeFromHdf5(self, hdf5File, projectFilePath, headless = False):
# Check the overall file version
ilastikVersion = hdf5File["ilastikVersion"].value
# This is the v0.5 import deserializer. Don't work with 0.6 projects (or anything else).
if ilastikVersion != 0.5:
return
# The 'working directory' for the purpose of constructing absolute
# paths from relative paths is the project file's directory.
projectDir = os.path.split(projectFilePath)[0]
self.topLevelOperator.WorkingDirectory.setValue( projectDir )
# Access the top group and the info group
try:
#dataset = hdf5File["DataSets"]["dataItem00"]["data"]
dataDir = hdf5File["DataSets"]
except KeyError:
# If our group (or subgroup) doesn't exist, then make sure the operator is empty
self.topLevelOperator.DatasetGroup.resize( 0 )
return
self.topLevelOperator.DatasetGroup.resize( len(dataDir) )
for index, (datasetDirName, datasetDir) in enumerate( sorted(dataDir.items()) ):
datasetInfo = DatasetInfo()
# We'll set up the link to the dataset in the old project file,
# but we'll set the location to ProjectInternal so that it will
# be copied to the new file when the project is saved.
datasetInfo.location = DatasetInfo.Location.ProjectInternal
# Some older versions of ilastik 0.5 stored the data in tzyxc order.
# Some power-users can enable a command-line flag that tells us to
# transpose the data back to txyzc order when we import the old project.
default_axis_order = ilastik.utility.globals.ImportOptions.default_axis_order
if default_axis_order is not None:
import warnings
warnings.warn( "Using a strange axis order to import ilastik 0.5 projects: {}".format( default_axis_order ) )
datasetInfo.axistags = vigra.defaultAxistags(default_axis_order)
# Write to the 'private' members to avoid resetting the dataset id
totalDatasetPath = projectFilePath + '/DataSets/' + datasetDirName + '/data'
datasetInfo._filePath = totalDatasetPath
datasetInfo._datasetId = datasetDirName # Use the old dataset name as the new dataset id
datasetInfo.nickname = "{} (imported from v0.5)".format( datasetDirName )
# Give the new info to the operator
self.topLevelOperator.DatasetGroup[index][0].setValue(datasetInfo)
def replaceWithStack(self, roleIndex, laneIndex):
"""
The user clicked the "Import Stack Files" button.
"""
stackDlg = StackFileSelectionWidget(self)
stackDlg.exec_()
if stackDlg.result() != QDialog.Accepted :
return
files = stackDlg.selectedFiles
if len(files) == 0:
return
info = DatasetInfo()
info.filePath = "//".join( files )
prefix = os.path.commonprefix(files)
info.nickname = PathComponents(prefix).filenameBase + "..."
# Allow labels by default if this gui isn't being used for batch data.
info.allowLabels = ( self.guiMode == GuiMode.Normal )
info.fromstack = True
originalNumLanes = len(self.topLevelOperator.DatasetGroup)
if laneIndex is None:
laneIndex = self._findFirstEmptyLane(roleIndex)
if len(self.topLevelOperator.DatasetGroup) < laneIndex+1:
self.topLevelOperator.DatasetGroup.resize(laneIndex+1)
def importStack():
self.guiControlSignal.emit( ControlCommand.DisableAll )
# Serializer will update the operator for us, which will propagate to the GUI.
try:
self.serializer.importStackAsLocalDataset( info )
try:
self.topLevelOperator.DatasetGroup[laneIndex][roleIndex].setValue(info)
except DatasetConstraintError as ex:
# Give the user a chance to repair the problem.
filename = files[0] + "\n...\n" + files[-1]
if not self.handleDatasetConstraintError( info, filename, ex, roleIndex, laneIndex ):
self.topLevelOperator.DatasetGroup.resize(originalNumLanes)
finally:
self.guiControlSignal.emit( ControlCommand.Pop )
req = Request( importStack )
req.notify_failed( partial(self.handleFailedStackLoad, files, originalNumLanes ) )
req.submit()
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
def addFileNames(self, fileNames, roleIndex, startingLane=None):
"""
Add the given filenames to both the GUI table and the top-level operator inputs.
If startingLane is None, the filenames will be *appended* to the role's list of files.
