          else:
              logger.debug("Launching aligner")
              self.launch_aligner()
  
      def launch_aligner(self):
          """ Launch the face aligner """
          logger.debug("Launching Aligner")
          kwargs = {"in_queue": self.queues["extract_align_in"],
                    "out_queue": self.queues["extract_align_out"]}
  
 
</s> remove     def launch(self):
        """ Launches the plugins
            This can be called multiple times depending on the phase/whether multiprocessing
            is enabled.

            If multiprocessing:
                launches both plugins, but aligner first so that it's VRAM can be allocated
                prior to giving the remaining to the detector
            If not multiprocessing:
                Launches the relevant plugin for the current phase """
        if self.is_parallel:
            logger.debug("Launching aligner and detector")
            self.launch_aligner()
            self.launch_detector()
        elif self.phase == "detect":
            logger.debug("Launching detector")
            self.launch_detector()
        else:
            logger.debug("Launching aligner")
            self.launch_aligner()
 </s> add     @staticmethod
    def _load_aligner(aligner, configfile, normalize_method):
        """ Set global arguments and load aligner plugin """
        aligner_name = aligner.replace("-", "_").lower()
        logger.debug("Loading Aligner: '%s'", aligner_name)
        aligner = PluginLoader.get_aligner(aligner_name)(configfile=configfile,
                                                         normalize_method=normalize_method)
        return aligner </s> remove         kwargs = {"in_queue": self.queues["extract_align_in"],
                  "out_queue": self.queues["extract_align_out"]}

        process = SpawnProcess(self.aligner.run, **kwargs)
        event = process.event
        error = process.error
        process.start()
        self.processes.append(process)

        # Wait for Aligner to take it's VRAM
        # The first ever load of the model for FAN has reportedly taken
        # up to 3-4 minutes, hence high timeout.
        # TODO investigate why this is and fix if possible
        for mins in reversed(range(5)):
            for seconds in range(60):
                event.wait(seconds)
                if event.is_set():
                    break
                if error.is_set():
                    break
            if event.is_set():
                break
            if mins == 0 or error.is_set():
                raise ValueError("Error initializing Aligner")
            logger.info("Waiting for Aligner... Time out in %s minutes", mins)

 </s> add         kwargs = dict(in_queue=self._queues["extract_align_in"],
                      out_queue=self._queues["extract_align_out"],
                      queue_size=self._queue_size)
        self._aligner.initialize(**kwargs)
        self._aligner.start() </s> remove     def launch_detector(self):
 </s> add     def _launch_detector(self): </s> remove     def launch_aligner(self):
 </s> add     @staticmethod
    def launch_aligner(): </s> remove         out_queue = queue_manager.get_queue("out")
        kwargs = {"in_queue": queue_manager.get_queue("in"),
                  "out_queue": out_queue}

        for plugin in ("fan", "cv2_dnn"):
            aligner = PluginLoader.get_aligner(plugin)(loglevel=self.args.loglevel)
            process = SpawnProcess(aligner.run, **kwargs)
            event = process.event
            process.start()
            # Wait for Aligner to take init
            # The first ever load of the model for FAN has reportedly taken
            # up to 3-4 minutes, hence high timeout.
            event.wait(300)

            if not event.is_set():
                if plugin == "fan":
                    process.join()
                    logger.error("Error initializing FAN. Trying CV2-DNN")
                    continue
                else:
                    raise ValueError("Error inititalizing Aligner")
            if plugin == "cv2_dnn":
                return

            try:
                err = None
                err = out_queue.get(True, 1)
            except QueueEmpty:
                pass
            if not err:
                break
            process.join()
            logger.error("Error initializing FAN. Trying CV2-DNN")
 </s> add         kwargs = dict(in_queue=queue_manager.get_queue("in"),
                      out_queue=queue_manager.get_queue("out"),
                      queue_size=8)
        aligner = PluginLoader.get_aligner("fan")(normalize_method="hist")
        aligner.batchsize = 1
        aligner.initialize(**kwargs)
        aligner.start()
    def _launch_aligner(self):
          else:
              logger.debug("Launching aligner")
              self.launch_aligner()
  
    def _launch_aligner(self):
          """ Launch the face aligner """
          logger.debug("Launching Aligner")
          kwargs = {"in_queue": self.queues["extract_align_in"],
                    "out_queue": self.queues["extract_align_out"]}
  
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 
plugins/extract/pipeline.py