          self.model = MTCNN(self.model_path, self.config["allow_growth"], **self.kwargs)
  
      def process_input(self, batch):
          """ Compile the detection image(s) for prediction """
          batch["feed"] = (batch["scaled_image"] - 127.5) / 127.5
          return batch
  
      def predict(self, batch):
          """ Run model to get predictions """
          prediction, points = self.model.detect_faces(batch["feed"])
 
</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"]
 </s> add         batch["feed"] = batch["image"] </s> remove         batch["feed"] = cv2.dnn.blobFromImages(batch["scaled_image"],  # pylint: disable=no-member
 </s> add         batch["feed"] = cv2.dnn.blobFromImages(batch["image"],  # pylint: disable=no-member </s> remove         faces, batch["roi"] = self.align_image(batch["detected_faces"])
 </s> add         faces, batch["roi"] = self.align_image(batch) </s> remove     def align_image(self, detected_faces):
 </s> add     def align_image(self, batch):
        batch["feed"] = (batch["image"] - 127.5) / 127.5
          self.model = MTCNN(self.model_path, self.config["allow_growth"], **self.kwargs)
  
      def process_input(self, batch):
          """ Compile the detection image(s) for prediction """
        batch["feed"] = (batch["image"] - 127.5) / 127.5
          return batch
  
      def predict(self, batch):
          """ Run model to get predictions """
          prediction, points = self.model.detect_faces(batch["feed"])
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/detect/mtcnn.py