                   filename: str,
                   image: "np.ndarray",
                   detected_faces: Optional[List["DetectedFace"]] = None) -> None:
          logger.trace("Initializing %s: (filename: '%s', image shape: %s, "  # type: ignore
                       "detected_faces: %s)", self.__class__.__name__, filename, image.shape,
                       detected_faces)
          self._filename = filename
          self._image: Optional["np.ndarray"] = image
          self._image_shape = cast(Tuple[int, int, int], image.shape)
          self._detected_faces: List["DetectedFace"] = ([] if detected_faces is None
                                                        else detected_faces)
 
</s> remove                  detected_faces: Optional[List["DetectedFace"]] = None) -> None:
 </s> add                  detected_faces: Optional[List["DetectedFace"]] = None,
                 is_aligned: bool = False) -> None: </s> remove         self._frame_metadata: Dict[str, Any] = {}
 </s> add         self._is_aligned = is_aligned
        self._frame_metadata: Optional["PNGHeaderSourceDict"] = None </s> remove     def add_frame_metadata(self, metadata: Dict[str, Any]) -> None:
 </s> add     def add_frame_metadata(self, metadata: "PNGHeaderSourceDict") -> None: </s> remove     def save(self, filename: str, image: bytes, sub_folder: Optional[str] = None) -> None:
 </s> add     def save(self,
             filename: str,
             image: Union[bytes, np.ndarray],
             sub_folder: Optional[str] = None) -> None:
                     "detected_faces: %s, is_aligned: %s)", self.__class__.__name__, filename,
                     image.shape, detected_faces, is_aligned)
                   filename: str,
                   image: "np.ndarray",
                   detected_faces: Optional[List["DetectedFace"]] = None) -> None:
          logger.trace("Initializing %s: (filename: '%s', image shape: %s, "  # type: ignore
                     "detected_faces: %s, is_aligned: %s)", self.__class__.__name__, filename,
                     image.shape, detected_faces, is_aligned)
                     "detected_faces: %s, is_aligned: %s)", self.__class__.__name__, filename,
                     image.shape, detected_faces, is_aligned)
          self._filename = filename
          self._image: Optional["np.ndarray"] = image
          self._image_shape = cast(Tuple[int, int, int], image.shape)
          self._detected_faces: List["DetectedFace"] = ([] if detected_faces is None
                                                        else detected_faces)
keep keep keep keep replace replace keep keep keep keep keep
 Update Face Filter
  - Remove old face filter
  - plugins.extract.pipeline: Expose plugins directly
  - Change `is_aligned` from plugin level to ExtractMedia level
  - Allow extract pipeline to take faceswap aligned images
  - Add ability for recognition plugins to accept aligned faces as input
  - Add face filter to recognition plugin
  - Move extractor pipeline IO ops to own class 
plugins/extract/pipeline.py