def make_cmdline_parser(cls, starting_parser=None):
"""
Returns a command line parser that includes all parameters from the parent applet and adds export_plugin.
"""
arg_parser = DataExportApplet.make_cmdline_parser(starting_parser)
arg_parser.add_argument('--export_plugin',
help='Plugin name for exporting tracking results',
required=False,
default=None)
return arg_parser
def make_cmdline_parser(cls, starting_parser=None):
"""
Returns a command line parser that includes all parameters from the parent applet and adds export_plugin.
"""
arg_parser = DataExportApplet.make_cmdline_parser(starting_parser)
arg_parser.add_argument(
'--export_plugin',
help='Plugin name for exporting tracking results',
required=False,
default=None)
return arg_parser
def make_cmdline_parser(cls, starting_parser=None):
"""
Returns a command line parser that includes all parameters from the parent applet and adds export_plugin.
"""
arg_parser = DataExportApplet.make_cmdline_parser(starting_parser)
arg_parser.add_argument('--export_plugin',
help='Plugin name for exporting tracking results',
required=False,
default=None)
arg_parser.add_argument('--big_data_viewer_xml_file',
help='Path to BigDataViewer XML file. Required if export_plugin=Fiji-MaMuT',
required=False,
default=None)
return arg_parser
Return a list of all the internal datasets in an hdf5 file.
"""
allkeys = []
f.visit(allkeys.append)
dataset_keys = [key for key in allkeys if isinstance(f[key], h5py.Dataset)]
return dataset_keys
if __name__ == "__main__":
import sys
import argparse
# sys.argv += "/tmp/example_slice.h5/data /tmp/example_slice2.h5/data --export_drange=(0,255) --output_format=png --pipeline_result_drange=(1,2)".split()
# 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)
def all_dataset_internal_paths(f):
"""
Return a list of all the internal datasets in an hdf5 file.
"""
allkeys = []
f.visit(allkeys.append)
dataset_keys = filter(lambda key: isinstance(f[key], h5py.Dataset),
allkeys)
return dataset_keys
if __name__ == "__main__":
import sys
import argparse
# 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.")
