  
      # Extraction pipeline
      def align_faces(self, detector_name, aligner_name, loglevel, multiprocess):
          """ Use the requested detectors to retrieve landmarks for filter images """
          extractor = Extractor(detector_name, aligner_name, loglevel, multiprocess=multiprocess)
          self.run_extractor(extractor)
          del extractor
          self.load_aligned_face()
  
      def run_extractor(self, extractor):
 
</s> remove     def align_faces(self, detector_name, aligner_name, loglevel, multiprocess):
 </s> add     def align_faces(self, detector_name, aligner_name, multiprocess):
 </s> remove         exception = False
 </s> add  </s> remove     def load_face_filter(self, filter_lists, ref_threshold, aligner, detector, loglevel,
 </s> add     def load_face_filter(self, filter_lists, ref_threshold, aligner, detector, </s> remove         """ Return true if this is the final extractor pass """
        retval = self.is_parallel or self.phase == "align"
 </s> add         """ bool, Return ``True`` if this is the final extractor pass otherwise ``False``

        Useful for iterating over the pipeline :attr:`passes` or :func:`detected_faces` and
        handling accordingly.

        Example
        -------
        >>> for face in extractor.detected_faces():
        >>>     if extractor.final_pass:
        >>>         <do final processing>
        >>>     else:
        >>>         <do intermediate processing>
        >>>         extractor.input_queue.put({"filename": "path/to/image/file",
        >>>                                    "image": np.array(image),
        >>>                                    "detected_faces": [<DetectedFace objects]})
        """
        retval = self._is_parallel or self.phase == "align" </s> remove         """ Return the number of passes the extractor needs to make """
        retval = 1 if self.is_parallel else 2
 </s> add         """ int: Returns the total number of passes the extractor needs to make.

        This is calculated on several factors (vram available, plugin choice,
        :attr:`multiprocess` etc.). It is useful for iterating over the pipeline
        and handling accordingly.

        Example
        -------
        >>> for phase in extractor.passes:
        >>>     if phase == 1:
        >>>         extractor.input_queue.put({"filename": "path/to/image/file",
        >>>                                    "image": np.array(image)})
        >>>     else:
        >>>         extractor.input_queue.put({"filename": "path/to/image/file",
        >>>                                    "image": np.array(image),
        >>>                                    "detected_faces": [<DetectedFace objects]})
        """
        retval = 1 if self._is_parallel else 2
        extractor = Extractor(detector_name, aligner_name, multiprocess=multiprocess)

  
      # Extraction pipeline
      def align_faces(self, detector_name, aligner_name, loglevel, multiprocess):
          """ Use the requested detectors to retrieve landmarks for filter images """
        extractor = Extractor(detector_name, aligner_name, multiprocess=multiprocess)
          self.run_extractor(extractor)
          del extractor
          self.load_aligned_face()
  
      def run_extractor(self, extractor):
keep keep keep keep replace keep keep keep keep keep
 De-Multiprocess Extract (#871)

* requirements.txt: - Pin opencv to 4.1.1 (fixes cv2-dnn error)

* lib.face_detect.DetectedFace: change LandmarksXY to landmarks_xy. Add left, right, top, bottom attributes

* lib.model.session: Session manager for loading models into different graphs (for Nvidia + CPU)

* plugins.extract._base: New parent class for all extract plugins

* plugins.extract.pipeline. Remove MultiProcessing. Dynamically limit batchsize for Nvidia cards. Remove loglevel input

* S3FD + FAN plugins. Standardise to Keras version for all backends

* Standardize all extract plugins to new threaded codebase

* Documentation. Start implementing Numpy style docstrings for Sphinx Documentation

* Remove s3fd_amd. Change convert OTF to expect DetectedFace object

* faces_detect - clean up and documentation

* Remove PoolProcess

* Migrate manual tool to new extract workflow

* Remove AMD specific extractor code from cli and plugins

* Sort tool to new extract workflow

* Remove multiprocessing from project

* Remove multiprocessing queues from QueueManager

* Remove multiprocessing support from logger

* Move face_filter to new extraction pipeline

* Alignments landmarksXY > landmarks_xy and legacy handling

* Intercept get_backend for sphinx doc build

# Add Sphinx documentation 
lib/face_filter.py