def make_training_data(direc_name,
file_name_save,
channel_names,
dimensionality,
training_direcs=None,
raw_image_direc='raw',
annotation_direc='annotated',
annotation_name='feature',
reshape_size=None,
**kwargs):
"""Wrapper function for other make_training_data functions (2d, 3d)
Calls one of the above functions based on the dimensionality of the data.
"""
# Validate Arguments
if not isinstance(dimensionality, (int, float)):
raise ValueError(
'Data dimensionality should be an integer value, typically 2 or 3. '
'Recieved {}'.format(type(dimensionality).__name__))
if not isinstance(channel_names, (list, )):
raise ValueError(
'channel_names should be a list of strings (e.g. [\'DAPI\']). '
'Found {}'.format(type(channel_names).__name__))
if training_direcs is None:
training_direcs = get_immediate_subdirs(direc_name)
dimensionality = int(dimensionality)
if dimensionality == 2:
make_training_data_2d(direc_name,
file_name_save,
channel_names,
training_direcs=training_direcs,
reshape_size=reshape_size,
raw_image_direc=raw_image_direc,
annotation_name=annotation_name,
annotation_direc=annotation_direc)
elif dimensionality == 3:
make_training_data_3d(direc_name,
file_name_save,
channel_names,
training_direcs=training_direcs,
annotation_name=annotation_name,
raw_image_direc=raw_image_direc,
annotation_direc=annotation_direc,
reshape_size=reshape_size,
montage_mode=kwargs.get('montage_mode', False),
num_frames=kwargs.get('num_frames', 50))
else:
raise NotImplementedError('make_training_data is not implemented for '
'dimensionality {}'.format(dimensionality))
def test_get_immediate_subdirs(self):
dirs = []
temp_dir = self.get_temp_dir()
self.addCleanup(shutil.rmtree, temp_dir)
for x in range(2, -1, -1): # iterate backwards to test sorting
sub_dir = os.path.join(temp_dir, str(x))
try:
os.makedirs(sub_dir)
except OSError as err:
if err.errno != os.errno.EEXIST:
raise
dirs.append(str(x))
subdirs = io_utils.get_immediate_subdirs(temp_dir)
self.assertListEqual(subdirs, list(reversed(dirs)))