          faces = self._normalize_faces(faces)
          batch["feed"] = np.array(faces, dtype="float32")[..., :3].transpose((0, 3, 1, 2))
          return batch
  
      def align_image(self, detected_faces):
          """ Align the incoming image for prediction """
          logger.trace("Aligning image around center")
          sizes = (self.input_size, self.input_size)
          rois = []
          faces = []
 
</s> remove         faces, batch["roi"] = self.align_image(batch["detected_faces"])
 </s> add         faces, batch["roi"] = self.align_image(batch) </s> remove         for face in detected_faces:
 </s> add         for face, image in zip(batch["detected_faces"], batch["image"]): </s> remove         batch["feed"] = batch["scaled_image"]
 </s> add         batch["feed"] = batch["image"] </s> remove         batch["feed"] = self.model.prepare_batch(batch["scaled_image"])
 </s> add         batch["feed"] = self.model.prepare_batch(batch["image"]) </s> remove         batch["feed"] = (batch["scaled_image"] - 127.5) / 127.5
 </s> add         batch["feed"] = (batch["image"] - 127.5) / 127.5
    def align_image(self, batch):
          faces = self._normalize_faces(faces)
          batch["feed"] = np.array(faces, dtype="float32")[..., :3].transpose((0, 3, 1, 2))
          return batch
  
    def align_image(self, batch):
          """ Align the incoming image for prediction """
          logger.trace("Aligning image around center")
          sizes = (self.input_size, self.input_size)
          rois = []
          faces = []
keep keep keep keep replace keep keep keep keep keep
 plugins.extract - Create ExtractMedia class for pipeline flow
Bugfix - Fix memory leak in extract 
plugins/extract/align/cv2_dnn.py