Python StreamlineLinearRegistration.optimize方法代碼示例

# 需要導入模塊: from dipy.align.streamlinear import StreamlineLinearRegistration [as 別名]
# 或者: from dipy.align.streamlinear.StreamlineLinearRegistration import optimize [as 別名]
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')

# 需要導入模塊: from dipy.align.streamlinear import StreamlineLinearRegistration [as 別名]
# 或者: from dipy.align.streamlinear.StreamlineLinearRegistration import optimize [as 別名]
def test_cascade_of_optimizations():

    cingulum_bundles = two_cingulum_bundles()

    cb1 = cingulum_bundles[0]
    cb1 = set_number_of_points(cb1, 20)

    test_x0 = np.array([10, 4, 3, 0, 20, 10, 1.5, 1.5, 1.5, 0., 0.2, 0])

    cb2 = transform_streamlines(cingulum_bundles[0],
                                compose_matrix44(test_x0))
    cb2 = set_number_of_points(cb2, 20)

    print('first rigid')
    slr = StreamlineLinearRegistration(x0=6)
    slm = slr.optimize(cb1, cb2)

    print('then similarity')
    slr2 = StreamlineLinearRegistration(x0=7)
    slm2 = slr2.optimize(cb1, cb2, slm.matrix)

    print('then affine')
    slr3 = StreamlineLinearRegistration(x0=12, options={'maxiter': 50})
    slm3 = slr3.optimize(cb1, cb2, slm2.matrix)

    assert_(slm2.fopt < slm.fopt)
    assert_(slm3.fopt < slm2.fopt)

# 需要導入模塊: from dipy.align.streamlinear import StreamlineLinearRegistration [as 別名]
# 或者: from dipy.align.streamlinear.StreamlineLinearRegistration import optimize [as 別名]
    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

# 需要導入模塊: from dipy.align.streamlinear import StreamlineLinearRegistration [as 別名]
# 或者: from dipy.align.streamlinear.StreamlineLinearRegistration import optimize [as 別名]
def test_affine_real_bundles():

    bundle_initial = fornix_streamlines()
    bundle_initial, shift = center_streamlines(bundle_initial)
    bundle = bundle_initial[:20]
    xgold = [0, 4, 2, 0, 10, 10, 1.2, 1.1, 1., 0., 0.2, 0.]
    mat = compose_matrix44(xgold)
    bundle2 = transform_streamlines(bundle_initial[:20], mat)

    x0 = np.array([0, 0, 0, 0, 0, 0, 1., 1., 1., 0, 0, 0])

    x = 25

    bounds = [(-x, x), (-x, x), (-x, x),
              (-x, x), (-x, x), (-x, x),
              (0.1, 1.5), (0.1, 1.5), (0.1, 1.5),
              (-1, 1), (-1, 1), (-1, 1)]

    options = {'maxcor': 10, 'ftol': 1e-7, 'gtol': 1e-5, 'eps': 1e-8}

    metric = BundleMinDistanceMatrixMetric()

    slr = StreamlineLinearRegistration(metric=metric,
                                       x0=x0,
                                       method='L-BFGS-B',
                                       bounds=bounds,
                                       verbose=True,
                                       options=options)
    slm = slr.optimize(bundle, bundle2)

    new_bundle2 = slm.transform(bundle2)

    slr2 = StreamlineLinearRegistration(metric=metric,
                                        x0=x0,
                                        method='Powell',
                                        bounds=None,
                                        verbose=True,
                                        options=None)

    slm2 = slr2.optimize(bundle, new_bundle2)

    new_bundle2 = slm2.transform(new_bundle2)

    evaluate_convergence(bundle, new_bundle2)

# 需要導入模塊: from dipy.align.streamlinear import StreamlineLinearRegistration [as 別名]
# 或者: from dipy.align.streamlinear.StreamlineLinearRegistration import optimize [as 別名]
def test_stream_rigid():

    static = fornix_streamlines()[:20]
    moving = fornix_streamlines()[20:40]
    static_center, shift = center_streamlines(static)

    mat = compose_matrix44([0, 0, 0, 0, 40, 0])
    moving = transform_streamlines(moving, mat)

    srr = StreamlineLinearRegistration()
    sr_params = srr.optimize(static, moving)
    moved = transform_streamlines(moving, sr_params.matrix)

    srr = StreamlineLinearRegistration(verbose=True)
    srm = srr.optimize(static, moving)
    moved2 = transform_streamlines(moving, srm.matrix)
    moved3 = srm.transform(moving)

    assert_array_almost_equal(moved[0], moved2[0], decimal=3)
    assert_array_almost_equal(moved2[0], moved3[0], decimal=3)

