          logger.trace("Rotating bounding box")
          bounding_box = rotate_landmarks(bounding_box, rotation_matrix)
          return bounding_box
  
      @staticmethod
      def rotate_image_by_angle(image, angle,
                                rotated_width=None, rotated_height=None):
          """ Rotate an image by a given angle.
              From: https://stackoverflow.com/questions/22041699 """
  
          logger.trace("Rotating image: (angle: %s, rotated_width: %s, rotated_height: %s)",
                       angle, rotated_width, rotated_height)
 
</s> remove         logger.trace("Rotating image: (angle: %s, rotated_width: %s, rotated_height: %s)",
                     angle, rotated_width, rotated_height)
 </s> add         logger.trace("Rotating image: (image: %s, angle: %s)", image.shape, angle)
        channels_first = image.shape[0] <= 4
        if channels_first:
            image = np.moveaxis(image, 0, 2)
 </s> remove     def rotate_rect(bounding_box, rotation_matrix):
 </s> add     def _rotate_rect(bounding_box, rotation_matrix): </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         Pass in a DetectedFace object, Alignments dict or bounding box dict
        (as defined in lib/plugins/extract/detect/_base.py) """
 </s> add         Pass in a DetectedFace object or Alignments dict """
    def _rotate_image_by_angle(self, image, angle):
          logger.trace("Rotating bounding box")
          bounding_box = rotate_landmarks(bounding_box, rotation_matrix)
          return bounding_box
  
    def _rotate_image_by_angle(self, image, angle):
    def _rotate_image_by_angle(self, image, angle):
    def _rotate_image_by_angle(self, image, angle):
          """ Rotate an image by a given angle.
              From: https://stackoverflow.com/questions/22041699 """
  
          logger.trace("Rotating image: (angle: %s, rotated_width: %s, rotated_height: %s)",
                       angle, rotated_width, rotated_height)
keep keep keep keep 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 
plugins/extract/detect/_base.py