Python Opencv SolvePnP yields wrong translation vector

I am attempting to calibrate and find the location and rotation of a single virtual camera in Blender 3d using homography. I am using Blender so that I can double check my results before I move on to the real world where that is more difficult. I rendered ten pictures of a chess board in various locations and rotations in the view of my stationary camera. With OpenCV's Python, I used `cv2.calibrateCamera` to find the intrinsic matrix from the detected corners of the chess board in the ten images and then used that in `cv2.solvePnP` to find the extrinsic parameters(translation and rotation). However, though the estimated parameters were close to the actual ones, there is something fishy going on. My initial estimation of the translation was `(-0.11205481,-0.0490256,8.13892491)`. The actual location was `(0,0,8.07105)`. Pretty close right? But, when I moved and rotated the camera slightly and rerendered the images, the estimated translation became farther off. Estimated: `(-0.15933154,0.13367286,9.34058867)`. Actual: `(-1.7918,-1.51073,9.76597)`. The Z value is close, but the X and the Y are not. I am utterly confused. If anybody can help me sort through this, I would be highly grateful. Here is the code (it's based off of the Python2 calibrate example supplied with OpenCV): ``` #imports left out USAGE = ''' USAGE: calib.py [--save <filename>] [--debug <output path>] [--square_size] [<image mask>] ''' args, img_mask = getopt.getopt(sys.argv[1:], '', ['save=', 'debug=', 'square_size=']) args = dict(args) try: img_mask = img_mask[0] except: img_mask = '../cpp/0*.png' img_names = glob(img_mask) debug_dir = args.get('--debug') square_size = float(args.get('--square_size', 1.0)) pattern_size = (5, 8) pattern_points = np.zeros( (np.prod(pattern_size), 3), np.float32 ) pattern_points[:,:2] = np.indices(pattern_size).T.reshape(-1, 2) pattern_points *= square_size obj_points = [] img_points = [] h, w = 0, 0 count = 0 for fn in img_names: print 'processing %s...' % fn, img = cv2.imread(fn, 0) h, w = img.shape[:2] found, corners = cv2.findChessboardCorners(img, pattern_size) if found: if count == 0: #corners first is a list of the image points for just the first image. #This is the image I know the object points for and use in solvePnP corners_first = [] for val in corners: corners_first.append(val[0]) np_corners_first = np.asarray(corners_first,np.float64) count+=1 term = ( cv2.TERM_CRITERIA_EPS + cv2.TERM_CRITERIA_COUNT, 30, 0.1 ) cv2.cornerSubPix(img, corners, (5, 5), (-1, -1), term) if debug_dir: vis = cv2.cvtColor(img, cv2.COLOR_GRAY2BGR) cv2.drawChessboardCorners(vis, pattern_size, corners, found) path, name, ext = splitfn(fn) cv2.imwrite('%s/%s_chess.bmp' % (debug_dir, name), vis) if not found: print 'chessboard not found' continue img_points.append(corners.reshape(-1, 2)) obj_points.append(pattern_points) print 'ok' rms, camera_matrix, dist_coefs, rvecs, tvecs = cv2.calibrateCamera(obj_points, img_points, (w, h)) print "RMS:", rms print "camera matrix:\n", camera_matrix print "distortion coefficients: ", dist_coefs.ravel() cv2.destroyAllWindows() np_xyz = np.array(xyz,np.float64).T #xyz list is from file. Not shown here for brevity camera_matrix2 = np.asarray(camera_matrix,np.float64) np_dist_coefs = np.asarray(dist_coefs[:,:],np.float64) found,rvecs_new,tvecs_new = cv2.solvePnP(np_xyz, np_corners_first,camera_matrix2,np_dist_coefs) np_rodrigues = np.asarray(rvecs_new[:,:],np.float64) print np_rodrigues.shape rot_matrix = cv2.Rodrigues(np_rodrigues)[0] def rot_matrix_to_euler(R): y_rot = asin(R[2][0]) x_rot = acos(R[2][2]/cos(y_rot)) z_rot = acos(R[0][0]/cos(y_rot)) y_rot_angle = y_rot *(180/pi) x_rot_angle = x_rot *(180/pi) z_rot_angle = z_rot *(180/pi) return x_rot_angle,y_rot_angle,z_rot_angle print "Euler_rotation = ",rot_matrix_to_euler(rot_matrix) print "Translation_Matrix = ", tvecs_new ```