                  logger.trace("aligned landmarks: %s", lms)
                  self._cache["landmarks"][0] = lms
          return self._cache["landmarks"][0]
  
      @property
      def interpolators(self):
          """ tuple: (`interpolator` and `reverse interpolator`) for the :attr:`adjusted matrix`. """
          with self._cache["interpolators"][1]:
 
</s> remove         if self._globals.filter_mode != "No Faces":
            for frame_idx, face_count in enumerate(self._detected_faces.face_count_per_index):
                if face_count <= 1 and self._globals.filter_mode == "Multiple Faces":
                    continue
                for face_idx in range(face_count):
                    frame_indices.append(frame_idx)
                    face_indices.append(face_idx)
        logger.trace("frame_indices: %s, face_indices: %s", frame_indices, face_indices)
 </s> add         face_counts = self._detected_faces.face_count_per_index  # Copy to avoid recalculations

        for frame_idx in self.frames_list:
            for face_idx in range(face_counts[frame_idx]):
                frame_indices.append(frame_idx)
                face_indices.append(face_idx)

    @property
    def normalized_landmarks(self):
        """ :class:`numpy.ndarray`: The 68 point facial landmarks normalized to 0.0 - 1.0 as
        aligned by Umeyama. """
        with self._cache["landmarks_normalized"][1]:
            if self._cache["landmarks_normalized"][0] is None:
                lms = np.expand_dims(self._frame_landmarks, axis=1)
                lms = cv2.transform(lms, self._matrices["legacy"], lms.shape).squeeze()
                logger.trace("normalized landmarks: %s", lms)
                self._cache["landmarks_normalized"][0] = lms
        return self._cache["landmarks_normalized"][0]

                  logger.trace("aligned landmarks: %s", lms)
                  self._cache["landmarks"][0] = lms
          return self._cache["landmarks"][0]
  
    @property
    def normalized_landmarks(self):
        """ :class:`numpy.ndarray`: The 68 point facial landmarks normalized to 0.0 - 1.0 as
        aligned by Umeyama. """
        with self._cache["landmarks_normalized"][1]:
            if self._cache["landmarks_normalized"][0] is None:
                lms = np.expand_dims(self._frame_landmarks, axis=1)
                lms = cv2.transform(lms, self._matrices["legacy"], lms.shape).squeeze()
                logger.trace("normalized landmarks: %s", lms)
                self._cache["landmarks_normalized"][0] = lms
        return self._cache["landmarks_normalized"][0]

      @property
      def interpolators(self):
          """ tuple: (`interpolator` and `reverse interpolator`) for the :attr:`adjusted matrix`. """
          with self._cache["interpolators"][1]:
keep keep keep add keep keep keep keep
 Add Misalignment Detection
  - lib.align.AlignedFace - Add average_distance property (distance from mean_face)
  - tools.manual - Add misaligned Faces filter
  - tools.sort - Add sort by distance (misaligned sort)Add "Misaligned Faces" filter to manual tool 
lib/align/aligned_face.py