  def log_setup(loglevel, logfile, command, is_gui=False):
      """ initial log set up. """
      numeric_loglevel = get_loglevel(loglevel)
      root_loglevel = min(logging.DEBUG, numeric_loglevel)
      set_root_logger(loglevel=root_loglevel)
      log_format = FaceswapFormatter("%(asctime)s %(processName)-15s %(threadName)-15s "
                                     "%(module)-15s %(funcName)-25s %(levelname)-8s %(message)s",
                                     datefmt="%m/%d/%Y %H:%M:%S")
      f_handler = file_handler(numeric_loglevel, logfile, log_format, command)
      s_handler = stream_handler(numeric_loglevel, is_gui)
 
</s> remove 
    q_listener = QueueListener(LOG_QUEUE, f_handler, s_handler, c_handler,
                               respect_handler_level=True)
    q_listener.start()
 </s> add     rootlogger.addHandler(f_handler)
    rootlogger.addHandler(s_handler)
    rootlogger.addHandler(c_handler) </s> remove     q_handler = QueueHandler(queue)
    rootlogger.addHandler(q_handler)
 </s> add  </s> remove             return image, None
        return self.rotate_image_by_angle(image, angle)
 </s> add             # Set the initial batch so we always rotate from zero
            batch["initial_feed"] = batch["feed"].copy()
            return

        retval = dict()
        for img, faces, rotmat in zip(batch["initial_feed"], batch["prediction"], batch["rotmat"]):
            if faces.any():
                image = np.zeros_like(img)
                matrix = rotmat
            else:
                image, matrix = self._rotate_image_by_angle(img, angle)
            retval.setdefault("feed", []).append(image)
            retval.setdefault("rotmat", []).append(matrix)
        batch["feed"] = np.array(retval["feed"], dtype="float32")
        batch["rotmat"] = retval["rotmat"] </s> remove         self.landmarksXY = landmarksXY
        self.hash = None  # Hash must be set when the file is saved due to image compression
 </s> add         self.landmarks_xy = landmarks_xy
        self.hash = 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` """
    rootlogger = set_root_logger(loglevel=root_loglevel)
  def log_setup(loglevel, logfile, command, is_gui=False):
      """ initial log set up. """
      numeric_loglevel = get_loglevel(loglevel)
      root_loglevel = min(logging.DEBUG, numeric_loglevel)
    rootlogger = set_root_logger(loglevel=root_loglevel)
      log_format = FaceswapFormatter("%(asctime)s %(processName)-15s %(threadName)-15s "
                                     "%(module)-15s %(funcName)-25s %(levelname)-8s %(message)s",
                                     datefmt="%m/%d/%Y %H:%M:%S")
      f_handler = file_handler(numeric_loglevel, logfile, log_format, command)
      s_handler = stream_handler(numeric_loglevel, is_gui)
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/logger.py