# 需要導入模塊: from dipy.align.streamlinear import StreamlineLinearRegistration [as 別名]
# 或者: from dipy.align.streamlinear.StreamlineLinearRegistration import optimize [as 別名]
def test_evolution_of_previous_iterations():

    static = fornix_streamlines()[:20]
    moving = fornix_streamlines()[:20]

    moving = [m + np.array([10., 0., 0.]) for m in moving]

    slr = StreamlineLinearRegistration(evolution=True)

    from dipy.core.optimize import SCIPY_LESS_0_12

    if not SCIPY_LESS_0_12:

        slm = slr.optimize(static, moving)

        assert_equal(len(slm.matrix_history), slm.iterations)

# 需要導入模塊: from dipy.align.streamlinear import StreamlineLinearRegistration [as 別名]
# 或者: from dipy.align.streamlinear.StreamlineLinearRegistration import optimize [as 別名]
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)

# 需要導入模塊: from dipy.align.streamlinear import StreamlineLinearRegistration [as 別名]
# 或者: from dipy.align.streamlinear.StreamlineLinearRegistration import optimize [as 別名]
def test_rigid_partial_real_bundles():

    static = fornix_streamlines()[:20]
    moving = fornix_streamlines()[20:40]
    static_center, shift = center_streamlines(static)
    moving_center, shift2 = center_streamlines(moving)

    print(shift2)
    mat = compose_matrix(translate=np.array([0, 0, 0.]),
                         angles=np.deg2rad([40, 0, 0.]))
    moved = transform_streamlines(moving_center, mat)

    srr = StreamlineLinearRegistration()

    srm = srr.optimize(static_center, moved)
    print(srm.fopt)
    print(srm.iterations)
    print(srm.funcs)

    moving_back = srm.transform(moved)
    print(srm.matrix)

    static_center = set_number_of_points(static_center, 100)
    moving_center = set_number_of_points(moving_back, 100)

    vol = np.zeros((100, 100, 100))
    spts = np.concatenate(static_center, axis=0)
    spts = np.round(spts).astype(np.int) + np.array([50, 50, 50])

    mpts = np.concatenate(moving_center, axis=0)
    mpts = np.round(mpts).astype(np.int) + np.array([50, 50, 50])

    for index in spts:
        i, j, k = index
        vol[i, j, k] = 1

    vol2 = np.zeros((100, 100, 100))
    for index in mpts:
        i, j, k = index
        vol2[i, j, k] = 1

    overlap = np.sum(np.logical_and(vol, vol2)) / float(np.sum(vol2))

    assert_equal(overlap * 100 > 40, True)

# 需要導入模塊: from dipy.align.streamlinear import StreamlineLinearRegistration [as 別名]
# 或者: from dipy.align.streamlinear.StreamlineLinearRegistration import optimize [as 別名]
def streamline_registration(moving, static, n_points=100,
                            native_resampled=False):
    """
    Register two collections of streamlines ('bundles') to each other

    Parameters
    ----------
    moving, static : lists of 3 by n, or str
        The two bundles to be registered. Given either as lists of arrays with
        3D coordinates, or strings containing full paths to these files.

    n_points : int, optional
        How many points to resample to. Default: 100.

    native_resampled : bool, optional
        Whether to return the moving bundle in the original space, but
        resampled in the static space to n_points.

    Returns
    -------
    aligned : list
        Streamlines from the moving group, moved to be closely matched to
        the static group.

    matrix : array (4, 4)
        The affine transformation that takes us from 'moving' to 'static'
    """
    # Load the streamlines, if you were given a file-name
    if isinstance(moving, str):
        moving = sut.read_trk(moving)
    if isinstance(static, str):
        static = sut.read_trk(static)

    srr = StreamlineLinearRegistration()
    srm = srr.optimize(static=set_number_of_points(static, n_points),
                       moving=set_number_of_points(moving, n_points))

    aligned = srm.transform(moving)
    if native_resampled:
        aligned = set_number_of_points(aligned, n_points)
        aligned = move_streamlines(aligned, np.linalg.inv(srm.matrix))

    return aligned, srm.matrix

# 需要導入模塊: from dipy.align.streamlinear import StreamlineLinearRegistration [as 別名]
# 或者: from dipy.align.streamlinear.StreamlineLinearRegistration import optimize [as 別名]
def test_similarity_real_bundles():

