  
      def __init__(self, model_path, allow_growth, minsize, threshold, factor):
          """
          minsize: minimum faces' size
          threshold: threshold=[th1, th2, th3], th1-3 are three steps's threshold
          factor: the factor used to create a scaling pyramid of face sizes to
                  detect in the image.
          pnet, rnet, onet: caffemodel
          """
          logger.debug("Initializing: %s: (model_path: '%s', allow_growth: %s, minsize: %s, "
 
</s> remove         self.valid = True  # Set to False if invalid params passed in to disable
 </s> add         self.valid = True  # Set to False if invalid parameters passed in to disable </s> remove     """ Parent class for Post Processing Actions
        Usuable in Extract or Convert or both
 </s> add     """ Parent class for Post Processing Actions. Usable in Extract or Convert or both </s> remove         if isinstance(item, dict):
            logger.trace("item: %s, queue: %s",
                         {k: v.shape if isinstance(v, np.ndarray) else v
                          for k, v in item.items()},
                         queue)
 </s> add         if isinstance(item, ExtractMedia):
            logger.trace("filename: '%s', image shape: %s, detected_faces: %s, queue: %s, "
                         "item: %s",
                         item.filename, item.image_shape, item.detected_faces, queue, item)
            self._extract_media[item.filename] = item </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
        threshold: threshold=[th1, th2, th3], th1-3 are three steps threshold
  
      def __init__(self, model_path, allow_growth, minsize, threshold, factor):
          """
          minsize: minimum faces' size
        threshold: threshold=[th1, th2, th3], th1-3 are three steps threshold
          factor: the factor used to create a scaling pyramid of face sizes to
                  detect in the image.
          pnet, rnet, onet: caffemodel
          """
          logger.debug("Initializing: %s: (model_path: '%s', allow_growth: %s, minsize: %s, "
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