"""
infos = []
if startingLane is None or startingLane == -1:
startingLane = len(self.topLevelOperator.DatasetGroup)
endingLane = startingLane+len(fileNames)-1
else:
assert startingLane < len(self.topLevelOperator.DatasetGroup)
max_files = len(self.topLevelOperator.DatasetGroup) - \
startingLane
if len(fileNames) > max_files:
msg = "You selected {num_selected} files for {num_slots} "\
"slots. To add new files use the 'Add new...' option "\
"in the context menu or the button in the last row."\
.format(num_selected=len(fileNames),
num_slots=max_files)
QMessageBox.critical( self, "Too many files", msg )
return
endingLane = min(startingLane+len(fileNames)-1,
len(self.topLevelOperator.DatasetGroup))
if self._max_lanes and endingLane >= self._max_lanes:
msg = "You may not add more than {} file(s) to this workflow. Please try again.".format( self._max_lanes )
QMessageBox.critical( self, "Too many files", msg )
return
# Assign values to the new inputs we just allocated.
# The GUI will be updated by callbacks that are listening to slot changes
for i, filePath in enumerate(fileNames):
datasetInfo = DatasetInfo()
cwd = self.topLevelOperator.WorkingDirectory.value
absPath, relPath = getPathVariants(filePath, cwd)
# Relative by default, unless the file is in a totally different tree from the working directory.
if relPath is not None and len(os.path.commonprefix([cwd, absPath])) > 1:
datasetInfo.filePath = relPath
else:
datasetInfo.filePath = absPath
datasetInfo.nickname = PathComponents(absPath).filenameBase
h5Exts = ['.ilp', '.h5', '.hdf5']
if os.path.splitext(datasetInfo.filePath)[1] in h5Exts:
datasetNames = self.getPossibleInternalPaths( absPath )
if len(datasetNames) > 0:
datasetInfo.filePath += str(datasetNames[0])
else:
raise RuntimeError("HDF5 file %s has no image datasets" % datasetInfo.filePath)
# Allow labels by default if this gui isn't being used for batch data.
datasetInfo.allowLabels = ( self.guiMode == GuiMode.Normal )
infos.append(datasetInfo)
# if no exception was thrown, set up the operator now
opTop = self.topLevelOperator
originalSize = len(opTop.DatasetGroup)
if len( opTop.DatasetGroup ) < endingLane+1:
opTop.DatasetGroup.resize( endingLane+1 )
for laneIndex, info in zip(range(startingLane, endingLane+1), infos):
try:
self.topLevelOperator.DatasetGroup[laneIndex][roleIndex].setValue( info )
except DatasetConstraintError as ex:
return_val = [False]
# Give the user a chance to fix the problem
self.handleDatasetConstraintError(info, info.filePath, ex, roleIndex, laneIndex, return_val)
if not return_val[0]:
# Not successfully repaired. Roll back the changes and give up.
opTop.DatasetGroup.resize( originalSize )
break
except OpDataSelection.InvalidDimensionalityError as ex:
opTop.DatasetGroup.resize( originalSize )
QMessageBox.critical( self, "Dataset has different dimensionality", ex.message )
break
except:
QMessageBox.critical( self, "Dataset Load Error", "Wasn't able to load your dataset into the workflow. See console for details." )
opTop.DatasetGroup.resize( originalSize )
raise
# If we succeeded in adding all images, show the first one.
if laneIndex == endingLane:
self.showDataset(startingLane, roleIndex)
# Notify the workflow that something that could affect applet readyness has occurred.
self.parentApplet.appletStateUpdateRequested.emit()
self.updateInternalPathVisiblity()
def addFileNames(self, fileNames, roleIndex, startingLane=None):
"""
Add the given filenames to both the GUI table and the top-level operator inputs.
If startingLane is None, the filenames will be *appended* to the role's list of files.
"""
infos = []
if startingLane is None:
startingLane = self._findFirstEmptyLane(roleIndex)
endingLane = startingLane+len(fileNames)-1
else:
assert startingLane < len(self.topLevelOperator.DatasetGroup)
endingLane = startingLane+len(fileNames)-1
# Assign values to the new inputs we just allocated.
# The GUI will be updated by callbacks that are listening to slot changes
for i, filePath in enumerate(fileNames):
datasetInfo = DatasetInfo()
cwd = self.topLevelOperator.WorkingDirectory.value
if not areOnSameDrive(filePath,cwd):
QMessageBox.critical(self, "Drive Error","Data must be on same drive as working directory.")
return
absPath, relPath = getPathVariants(filePath, cwd)
# Relative by default, unless the file is in a totally different tree from the working directory.
if len(os.path.commonprefix([cwd, absPath])) > 1:
datasetInfo.filePath = relPath
else:
datasetInfo.filePath = absPath
datasetInfo.nickname = PathComponents(absPath).filenameBase
h5Exts = ['.ilp', '.h5', '.hdf5']
if os.path.splitext(datasetInfo.filePath)[1] in h5Exts:
datasetNames = self.getPossibleInternalPaths( absPath )
if len(datasetNames) > 0:
datasetInfo.filePath += str(datasetNames[0])
else:
raise RuntimeError("HDF5 file %s has no image datasets" % datasetInfo.filePath)
