def test_all_data(self):
folder = 'testing_data'
all_files = [
os.path.join(folder, f)
for f in os.listdir(folder)
if os.path.isfile(os.path.join(folder, f))]
for f in all_files:
if f.endswith('nii.gz'):
loaded_shape = image_loader.load_image_obj(f).get_data().shape
print(loaded_shape)
self.assertGreaterEqual(5, len(loaded_shape))
else:
with self.assertRaisesRegexp(ValueError, ''):
image_loader.load_image_obj(f)
def test_all_data(self):
folder = 'testing_data'
all_files = [
os.path.join(folder, f) for f in os.listdir(folder)
if os.path.isfile(os.path.join(folder, f))
]
for f in all_files:
if f.endswith('nii.gz'):
loaded_shape = image_loader.load_image_obj(f).get_data().shape
print(loaded_shape)
self.assertGreaterEqual(5, len(loaded_shape))
else:
with self.assertRaisesRegexp(ValueError, ''):
image_loader.load_image_obj(f)
def infer_ndims_from_file(file_path, loader=None):
# todo: loader specified by the user is not used for ndims infer.
image_header = load_image_obj(file_path, loader).header
try:
return int(image_header['dim'][0])
except TypeError:
pass
try:
return int(len(image_header.get_data_shape()))
except (TypeError, AttributeError):
pass
tf.logging.fatal('unsupported file header in: {}'.format(file_path))
raise IOError('could not get ndims from file header, please '
'consider convert image files to NifTI format.')
def infer_ndims_from_file(file_path, loader=None):
# todo: loader specified by the user is not used for ndims infer.
image_header = load_image_obj(file_path, loader).header
try:
return int(image_header['dim'][0])
except TypeError:
pass
try:
return int(len(image_header.get_data_shape()))
except (TypeError, AttributeError):
pass
tf.logging.fatal('unsupported file header in: {}'.format(file_path))
raise IOError('could not get ndims from file header, please '
'consider convert image files to NifTI format.')
def dtype(self):
"""
data type property of the input images.
:return: a tuple of input image data types
``len(self.dtype) == len(self.file_path)``
"""
if not self._dtype:
try:
self._dtype = tuple(
load_image_obj(_file, _loader).header.get_data_dtype()
for _file, _loader in zip(self.file_path, self.loader))
except (IOError, TypeError, AttributeError):
tf.logging.warning('could not decide image data type')
self._dtype = (np.dtype(np.float32),) * len(self.file_path)
return self._dtype
def dtype(self):
"""
data type property of the input images.
:return: a tuple of input image data types
``len(self.dtype) == len(self.file_path)``
"""
if not self._dtype:
try:
self._dtype = tuple(
load_image_obj(_file, _loader).header.get_data_dtype()
for _file, _loader in zip(self.file_path, self.loader))
except (IOError, TypeError, AttributeError):
tf.logging.warning('could not decide image data type')
self._dtype = (np.dtype(np.float32),) * len(self.file_path)
return self._dtype
def _load_header(self):
"""
read original header for pixdim and affine info
:return:
"""
self._original_pixdim = []
self._original_affine = []
for file_i, loader_i in zip(self.file_path, self.loader):
image_obj = load_image_obj(file_i, loader_i)
try:
misc.correct_image_if_necessary(image_obj)
self._original_pixdim.append(image_obj.header.get_zooms()[:3])
self._original_affine.append(image_obj.affine)
except (TypeError, IndexError, AttributeError):
tf.logging.fatal('could not read header from %s', file_i)
raise ValueError
def _load_header(self):
"""
read original header for pixdim and affine info
:return:
"""
self._original_pixdim = []
self._original_affine = []
for file_i, loader_i in zip(self.file_path, self.loader):
image_obj = load_image_obj(file_i, loader_i)
try:
misc.correct_image_if_necessary(image_obj)
self._original_pixdim.append(image_obj.header.get_zooms()[:3])
self._original_affine.append(image_obj.affine)
except (TypeError, IndexError, AttributeError):
tf.logging.fatal('could not read header from %s', file_i)
raise ValueError
def infer_ndims_from_file(file_path, loader=None):
"""
Get spatial rank of the image file.
