          face.h = int(height)
          face.r = 0
          if len(rotated) > 1:
              rotated_landmarks = [tuple(point) for point in rotated[1].tolist()]
              face.landmarksXY = rotated_landmarks
      elif isinstance(face, dict) and "x" in face:
          face["x"] = int(pt_x)
          face["y"] = int(pt_y)
          face["w"] = int(width)
          face["h"] = int(height)
          face["r"] = 0
          if len(rotated) > 1:
              rotated_landmarks = [tuple(point) for point in rotated[1].tolist()]
              face["landmarksXY"] = rotated_landmarks
      else:
          face["left"] = int(pt_x)
          face["top"] = int(pt_y)
          face["right"] = int(pt_x1)
          face["bottom"] = int(pt_y1)
 
</s> remove         landmarks = face.landmarksXY
 </s> add         landmarks = face.landmarks_xy </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             passed_angles = [
                int(angle)
                for angle in rotation.split(",")
                if int(angle) != 0
            ]
 </s> add             passed_angles = [int(angle)
                             for angle in rotation.split(",")
                             if int(angle) != 0] </s> remove                 exception = faces.get("exception", False)
                if exception:
                    break
 </s> add 
            face.landmarks_xy = rotated_landmarks
          face.h = int(height)
          face.r = 0
          if len(rotated) > 1:
              rotated_landmarks = [tuple(point) for point in rotated[1].tolist()]
            face.landmarks_xy = rotated_landmarks
            face.landmarks_xy = rotated_landmarks
            face.landmarks_xy = rotated_landmarks
            face.landmarks_xy = rotated_landmarks
            face.landmarks_xy = rotated_landmarks
            face.landmarks_xy = rotated_landmarks
            face.landmarks_xy = rotated_landmarks
            face.landmarks_xy = rotated_landmarks
            face.landmarks_xy = rotated_landmarks
            face.landmarks_xy = rotated_landmarks
      else:
          face["left"] = int(pt_x)
          face["top"] = int(pt_y)
          face["right"] = int(pt_x1)
          face["bottom"] = int(pt_y1)
keep keep keep keep replace 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 
lib/utils.py