title: opencv cv2.remap creates strange images
body: <p>I'm new to stereo calibration and tried to get the matrices of a stereo camera and then remap the images, so I could use them for depth-map generation.
  I used the sample images <code>left*.jpg</code> and <code>right*.jpg</code> and parts of this code: <a href="http://docs.opencv.org/3.0-beta/doc/py_tutorials/py_calib3d/py_calibration/py_calibration.html#calibration" rel="nofollow noreferrer">http://docs.opencv.org/3.0-beta/doc/py_tutorials/py_calib3d/py_calibration/py_calibration.html#calibration</a> (and also from <a href="https://github.com/Itseez/opencv/blob/master/samples/python/calibrate.py" rel="nofollow noreferrer">samples/python/calibrate.py</a>)</p>
  
  <p>So my code is the following:</p>
  
  <pre><code>import cv2
  ...
  img_names = [glob('../data/left*.jpg'), glob('../data/right*.jpg')]
  obj_points = [[], []]
  img_points = [[], []]
  h = ... #
  w = ... #
  
  ... # get the obj_points and img_points from both image sets like in the examples
  # This works fine, I viewed the results with cv2.drawChessboardCorners
  
  rms_l, camera_matrix_l, dist_coeffs_l, _, _ = cv2.calibrateCamera(obj_points[0], img_points[0], (w, h), None, None)
  rms_r, camera_matrix_r, dist_coeffs_r, _, _ = cv2.calibrateCamera(obj_points[1], img_points[1], (w, h), None, None)
  
  stereo_flags = 0
  stereo_flags |= cv2.CALIB_USE_INTRINSIC_GUESS
  stereo_flags |= cv2.CALIB_FIX_INTRINSIC
  
  # run stereo calibration
  rms_stereo, camera_matrix_l, dist_coeffs_l, camera_matrix_r, dist_coeffs_r, R, T, E, F  = cv2.stereoCalibrate(obj_points[0], img_points[0], img_points[1], camera_matrix_l, dist_coeffs_l, camera_matrix_r, dist_coeffs_r, (w, h), flags=stereo_flags)
  
  # run stereo rectification
  rectification_matrix_l, rectification_matrix_r, projection_matrix_l, projection_matrix_r, _, _, _ = cv2.stereoRectify(camera_matrix_l, dist_coeffs_l, camera_matrix_r, dist_coeffs_r, (w, h), R, T)
  
  map1, map2 = [], []
  map1[0], map2[0] = cv2.initUndistortRectifyMap(camera_matrix_l, dist_coeffs_l, rectification_matrix_l, projection_matrix_l, (w, h), cv2.CV_16SC2)
  map1[1], map2[1] = cv2.initUndistortRectifyMap(camera_matrix_r, dist_coeffs_r, rectification_matrix_r, projection_matrix_r, (w, h), cv2.CV_16SC2)
  
  print ... # see below
  
  for in in range(2):
      img = cv2.imread(img_names[i][0], 0)
      imgt = cv2.remap(img, map1[i], map2[i], cv2.INTER_LINEAR)
      cv2.imshow('image', imgt)
      cv2.waitKey(0)
  </code></pre>
  
  <p>But then, my the resulting images are very strange (left and right):</p>
  
  <p><a href="https://i.stack.imgur.com/i5hBi.png" rel="nofollow noreferrer"><img src="https://i.stack.imgur.com/i5hBi.png" alt="Left image"></a>
  <a href="https://i.stack.imgur.com/6oIZe.png" rel="nofollow noreferrer"><img src="https://i.stack.imgur.com/6oIZe.png" alt="Right image"></a></p>
  
  <p>Now is my question, what can be my mistake? Did I calculated the matrices not correct or inserted wrong parameters? I saw many examples which are using those functions in a similar way, but altough their results are not like mine.</p>
  
  <p><strong>EDIT:</strong> Here are my matrices:</p>
  
  <pre><code>---------- Camera Stereo ----------
  RMS: 54.0798238093
  camera matrix left:
  [[ 532.80990768    0.          342.49522241]
   [   0.          532.93344826  233.88792572]
   [   0.            0.            1.        ]]
  distortion coefficients left:  [ -2.81325825e-01   2.91151900e-02   1.21234424e-03  -1.40823847e-04
     1.54861062e-01]
  camera matrix right:
  [[ 537.43775429    0.          327.6113408 ]
   [   0.          536.95118843  248.89561922]
   [   0.            0.            1.        ]]
  distortion coefficients right:  [-0.29741745  0.14930176 -0.00077008  0.00032599 -0.06565751]
  R:
  [[ 0.81520177  0.24703546  0.52385071]
   [ 0.42427129  0.36098612 -0.83047149]
   [-0.39425873  0.89925664  0.18946647]]
  Rectification matrix Left :
  [[ 0.84817656  0.5225405  -0.08687895]
   [-0.44783099  0.61975759 -0.64447493]
   [-0.28292036  0.58553561  0.75967369]]
  Rectification matrix Right :
  [[ 0.77500946  0.62063732  0.11903635]
   [-0.54957916  0.56894011  0.61177602]
   [ 0.31196648 -0.5395521   0.7820233 ]]
  Projection Matrix Left :
  [[ 448.32924881    0.          328.40793514    0.        ]
   [   0.          448.32924881  215.26811713    0.        ]
   [   0.            0.            1.            0.        ]]
  Projection Matrix Right :
  [[  4.48329249e+02   0.00000000e+00   3.28407935e+02   0.00000000e+00]
   [  0.00000000e+00   4.48329249e+02   2.15268117e+02   1.97345858e+04]
   [  0.00000000e+00   0.00000000e+00   1.00000000e+00   0.00000000e+00]]
  </code></pre>