import numpy as np
from nibabel.wrapstruct import LabeledWrapStruct as LWS
[docs]def shape_zoom_affine(shape, zooms, x_flip=True, y_flip=False):
"""
Get affine implied by given shape and zooms.
We get the translations from the center of the image (implied by
`shape`).
Parameters
----------
shape : (N,) array-like
shape of image data. ``N`` is the number of dimensions
zooms : (N,) array-like
zooms (voxel sizes) of the image
x_flip : {True, False}
whether to flip the X row of the affine. Corresponds to
radiological storage on disk.
y_flip : {False, True}
whether to flip the Y row of the affine. Corresponds to
DICOM storage on disk when x_flip is also True.
Returns
-------
aff : (4,4) array
affine giving correspondance of voxel coordinates to mm
coordinates, taking the center of the image as origin
Examples
--------
>>> shape = (3, 5, 7)
>>> zooms = (3, 2, 1)
>>> shape_zoom_affine((3, 5, 7), (3, 2, 1))
array([[-3., 0., 0., 3.],
[ 0., 2., 0., -4.],
[ 0., 0., 1., -3.],
[ 0., 0., 0., 1.]])
>>> shape_zoom_affine((3, 5, 7), (3, 2, 1), False)
array([[ 3., 0., 0., -3.],
[ 0., 2., 0., -4.],
[ 0., 0., 1., -3.],
[ 0., 0., 0., 1.]])
"""
shape = np.asarray(shape)
zooms = np.array(zooms) # copy because of flip below
ndims = len(shape)
if ndims != len(zooms):
raise ValueError("Should be same length of zooms and shape")
if ndims >= 3:
shape = shape[:3]
zooms = zooms[:3]
else:
full_shape = np.ones((3,))
full_zooms = np.ones((3,))
full_shape[:ndims] = shape[:]
full_zooms[:ndims] = zooms[:]
shape = full_shape
zooms = full_zooms
if x_flip:
zooms[0] *= -1
if y_flip:
zooms[1] *= -1
# Get translations from center of image
origin = (shape - 1) / 2.0
aff = np.eye(4)
aff[:3, :3] = np.diag(zooms)
aff[:3, -1] = -origin * zooms
return aff
[docs]class LabeledWrapStruct(LWS):
def __setitem__(self, item, value):
self._structarr[item] = np.asanyarray(value)