      @staticmethod
      def _padding_from_coverage(size, coverage_ratio):
          """ Return the image padding for a face from coverage_ratio set against a
              pre-padded training image """
          padding = int((size * (coverage_ratio - 0.625)) / 2)
          logger.trace(padding)
          return padding
  
      def load_feed_face(self, image, size=64, coverage_ratio=0.625, dtype=None):
          """ Align a face in the correct dimensions for feeding into a model.
 
</s> remove     @staticmethod
    def _padding_from_coverage(size, coverage_ratio):
 </s> add     def _padding_from_coverage(self, size, coverage_ratio): </s> remove         self.face = image[self.top: self.bottom,
                          self.left: self.right]
 </s> add         self.image = image[self.top: self.bottom,
                           self.left: self.right] </s> remove     def training_coverage(self):
        """ The coverage ratio to add for training images """
        return 1.0
 </s> add     def _extract_ratio(self):
        """ float: The ratio of padding to add for training images """
        return 0.375 </s> remove     def load_aligned(self, image, size=256, coverage_ratio=1.0, dtype=None):
 </s> add     def load_aligned(self, image, size=256, dtype=None): </s> remove         """ numpy.ndarray: The adjusted matrix face sized for refence against a face coming out of
 </s> add         """ numpy.ndarray: The adjusted matrix face sized for reference against a face coming out of
        adjusted_ratio = coverage_ratio - (1 - self._extract_ratio)
        padding = round((size * adjusted_ratio) / 2)
      @staticmethod
      def _padding_from_coverage(size, coverage_ratio):
          """ Return the image padding for a face from coverage_ratio set against a
              pre-padded training image """
        adjusted_ratio = coverage_ratio - (1 - self._extract_ratio)
        padding = round((size * adjusted_ratio) / 2)
          logger.trace(padding)
          return padding
  
      def load_feed_face(self, image, size=64, coverage_ratio=0.625, dtype=None):
          """ Align a face in the correct dimensions for feeding into a model.
keep keep keep keep replace keep keep keep keep keep
 Smart Mask - Extract code review

- Lint simple_tests.py
- Only reformat alignments file if it exists otherwise change filename
- Update legacy alignments to new format at all stages
- faces_detect.Mask.from_dict - logging format fix
- convert.py fix otf for new pipeline
- cli.py - Add note that masks not used. Revert convert masks
- faces_detect.py - Revert non-extract code
- Add .p and .pickle extensions for serializer
- plugins/extract revert some changes
- scripts/fsmedia - Revert code changes
- Pipeline - cleanup
- Consistant alpha channel stripping (fixes single-process)
- Store landmarks as numpy array
- Code attribution
- Normalize feed face and reference face to 0.0 - 1.0 in convert
- Lock in mask VRAM sized
- Add documentation to plugin_loader
- Update alignments tool to work with new format 
lib/faces_detect.py