          """ Apply the filter to the incoming batch
  
          Parameters
          ----------
          batch: list
              List of detected face objects to filter out on size
          minimum_dimension: int
              The minimum (height, width) of the original frame
  
          Returns
 
</s> remove         Checks against `extract_every_n` and the existence of alignments data (can exist if
        `skip_existing` or `skip_existing_faces` has been provided) and compiles a list of frame
        indices that should not be processed, providing these to :class:`lib.image.ImagesLoader`.
 </s> add     @classmethod
    def _validate_inputs(cls,
                         filter_files: Optional[Union[str, List[str]]],
                         nfilter_files: Optional[Union[str, List[str]]]) -> Tuple[List[str],
                                                                                  List[str]]:
        """ Validates that the given filter/nfilter files exist, are image files and are unique

        Parameters
        ----------
        filter_files: str, list or ``None``
            The list of filter file(s) passed in as command line arguments
        nfilter_files: str, list or ``None``
            The list of nfilter file(s) passed in as command line arguments

        Returns
        -------
        filter_files: list
            List of full paths to filter files
        nfilter_files: list
            List of full paths to nfilter files </s> remove         The original frame
 </s> add         The original frame or a faceswap aligned face image </s> remove         Should only be called from  :class:`lib.cli.launcher.ScriptExecutor`
 </s> add         if len(item.detected_faces) > 1 and is_filter:
            logger.warning("%s faces detected for filter in '%s'. These identies will not be used",
                           len(item.detected_faces), os.path.basename(item.filename))
            filelist.pop(idx)
            embeddings.pop(idx)
            return

        if len(item.detected_faces) > 1 and not is_filter:
            logger.warning("%s faces detected for nfilter in '%s'. All of these identies will be "
                           "used", len(item.detected_faces), os.path.basename(item.filename))
            embeddings[idx] = identities
            return

    def _identity_from_extractor(self, file_list: List[str], aligned: List[str]) -> None:
        """ Obtain the identity embeddings from the extraction pipeline

        Parameters
        ----------
        filesile_list: list
            List of full path to images to run through the extraction pipeline
        aligned: list
            List of full path to images that exist in attr:`filelist` that are faceswap aligned
            images </s> remove 

class FaceFilter(PostProcessAction):
    """ Filter in or out faces based on input image(s). Extract or Convert

    Parameters
    -----------
    args: tuple
        Unused
    kwargs: dict
        Keyword arguments for face filter:

        * **detector** (`str`) - The detector to use

        * **aligner** (`str`) - The aligner to use

        * **multiprocess** (`bool`) - Whether to run the extraction pipeline in single process \
        mode or not

        * **ref_threshold** (`float`) - The reference threshold for a positive match

        * **filter_lists** (`dict`) - The filter and nfilter image paths
    """

    def __init__(self, *args, **kwargs) -> None:
        super().__init__(*args, **kwargs)
        logger.info("Extracting and aligning face for Face Filter...")
        self._filter = self._load_face_filter(**kwargs)
        logger.debug("Initialized %s", self.__class__.__name__)

    def _load_face_filter(self,
                          filter_lists: Dict[str, str],
                          ref_threshold: float,
                          aligner: str,
                          detector: str,
                          multiprocess: bool) -> Optional[FilterFunc]:
        """ Set up and load the :class:`~lib.face_filter.FaceFilter`.

        Parameters
        ----------
        filter_lists: dict
            The filter and nfilter image paths
        ref_threshold: float
            The reference threshold for a positive match
        aligner: str
            The aligner to use
        detector: str
            The detector to use
        multiprocess: bool
            Whether to run the extraction pipeline in single process mode or not

        Returns
        -------
        :class:`~lib.face_filter.FaceFilter`
            The face filter
        """
        if not any(val for val in filter_lists.values()):
            return None

        facefilter = None
        filter_files = [self._set_face_filter(f_type, filter_lists[f_type])
                        for f_type in get_args(Literal["filter", "nfilter"])]

        if any(filters for filters in filter_files):
            facefilter = FilterFunc(filter_files[0],
                                    filter_files[1],
                                    detector,
                                    aligner,
                                    multiprocess,
                                    ref_threshold)
            logger.debug("Face filter: %s", facefilter)
        else:
            self._valid = False
        return facefilter

    @classmethod
    def _set_face_filter(cls,
                         f_type: Literal["filter", "nfilter"],
                         f_args: Union[str, List[str]]) -> List[str]:
        """ Check filter files exist and add the filter file paths to a list.

        Parameters
        ----------
        f_type: {"filter", "nfilter"}
            The type of filter to create this list for
        f_args: str or list
            The filter image(s) to use

        Returns
        -------
        list
            The confirmed existing paths to filter files to use
        """
        if not f_args:
            return []

        logger.info("%s: %s", f_type.title(), f_args)
        filter_files = f_args if isinstance(f_args, list) else [f_args]
        filter_files = [fpath for fpath in filter_files if os.path.exists(fpath)]
        if not filter_files:
            logger.warning("Face %s files were requested, but no files could be found. This "
                           "filter will not be applied.", f_type)
        logger.debug("Face Filter files: %s", filter_files)
        return filter_files

    def process(self, extract_media: "ExtractMedia") -> None:
        """ Filters in or out any wanted or unwanted faces based on command line arguments.

        Parameters
        ----------
        extract_media: :class:`~plugins.extract.pipeline.ExtractMedia`
            The :class:`~plugins.extract.pipeline.ExtractMedia` object to perform the
            face filtering on.
        """
        if not self._filter:
            return
        ret_faces = []
        for idx, detect_face in enumerate(extract_media.detected_faces):
            check_item = detect_face["face"] if isinstance(detect_face, dict) else detect_face
            if not self._filter.check(extract_media.image, check_item):
                logger.verbose("Skipping not recognized face: (Frame: %s Face %s)",  # type: ignore
                               extract_media.filename, idx)
                continue
            logger.trace("Accepting recognised face. Frame: %s. Face: %s",  # type: ignore
                         extract_media.filename, idx)
            ret_faces.append(detect_face)
        extract_media.add_detected_faces(ret_faces)
 </s> add 
        faces: list
          """ Apply the filter to the incoming batch
  
          Parameters
          ----------
        faces: list
              List of detected face objects to filter out on size
          minimum_dimension: int
              The minimum (height, width) of the original frame
  
          Returns
keep keep keep keep 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/align/_base.py