    bundle_initial = fornix_streamlines()
    bundle_initial, shift = center_streamlines(bundle_initial)
    bundle = bundle_initial[:20]
    xgold = [0, 0, 10, 0, 0, 0, 1.5]
    mat = compose_matrix44(xgold)
    bundle2 = transform_streamlines(bundle_initial[:20], mat)

    metric = BundleMinDistanceMatrixMetric()
    x0 = np.array([0, 0, 0, 0, 0, 0, 1], 'f8')

    slr = StreamlineLinearRegistration(metric=metric,
                                       x0=x0,
                                       method='Powell',
                                       bounds=None,
                                       verbose=False)

    slm = slr.optimize(bundle, bundle2)
    new_bundle2 = slm.transform(bundle2)
    evaluate_convergence(bundle, new_bundle2)

# 需要導入模塊: from dipy.align.streamlinear import StreamlineLinearRegistration [as 別名]
# 或者: from dipy.align.streamlinear.StreamlineLinearRegistration import optimize [as 別名]
Here we will use 20 points. This step is not optional. Inputting streamlines
with different number of points will break the theoretical advantages of using
the SLR as explained in [Garyfallidis15]_.
"""

cb_subj1 = set_number_of_points(cb_subj1, 20)
cb_subj2 = set_number_of_points(cb_subj2, 20)

"""
Let's say now that we want to move the ``cb_subj2`` (moving) so that it can be
aligned with ``cb_subj1`` (static). Here is how this is done.
"""

srr = StreamlineLinearRegistration()

srm = srr.optimize(static=cb_subj1, moving=cb_subj2)

"""
After the optimization is finished we can apply the transformation to
``cb_subj2``.
"""

cb_subj2_aligned = srm.transform(cb_subj2)


def show_both_bundles(bundles, colors=None, show=False, fname=None):

    ren = fvtk.ren()
    ren.SetBackground(1.0, 1, 1)
    for (i, bundle) in enumerate(bundles):
        color = colors[i]

# 需要導入模塊: from dipy.align.streamlinear import StreamlineLinearRegistration [as 別名]
# 或者: from dipy.align.streamlinear.StreamlineLinearRegistration import optimize [as 別名]
ref_idx = data['ref_idx']
p_per_strm =20

ref_vec = set_number_of_points(data['streamlines'][ref_idx], p_per_strm)

srr = StreamlineLinearRegistration()

for i,strm in enumerate(data['streamlines']):
    print 'registering %d/%d' % (i,len(data['file'])-1)
    print '# streamlines = %d' %len(strm)
    if len(strm) == 0 or i==ref_idx:
        print 'skipping'
        continue
    mov_vec = set_number_of_points(strm, 20)
    srm = srr.optimize(static=ref_vec, moving=mov_vec)
    data['aligned_strms'].append(srm.transform(mov_vec))

from dipy.viz import fvtk
ren = fvtk.ren()
ren.SetBackground(1., 1, 1)

reflines = fvtk.streamtube(ref_vec, fvtk.colors.red, linewidth=0.2)
fvtk.add(ren, reflines)

for (i, bundle) in enumerate(data['aligned_strms']):
    lines = fvtk.streamtube(bundle, np.random.rand(3), linewidth=0.1)
    # lines.RotateX(-90)
    # lines.RotateZ(90)
    fvtk.add(ren, lines)

# 需要導入模塊: from dipy.align.streamlinear import StreamlineLinearRegistration [as 別名]
# 或者: from dipy.align.streamlinear.StreamlineLinearRegistration import optimize [as 別名]
              (0.5, 1.5), (0.5, 1.5), (0.5, 1.5),
              (-1, 1), (-1, 1), (-1, 1)]

if not affine:
    
    x0 = np.array([0, 0, 0, 0, 0, 0.])
    #default is BundleMinDistanceFast, rigid and L-BFGS-B
    metric = BundleMinDistance()
    method = 'Powell'#L-BFGS-B'
    bounds = None
    #bounds = [(-20, 20), (-20, 20), (-20, 20),
    #          (-30, 30), (-30, 30), (-30, 30)]

srr = StreamlineLinearRegistration(metric=metric, x0=x0, bounds=bounds)

srm = srr.optimize(static=t_tract_tmp, moving=s_tract_tmp)

"""
After the optimization is finished we can apply the learned transformation to
``s_tract``.
"""

s_tract_aligned = srm.transform(s_tract)

save_tracks_dpy(s_tract_aligned, out_file)
print 'Saved:  ' , out_file

                
def show_both_bundles(bundles, colors=None, show=False, fname=None):

    ren = fvtk.ren()