code: def postprocess_v(
      cur_gray, scoremap, prev_gray, pre_cfd, disflow, is_init
  ):
      """光流优化
      Args:
          cur_gray : 当前帧灰度图
          pre_gray : 前一帧灰度图
          pre_cfd  ：前一帧融合结果
          scoremap : 当前帧分割结果
          difflow  : 光流
          is_init : 是否第一帧
      Returns:
          fusion_cfd : 光流追踪图和预测结果融合图
      """
      h, w = scoremap.shape
      cur_cfd = scoremap.copy()
  
      if is_init:
          if h <= 64 or w <= 64:
              disflow.setFinestScale(1)
          elif h <= 160 or w <= 160:
              disflow.setFinestScale(2)
          else:
              disflow.setFinestScale(3)
          fusion_cfd = cur_cfd
      else:
          weights = np.ones((h, w), np.float32) * 0.3
          track_cfd, is_track, weights = human_seg_tracking(
              prev_gray, cur_gray, pre_cfd, weights, disflow
          )
          fusion_cfd = human_seg_track_fuse(
              track_cfd, cur_cfd, weights, is_track
          )
  
      return fusion_cfd
  
imports: import numpy
label_desc: Use image node_id 4 for calling a global function with example usage: postprocess_v(cur_gray, scoremap, prev_gray, pre_cfd, disflow, is_init) and returns: fusion_cfd