def bundle_min_distance_asymmetric_fast(t, static, moving, block_size):
""" MDF-based pairwise distance optimization function (MIN)
We minimize the distance between moving streamlines as they align
with the static streamlines.
Parameters
-----------
t : array
1D array. t is a vector of of affine transformation parameters with
size at least 6.
If size is 6, t is interpreted as translation + rotation.
If size is 7, t is interpreted as translation + rotation +
isotropic scaling.
If size is 12, t is interpreted as translation + rotation +
scaling + shearing.
static : array
N*M x 3 array. All the points of the static streamlines. With order of
streamlines intact. Where N is the number of streamlines and M
is the number of points per streamline.
moving : array
K*M x 3 array. All the points of the moving streamlines. With order of
streamlines intact. Where K is the number of streamlines and M
is the number of points per streamline.
block_size : int
Number of points per streamline. All streamlines in static and moving
should have the same number of points M.
Returns
-------
cost: float
"""
aff = compose_matrix44(t)
moving = np.dot(aff[:3, :3], moving.T).T + aff[:3, 3]
moving = np.ascontiguousarray(moving, dtype=np.float64)
rows = static.shape[0] / block_size
cols = moving.shape[0] / block_size
return _bundle_minimum_distance_asymmetric(static, moving,
rows,
cols,
block_size)
def bundle_min_distance_asymmetric_fast(t, static, moving, block_size):
""" MDF-based pairwise distance optimization function (MIN)
We minimize the distance between moving streamlines as they align
with the static streamlines.
Parameters
-----------
t : array
1D array. t is a vector of of affine transformation parameters with
size at least 6.
If size is 6, t is interpreted as translation + rotation.
If size is 7, t is interpreted as translation + rotation +
isotropic scaling.
If size is 12, t is interpreted as translation + rotation +
scaling + shearing.
static : array
N*M x 3 array. All the points of the static streamlines. With order of
streamlines intact. Where N is the number of streamlines and M
is the number of points per streamline.
moving : array
K*M x 3 array. All the points of the moving streamlines. With order of
streamlines intact. Where K is the number of streamlines and M
is the number of points per streamline.
block_size : int
Number of points per streamline. All streamlines in static and moving
should have the same number of points M.
Returns
-------
cost: float
"""
aff = compose_matrix44(t)
moving = np.dot(aff[:3, :3], moving.T).T + aff[:3, 3]
moving = np.ascontiguousarray(moving, dtype=np.float64)
rows = static.shape[0] / block_size
cols = moving.shape[0] / block_size
return _bundle_minimum_distance_asymmetric(static, moving,
rows,
cols,
block_size)