def image(data):
"""
Creates a NiBabel image for the supplied data. The image object will be of a generic
type and only made concrete during the saving process (nibabel.loadsave.save() takes
care of this). No special meta-data can be associated with the image.
@param data: a numpy array of arbitrary type
@return: a NiBabel image for data of generic type SpatialImage
"""
image = SpatialImage(data, None)
image.update_header()
return image
def mask_image(image: SpatialImage, mask: np.ndarray, data_type: type = None
) -> np.ndarray:
"""Mask image after optionally casting its type.
Parameters
----------
image
Image to mask. Can include time as the last dimension.
mask
Mask to apply. Must have the same shape as the image data.
data_type
Type to cast image to.
Returns
-------
np.ndarray
Masked image.
Raises
------
ValueError
Image data and masks have different shapes.
"""
image_data = image.get_data()
if image_data.shape[:3] != mask.shape:
raise ValueError("Image data and mask have different shapes.")
if data_type is not None:
cast_data = image_data.astype(data_type)
else:
cast_data = image_data
return cast_data[mask]
def image_new(data, filename):
"""
Creates a NiBabel image for the supplied data. The function intends to directly
create the appropriate image type, depending on the image header.
@param data: a numpy array of arbitrary type
@param filename the intended filename with the file ending telling the image type
@return: a NiBabel image for data of any nibabel image type
"""
# extract suffix and return apropriate image
suffix = filename.split('.')[-1].lower()
if not suffix in __suffix_to_type:
suffix = '.'.join(map(lambda x: x.lower(), filename.split('.')[-2:]))
if not suffix in __suffix_to_type:
image = SpatialImage(data, None)
image.update_header()
return image
image = __suffix_to_type[suffix](data, None)
image.update_header()
return image
def images(spatial_image: SpatialImage) -> Iterable[SpatialImage]:
images = [spatial_image]
image_data = spatial_image.get_data().copy()
image_data[1, 1, 1, 0] = 2
images.append(Nifti1Pair(image_data, np.eye(4)))
return images
def test_resample_equiv(from_shape, from_affine, to_shape, to_affine, order,
seed):
"""Test resampling equivalences."""
rng = np.random.RandomState(seed)
from_data = rng.randn(*from_shape)
is_rand = False
if isinstance(to_affine, str):
assert to_affine == 'rand'
to_affine = _rand_affine(rng)
is_rand = True
if isinstance(from_affine, str):
assert from_affine == 'rand'
from_affine = _rand_affine(rng)
is_rand = True
to_affine = np.array(to_affine, float)
assert to_affine.shape == (4, 4)
from_affine = np.array(from_affine, float)
assert from_affine.shape == (4, 4)
#
# 1. nibabel.processing.resample_from_to
#
# for a 1mm iso / 256 -> 5mm / 51 one sample takes ~486 ms
from nibabel.processing import resample_from_to
from nibabel.spatialimages import SpatialImage
start = np.linalg.norm(from_data)
got_nibabel = resample_from_to(SpatialImage(from_data, from_affine),
(to_shape, to_affine),
order=order).get_fdata()
end = np.linalg.norm(got_nibabel)
assert end > 0.05 * start # not too much power lost
#
# 2. dipy.align.imaffine
#
# ~366 ms
import dipy.align.imaffine
interp = 'linear' if order == 1 else 'nearest'
got_dipy = dipy.align.imaffine.AffineMap(
None, to_shape, to_affine, from_shape,
from_affine).transform(from_data,
interpolation=interp,
resample_only=True)
# XXX possibly some error in dipy or nibabel (/SciPy), or some boundary
# condition?
nib_different = ((is_rand and order == 1)
or (from_affine[0, 0] == 2.
and not np.allclose(from_affine, to_affine)))
nib_different = nib_different and not (is_rand and from_affine[0, 0] == 2
and order == 0)
if nib_different:
assert not np.allclose(got_dipy, got_nibabel), 'nibabel fixed'
else:
assert_allclose(got_dipy, got_nibabel, err_msg='dipy<->nibabel')
#
# 3. mne.source_space._grid_interp
#
# ~339 ms
trans = np.linalg.inv(from_affine) @ to_affine # to -> from
interp = _grid_interp(from_shape, to_shape, trans, order=order)
got_mne = np.asarray(interp @ from_data.ravel(order='F')).reshape(
to_shape, order='F')
if order == 1:
assert_allclose(got_mne, got_dipy, err_msg='MNE<->dipy')
else:
perc = 100 * np.isclose(got_mne, got_dipy).mean()
assert 83 < perc <= 100
def tract_generate_mask(optional_flags, tractography, image, file_output):
image = nibabel.load(image)
mask = tract_operations.tract_mask(image, tractography)
return SpatialImage(mask, image.get_affine())
def images(spatial_image: SpatialImage) -> Iterable[SpatialImage]:
images = [spatial_image]
image_data = spatial_image.get_data().copy()
image_data[1, 1, 1] = 2
images.append(Nifti1Pair(image_data, np.eye(4)))
return images