# Allow labels by default if this gui isn't being used for batch data.
datasetInfo.allowLabels = ( self.guiMode == GuiMode.Normal )
infos.append(datasetInfo)
# if no exception was thrown, set up the operator now
opTop = self.topLevelOperator
originalSize = len(opTop.DatasetGroup)
if len( opTop.DatasetGroup ) < endingLane+1:
opTop.DatasetGroup.resize( endingLane+1 )
for laneIndex, info in zip(range(startingLane, endingLane+1), infos):
try:
self.topLevelOperator.DatasetGroup[laneIndex][roleIndex].setValue( info )
except DatasetConstraintError as ex:
# Give the user a chance to fix the problem
if not self.handleDatasetConstraintError(info, info.filePath, ex, roleIndex, laneIndex):
opTop.DatasetGroup.resize( originalSize )
break
except:
QMessageBox.critical( self, "Dataset Load Error", "Wasn't able to load your dataset into the workflow. See console for details." )
opTop.DatasetGroup.resize( originalSize )
raise
self.updateInternalPathVisiblity()
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("*******************************************************************************************")
def _readDatasetInfo(self, infoGroup, localDataGroup, projectFilePath, headless):
# Unready datasets are represented with an empty group.
if len( infoGroup ) == 0:
return None, False
datasetInfo = DatasetInfo()
# Make a reverse-lookup of the location storage strings
LocationLookup = { v:k for k,v in self.LocationStrings.items() }
datasetInfo.location = LocationLookup[ str(infoGroup['location'].value) ]
# Write to the 'private' members to avoid resetting the dataset id
datasetInfo._filePath = infoGroup['filePath'].value
datasetInfo._datasetId = infoGroup['datasetId'].value
try:
datasetInfo.allowLabels = infoGroup['allowLabels'].value
except KeyError:
pass
try:
datasetInfo.drange = tuple( infoGroup['drange'].value )
except KeyError:
pass
try:
datasetInfo.nickname = infoGroup['nickname'].value
except KeyError:
datasetInfo.nickname = PathComponents(datasetInfo.filePath).filenameBase
try:
tags = vigra.AxisTags.fromJSON( infoGroup['axistags'].value )
datasetInfo.axistags = tags
except KeyError:
# Old projects just have an 'axisorder' field instead of full axistags
try:
axisorder = infoGroup['axisorder'].value
datasetInfo.axistags = vigra.defaultAxistags(axisorder)
except KeyError:
pass
# If the data is supposed to be in the project,
# check for it now.
if datasetInfo.location == DatasetInfo.Location.ProjectInternal:
if not datasetInfo.datasetId in localDataGroup.keys():
raise RuntimeError("Corrupt project file. Could not find data for " + infoGroup.name)
dirty = False
# If the data is supposed to exist outside the project, make sure it really does.
if datasetInfo.location == DatasetInfo.Location.FileSystem and not isUrl(datasetInfo.filePath):
pathData = PathComponents( datasetInfo.filePath, os.path.split(projectFilePath)[0])
filePath = pathData.externalPath
if not os.path.exists(filePath):
if headless:
raise RuntimeError("Could not find data at " + filePath)
filt = "Image files (" + ' '.join('*.' + x for x in OpDataSelection.SupportedExtensions) + ')'
newpath = self.repairFile(filePath, filt)
if pathData.internalPath is not None:
newpath += pathData.internalPath
datasetInfo._filePath = getPathVariants(newpath , os.path.split(projectFilePath)[0])[0]
dirty = True
return datasetInfo, dirty
def _readDatasetInfo(self, infoGroup, localDataGroup, projectFilePath, headless):
# Unready datasets are represented with an empty group.
if len( infoGroup ) == 0:
return None, False
datasetInfo = DatasetInfo()
# Make a reverse-lookup of the location storage strings
LocationLookup = { v:k for k,v in self.LocationStrings.items() }
datasetInfo.location = LocationLookup[ str(infoGroup['location'].value) ]
# Write to the 'private' members to avoid resetting the dataset id
datasetInfo._filePath = infoGroup['filePath'].value
datasetInfo._datasetId = infoGroup['datasetId'].value
try:
datasetInfo.allowLabels = infoGroup['allowLabels'].value
except KeyError:
pass
try:
datasetInfo.drange = tuple( infoGroup['drange'].value )
except KeyError:
pass
try:
datasetInfo.nickname = infoGroup['nickname'].value
except KeyError:
datasetInfo.nickname = PathComponents(datasetInfo.filePath).filenameBase
try:
datasetInfo.fromstack = infoGroup['fromstack'].value
except KeyError:
# Guess based on the storage setting and original filepath
datasetInfo.fromstack = ( datasetInfo.location == DatasetInfo.Location.ProjectInternal
and ( ('?' in datasetInfo._filePath) or (os.path.pathsep in datasetInfo._filePath) ) )
try:
tags = vigra.AxisTags.fromJSON( infoGroup['axistags'].value )
datasetInfo.axistags = tags
except KeyError:
# Old projects just have an 'axisorder' field instead of full axistags
try:
axisorder = infoGroup['axisorder'].value
datasetInfo.axistags = vigra.defaultAxistags(axisorder)
except KeyError:
pass
try:
start, stop = map( tuple, infoGroup['subvolume_roi'].value )
datasetInfo.subvolume_roi = (start, stop)
except KeyError:
