def test_face_normals_random(self):
"""Tests the computation of mesh face normals in each axis."""
tensor_vertex_size = np.random.randint(1, 3)
tensor_out_shape = np.random.randint(1, 5, size=tensor_vertex_size)
tensor_out_shape = tensor_out_shape.tolist()
tensor_vertex_shape = list(tensor_out_shape)
tensor_vertex_shape[-1] *= 3
tensor_index_shape = tensor_out_shape[-1]
for i in range(3):
vertices = np.random.random(size=tensor_vertex_shape + [3])
indices = np.arange(tensor_vertex_shape[-1])
np.random.shuffle(indices)
indices = np.reshape(indices,
newshape=[1] * (tensor_vertex_size - 1) \
+ [tensor_index_shape, 3])
indices = np.tile(indices, tensor_vertex_shape[:-1] + [1, 1])
vertices[..., i] = 0.
expected = np.zeros(shape=tensor_out_shape + [3],
dtype=vertices.dtype)
expected[..., i] = 1.
faces = normals.gather_faces(vertices, indices)
self.assertAllClose(tf.abs(normals.face_normals(faces)),
expected,
rtol=1e-3)
def test_face_normals_jacobian_random(self):
"""Test the Jacobian of the face normals function."""
tensor_vertex_size = np.random.randint(1, 3)
tensor_out_shape = np.random.randint(1, 5, size=tensor_vertex_size)
tensor_out_shape = tensor_out_shape.tolist()
tensor_vertex_shape = list(tensor_out_shape)
tensor_vertex_shape[-1] *= 3
tensor_index_shape = tensor_out_shape[-1]
vertex_init = np.random.random(size=tensor_vertex_shape + [3])
index_init = np.arange(tensor_vertex_shape[-1])
np.random.shuffle(index_init)
index_init = np.reshape(index_init, newshape=[1] * (tensor_vertex_size - 1) \
+ [tensor_index_shape, 3])
index_init = np.tile(index_init, tensor_vertex_shape[:-1] + [1, 1])
vertex_tensor = tf.convert_to_tensor(value=vertex_init)
index_tensor = tf.convert_to_tensor(value=index_init)
face_tensor = normals.gather_faces(vertex_tensor, index_tensor)
y = normals.face_normals(face_tensor)
self.assert_jacobian_is_correct(vertex_tensor,
vertex_init,
y,
atol=1e-4,
delta=1e-9)
def face_normals(vertex_tensor): face_tensor = normals.gather_faces(vertex_tensor, index_tensor) return normals.face_normals(face_tensor)