def __init__(self, image, control_points, sigma, grid_coords=False, affine=None):
"""
control_points: a Nx3 array of world coordinates
if grid_coords is True, both `control_points` and `sigma` are
interpreted in voxel coordinates.
"""
nparams = np.prod(control_points.shape)
self._generic_init(image, affine, nparams)
fromworld = inverse_affine(self._toworld)
if grid_coords:
self._control_points = apply_affine(self._toworld, control_points)
tmp = control_points
else:
self._control_points = np.asarray(control_points)
tmp = apply_affine(fromworld, control_points)
# TODO : make sure the control point indices fall within the
# subgrid and maybe raise a warning if rounding is too severe
tmp = np.round(tmp).astype('int')
self._idx_control_points = tuple([tmp[:,:,:,i] for i in range(tmp.shape[3])])
self._sigma = sigma*np.ones(3)
self._grid_sigma = np.abs(np.diagonal(fromworld)[0:-1]*sigma)
self._norma = np.prod(np.sqrt(2*np.pi)*self._grid_sigma)
def test_apply_affine():
XYZ = (100*(np.random.rand(10,11,12,3)-.5)).astype('int')
T = np.eye(4)
T[0:3,0:3] = np.random.rand(3,3)
T[0:3,3] = 100*(np.random.rand(3)-.5)
_XYZ = apply_affine(inverse_affine(T), apply_affine(T, XYZ))
assert_almost_equal(_XYZ, XYZ)