def test_kwargs(self):
spatial_size = (32, 64, 128)
test_image = np.random.rand(*spatial_size)
with tempfile.TemporaryDirectory() as tempdir:
filename = os.path.join(tempdir, "test_image.nii.gz")
itk_np_view = itk.image_view_from_array(test_image)
itk.imwrite(itk_np_view, filename)
loader = LoadImage(image_only=True)
reader = ITKReader(fallback_only=False)
loader.register(reader)
result = loader(filename)
reader = ITKReader()
img = reader.read(filename, fallback_only=False)
result_raw = reader.get_data(img)
result_raw = MetaTensor.ensure_torch_and_prune_meta(*result_raw)
self.assertTupleEqual(result.shape, result_raw.shape)
def __call__(self,
filename: Union[Sequence[PathLike], PathLike],
reader: Optional[ImageReader] = None):
"""
Load image file and metadata from the given filename(s).
If `reader` is not specified, this class automatically chooses readers based on the
reversed order of registered readers `self.readers`.
Args:
filename: path file or file-like object or a list of files.
will save the filename to meta_data with key `filename_or_obj`.
if provided a list of files, use the filename of first file to save,
and will stack them together as multi-channels data.
if provided directory path instead of file path, will treat it as
DICOM images series and read.
reader: runtime reader to load image file and metadata.
"""
filename = tuple(f"{Path(s).expanduser()}"
for s in ensure_tuple(filename)) # allow Path objects
img, err = None, []
if reader is not None:
img = reader.read(filename) # runtime specified reader
else:
for reader in self.readers[::-1]:
if self.auto_select: # rely on the filename extension to choose the reader
if reader.verify_suffix(filename):
img = reader.read(filename)
break
else: # try the user designated readers
try:
img = reader.read(filename)
except Exception as e:
err.append(traceback.format_exc())
logging.getLogger(self.__class__.__name__).debug(
e, exc_info=True)
logging.getLogger(self.__class__.__name__).info(
f"{reader.__class__.__name__}: unable to load {filename}.\n"
)
else:
err = []
break
if img is None or reader is None:
if isinstance(filename, tuple) and len(filename) == 1:
filename = filename[0]
msg = "\n".join([f"{e}" for e in err])
raise RuntimeError(
f"{self.__class__.__name__} cannot find a suitable reader for file: {filename}.\n"
" Please install the reader libraries, see also the installation instructions:\n"
" https://docs.monai.io/en/latest/installation.html#installing-the-recommended-dependencies.\n"
f" The current registered: {self.readers}.\n{msg}")
img_array: NdarrayOrTensor
img_array, meta_data = reader.get_data(img)
img_array = convert_to_dst_type(img_array,
dst=img_array,
dtype=self.dtype)[0]
if not isinstance(meta_data, dict):
raise ValueError("`meta_data` must be a dict.")
# make sure all elements in metadata are little endian
meta_data = switch_endianness(meta_data, "<")
meta_data[
Key.
FILENAME_OR_OBJ] = f"{ensure_tuple(filename)[0]}" # Path obj should be strings for data loader
img = MetaTensor.ensure_torch_and_prune_meta(img_array, meta_data,
self.simple_keys)
if self.ensure_channel_first:
img = EnsureChannelFirst()(img)
if self.image_only:
return img
return img, img.meta # for compatibility purpose
