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Use of common setUpClass method
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@roderick-koehle
roderick-koehle authored Jul 9, 2021
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121 changes: 60 additions & 61 deletions python/gtsam/tests/test_Cal3Unified.py
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import gtsam
from gtsam.utils.test_case import GtsamTestCase
from gtsam.symbol_shorthand import K, L, P


class TestCal3Unified(GtsamTestCase):

@classmethod
def setUpClass(cls):
"""
Stereographic fisheye projection
An equidistant fisheye projection with focal length f is defined
as the relation r/f = 2*tan(theta/2), with r being the radius in the
image plane and theta the incident angle of the object point.
"""
x, y, z = 1.0, 0.0, 1.0
r = np.linalg.norm([x, y, z])
u, v = 2*x/(z+r), 0.0
#u, v = 2*np.tan(np.arctan2(x, z)/2), 0.0
cls.obj_point = np.array([x, y, z])
cls.img_point = np.array([u, v])

fx, fy, s, u0, v0 = 2, 2, 0, 0, 0
k1, k2, p1, p2 = 0, 0, 0, 0
xi = 1
cls.stereographic = gtsam.Cal3Unified(fx, fy, s, u0, v0, k1, k2, p1, p2, xi)

def test_Cal3Unified(self):
K = gtsam.Cal3Unified()
self.assertEqual(K.fx(), 1.)
self.assertEqual(K.fx(), 1.)

def test_distortion(self):
"Stereographic fisheye model of focal length f, defined as r/f = 2*tan(theta/2)"
fx, fy, s, u0, v0 = 2, 2, 0, 0, 0
k1, k2, p1, p2 = 0, 0, 0, 0
xi = 1
stereographic = gtsam.Cal3Unified(fx, fy, s, u0, v0, k1, k2, p1, p2, xi)
x, y, z = 1, 0, 1
u, v = stereographic.uncalibrate([x, y])
"""Stereographic fisheye model of focal length f, defined as r/f = 2*tan(theta/2)"""
x, y, z = self.obj_point
r = np.linalg.norm([x, y, z])
# Note: 2*tan(atan2(x, z)/2) = 2/(1+sqrt(x^2+z^2))
self.assertAlmostEqual(2*np.tan(np.arctan2(x, z)/2), 2/(1+r))
self.assertAlmostEqual(u, 2/(1+r))
x2, y2 = stereographic.calibrate([u, v])
self.assertAlmostEqual(x2, x)
# Note: 2*tan(atan2(x, z)/2) = 2*x/(z+sqrt(x^2+z^2))
self.assertAlmostEqual(2*np.tan(np.arctan2(x, z)/2), 2*x/(z+r))
perspective_pt = self.obj_point[0:2]/self.obj_point[2]
distorted_pt = self.stereographic.uncalibrate(perspective_pt)
rectified_pt = self.stereographic.calibrate(distorted_pt)
self.gtsamAssertEquals(distorted_pt, self.img_point)
self.gtsamAssertEquals(rectified_pt, perspective_pt)

def test_pinhole(self):
"Spatial stereographic camera projection"
x, y, z = 1.0, 0.0, 1.0
r = np.linalg.norm([x, y, z])
u, v = 2/(1+r), 0.0
objPoint = np.array([x, y, z])
imgPoint = np.array([u, v])
fx, fy, s, u0, v0 = 2, 2, 0, 0, 0
k1, k2, p1, p2 = 0, 0, 0, 0
xi = 1
stereographic = gtsam.Cal3Unified(fx, fy, s, u0, v0, k1, k2, p1, p2, xi)
"""Spatial stereographic camera projection"""
x, y, z = self.obj_point
u, v = self.img_point
r = np.linalg.norm(self.obj_point)
pose = gtsam.Pose3()
camera = gtsam.PinholeCameraCal3Unified(pose, stereographic)
pt1 = camera.Project(objPoint)
camera = gtsam.PinholeCameraCal3Unified(pose, self.stereographic)
pt1 = camera.Project(self.obj_point)
self.gtsamAssertEquals(pt1, np.array([x/z, y/z]))
pt2 = camera.project(objPoint)
self.gtsamAssertEquals(pt2, np.array([u, v]))
obj1 = camera.backproject([u, v], z)
self.gtsamAssertEquals(obj1, np.array([x, y, z]))
r1 = camera.range(np.array([x, y, z]))
pt2 = camera.project(self.obj_point)
self.gtsamAssertEquals(pt2, self.img_point)
obj1 = camera.backproject(self.img_point, z)
self.gtsamAssertEquals(obj1, self.obj_point)
r1 = camera.range(self.obj_point)
self.assertEqual(r1, r)

def test_generic_factor(self):
"Evaluate residual using pose and point as state variables"
fx, fy, s, u0, v0 = 2, 2, 0, 0, 0
k1, k2, p1, p2 = 0, 0, 0, 0
xi = 1
objPoint = np.array([1, 0, 1])
r = np.linalg.norm(objPoint)
imgPoint = np.array([2/(1+r), 0])
"""Evaluate residual using pose and point as state variables"""
graph = gtsam.NonlinearFactorGraph()
state = gtsam.Values()
measured = imgPoint
noiseModel = gtsam.noiseModel.Isotropic.Sigma(2, 1)
poseKey = gtsam.symbol_shorthand.P(0)
pointKey = gtsam.symbol_shorthand.L(0)
k = gtsam.Cal3Unified(fx, fy, s, u0, v0, k1, k2, p1, p2, xi)
state.insert_pose3(poseKey, gtsam.Pose3())
state.insert_point3(pointKey, gtsam.Point3(objPoint))
factor = gtsam.GenericProjectionFactorCal3Unified(measured, noiseModel, poseKey, pointKey, k)
measured = self.img_point
noise_model = gtsam.noiseModel.Isotropic.Sigma(2, 1)
pose_key, point_key = P(0), L(0)
k = self.stereographic
state.insert_pose3(pose_key, gtsam.Pose3())
state.insert_point3(point_key, self.obj_point)
factor = gtsam.GenericProjectionFactorCal3Unified(measured, noise_model, pose_key, point_key, k)
graph.add(factor)
score = graph.error(state)
self.assertAlmostEqual(score, 0)

def test_sfm_factor2(self):
"Evaluate residual with camera, pose and point as state variables"
fx, fy, s, u0, v0 = 2, 2, 0, 0, 0
k1, k2, p1, p2 = 0, 0, 0, 0
xi = 1
objPoint = np.array([1, 0, 1])
r = np.linalg.norm(objPoint)
imgPoint = np.array([2/(1+r), 0])
"""Evaluate residual with camera, pose and point as state variables"""
r = np.linalg.norm(self.obj_point)
graph = gtsam.NonlinearFactorGraph()
state = gtsam.Values()
measured = imgPoint
noiseModel = gtsam.noiseModel.Isotropic.Sigma(2, 1)
cameraKey = gtsam.symbol_shorthand.K(0)
poseKey = gtsam.symbol_shorthand.P(0)
landmarkKey = gtsam.symbol_shorthand.L(0)
k = gtsam.Cal3Unified(fx, fy, s, u0, v0, k1, k2, p1, p2, xi)
state.insert_cal3unified(cameraKey, k)
state.insert_pose3(poseKey, gtsam.Pose3())
state.insert_point3(landmarkKey, gtsam.Point3(objPoint))
factor = gtsam.GeneralSFMFactor2Cal3Unified(measured, noiseModel, poseKey, landmarkKey, cameraKey)
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)
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