title: Python Opencv SolvePnP yields wrong translation vector
body: <p>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.</p>
  
  <p>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 <code>cv2.calibrateCamera</code> to find the intrinsic matrix from the detected corners of the chess board in the ten images and then used that in <code>cv2.solvePnP</code> to find the extrinsic parameters(translation and rotation).</p>
  
  <p>However, though the estimated parameters were close to the actual ones, there is something fishy going on. My initial estimation of the translation was <code>(-0.11205481,-0.0490256,8.13892491)</code>. The actual location was <code>(0,0,8.07105)</code>. Pretty close right?</p>
  
  <p>But, when I moved and rotated the camera slightly and rerendered the images, the estimated translation became farther off. Estimated: <code>(-0.15933154,0.13367286,9.34058867)</code>. Actual: <code>(-1.7918,-1.51073,9.76597)</code>. The Z value is close, but the X and the Y are not.</p>
  
  <p>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):</p>
  
  <pre><code>#imports left out
  USAGE = '''
  USAGE: calib.py [--save &lt;filename&gt;] [--debug &lt;output path&gt;] [--square_size] [&lt;image mask&gt;]
  '''   
  
  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
  </code></pre>