示例#1
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def test_reorder_img_mirror():
    affine = np.array([[-1.1, -0., 0., 0.], [-0., -1.2, 0., 0.],
                       [-0., -0., 1.3, 0.], [0., 0., 0., 1.]])
    img = Nifti1Image(np.zeros((4, 6, 8)), affine=affine)
    reordered = reorder_img(img)
    np.testing.assert_allclose(
        get_bounds(reordered.shape, reordered.affine),
        get_bounds(img.shape, img.affine),
    )
示例#2
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def test_reorder_img_mirror():
    affine = np.array([
        [-1.1, -0., 0., 0.],
        [-0., -1.2, 0., 0.],
        [-0., -0., 1.3, 0.],
        [0.,  0., 0., 1.]
    ])
    img = Nifti1Image(np.zeros((4, 6, 8)), affine=affine)
    reordered = reorder_img(img)
    np.testing.assert_allclose(
        get_bounds(reordered.shape, reordered.affine),
        get_bounds(img.shape, img.affine),
    )
示例#3
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def test_reorder_img():
    # We need to test on a square array, as rotation does not change
    # shape, whereas reordering does.
    shape = (5, 5, 5, 2, 2)
    rng = np.random.RandomState(42)
    data = rng.rand(*shape)
    affine = np.eye(4)
    affine[:3, -1] = 0.5 * np.array(shape[:3])
    ref_img = Nifti1Image(data, affine)
    # Test with purely positive matrices and compare to a rotation
    for theta, phi in np.random.randint(4, size=(5, 2)):
        rot = rotation(theta * np.pi / 2, phi * np.pi / 2)
        rot[np.abs(rot) < 0.001] = 0
        rot[rot > 0.9] = 1
        rot[rot < -0.9] = 1
        b = 0.5 * np.array(shape[:3])
        new_affine = from_matrix_vector(rot, b)
        rot_img = resample_img(ref_img, target_affine=new_affine)
        np.testing.assert_array_equal(rot_img.get_affine(), new_affine)
        np.testing.assert_array_equal(rot_img.get_data().shape, shape)
        reordered_img = reorder_img(rot_img)
        np.testing.assert_array_equal(reordered_img.get_affine()[:3, :3],
                                      np.eye(3))
        np.testing.assert_almost_equal(reordered_img.get_data(),
                                       data)

    # Create a non-diagonal affine, and check that we raise a sensible
    # exception
    affine[1, 0] = 0.1
    ref_img = Nifti1Image(data, affine)
    testing.assert_raises_regex(ValueError, 'Cannot reorder the axes',
                                reorder_img, ref_img)

    # Test that no exception is raised when resample='continuous'
    reorder_img(ref_img, resample='continuous')

    # Test that resample args gets passed to resample_img
    interpolation = 'nearest'
    reordered_img = reorder_img(ref_img, resample=interpolation)
    resampled_img = resample_img(ref_img,
                                 target_affine=reordered_img.get_affine(),
                                 interpolation=interpolation)
    np.testing.assert_array_equal(reordered_img.get_data(),
                                  resampled_img.get_data())

    # Make sure invalid resample argument is included in the error message
    interpolation = 'an_invalid_interpolation'
    pattern = "interpolation must be either.+{0}".format(interpolation)
    testing.assert_raises_regex(ValueError, pattern,
                                reorder_img, ref_img,
                                resample=interpolation)

