          logger.trace("Loaded aligned face: %s", {k: str(v) if isinstance(v, np.ndarray) else v
                                                   for k, v in self.aligned.items()
                                                   if k != "face"})
  
      @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
 
</s> remove         padding = int((size * (coverage_ratio - 0.625)) / 2)
 </s> add         adjusted_ratio = coverage_ratio - (1 - self._extract_ratio)
        padding = round((size * adjusted_ratio) / 2) </s> remove             face = AlignerExtract().transform(
                image,
                self.aligned["matrix"],
                size,
                self.aligned["padding"])
 </s> add             face = AlignerExtract().transform(image, self.aligned["matrix"], size, padding) </s> remove         for key in ("mask", "affine_matrix", "frame_dims", "intepolator", "stored_size"):
 </s> add         for key in ("mask", "affine_matrix", "frame_dims", "interpolator", "stored_size"): </s> remove             ``affine_matrix``, ``frame_dims``, ``intepolator``, ``stored_size``
 </s> add             ``affine_matrix``, ``frame_dims``, ``interpolator``, ``stored_size``
    def _padding_from_coverage(self, size, coverage_ratio):
          logger.trace("Loaded aligned face: %s", {k: str(v) if isinstance(v, np.ndarray) else v
                                                   for k, v in self.aligned.items()
                                                   if k != "face"})
  
    def _padding_from_coverage(self, size, coverage_ratio):
    def _padding_from_coverage(self, 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
keep keep keep keep replace 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