          self.y = alignment["y"]
          self.h = alignment["h"]
          landmarks = alignment["landmarks_xy"]
          if not isinstance(landmarks, np.ndarray):
              landmarks = np.array(landmarks, dtype="int32")
          self.landmarks_xy = landmarks
          # Manual tool does not know the final hash so default to None
          self.hash = alignment.get("hash", None)
          # Manual tool and legacy alignments will not have a mask
          if alignment.get("mask", None) is not None:
 
</s> remove         if (hasattr(self.args, 'debug_landmarks')
                and self.args.debug_landmarks):
 </s> add         if (hasattr(self.args, 'debug_landmarks') and self.args.debug_landmarks): </s> remove             face.landmarks_xy = np.rint(landmarks).astype("int32")
 </s> add             face.landmarks_xy = landmarks </s> remove             cv2.rectangle(self.image,  # pylint: disable=no-member
                          top_left, bottom_right, color, thickness)
 </s> add             cv2.rectangle(self.image, top_left, bottom_right, color, thickness) </s> remove     """ Perform transformation to align and get landmarks """
 </s> add     """ Neural network to process face image into a segmentation mask of the face """ </s> remove     """ Perform transformation to align and get landmarks """
 </s> add     """ Neural network to process face image into a segmentation mask of the face """
            landmarks = np.array(landmarks, dtype="float32")
          self.y = alignment["y"]
          self.h = alignment["h"]
          landmarks = alignment["landmarks_xy"]
          if not isinstance(landmarks, np.ndarray):
            landmarks = np.array(landmarks, dtype="float32")
          self.landmarks_xy = landmarks
          # Manual tool does not know the final hash so default to None
          self.hash = alignment.get("hash", None)
          # Manual tool and legacy alignments will not have a mask
          if alignment.get("mask", None) is not None:
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/faces_detect.py