    # Test flipping an axis
    data = rng.rand(*shape)
    for i in (0, 1, 2):
        # Make a diagonal affine with a negative axis, and check that
        # can be reordered, also vary the shape
        shape = (i + 1, i + 2, 3 - i)
        affine = np.eye(4)
        affine[i, i] *= -1
        img = Nifti1Image(data, affine)
        orig_img = copy.copy(img)
        #x, y, z = img.get_world_coords()
        #sample = img.values_in_world(x, y, z)
        img2 = reorder_img(img)
        # Check that img has not been changed
        np.testing.assert_array_equal(img.get_affine(),
                                      orig_img.get_affine())
        np.testing.assert_array_equal(img.get_data(),
                                      orig_img.get_data())
        # Test that the affine is indeed diagonal:
        np.testing.assert_array_equal(img2.get_affine()[:3, :3],
                                      np.diag(np.diag(
                                              img2.get_affine()[:3, :3])))
        assert_true(np.all(np.diag(img2.get_affine()) >= 0))
示例#4
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def test_reorder_img():
    # We need to test on a square array, as rotation does not change
    # shape, whereas reordering does.
    shape = (5, 5, 5, 2, 2)
    rng = np.random.RandomState(42)
    data = rng.rand(*shape)
    affine = np.eye(4)
    affine[:3, -1] = 0.5 * np.array(shape[:3])
    ref_img = Nifti1Image(data, affine)
    # Test with purely positive matrices and compare to a rotation
    for theta, phi in np.random.randint(4, size=(5, 2)):
        rot = rotation(theta * np.pi / 2, phi * np.pi / 2)
        rot[np.abs(rot) < 0.001] = 0
        rot[rot > 0.9] = 1
        rot[rot < -0.9] = 1
        b = 0.5 * np.array(shape[:3])
        new_affine = from_matrix_vector(rot, b)
        rot_img = resample_img(ref_img, target_affine=new_affine)
        np.testing.assert_array_equal(rot_img.affine, new_affine)
        np.testing.assert_array_equal(rot_img.get_data().shape, shape)
        reordered_img = reorder_img(rot_img)
        np.testing.assert_array_equal(reordered_img.affine[:3, :3], np.eye(3))
        np.testing.assert_almost_equal(reordered_img.get_data(), data)

    # Create a non-diagonal affine, and check that we raise a sensible
    # exception
    affine[1, 0] = 0.1
    ref_img = Nifti1Image(data, affine)
    testing.assert_raises_regex(ValueError, 'Cannot reorder the axes',
                                reorder_img, ref_img)

    # Test that no exception is raised when resample='continuous'
    reorder_img(ref_img, resample='continuous')

    # Test that resample args gets passed to resample_img
    interpolation = 'nearest'
    reordered_img = reorder_img(ref_img, resample=interpolation)
    resampled_img = resample_img(ref_img,
                                 target_affine=reordered_img.affine,
                                 interpolation=interpolation)
    np.testing.assert_array_equal(reordered_img.get_data(),
                                  resampled_img.get_data())

    # Make sure invalid resample argument is included in the error message
    interpolation = 'an_invalid_interpolation'
    pattern = "interpolation must be either.+{0}".format(interpolation)
    testing.assert_raises_regex(ValueError,
                                pattern,
                                reorder_img,
                                ref_img,
                                resample=interpolation)

    # Test flipping an axis
    data = rng.rand(*shape)
    for i in (0, 1, 2):
        # Make a diagonal affine with a negative axis, and check that
        # can be reordered, also vary the shape
        shape = (i + 1, i + 2, 3 - i)
        affine = np.eye(4)
        affine[i, i] *= -1
        img = Nifti1Image(data, affine)
        orig_img = copy.copy(img)
        #x, y, z = img.get_world_coords()
        #sample = img.values_in_world(x, y, z)
        img2 = reorder_img(img)
        # Check that img has not been changed
        np.testing.assert_array_equal(img.affine, orig_img.affine)
        np.testing.assert_array_equal(img.get_data(), orig_img.get_data())
        # Test that the affine is indeed diagonal:
        np.testing.assert_array_equal(img2.affine[:3, :3],
                                      np.diag(np.diag(img2.affine[:3, :3])))
        assert_true(np.all(np.diag(img2.affine) >= 0))
示例#5
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def test_mni152template_is_reordered():
    """See issue #2550"""
    reordered_mni = reorder_img(load_mni152_template())
    assert np.allclose(get_data(reordered_mni), get_data(MNI152TEMPLATE))
    assert np.allclose(reordered_mni.affine, MNI152TEMPLATE.affine)
    assert np.allclose(reordered_mni.shape, MNI152TEMPLATE.shape)