def test_identity_position_field_different_output_resolution(self):
subject = np.array([
[0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 1, 1, 1, 1, 0, 0],
[0, 0, 1, 1, 1, 1, 0, 0],
[0, 0, 1, 1, 1, 1, 0, 0],
[0, 0, 1, 1, 1, 1, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0],
])
subject_resolution = 1
output_resolution = 2
output_shape = None
position_field_resolution = subject_resolution
position_field = _lddmm_utilities._compute_coords(
subject.shape, position_field_resolution)
deformed_subject = _transform_image(
subject=subject,
subject_resolution=subject_resolution,
output_resolution=output_resolution,
output_shape=output_shape,
position_field=position_field,
position_field_resolution=position_field_resolution,
)
expected_output = np.array([
[0, 0, 0, 0],
[0, 1, 1, 0],
[0, 1, 1, 0],
[0, 0, 0, 0],
])
assert np.allclose(deformed_subject, expected_output)
def test_identity_position_fields(self, deform_to):
subject = np.array([
[0, 0, 0, 0],
[0, 1, 1, 0],
[0, 1, 1, 0],
[0, 0, 0, 0],
])
subject_resolution = 1
output_resolution = None
output_shape = None
template_shape = (3, 4)
template_resolution = 1
target_shape = (2, 5)
target_resolution = 1
extrapolation_fill_value = np.quantile(subject, 10**-subject.ndim)
affine_phi = _lddmm_utilities._compute_coords(template_shape,
template_resolution)
phi_inv_affine_inv = _lddmm_utilities._compute_coords(
target_shape, target_resolution)
expected_output = _transform_image(
subject,
subject_resolution,
output_resolution,
output_shape,
position_field=affine_phi
if deform_to == "template" else phi_inv_affine_inv,
position_field_resolution=template_resolution
if deform_to == "template" else target_resolution,
extrapolation_fill_value=extrapolation_fill_value,
)
deformed_subject = lddmm_transform_image(
subject=subject,
subject_resolution=subject_resolution,
output_resolution=output_resolution,
deform_to=deform_to,
extrapolation_fill_value=extrapolation_fill_value,
affine_phi=affine_phi,
phi_inv_affine_inv=phi_inv_affine_inv,
template_resolution=template_resolution,
target_resolution=target_resolution,
)
assert np.array_equal(deformed_subject, expected_output)
def test_constant_position_field_trivial_extrapolation(self):
# Note: applying a leftward shift to the position_field is done by subtracting 1 from the appropriate dimension.
# The corresponding effect on the deformed_subject is a shift to the right.
subject = np.array([
[0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 1, 1, 1, 1, 0, 0],
[0, 0, 1, 1, 1, 1, 0, 0],
[0, 0, 1, 1, 1, 1, 0, 0],
[0, 0, 1, 1, 1, 1, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0],
])
subject_resolution = 1
output_resolution = 1
output_shape = None
position_field_resolution = subject_resolution
position_field = _lddmm_utilities._compute_coords(
subject.shape, position_field_resolution) + [
0,
-1,
] # Shift to the left by 1.
deformed_subject = _transform_image(
subject=subject,
subject_resolution=subject_resolution,
output_resolution=output_resolution,
output_shape=output_shape,
position_field=position_field,
position_field_resolution=position_field_resolution,
)
expected_output = np.array([
[0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 1, 1, 1, 1, 0],
[0, 0, 0, 1, 1, 1, 1, 0],
[0, 0, 0, 1, 1, 1, 1, 0],
[0, 0, 0, 1, 1, 1, 1, 0],
[0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0],
])
assert np.allclose(deformed_subject, expected_output)
def test_rotational_position_field(self):
# Note: applying an affine indicating a clockwise-rotation to a position_field produces a position _ield rotated counter-clockwise.
# The corresponding effect on the deformed_subject is a counter-clockwise rotation.
subject = np.array([
[0, 1, 0, 0],
[0, 1, 0, 0],
[0, 1, 0, 0],
[0, 1, 1, 1],
])
subject_resolution = 1
output_resolution = 1
output_shape = None
position_field_resolution = subject_resolution
# Indicates a 90 degree rotation to the right.
affine = np.array([
[0, 1, 0],
[-1, 0, 0],
[0, 0, 1],
])
position_field = _lddmm_utilities._multiply_coords_by_affine(
affine,
_lddmm_utilities._compute_coords(subject.shape,
position_field_resolution),
)
deformed_subject = _transform_image(
subject=subject,
subject_resolution=subject_resolution,
output_resolution=output_resolution,
output_shape=output_shape,
position_field=position_field,
position_field_resolution=position_field_resolution,
)
expected_output = np.array([
[0, 0, 0, 1],
[0, 0, 0, 1],
[1, 1, 1, 1],
[0, 0, 0, 0],
])
assert np.allclose(deformed_subject, expected_output)
def test_identity_position_field_equal_output_resolution(self):
subject = np.arange(3 * 4).reshape(3, 4)
subject_resolution = 1
output_resolution = 1
output_shape = None
position_field_resolution = subject_resolution
position_field = _lddmm_utilities._compute_coords(
subject.shape, position_field_resolution)
deformed_subject = _transform_image(
subject=subject,
subject_resolution=subject_resolution,
output_resolution=output_resolution,
output_shape=output_shape,
position_field=position_field,
position_field_resolution=position_field_resolution,
)
expected_output = subject
assert np.allclose(deformed_subject, expected_output)
def test_constant_position_field_linear_extrapolation(self):
# Idiosyncratic extrapolation behavior is demonstrated with a nonzero gradient at the extrapolated edge.
subject = np.array([
[0, 0, 0, 0],
[0, 1, 1, 0],
[0, 1, 1, 0],
[0, 0, 0, 0],
])
subject_resolution = 1
output_resolution = 1
output_shape = None
position_field_resolution = subject_resolution
position_field = _lddmm_utilities._compute_coords(
subject.shape, position_field_resolution) + [
0,
-1,
] # Shift to the left by 1.
deformed_subject = _transform_image(
subject=subject,
subject_resolution=subject_resolution,
output_resolution=output_resolution,
output_shape=output_shape,
position_field=position_field,
position_field_resolution=position_field_resolution,
)
expected_output = np.array([
[0, 0, 0, 0],
[-1, 0, 1, 1],
[-1, 0, 1, 1],
[0, 0, 0, 0],
])
assert np.allclose(deformed_subject, expected_output)