          batch_dst = (batch_dst_points + np.random.normal(size=batch_dst_points.shape,
                                                           scale=2.0)).astype("int32")
  
          face_cores = [cv2.convexHull(np.concatenate([src[17:], dst[17:]], axis=0))
                        for src, dst in zip(batch_src_points, batch_dst)]
  
          batch_src = np.append(batch_src_points, edge_anchors, axis=1)
          batch_dst = np.append(batch_dst, edge_anchors, axis=1)
  
          rem_indices = [list(set(idx for fpl in (src, dst)
 
</s> remove                                                          scale=2.0)).astype("int32")
 </s> add                                                          scale=2.0)) </s> remove         aligned_landmarks_68 = cv2.transform(aligned_landmarks_68,  # pylint: disable=no-member
 </s> add         aligned_landmarks_68 = cv2.transform(aligned_landmarks_68,
 </s> remove         points = cv2.transform(points,  # pylint: disable=no-member
                               matrix, points.shape)
 </s> add         points = cv2.transform(points, matrix, points.shape)
 </s> remove                 item = np.concatenate(item)
                hull = cv2.convexHull(item).astype("int32")  # pylint: disable=no-member
 </s> add                 item = np.rint(np.concatenate(item)).astype("int32")
                hull = cv2.convexHull(item) </s> remove                 item = np.concatenate(item)
                hull = cv2.convexHull(item).astype("int32")  # pylint: disable=no-member
 </s> add                 item = np.rint(np.concatenate(item)).astype("int32")
                hull = cv2.convexHull(item)
                      for src, dst in zip(batch_src_points.astype("int32"),
                                          batch_dst.astype("int32"))]
          batch_dst = (batch_dst_points + np.random.normal(size=batch_dst_points.shape,
                                                           scale=2.0)).astype("int32")
  
          face_cores = [cv2.convexHull(np.concatenate([src[17:], dst[17:]], axis=0))
                      for src, dst in zip(batch_src_points.astype("int32"),
                                          batch_dst.astype("int32"))]
  
          batch_src = np.append(batch_src_points, edge_anchors, axis=1)
          batch_dst = np.append(batch_dst, edge_anchors, axis=1)
  
          rem_indices = [list(set(idx for fpl in (src, dst)
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/training_data.py