          return self.feed["face"]
  
      @property
      def _feed_matrix(self):
          """ numpy.ndarray: The adjusted matrix face sized for feeding into a model. Only available
          after :func:`load_feed_face` has been called with an image, otherwise returns ``None`` """
 
</s> remove         adjusted_ratio = coverage_ratio - (1 - self._extract_ratio)
        padding = round((size * adjusted_ratio) / 2)
 </s> add         padding = int((size * (coverage_ratio - 0.625)) / 2) </s> remove     def _extract_ratio(self):
        """ float: The ratio of padding to add for training images """
        return 0.375
 </s> add     def training_coverage(self):
        """ The coverage ratio to add for training images """
        return 1.0 </s> remove         return self.config.get("mask_type", None) is not None
 </s> add         return self.config.get("replicate_input_mask", False) </s> remove         * **landmarks** (`numpy.ndarray`, `optional`). Required if using a :attr:`mask_type` is \
        not ``None`` or :attr:`warp_to_landmarks` is ``True``. The 68 point face landmarks from \
        an alignments file.
 </s> add         * **landmarks** (`numpy.ndarray`, `optional`). Required if :attr:`warp_to_landmarks` is \
        ``True``. The 68 point face landmarks from an alignments file. </s> remove     def load_aligned(self, image, size=256, dtype=None):
 </s> add     def load_aligned(self, image, size=256, coverage_ratio=1.0, dtype=None):
    @property
    def feed_landmarks(self):
        """ numpy.ndarray: The 68 point landmarks location transposed to the feed face box.
        Only available after :func:`load_reference_face` has been called, otherwise returns
        ``None``"""
        if not self.feed:
            return None
        landmarks = AlignerExtract().transform_points(self.landmarks_xy,
                                                      self.feed["matrix"],
                                                      self.feed["size"],
                                                      self.feed["padding"])
        logger.trace("Returning: %s", landmarks)
        return landmarks

          return self.feed["face"]
  
    @property
    def feed_landmarks(self):
        """ numpy.ndarray: The 68 point landmarks location transposed to the feed face box.
        Only available after :func:`load_reference_face` has been called, otherwise returns
        ``None``"""
        if not self.feed:
            return None
        landmarks = AlignerExtract().transform_points(self.landmarks_xy,
                                                      self.feed["matrix"],
                                                      self.feed["size"],
                                                      self.feed["padding"])
        logger.trace("Returning: %s", landmarks)
        return landmarks

      @property
      def _feed_matrix(self):
          """ numpy.ndarray: The adjusted matrix face sized for feeding into a model. Only available
          after :func:`load_feed_face` has been called with an image, otherwise returns ``None`` """
keep add keep keep keep keep
 Smart Mask Exposure for Extraction & Training (#831)

Smart Masks - Initial Commit 
lib/faces_detect.py