                  logger.trace("Continuing. Current batchsize: %s", faces_seen)
                  continue
  
              if batch:
                  detected_batch = [detected_face for item in batch
                                    for detected_face in item["detected_faces"]]
                  if faces_seen != 0:
                      feed_faces = self.compile_feed_faces(detected_batch)
 
</s> remove                 feed_faces = self.compile_feed_faces(detected_batch)
                predicted = self.predict(feed_faces)
 </s> add                 if faces_seen != 0:
                    feed_faces = self.compile_feed_faces(detected_batch)
                    predicted = self.predict(feed_faces)
                else:
                    predicted = list() </s> remove                 if faces_count != 0:
                    self.pre_process.do_actions(item)
                    self.faces_count += faces_count
 </s> add                 self.faces_count += faces_count </s> remove         for idx, detected_face in enumerate(output_item["detected_faces"]):
            if not self.filter.check(detected_face["face"]):
 </s> add         for idx, detect_face in enumerate(output_item["detected_faces"]):
            check_item = detect_face["face"] if isinstance(detect_face, dict) else detect_face
            if not self.filter.check(check_item): </s> remove                                detected_face["file_location"].parts[-1], idx)
 </s> add                                output_item["filename"], idx) </s> remove         self.pre_process = PostProcess(arguments)
 </s> add 
                logger.trace("Batching to predictor. Frames: %s, Faces: %s",
                             len(batch), faces_seen)
                  logger.trace("Continuing. Current batchsize: %s", faces_seen)
                  continue
  
              if batch:
                logger.trace("Batching to predictor. Frames: %s, Faces: %s",
                             len(batch), faces_seen)
                  detected_batch = [detected_face for item in batch
                                    for detected_face in item["detected_faces"]]
                  if faces_seen != 0:
                      feed_faces = self.compile_feed_faces(detected_batch)
keep keep keep add keep keep keep keep
 Bugfix: Filter and NFilter for convert 
scripts/convert.py