:param file_path:
:param loader:
:return:
"""
image_header = load_image_obj(file_path, loader).header
try:
return int(image_header['dim'][0])
except (TypeError, KeyError, IndexError):
pass
try:
return int(len(image_header.get_data_shape()))
except (TypeError, AttributeError):
pass
tf.logging.fatal('unsupported file header in: {}'.format(file_path))
raise IOError('could not get ndims from file header, please '
'consider convert image files to NifTI format.')
def shape(self):
"""
This function read image shape info from the headers
The lengths in the fifth dim of multiple images are summed
as a multi-mod representation.
The fourth dim corresponding to different time sequences
is ignored.
:return: a tuple of integers as image shape
"""
if self._original_shape is None:
try:
self._original_shape = tuple(
load_image_obj(_file, _loader).header['dim'][1:6]
for _file, _loader in zip(self.file_path, self.loader))
except (IOError, KeyError, AttributeError, IndexError):
tf.logging.fatal('unknown image shape from header %s',
self.file_path)
raise ValueError
try:
non_modality_shapes = \
set([tuple(shape[:4].tolist())
for shape in self._original_shape])
assert len(non_modality_shapes) == 1
except (TypeError, IndexError, AssertionError):
tf.logging.fatal(
"could not combining multimodal images: "
"shapes not consistent %s -- %s", self.file_path,
self._original_shape)
raise ValueError
n_modalities = \
np.sum([int(shape[4]) for shape in self._original_shape])
self._original_shape = non_modality_shapes.pop() + (n_modalities, )
self._original_shape = \
tuple([shape_i if shape_i > 0 else 1
for shape_i in self._original_shape])
return self._original_shape
def shape(self):
"""
This function read image shape info from the headers
The lengths in the fifth dim of multiple images are summed
as a multi-mod representation.
The fourth dim corresponding to different time sequences
is ignored.
:return: a tuple of integers as image shape
"""
if self._original_shape is None:
try:
self._original_shape = tuple(
load_image_obj(_file, _loader).header['dim'][1:6]
for _file, _loader in zip(self.file_path, self.loader))
except (IOError, KeyError, AttributeError, IndexError):
tf.logging.fatal(
'unknown image shape from header %s', self.file_path)
raise ValueError
try:
non_modality_shapes = \
set([tuple(shape[:4].tolist())
for shape in self._original_shape])
assert len(non_modality_shapes) == 1
except (TypeError, IndexError, AssertionError):
tf.logging.fatal("could not combining multimodal images: "
"shapes not consistent %s -- %s",
self.file_path, self._original_shape)
raise ValueError
n_modalities = \
np.sum([int(shape[4]) for shape in self._original_shape])
self._original_shape = non_modality_shapes.pop() + (n_modalities,)
self._original_shape = \
tuple([shape_i if shape_i > 0 else 1
for shape_i in self._original_shape])
return self._original_shape
def load_2d_image(self, loader=None):
data = image_loader.load_image_obj(CASE_LOGO_2D,
loader=loader).get_data()
self.assertAllClose(data.shape, (400, 677, 1, 1, 4))
def test_nibabel_3d(self):
data = image_loader.load_image_obj(CASE_NIBABEL_3D).get_data()
self.assertAllClose(data.shape, (256, 168, 256))
def _load_single_file(cls, file_path, loader, dtype=np.float32):
image_obj = load_image_obj(file_path, loader)
image_data = image_obj.get_data() # new API: get_fdata()
image_data = misc.expand_to_5d(image_data)
return image_data.astype(dtype)
def load_2d_image(self, loader=None):
data = image_loader.load_image_obj(CASE_LOGO_2D, loader=loader).get_data()
self.assertAllClose(data.shape, (400, 677, 1, 1, 4))
def test_nibabel_3d(self):
data = image_loader.load_image_obj(CASE_NIBABEL_3D).get_data()
self.assertAllClose(data.shape, (256, 168, 256))
def _load_single_file(cls, file_path, loader, dtype=np.float32):
image_obj = load_image_obj(file_path, loader)
image_data = image_obj.get_data() # new API: get_fdata()
image_data = misc.expand_to_5d(image_data)
return image_data.astype(dtype)
