def test_projection_matrix():
mesh = flat_mesh(5, 7, 4)
img = z_const_img(5, 7, 13)
proj = surface._projection_matrix(
mesh, np.eye(4), img.shape, radius=2., n_points=10)
# proj matrix has shape (n_vertices, img_size)
assert_equal(proj.shape, (5 * 7, 5 * 7 * 13))
# proj.dot(img) should give the values of img at the vertices' locations
values = proj.dot(img.ravel()).reshape((5, 7))
assert_array_almost_equal(values, img[:, :, 0])
mesh = flat_mesh(5, 7)
proj = surface._projection_matrix(
mesh, np.eye(4), (5, 7, 1), radius=.1, n_points=10)
assert_array_almost_equal(proj.toarray(), np.eye(proj.shape[0]))
mask = np.ones(img.shape, dtype=int)
mask[0] = 0
proj = surface._projection_matrix(
mesh, np.eye(4), img.shape, radius=2., n_points=10, mask=mask)
proj = proj.toarray()
# first row of the mesh is masked
assert_array_almost_equal(proj.sum(axis=1)[:7], np.zeros(7))
assert_array_almost_equal(proj.sum(axis=1)[7:], np.ones(proj.shape[0] - 7))
# mask and img should have the same shape
assert_raises(ValueError, surface._projection_matrix,
mesh, np.eye(4), img.shape, mask=np.ones((3, 3, 2)))
def test_projection_matrix():
mesh = flat_mesh(5, 7, 4)
img = z_const_img(5, 7, 13)
proj = surface._projection_matrix(
mesh, np.eye(4), img.shape, radius=2., n_points=10)
# proj matrix has shape (n_vertices, img_size)
assert proj.shape == (5 * 7, 5 * 7 * 13)
# proj.dot(img) should give the values of img at the vertices' locations
values = proj.dot(img.ravel()).reshape((5, 7))
assert_array_almost_equal(values, img[:, :, 0])
mesh = flat_mesh(5, 7)
proj = surface._projection_matrix(
mesh, np.eye(4), (5, 7, 1), radius=.1, n_points=10)
assert_array_almost_equal(proj.toarray(), np.eye(proj.shape[0]))
mask = np.ones(img.shape, dtype=int)
mask[0] = 0
proj = surface._projection_matrix(
mesh, np.eye(4), img.shape, radius=2., n_points=10, mask=mask)
proj = proj.toarray()
# first row of the mesh is masked
assert_array_almost_equal(proj.sum(axis=1)[:7], np.zeros(7))
assert_array_almost_equal(proj.sum(axis=1)[7:], np.ones(proj.shape[0] - 7))
# mask and img should have the same shape
pytest.raises(ValueError, surface._projection_matrix,
mesh, np.eye(4), img.shape, mask=np.ones((3, 3, 2)))
def test_sampling_between_surfaces(projection):
projector = {"nearest": surface._nearest_voxel_sampling,
"linear": surface._interpolation_sampling}[projection]
mesh = flat_mesh(13, 7, 3.)
inner_mesh = flat_mesh(13, 7, 1)
img = z_const_img(5, 7, 13).T
projection = projector(
[img], mesh, np.eye(4),
kind="auto", n_points=100, inner_mesh=inner_mesh)
assert_array_almost_equal(
projection.ravel(), img[:, :, 1:4].mean(axis=-1).ravel())
def test_sample_locations():
# check positions of samples on toy example, with an affine != identity
# flat horizontal mesh
mesh = flat_mesh(5, 7)
affine = np.diagflat([10, 20, 30, 1])
inv_affine = np.linalg.inv(affine)
# transform vertices to world space
vertices = np.asarray(
resampling.coord_transform(*mesh[0].T, affine=affine)).T
# compute by hand the true offsets in voxel space
# (transformed by affine^-1)
ball_offsets = surface._load_uniform_ball_cloud(10)
ball_offsets = np.asarray(
resampling.coord_transform(*ball_offsets.T, affine=inv_affine)).T
line_offsets = np.zeros((10, 3))
line_offsets[:, 2] = np.linspace(1, -1, 10)
line_offsets = np.asarray(
resampling.coord_transform(*line_offsets.T, affine=inv_affine)).T
# check we get the same locations
for kind, offsets in [('line', line_offsets), ('ball', ball_offsets)]:
locations = surface._sample_locations(
[vertices, mesh[1]], affine, 1., kind=kind, n_points=10)
true_locations = np.asarray([vertex + offsets for vertex in mesh[0]])
assert_array_equal(locations.shape, true_locations.shape)
assert_array_almost_equal(true_locations, locations)
pytest.raises(ValueError, surface._sample_locations,
mesh, affine, 1., kind='bad_kind')
def test_vertex_outer_normals():
# compute normals for a flat horizontal mesh, they should all be (0, 0, 1)
mesh = flat_mesh(5, 7)
computed_normals = surface._vertex_outer_normals(mesh)
true_normals = np.zeros((len(mesh[0]), 3))
true_normals[:, 2] = 1
assert_array_almost_equal(computed_normals, true_normals)
def test_sample_locations():
# check positions of samples on toy example, with an affine != identity
# flat horizontal mesh
mesh = flat_mesh(5, 7)
