def create_instance(cls, file_path, **kwargs):
"""
Read image headers and create image instance.
:param file_path: a file path or a sequence of file paths
:param kwargs: output properties for transforming the image data
array into a desired format
:return: an image instance
"""
if file_path is None:
tf.logging.fatal('No file_path provided, '
'please check input sources in config file')
raise ValueError
image_type = None
try:
if os.path.isfile(file_path):
ndims = misc.infer_ndims_from_file(file_path)
image_type = cls.INSTANCE_DICT.get(ndims, None)
except TypeError:
pass
if image_type is None:
try:
assert all([os.path.isfile(path) for path in file_path])
ndims = misc.infer_ndims_from_file(file_path[0])
ndims = ndims + (1 if len(file_path) > 1 else 0)
image_type = cls.INSTANCE_DICT.get(ndims, None)
except AssertionError:
tf.logging.fatal('Could not load file: %s', file_path)
raise IOError
if image_type is None:
tf.logging.fatal('Not supported image type: %s', file_path)
raise NotImplementedError
return image_type(file_path, **kwargs)
def create_instance(cls, file_path, **kwargs):
"""
Read image headers and create image instance.
:param file_path: a file path or a sequence of file paths
:param kwargs: output properties for transforming the image data
array into a desired format
:return: an image instance
"""
if file_path is None:
tf.logging.fatal('No file_path provided, '
'please check input sources in config file')
raise ValueError
ndims = 0
image_type = None
home_folder = NiftyNetGlobalConfig().get_niftynet_home_folder()
try:
file_path = resolve_file_name(file_path, ('.', home_folder))
if os.path.isfile(file_path):
loader = kwargs.get('loader', None) or None
ndims = misc.infer_ndims_from_file(file_path, loader)
image_type = cls.INSTANCE_DICT.get(ndims, None)
except (TypeError, IOError, AttributeError):
pass
if image_type is None:
try:
file_path = [
resolve_file_name(path, ('.', home_folder))
for path in file_path
]
loader = kwargs.get('loader', None) or (None, )
ndims = misc.infer_ndims_from_file(file_path[0], loader[0])
ndims = ndims + (1 if len(file_path) > 1 else 0)
image_type = cls.INSTANCE_DICT.get(ndims, None)
except (AssertionError, TypeError, IOError, AttributeError):
tf.logging.fatal('Could not load file: %s', file_path)
raise IOError
if image_type is None:
tf.logging.fatal('Not supported image type from:\n%s', file_path)
raise NotImplementedError(
"unrecognised spatial rank {}".format(ndims))
return image_type(file_path, **kwargs)
def create_instance(cls, file_path, **kwargs):
"""
Read image headers and create image instance.
:param file_path: a file path or a sequence of file paths
:param kwargs: output properties for transforming the image data
array into a desired format
:return: an image instance
"""
if file_path is None:
tf.logging.fatal('No file_path provided, '
'please check input sources in config file')
raise ValueError
ndims = 0
image_type = None
home_folder = NiftyNetGlobalConfig().get_niftynet_home_folder()
try:
file_path = resolve_file_name(file_path, ('.', home_folder))
if os.path.isfile(file_path):
loader = kwargs.get('loader', None) or None
ndims = misc.infer_ndims_from_file(file_path, loader)
image_type = cls.INSTANCE_DICT.get(ndims, None)
except (TypeError, IOError, AttributeError):
pass
if image_type is None:
try:
file_path = [resolve_file_name(path, ('.', home_folder))
for path in file_path]
loader = kwargs.get('loader', None) or (None,)
ndims = misc.infer_ndims_from_file(file_path[0], loader[0])
ndims = ndims + (1 if len(file_path) > 1 else 0)
image_type = cls.INSTANCE_DICT.get(ndims, None)
except (AssertionError, TypeError, IOError, AttributeError):
tf.logging.fatal('Could not load file: %s', file_path)
raise IOError
if image_type is None:
tf.logging.fatal('Not supported image type from:\n%s', file_path)
raise NotImplementedError(
"unrecognised spatial rank {}".format(ndims))
return image_type(file_path, **kwargs)