                 "image": image}
          self.extractor.input_queue.put(inp)
          faces = next(self.extractor.detected_faces())
  
          landmarks = faces["landmarks"]
          detected_faces = faces["detected_faces"]
          final_faces = list()
  
          for idx, face in enumerate(detected_faces):
              detected_face = DetectedFace()
              detected_face.from_bounding_box_dict(face)
              detected_face.landmarksXY = landmarks[idx]
              final_faces.append(detected_face)
          return final_faces
  
      # Saving tasks
      def save(self, completion_event):
          """ Save the converted images """
 
</s> remove         landmarks = faces["landmarks"]
 </s> add  </s> remove         for idx, face in enumerate(detected_faces):
            detected_face = DetectedFace()
            detected_face.from_bounding_box_dict(face, image)
            detected_face.landmarksXY = landmarks[idx]
            detected_face.load_aligned(image, size=size, align_eyes=align_eyes)
 </s> add         for face in detected_faces:
            face.load_aligned(image, size=size, align_eyes=align_eyes) </s> remove                                 "face": detected_face})
 </s> add                                 "face": face}) </s> remove                     detected_faces = [detected_faces[0]]
                self.filters[filename]["detected_faces"] = detected_faces

                # Aligner output
                if extractor.final_pass:
                    landmarks = faces["landmarks"]
                    self.filters[filename]["landmarks"] = landmarks
 </s> add                 self.filters[filename]["detected_face"] = detected_faces[0]
 </s> remove             bounding_box = face["detected_faces"][0]
 </s> add 
        final_faces = [face for face in faces["detected_faces"]]
                 "image": image}
          self.extractor.input_queue.put(inp)
          faces = next(self.extractor.detected_faces())
  
        final_faces = [face for face in faces["detected_faces"]]
        final_faces = [face for face in faces["detected_faces"]]
        final_faces = [face for face in faces["detected_faces"]]
        final_faces = [face for face in faces["detected_faces"]]
        final_faces = [face for face in faces["detected_faces"]]
        final_faces = [face for face in faces["detected_faces"]]
        final_faces = [face for face in faces["detected_faces"]]
        final_faces = [face for face in faces["detected_faces"]]
        final_faces = [face for face in faces["detected_faces"]]
          return final_faces
  
      # Saving tasks
      def save(self, completion_event):
          """ Save the converted images """
keep keep keep keep replace replace replace replace replace replace replace replace 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 
scripts/convert.py