def test_rigid_real_bundles():
bundle_initial = fornix_streamlines()[:20]
bundle, shift = center_streamlines(bundle_initial)
mat = compose_matrix44([0, 0, 20, 45., 0, 0])
bundle2 = transform_streamlines(bundle, mat)
bundle_sum_distance = BundleSumDistanceMatrixMetric()
srr = StreamlineLinearRegistration(bundle_sum_distance,
x0=np.zeros(6),
method='Powell')
new_bundle2 = srr.optimize(bundle, bundle2).transform(bundle2)
evaluate_convergence(bundle, new_bundle2)
bundle_min_distance = BundleMinDistanceMatrixMetric()
srr = StreamlineLinearRegistration(bundle_min_distance,
x0=np.zeros(6),
method='Powell')
new_bundle2 = srr.optimize(bundle, bundle2).transform(bundle2)
evaluate_convergence(bundle, new_bundle2)
assert_raises(ValueError, StreamlineLinearRegistration, method='Whatever')
def _register_neighb_to_model(self, model_bundle, neighb_streamlines,
metric=None, x0=None, bounds=None,
select_model=400, select_target=600,
method='L-BFGS-B',
nb_pts=20, num_threads=None):
if self.verbose:
print('# Local SLR of neighb_streamlines to model')
t = time()
if metric is None or metric == 'symmetric':
metric = BundleMinDistanceMetric(num_threads=num_threads)
if metric == 'asymmetric':
metric = BundleMinDistanceAsymmetricMetric()
if metric == 'diagonal':
metric = BundleSumDistanceMatrixMetric()
if x0 is None:
x0 = 'similarity'
if bounds is None:
bounds = [(-30, 30), (-30, 30), (-30, 30),
(-45, 45), (-45, 45), (-45, 45), (0.8, 1.2)]
# TODO this can be speeded up by using directly the centroids
static = select_random_set_of_streamlines(model_bundle,
select_model, rng=self.rng)
moving = select_random_set_of_streamlines(neighb_streamlines,
select_target, rng=self.rng)
static = set_number_of_points(static, nb_pts)
moving = set_number_of_points(moving, nb_pts)
slr = StreamlineLinearRegistration(metric=metric, x0=x0,
bounds=bounds,
method=method)
slm = slr.optimize(static, moving)
transf_streamlines = neighb_streamlines.copy()
transf_streamlines._data = apply_affine(
slm.matrix, transf_streamlines._data)
transf_matrix = slm.matrix
slr_bmd = slm.fopt
slr_iterations = slm.iterations
if self.verbose:
print(' Square-root of BMD is %.3f' % (np.sqrt(slr_bmd),))
if slr_iterations is not None:
print(' Number of iterations %d' % (slr_iterations,))
print(' Matrix size {}'.format(slm.matrix.shape))
original = np.get_printoptions()
np.set_printoptions(3, suppress=True)
print(transf_matrix)
print(slm.xopt)
np.set_printoptions(**original)
print(' Duration %0.3f sec. \n' % (time() - t,))
return transf_streamlines, slr_bmd
def test_rigid_parallel_lines():
bundle_initial = simulated_bundle()
bundle, shift = center_streamlines(bundle_initial)
mat = compose_matrix44([20, 0, 10, 0, 40, 0])
bundle2 = transform_streamlines(bundle, mat)
bundle_sum_distance = BundleSumDistanceMatrixMetric()
options = {'maxcor': 100, 'ftol': 1e-9, 'gtol': 1e-16, 'eps': 1e-3}
srr = StreamlineLinearRegistration(metric=bundle_sum_distance,
x0=np.zeros(6),
method='L-BFGS-B',
bounds=None,
options=options)
new_bundle2 = srr.optimize(bundle, bundle2).transform(bundle2)
evaluate_convergence(bundle, new_bundle2)