pass
# If the data is supposed to be in the project,
# check for it now.
if datasetInfo.location == DatasetInfo.Location.ProjectInternal:
if not datasetInfo.datasetId in localDataGroup.keys():
raise RuntimeError("Corrupt project file. Could not find data for " + infoGroup.name)
dirty = False
# If the data is supposed to exist outside the project, make sure it really does.
if datasetInfo.location == DatasetInfo.Location.FileSystem and not isUrl(datasetInfo.filePath):
pathData = PathComponents( datasetInfo.filePath, os.path.split(projectFilePath)[0])
filePath = pathData.externalPath
if not os.path.exists(filePath):
if headless:
raise RuntimeError("Could not find data at " + filePath)
filt = "Image files (" + ' '.join('*.' + x for x in OpDataSelection.SupportedExtensions) + ')'
newpath = self.repairFile(filePath, filt)
if pathData.internalPath is not None:
newpath += pathData.internalPath
datasetInfo._filePath = getPathVariants(newpath , os.path.split(projectFilePath)[0])[0]
dirty = True
return datasetInfo, dirty
def addFileNames(self, fileNames, roleIndex, startingLane=None):
"""
Add the given filenames to both the GUI table and the top-level operator inputs.
If startingLane is None, the filenames will be *appended* to the role's list of files.
"""
infos = []
if startingLane is None:
startingLane = self._findFirstEmptyLane(roleIndex)
endingLane = startingLane + len(fileNames) - 1
else:
assert startingLane < len(self.topLevelOperator.DatasetGroup)
endingLane = startingLane + len(fileNames) - 1
if self._max_lanes and endingLane >= self._max_lanes:
msg = "You may not add more than {} file(s) to this workflow. Please try again.".format(
self._max_lanes)
QMessageBox.critical(self, "Too many files", msg)
return
# Assign values to the new inputs we just allocated.
# The GUI will be updated by callbacks that are listening to slot changes
for i, filePath in enumerate(fileNames):
datasetInfo = DatasetInfo()
cwd = self.topLevelOperator.WorkingDirectory.value
if not areOnSameDrive(filePath, cwd):
QMessageBox.critical(
self, "Drive Error",
"Data must be on same drive as working directory.")
return
absPath, relPath = getPathVariants(filePath, cwd)
# Relative by default, unless the file is in a totally different tree from the working directory.
if len(os.path.commonprefix([cwd, absPath])) > 1:
datasetInfo.filePath = relPath
else:
datasetInfo.filePath = absPath
datasetInfo.nickname = PathComponents(absPath).filenameBase
h5Exts = ['.ilp', '.h5', '.hdf5']
if os.path.splitext(datasetInfo.filePath)[1] in h5Exts:
datasetNames = self.getPossibleInternalPaths(absPath)
if len(datasetNames) > 0:
datasetInfo.filePath += str(datasetNames[0])
else:
raise RuntimeError("HDF5 file %s has no image datasets" %
datasetInfo.filePath)
# Allow labels by default if this gui isn't being used for batch data.
datasetInfo.allowLabels = (self.guiMode == GuiMode.Normal)
infos.append(datasetInfo)
# if no exception was thrown, set up the operator now
opTop = self.topLevelOperator
originalSize = len(opTop.DatasetGroup)
if len(opTop.DatasetGroup) < endingLane + 1:
opTop.DatasetGroup.resize(endingLane + 1)
for laneIndex, info in zip(range(startingLane, endingLane + 1), infos):
try:
self.topLevelOperator.DatasetGroup[laneIndex][
roleIndex].setValue(info)
except DatasetConstraintError as ex:
return_val = [False]
# Give the user a chance to fix the problem
self.handleDatasetConstraintError(info, info.filePath, ex,
roleIndex, laneIndex,
return_val)
if not return_val[0]:
# Not successfully repaired. Roll back the changes and give up.
opTop.DatasetGroup.resize(originalSize)
break
except OpDataSelection.InvalidDimensionalityError as ex:
opTop.DatasetGroup.resize(originalSize)
QMessageBox.critical(self,
"Dataset has different dimensionality",
ex.message)
break
except:
QMessageBox.critical(
self, "Dataset Load Error",
"Wasn't able to load your dataset into the workflow. See console for details."
)
opTop.DatasetGroup.resize(originalSize)
raise
self.updateInternalPathVisiblity()