              self.aligned["matrix"] = get_align_mat(self)
              self.aligned["face"] = None
          if image is not None and self.aligned["face"] is None:
              logger.trace("Getting aligned face")
              face = AlignerExtract().transform(
                  image,
                  self.aligned["matrix"],
                  size,
                  self.aligned["padding"])
              self.aligned["face"] = face if dtype is None else face.astype(dtype)
  
          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"})
 
</s> remove             self.aligned["padding"] = self._padding_from_coverage(size, coverage_ratio)
 </s> add             self.aligned["padding"] = padding </s> remove     @staticmethod
    def _padding_from_coverage(size, coverage_ratio):
 </s> add     def _padding_from_coverage(self, size, coverage_ratio): </s> remove 
        face = AlignerExtract().transform(image,
                                          self.feed["matrix"],
                                          size,
                                          self.feed["padding"])
 </s> add         face = AlignerExtract().transform(image, self.feed["matrix"], size, self.feed["padding"]) </s> remove             ``affine_matrix``, ``frame_dims``, ``intepolator``, ``stored_size``
 </s> add             ``affine_matrix``, ``frame_dims``, ``interpolator``, ``stored_size``
            face = AlignerExtract().transform(image, self.aligned["matrix"], size, padding)
              self.aligned["matrix"] = get_align_mat(self)
              self.aligned["face"] = None
          if image is not None and self.aligned["face"] is None:
              logger.trace("Getting aligned face")
            face = AlignerExtract().transform(image, self.aligned["matrix"], size, padding)
            face = AlignerExtract().transform(image, self.aligned["matrix"], size, padding)
            face = AlignerExtract().transform(image, self.aligned["matrix"], size, padding)
            face = AlignerExtract().transform(image, self.aligned["matrix"], size, padding)
            face = AlignerExtract().transform(image, self.aligned["matrix"], size, padding)
              self.aligned["face"] = face if dtype is None else face.astype(dtype)
  
          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"})
keep keep keep keep replace replace replace 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