Beispiel #1
0
def test_peak_direction_tracker():
    """This tests that the Peaks And Metrics Direction Getter plays nice
    LocalTracking and produces reasonable streamlines in a simple example.
    """
    sphere = HemiSphere.from_sphere(unit_octahedron)

    # A simple image with three possible configurations, a vertical tract,
    # a horizontal tract and a crossing
    peaks_values_lookup = np.array([[0., 0.],
                                    [1., 0.],
                                    [1., 0.],
                                    [0.5, 0.5]])
    peaks_indices_lookup = np.array([[-1, -1],
                                     [0, -1],
                                     [1, -1],
                                     [0,  1]])
    # PeaksAndMetricsDirectionGetter needs at 3 slices on each axis to work
    simple_image = np.zeros([5, 6, 3], dtype=int)
    simple_image[:, :, 1] = np.array([[0, 1, 0, 1, 0, 0],
                                      [0, 1, 0, 1, 0, 0],
                                      [0, 3, 2, 2, 2, 0],
                                      [0, 1, 0, 0, 0, 0],
                                      [0, 1, 0, 0, 0, 0],
                                      ])

    dg = PeaksAndMetrics()
    dg.sphere = sphere
    dg.peak_values = peaks_values_lookup[simple_image]
    dg.peak_indices = peaks_indices_lookup[simple_image]
    dg.ang_thr = 90

    mask = (simple_image >= 0).astype(float)
    tc = ThresholdTissueClassifier(mask, 0.5)
    seeds = [np.array([1., 1., 1.]),
             np.array([2., 4., 1.]),
             np.array([1., 3., 1.]),
             np.array([4., 4., 1.])]

    streamlines = LocalTracking(dg, tc, seeds, np.eye(4), 1.)

    expected = [np.array([[0., 1., 1.],
                          [1., 1., 1.],
                          [2., 1., 1.],
                          [3., 1., 1.],
                          [4., 1., 1.]]),
                np.array([[2., 0., 1.],
                          [2., 1., 1.],
                          [2., 2., 1.],
                          [2., 3., 1.],
                          [2., 4., 1.],
                          [2., 5., 1.]]),
                np.array([[0., 3., 1.],
                          [1., 3., 1.],
                          [2., 3., 1.],
                          [2., 4., 1.],
                          [2., 5., 1.]]),
                np.array([[4., 4., 1.]])]

    for i, sl in enumerate(streamlines):
        npt.assert_(np.allclose(sl, expected[i]))
def test_peak_direction_tracker():
    """This tests that the Peaks And Metrics Direction Getter plays nice
    LocalTracking and produces reasonable streamlines in a simple example.
    """
    sphere = HemiSphere.from_sphere(unit_octahedron)

    # A simple image with three possible configurations, a vertical tract,
    # a horizontal tract and a crossing
    peaks_values_lookup = np.array([[0., 0.],
                                    [1., 0.],
                                    [1., 0.],
                                    [0.5, 0.5]])
    peaks_indices_lookup = np.array([[-1, -1],
                                     [0, -1],
                                     [1, -1],
                                     [0,  1]])
    # PeaksAndMetricsDirectionGetter needs at 3 slices on each axis to work
    simple_image = np.zeros([5, 6, 3], dtype=int)
    simple_image[:, :, 1] = np.array([[0, 1, 0, 1, 0, 0],
                                      [0, 1, 0, 1, 0, 0],
                                      [0, 3, 2, 2, 2, 0],
                                      [0, 1, 0, 0, 0, 0],
                                      [0, 1, 0, 0, 0, 0],
                                      ])

    dg = PeaksAndMetrics()
    dg.sphere = sphere
    dg.peak_values = peaks_values_lookup[simple_image]
    dg.peak_indices = peaks_indices_lookup[simple_image]
    dg.ang_thr = 90

    mask = (simple_image >= 0).astype(float)
    tc = ThresholdTissueClassifier(mask, 0.5)
    seeds = [np.array([1., 1., 1.]),
             np.array([2., 4., 1.]),
             np.array([1., 3., 1.]),
             np.array([4., 4., 1.])]

    streamlines = LocalTracking(dg, tc, seeds, np.eye(4), 1.)

    expected = [np.array([[0., 1., 1.],
                          [1., 1., 1.],
                          [2., 1., 1.],
                          [3., 1., 1.],
                          [4., 1., 1.]]),
                np.array([[2., 0., 1.],
                          [2., 1., 1.],
                          [2., 2., 1.],
                          [2., 3., 1.],
                          [2., 4., 1.],
                          [2., 5., 1.]]),
                np.array([[0., 3., 1.],
                          [1., 3., 1.],
                          [2., 3., 1.],
                          [2., 4., 1.],
                          [2., 5., 1.]]),
                np.array([[4., 4., 1.]])]

    for i, sl in enumerate(streamlines):
        npt.assert_(np.allclose(sl, expected[i]))