          self.file = self.get_location(folder, filename)
  
          self.data = self.load()
          logger.debug("Initialized %s", self.__class__.__name__)
  
      # << PROPERTIES >> #
  
      @property
      def frames_count(self):
 
</s> remove         self.detector = self.load_detector(detector, loglevel, rotate_images, min_size, configfile)
        self.aligner = self.load_aligner(aligner, loglevel, configfile, normalize_method)
        self.is_parallel = self.set_parallel_processing(multiprocess)
        self.processes = list()
        self.queues = self.add_queues()
 </s> add         self._queue_size = 32
        self._vram_buffer = 320  # Leave a buffer for VRAM allocation
        self._detector = self._load_detector(detector, rotate_images, min_size, configfile)
        self._aligner = self._load_aligner(aligner, configfile, normalize_method)
        self._is_parallel = self._set_parallel_processing(multiprocess)
        self._queues = self._add_queues() </s> remove     def init_extractor(self, loglevel):
 </s> add     def init_extractor(self): </s> remove         return (cv2.warpAffine(image,  # pylint: disable=no-member
 </s> add         image = cv2.warpAffine(image,  # pylint: disable=no-member
        self.update_legacy()
          self.file = self.get_location(folder, filename)
  
          self.data = self.load()
        self.update_legacy()
          logger.debug("Initialized %s", self.__class__.__name__)
  
      # << PROPERTIES >> #
  
      @property
      def frames_count(self):
keep keep add keep 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/alignments.py