          scale = size - 2 * padding
          translation = padding
          pad_mat = np.matrix([[scale, 0.0, translation], [0.0, scale, translation]])
          aligned_landmarks_68 = np.expand_dims(aligned_landmarks_68, axis=1)
          aligned_landmarks_68 = cv2.transform(aligned_landmarks_68,  # pylint: disable=no-member
                                               pad_mat,
                                               aligned_landmarks_68.shape)
          aligned_landmarks_68 = np.squeeze(aligned_landmarks_68)
          l_eye_points = aligned_landmarks_68[42:48].tolist()
          l_brow_points = aligned_landmarks_68[22:27].tolist()
 
</s> remove         interpolators = cv2.INTER_CUBIC, cv2.INTER_AREA  # pylint: disable=no-member
 </s> add         interpolators = cv2.INTER_CUBIC, cv2.INTER_AREA
 </s> remove         matrix = cv2.invertAffineTransform(matrix)  # pylint: disable=no-member
 </s> add         matrix = cv2.invertAffineTransform(matrix)
 </s> remove         interpolators = cv2.INTER_AREA, cv2.INTER_CUBIC  # pylint: disable=no-member
 </s> add         interpolators = cv2.INTER_AREA, cv2.INTER_CUBIC
 </s> remove         return cv2.transform(points, matrix)  # pylint: disable=no-member
 </s> add         return cv2.transform(points, matrix)
 </s> remove         mouth = mouth.reshape((-1, 2)).astype(int).flatten()
        l_eye_hull = cv2.convexHull(l_eye.reshape((-1, 2)))  # pylint: disable=no-member
        r_eye_hull = cv2.convexHull(r_eye.reshape((-1, 2)))  # pylint: disable=no-member
        mouth_hull = cv2.convexHull(mouth.reshape((-1, 2)))  # pylint: disable=no-member
 </s> add         mouth = mouth.reshape((-1, 2)).astype('int32').flatten()
        l_eye_hull = cv2.convexHull(l_eye.reshape((-1, 2)))
        r_eye_hull = cv2.convexHull(r_eye.reshape((-1, 2)))
        mouth_hull = cv2.convexHull(mouth.reshape((-1, 2)))

        aligned_landmarks_68 = cv2.transform(aligned_landmarks_68,

          scale = size - 2 * padding
          translation = padding
          pad_mat = np.matrix([[scale, 0.0, translation], [0.0, scale, translation]])
          aligned_landmarks_68 = np.expand_dims(aligned_landmarks_68, axis=1)
        aligned_landmarks_68 = cv2.transform(aligned_landmarks_68,
                                               pad_mat,
                                               aligned_landmarks_68.shape)
          aligned_landmarks_68 = np.squeeze(aligned_landmarks_68)
          l_eye_points = aligned_landmarks_68[42:48].tolist()
          l_brow_points = aligned_landmarks_68[22:27].tolist()
keep keep keep keep replace keep keep keep keep keep
 Landmarks stored and used as floating point numbers (#928)

* remove and fix int adjustments
* masking rounding 
lib/aligner.py