def test_correct(self, affine, scale, expected):
output = zoom_affine(affine, scale, diagonal=False)
ornt_affine = nib.orientations.ornt2axcodes(
nib.orientations.io_orientation(output))
ornt_output = nib.orientations.ornt2axcodes(
nib.orientations.io_orientation(affine))
np.testing.assert_array_equal(ornt_affine, ornt_output)
np.testing.assert_allclose(output, expected, rtol=1e-6, atol=1e-6)
def __call__(self, data_array, affine=None, interp_order=3):
"""
Args:
data_array (ndarray): in shape (num_channels, H[, W, ...]).
affine (matrix): (N+1)x(N+1) original affine matrix for spatially ND `data_array`. Defaults to identity.
interp_order (int): The order of the spline interpolation, default is 3.
The order has to be in the range 0-5.
https://docs.scipy.org/doc/scipy/reference/generated/scipy.ndimage.zoom.html
Returns:
data_array (resampled into `self.pixdim`), original pixdim, current pixdim.
"""
sr = data_array.ndim - 1
if sr <= 0:
raise ValueError(
"the array should have at least one spatial dimension.")
if affine is None:
# default to identity
affine = np.eye(sr + 1, dtype=np.float64)
affine_ = np.eye(sr + 1, dtype=np.float64)
else:
affine_ = to_affine_nd(sr, affine)
out_d = self.pixdim[:sr]
if out_d.size < sr:
out_d = np.append(out_d, [1.0] * (out_d.size - sr))
if np.any(out_d <= 0):
raise ValueError(f"pixdim must be positive, got {out_d}")
# compute output affine, shape and offset
new_affine = zoom_affine(affine_, out_d, diagonal=self.diagonal)
output_shape, offset = compute_shape_offset(data_array.shape[1:],
affine_, new_affine)
new_affine[:sr, -1] = offset[:sr]
transform = np.linalg.inv(affine_) @ new_affine
# adapt to the actual rank
transform_ = to_affine_nd(sr, transform)
# resample
dtype = data_array.dtype if self.dtype is None else self.dtype
output_data = []
for data in data_array:
data_ = scipy.ndimage.affine_transform(
data.astype(dtype),
matrix=transform_,
output_shape=output_shape,
order=interp_order,
mode=self.mode,
cval=self.cval,
)
output_data.append(data_)
output_data = np.stack(output_data)
new_affine = to_affine_nd(affine, new_affine)
return output_data, affine, new_affine
def test_diagonal(self, affine, scale, expected):
output = zoom_affine(affine, scale, diagonal=True)
np.testing.assert_allclose(output, expected, rtol=1e-6, atol=1e-6)