  
          self.feed["size"] = size
          self.feed["padding"] = self._padding_from_coverage(size, coverage_ratio)
          self.feed["matrix"] = get_align_mat(self)
  
          face = AlignerExtract().transform(image,
                                            self.feed["matrix"],
                                            size,
                                            self.feed["padding"])
          self.feed["face"] = face if dtype is None else face.astype(dtype)
  
          logger.trace("Loaded feed face. (face_shape: %s, matrix: %s)",
                       self.feed_face.shape, self.feed_matrix)
  
 
</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             self.aligned["padding"] = self._padding_from_coverage(size, coverage_ratio)
 </s> add             self.aligned["padding"] = padding </s> remove         self.face = None
        """ str: The hash of the face. This cannot be set until the file is saved due to image
        compression, but will be set if loading data from :func:`from_alignment` """
 </s> add  </s> remove         self.filename = filename
 </s> add 
        face = AlignerExtract().transform(image, self.feed["matrix"], size, self.feed["padding"])
  
          self.feed["size"] = size
          self.feed["padding"] = self._padding_from_coverage(size, coverage_ratio)
          self.feed["matrix"] = get_align_mat(self)
        face = AlignerExtract().transform(image, self.feed["matrix"], size, self.feed["padding"])
        face = AlignerExtract().transform(image, self.feed["matrix"], size, self.feed["padding"])
        face = AlignerExtract().transform(image, self.feed["matrix"], size, self.feed["padding"])
        face = AlignerExtract().transform(image, self.feed["matrix"], size, self.feed["padding"])
        face = AlignerExtract().transform(image, self.feed["matrix"], size, self.feed["padding"])
          self.feed["face"] = face if dtype is None else face.astype(dtype)
  
          logger.trace("Loaded feed face. (face_shape: %s, matrix: %s)",
                       self.feed_face.shape, self.feed_matrix)
  
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