def test_intrinsics_from_matrix_to_intrinsics_random(self):
"""Tests that converting intrinsics to a matrix and back is consistent."""
tensor_size = np.random.randint(3)
tensor_shape = np.random.randint(1, 10, size=(tensor_size)).tolist()
random_focal = np.random.normal(size=tensor_shape + [2])
random_principal_point = np.random.normal(size=tensor_shape + [2])
matrix = perspective.matrix_from_intrinsics(random_focal,
random_principal_point)
focal, principal_point = perspective.intrinsics_from_matrix(matrix)
self.assertAllClose(random_focal, focal, rtol=1e-3)
self.assertAllClose(random_principal_point, principal_point, rtol=1e-3)
def test_matrix_from_intrinsics_to_matrix_random(self):
"""Tests that converting a matrix to intrinsics and back is consistent."""
tensor_size = np.random.randint(3)
tensor_shape = np.random.randint(1, 10, size=(tensor_size)).tolist()
random_focal = np.random.normal(size=tensor_shape + [2])
random_principal_point = np.random.normal(size=tensor_shape + [2])
fx = random_focal[..., 0]
fy = random_focal[..., 1]
cx = random_principal_point[..., 0]
cy = random_principal_point[..., 1]
zero = np.zeros_like(fx)
one = np.ones_like(fx)
random_matrix = np.stack((fx, zero, cx, zero, fy, cy, zero, zero, one),
axis=-1).reshape(tensor_shape + [3, 3])
focal, principal_point = perspective.intrinsics_from_matrix(random_matrix)
matrix = perspective.matrix_from_intrinsics(focal, principal_point)
self.assertAllClose(random_matrix, matrix, rtol=1e-3)