def test_compose_transformations():
A = np.eye(4)
A[0, -1] = 10
B = np.eye(4)
B[0, -1] = -20
C = np.eye(4)
C[0, -1] = 10
CBA = compose_transformations(A, B, C)
assert_array_equal(CBA, np.eye(4))
assert_raises(ValueError, compose_transformations, A)
def optimize(self, static, moving, mat=None):
""" Find the minimum of the provided metric.
Parameters
----------
static : streamlines
Reference or fixed set of streamlines.
moving : streamlines
Moving set of streamlines.
mat : array
Transformation (4, 4) matrix to start the registration. ``mat``
is applied to moving. Default value None which means that initial
transformation will be generated by shifting the centers of moving
and static sets of streamlines to the origin.
Returns
-------
map : StreamlineRegistrationMap
"""
msg = 'need to have the same number of points. Use '
msg += 'set_number_of_points from dipy.tracking.streamline'
if not np.all(np.array(list(map(len, static))) == static[0].shape[0]):
raise ValueError('Static streamlines ' + msg)
if not np.all(np.array(list(map(len, moving))) == moving[0].shape[0]):
raise ValueError('Moving streamlines ' + msg)
if not np.all(np.array(list(map(len, moving))) == static[0].shape[0]):
raise ValueError('Static and moving streamlines ' + msg)
if mat is None:
static_centered, static_shift = center_streamlines(static)
moving_centered, moving_shift = center_streamlines(moving)
static_mat = compose_matrix44([static_shift[0], static_shift[1],
static_shift[2], 0, 0, 0])
moving_mat = compose_matrix44([-moving_shift[0], -moving_shift[1],
-moving_shift[2], 0, 0, 0])
else:
static_centered = static
moving_centered = transform_streamlines(moving, mat)
static_mat = np.eye(4)
moving_mat = mat
self.metric.setup(static_centered, moving_centered)
distance = self.metric.distance
if self.method == 'Powell':
if self.options is None:
self.options = {'xtol': 1e-6, 'ftol': 1e-6, 'maxiter': 1e6}
opt = Optimizer(distance, self.x0.tolist(),
method=self.method, options=self.options,
evolution=self.evolution)
if self.method == 'L-BFGS-B':
if self.options is None:
self.options = {'maxcor': 10, 'ftol': 1e-7,
'gtol': 1e-5, 'eps': 1e-8,
'maxiter': 100}
opt = Optimizer(distance, self.x0.tolist(),
method=self.method,
bounds=self.bounds, options=self.options,
evolution=self.evolution)
if self.verbose:
opt.print_summary()
opt_mat = compose_matrix44(opt.xopt)
mat = compose_transformations(moving_mat, opt_mat, static_mat)
mat_history = []
if opt.evolution is not None:
for vecs in opt.evolution:
mat_history.append(
compose_transformations(moving_mat,
compose_matrix44(vecs),
static_mat))
srm = StreamlineRegistrationMap(mat, opt.xopt, opt.fopt,
mat_history, opt.nfev, opt.nit)
del opt
return srm
def optimize(self, static, moving, mat=None):
""" Find the minimum of the provided metric.
Parameters
----------
static : streamlines
Reference or fixed set of streamlines.
moving : streamlines
Moving set of streamlines.
mat : array
Transformation (4, 4) matrix to start the registration. ``mat``
is applied to moving. Default value None which means that initial
transformation will be generated by shifting the centers of moving
and static sets of streamlines to the origin.
Returns
-------
map : StreamlineRegistrationMap
"""
msg = 'need to have the same number of points. Use '
msg += 'set_number_of_points from dipy.tracking.streamline'
if not np.all(np.array(list(map(len, static))) == static[0].shape[0]):
raise ValueError('Static streamlines ' + msg)
if not np.all(np.array(list(map(len, moving))) == moving[0].shape[0]):
raise ValueError('Moving streamlines ' + msg)
if not np.all(np.array(list(map(len, moving))) == static[0].shape[0]):
raise ValueError('Static and moving streamlines ' + msg)
if mat is None:
static_centered, static_shift = center_streamlines(static)
moving_centered, moving_shift = center_streamlines(moving)
static_mat = compose_matrix44(
[static_shift[0], static_shift[1], static_shift[2], 0, 0, 0])
moving_mat = compose_matrix44([
-moving_shift[0], -moving_shift[1], -moving_shift[2], 0, 0, 0
])
else:
static_centered = static
moving_centered = transform_streamlines(moving, mat)
static_mat = np.eye(4)
moving_mat = mat
self.metric.setup(static_centered, moving_centered)
distance = self.metric.distance
if self.method == 'Powell':
if self.options is None:
self.options = {'xtol': 1e-6, 'ftol': 1e-6, 'maxiter': 1e6}
opt = Optimizer(distance,
self.x0.tolist(),
method=self.method,
options=self.options,
evolution=self.evolution)
if self.method == 'L-BFGS-B':
if self.options is None:
self.options = {
'maxcor': 10,
'ftol': 1e-7,
'gtol': 1e-5,
'eps': 1e-8,
'maxiter': 100
}
opt = Optimizer(distance,
self.x0.tolist(),
method=self.method,
bounds=self.bounds,
options=self.options,
evolution=self.evolution)
if self.verbose:
opt.print_summary()
opt_mat = compose_matrix44(opt.xopt)
mat = compose_transformations(moving_mat, opt_mat, static_mat)
mat_history = []
if opt.evolution is not None:
for vecs in opt.evolution:
mat_history.append(
compose_transformations(moving_mat, compose_matrix44(vecs),
static_mat))
srm = StreamlineRegistrationMap(mat, opt.xopt, opt.fopt, mat_history,
opt.nfev, opt.nit)
del opt
return srm