affine = np.diagflat([10, 20, 30, 1])
inv_affine = np.linalg.inv(affine)
# transform vertices to world space
vertices = np.asarray(
resampling.coord_transform(*mesh[0].T, affine=affine)).T
# compute by hand the true offsets in voxel space
# (transformed by affine^-1)
ball_offsets = surface._load_uniform_ball_cloud(10)
ball_offsets = np.asarray(
resampling.coord_transform(*ball_offsets.T, affine=inv_affine)).T
line_offsets = np.zeros((10, 3))
line_offsets[:, 2] = np.linspace(-1, 1, 10)
line_offsets = np.asarray(
resampling.coord_transform(*line_offsets.T, affine=inv_affine)).T
# check we get the same locations
for kind, offsets in [('line', line_offsets), ('ball', ball_offsets)]:
locations = surface._sample_locations(
[vertices, mesh[1]], affine, 1., kind=kind, n_points=10)
true_locations = np.asarray([vertex + offsets for vertex in mesh[0]])
assert_array_equal(locations.shape, true_locations.shape)
assert_array_almost_equal(true_locations, locations)
assert_raises(ValueError, surface._sample_locations,
mesh, affine, 1., kind='bad_kind')
def test_vertex_outer_normals():
# compute normals for a flat horizontal mesh, they should all be (0, 0, 1)
mesh = flat_mesh(5, 7)
computed_normals = surface._vertex_outer_normals(mesh)
true_normals = np.zeros((len(mesh[0]), 3))
true_normals[:, 2] = 1
assert_array_almost_equal(computed_normals, true_normals)
def test_sampling():
mesh = flat_mesh(5, 7, 4)
img = z_const_img(5, 7, 13)
mask = np.ones(img.shape, dtype=int)
mask[0] = 0
projectors = [
surface._nearest_voxel_sampling, surface._interpolation_sampling
]
for kind in ('line', 'ball'):
for projector in projectors:
projection = projector([img],
mesh,
np.eye(4),
kind=kind,
radius=0.)
assert_array_almost_equal(projection.ravel(), img[:, :, 0].ravel())
projection = projector([img],
mesh,
np.eye(4),
kind=kind,
radius=0.,
mask=mask)
assert_array_almost_equal(projection.ravel()[7:], img[1:, :,
0].ravel())
assert np.isnan(projection.ravel()[:7]).all()
def test_sampling(kind, use_inner_mesh, projection):
mesh = flat_mesh(5, 7, 4)
img = z_const_img(5, 7, 13)
mask = np.ones(img.shape, dtype=int)
mask[0] = 0
projector = {
"nearest": surface._nearest_voxel_sampling,
"linear": surface._interpolation_sampling
}[projection]
inner_mesh = mesh if use_inner_mesh else None
projection = projector([img],
mesh,
np.eye(4),
kind=kind,
radius=0.,
inner_mesh=inner_mesh)
assert_array_almost_equal(projection.ravel(), img[:, :, 0].ravel())
projection = projector([img],
mesh,
np.eye(4),
kind=kind,
radius=0.,
mask=mask,
inner_mesh=inner_mesh)
assert_array_almost_equal(projection.ravel()[7:], img[1:, :, 0].ravel())
assert np.isnan(projection.ravel()[:7]).all()
def test_sample_locations_depth(depth, n_points):
mesh = flat_mesh(5, 7)
radius = 8.
locations = surface._sample_locations(
mesh, np.eye(4), radius, n_points=n_points, depth=depth)
offsets = np.asarray([[0., 0., - z * radius] for z in depth])
expected = np.asarray([vertex + offsets for vertex in mesh[0]])
assert np.allclose(locations, expected)
def test_sample_locations_between_surfaces(depth, n_points):
inner = flat_mesh(5, 7)
outer = inner[0] + [0., 0., 1.], inner[1]
locations = surface._sample_locations_between_surfaces(
outer, inner, np.eye(4), n_points=n_points, depth=depth)
if depth is None:
# can be simplified when we drop support for np 1.15
# (broadcasting linspace)
expected = np.asarray(
[np.linspace(b, a, n_points)
for (a, b) in zip(inner[0].ravel(), outer[0].ravel())])
expected = np.rollaxis(
expected.reshape((*outer[0].shape, n_points)), 2, 1)
else:
offsets = ([[0., 0., - z] for z in depth])
expected = np.asarray([vertex + offsets for vertex in outer[0]])
assert np.allclose(locations, expected)
def test_sampling():
mesh = flat_mesh(5, 7, 4)
img = z_const_img(5, 7, 13)
mask = np.ones(img.shape, dtype=int)
mask[0] = 0
projectors = [surface._nearest_voxel_sampling,
surface._interpolation_sampling]
for kind in ('line', 'ball'):
for projector in projectors:
projection = projector([img], mesh, np.eye(4),
kind=kind, radius=0.)
assert_array_almost_equal(projection.ravel(), img[:, :, 0].ravel())
projection = projector([img], mesh, np.eye(4),
kind=kind, radius=0., mask=mask)
assert_array_almost_equal(projection.ravel()[7:],
img[1:, :, 0].ravel())
assert_true(np.isnan(projection.ravel()[:7]).all())
