def getT_MC_and_Rt_errors(T_WM, pos_world, Rmat_error_loop, tvec_error_loop):
pos_world_homo = np.array([pos_world[0], pos_world[1], 0, 1])
pos_marker = np.dot(T_WM, pos_world_homo)
# print "pos_marker\n", pos_marker
# Create an initial camera on the center of the world
cam = Camera()
f = 800
cam.set_K(fx=f, fy=f, cx=320, cy=240) # Camera Matrix
cam.img_width = 320 * 2
cam.img_height = 240 * 2
cam = cam.clone()
cam.set_t(pos_marker[0], pos_marker[1], pos_marker[2], 'world')
cam.set_R_mat(Rt_matrix_from_euler_t.R_matrix_from_euler_t(0.0, 0, 0))
# print "cam.R\n",cam.R
cam.look_at([0, 0, 0])
# print "cam.Rt after look at\n",cam.R
objectPoints = np.copy(new_objectPoints)
imagePoints = np.array(cam.project(objectPoints, False))
new_imagePoints = np.copy(imagePoints)
# -----------------------------------------------------------------
new_imagePoints_noisy = cam.addnoise_imagePoints(new_imagePoints,
mean=0,
sd=4)
# print "new_imagePoints_noisy\n",new_imagePoints_noisy
debug = False
# TODO cv2.SOLVEPNP_DLS, cv2.SOLVEPNP_EPNP, cv2.SOLVEPNP_ITERATIVE
pnp_tvec, pnp_rmat = pose_pnp(new_objectPoints, new_imagePoints_noisy,
cam.K, debug, cv2.SOLVEPNP_ITERATIVE, False)
# Calculate errors
Cam_clone_cv2 = cam.clone_withPose(pnp_tvec, pnp_rmat)
tvec_error, Rmat_error = ef.calc_estimated_pose_error(
cam.get_tvec(), cam.R, Cam_clone_cv2.get_tvec(), pnp_rmat)
Rmat_error_loop.append(Rmat_error)
tvec_error_loop.append(tvec_error)
# print "cam.get_world_position()\n",cam.get_world_position()
t = np.eye(4)
t[:3, 3] = pnp_tvec[:3]
T_MC = np.dot(t, pnp_rmat)
return T_MC
homo_CV_error_loop = []
ippe_tvec_error_loop = []
ippe_rmat_error_loop = []
epnp_tvec_error_loop = []
epnp_rmat_error_loop = []
pnp_tvec_error_loop = []
pnp_rmat_error_loop = []
ImageNoise = 4
DataOut['ImageNoise'] = ImageNoise
# WE CREATE NOISY IMAGE POINTS (BASED ON THE TRUE VALUES) AND CALCULATE
# THE ERRORS WE THEN OBTAIN AN AVERAGE FOR EACH ONE
for j in range(homography_iters):
new_imagePoints_noisy = cam.addnoise_imagePoints(new_imagePoints,
mean=0,
sd=ImageNoise)
#Calculate the pose using IPPE (solution with least repro error)
normalizedimagePoints = cam.get_normalized_pixel_coordinates(
new_imagePoints_noisy)
ippe_tvec1, ippe_rmat1, ippe_tvec2, ippe_rmat2 = pose_ippe_both(
new_objectPoints, normalizedimagePoints, debug=False)
ippeCam1 = cam.clone_withPose(ippe_tvec1, ippe_rmat1)
ippeCam2 = cam.clone_withPose(ippe_tvec2, ippe_rmat2)
#Calculate the pose using solvepnp EPNP
debug = False
epnp_tvec, epnp_rmat = pose_pnp(new_objectPoints,
new_imagePoints_noisy, cam.K,
debug, cv2.SOLVEPNP_EPNP, False)
#cam.set_R_axisAngle(1.0, 0.0, 0.0, np.deg2rad(110.0))
#cam.set_t(0.0,-0.3,0.1, frame='world')
## Define a Display plane
pl = Plane(origin=np.array([0, 0, 0]),
normal=np.array([0, 0, 1]),
size=(0.3, 0.3),
n=(2, 2))
pl = CircularPlane()
pl.random(n=number_of_points, r=0.01, min_sep=0.01)
objectPoints = pl.get_points()
x1, y1, x2, y2, x3, y3, x4, y4 = gd.extract_objectpoints_vars(objectPoints)
imagePoints_true = np.array(cam.project(objectPoints, False))
imagePoints_measured = cam.addnoise_imagePoints(imagePoints_true, mean=0, sd=4)
repro = gd.repro_error_autograd(x1, y1, x2, y2, x3, y3, x4, y4, cam.P,
imagePoints_measured)
#%%
## CREATE A SET OF IMAGE POINTS FOR VALIDATION OF THE HOMOGRAPHY ESTIMATION
validation_plane = Plane(origin=np.array([0, 0, 0]),
normal=np.array([0, 0, 1]),
size=(0.3, 0.3),
n=(4, 4))
validation_plane.uniform()
## we create the gradient for the point distribution
normalize = False
n = 0.000000001 #condition number norm
gradient = gd.create_gradient(metric='condition_number', n=n)
