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),
)
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),
)
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))
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))
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)
