                       batch["filename"], batch["prediction"], batch["rotmat"])
          return batch
  
      # <<< DETECTION IMAGE COMPILATION METHODS >>> #
      def _compile_detection_image(self, input_image):
          """ Compile the detection image for feeding into the model"""
          image = self._convert_color(input_image)
  
          image_size = image.shape[:2]
          scale = self._set_scale(image_size)
          pad = self._set_padding(image_size, scale)
  
 
</s> remove         image_size = image.shape[:2]
        scale = self._set_scale(image_size)
        pad = self._set_padding(image_size, scale)
 </s> add         Parameters
        ----------
        item: :class:`plugins.extract.pipeline.ExtractMedia`
            The input item from the pipeline
        """
        image = item.get_image_copy(self.colorformat)
        scale = self._set_scale(item.image_size)
        pad = self._set_padding(item.image_size, scale) </s> remove         image = self._scale_image(image, image_size, scale)
 </s> add         image = self._scale_image(image, item.image_size, scale) </s> remove         The normalization method is dictated by the command line argument:
            -nh (--normalization)
 </s> add         The normalization method is dictated by the command line argument `-nh (--normalization)` </s> remove         >>> for face in extractor.detected_faces():
        >>>     filename = face["filename"]
        >>>     image = face["image"]
        >>>     detected_faces = face["detected_faces"]
 </s> add         >>> for extract_media in extractor.detected_faces():
        >>>     filename = extract_media.filename
        >>>     image = extract_media.image
        >>>     detected_faces = extract_media.detected_faces </s> remove             logger.trace("Yielding: (filename: '%s', image: %s, detected_faces: %s)",
                         retval["filename"], retval["image"].shape, len(retval["detected_faces"]))
            yield retval
 </s> add             logger.trace("Final Output: (filename: '%s', image shape: %s, detected_faces: %s, "
                         "item: %s)",
                         output.filename, output.image_shape, output.detected_faces, output)
            yield output
    def _compile_detection_image(self, item):
        """ Compile the detection image for feeding into the model
                       batch["filename"], batch["prediction"], batch["rotmat"])
          return batch
  
      # <<< DETECTION IMAGE COMPILATION METHODS >>> #
    def _compile_detection_image(self, item):
        """ Compile the detection image for feeding into the model
    def _compile_detection_image(self, item):
        """ Compile the detection image for feeding into the model
    def _compile_detection_image(self, item):
        """ Compile the detection image for feeding into the model
  
          image_size = image.shape[:2]
          scale = self._set_scale(image_size)
          pad = self._set_padding(image_size, scale)
  
keep keep keep keep replace replace replace keep keep keep keep keep
 plugins.extract - Create ExtractMedia class for pipeline flow
Bugfix - Fix memory leak in extract 
plugins/extract/detect/_base.py