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
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)
epnpCam = cam.clone_withPose(epnp_tvec, epnp_rmat)
#Calculate the pose using solvepnp ITERATIVE
pnp_tvec, pnp_rmat = pose_pnp(new_objectPoints,
new_imagePoints_noisy, cam.K, debug,
cv2.SOLVEPNP_ITERATIVE, False)
pnpCam = cam.clone_withPose(pnp_tvec, pnp_rmat)
new_objectPoints = objectPoints_des new_imagePoints = np.array(cam.project(new_objectPoints, False)) homography_iters = 1 for i in range(1000): # Xo = np.copy(new_objectPoints[[0,1,3],:]) #without the z coordinate (plane) # Xi = np.copy(new_imagePoints) # Aideal = ef.calculate_A_matrix(Xo, Xi) objectPoints_list.append(new_objectPoints) objectPoints_historic = np.hstack([objectPoints_historic,new_objectPoints]) imagePoints_list.append(new_imagePoints) #Calculate the pose using solvepnp ITERATIVE new_imagePoints_noisy = cam.addnoise_imagePoints(new_imagePoints, mean = 0, sd = 2) pnp_tvec, pnp_rmat = pose_pnp(new_objectPoints, new_imagePoints_noisy, cam.K, False, cv2.SOLVEPNP_ITERATIVE,False) pnpCam = cam.clone_withPose(pnp_tvec, pnp_rmat) x1,y1,x2,y2,x3,y3,x4,y4 = gd.extract_objectpoints_vars(new_objectPoints) repro = gd.repro_error_autograd(x1,y1,x2,y2,x3,y3,x4,y4,pnpCam.P, new_imagePoints_noisy) print "--------------------------------" print "Repro Error: ", repro print "--------------------------------" # # input_list = gd.extract_objectpoints_vars(new_objectPoints) # input_list.append(np.array(cam.P)) # # mat_cond = gd.matrix_condition_number_autograd(*input_list, normalize = False) # # input_list = gd.extract_objectpoints_vars(new_objectPoints) # input_list.append(np.array(cam.P)) # mat_cond_normalized = gd.matrix_condition_number_autograd(*input_list, normalize = True)
