def test_retract(self):
expected = gtsam.Cal3Unified(100 + 2, 105 + 3, 0.0 + 4, 320 + 5, 240 + 6,
1e-3 + 7, 2.0*1e-3 + 8, 3.0*1e-3 + 9, 4.0*1e-3 + 10, 0.1 + 1)
K = gtsam.Cal3Unified(100, 105, 0.0, 320, 240,
1e-3, 2.0*1e-3, 3.0*1e-3, 4.0*1e-3, 0.1)
d = np.array([2, 3, 4, 5, 6, 7, 8, 9, 10, 1], order='F')
actual = K.retract(d)
self.gtsamAssertEquals(actual, expected)
np.testing.assert_allclose(d, K.localCoordinates(actual))
def test_jacobian(self):
"""Evaluate jacobian at optical axis"""
obj_point_on_axis = np.array([0, 0, 1])
img_point = np.array([0.0, 0.0])
pose = gtsam.Pose3()
camera = gtsam.Cal3Unified()
state = gtsam.Values()
camera_key, pose_key, landmark_key = K(0), P(0), L(0)
state.insert_cal3unified(camera_key, camera)
state.insert_point3(landmark_key, obj_point_on_axis)
state.insert_pose3(pose_key, pose)
g = gtsam.NonlinearFactorGraph()
noise_model = gtsam.noiseModel.Unit.Create(2)
factor = gtsam.GeneralSFMFactor2Cal3Unified(img_point, noise_model,
pose_key, landmark_key,
camera_key)
g.add(factor)
f = g.error(state)
gaussian_factor_graph = g.linearize(state)
H, z = gaussian_factor_graph.jacobian()
self.assertAlmostEqual(f, 0)
self.gtsamAssertEquals(z, np.zeros(2))
self.gtsamAssertEquals(H @ H.T, 4 * np.eye(2))
Dcal = np.zeros((2, 10), order='F')
Dp = np.zeros((2, 2), order='F')
camera.calibrate(img_point, Dcal, Dp)
self.gtsamAssertEquals(
Dcal,
np.array([[0., 0., 0., -1., 0., 0., 0., 0., 0., 0.],
[0., 0., 0., 0., -1., 0., 0., 0., 0., 0.]]))
self.gtsamAssertEquals(Dp, np.array([[1., -0.], [-0., 1.]]))
def test_sfm_factor2(self):
"""Evaluate residual with camera, pose and point as state variables"""
r = np.linalg.norm(self.obj_point)
graph = gtsam.NonlinearFactorGraph()
state = gtsam.Values()
measured = self.img_point
noise_model = gtsam.noiseModel.Isotropic.Sigma(2, 1)
camera_key, pose_key, landmark_key = K(0), P(0), L(0)
k = self.stereographic
state.insert_cal3unified(camera_key, k)
state.insert_pose3(pose_key, gtsam.Pose3())
state.insert_point3(landmark_key, self.obj_point)
factor = gtsam.GeneralSFMFactor2Cal3Unified(measured, noise_model, pose_key, landmark_key, camera_key)
graph.add(factor)
score = graph.error(state)
self.assertAlmostEqual(score, 0)
def evaluate_jacobian(obj_point, img_point):
"""Evaluate jacobian at given object point"""
pose = gtsam.Pose3()
camera = gtsam.Cal3Fisheye()
state = gtsam.Values()
camera_key, pose_key, landmark_key = K(0), P(0), L(0)
state.insert_point3(landmark_key, obj_point)
state.insert_pose3(pose_key, pose)
g = gtsam.NonlinearFactorGraph()
noise_model = gtsam.noiseModel.Unit.Create(2)
factor = gtsam.GenericProjectionFactorCal3Fisheye(
img_point, noise_model, pose_key, landmark_key, camera)
g.add(factor)
f = g.error(state)
gaussian_factor_graph = g.linearize(state)
H, z = gaussian_factor_graph.jacobian()
return f, z, H
def test_Cal3Unified(self):
K = gtsam.Cal3Unified()
self.assertEqual(K.fx(), 1.)
self.assertEqual(K.fx(), 1.)
def test_Cal3Fisheye(self):
K = gtsam.Cal3Fisheye()
self.assertEqual(K.fx(), 1.)
self.assertEqual(K.fy(), 1.)