code: def human_seg_track_fuse(track_cfd, dl_cfd, dl_weights, is_track):
      """光流追踪图和人像分割结构融合
      输入参数:
          track_cfd: 光流追踪图
          dl_cfd: 当前帧分割结果
          dl_weights: 融合权重图
          is_track: 光流点匹配二值图
      返回
          cur_cfd: 光流跟踪图和人像分割结果融合图
      """
      fusion_cfd = dl_cfd.copy()
      is_track = is_track.astype(np.bool)
      fusion_cfd[is_track] = (
          dl_weights[is_track] * dl_cfd[is_track]
          + (1 - dl_weights[is_track]) * track_cfd[is_track]
      )
      # 确定区域
      index_certain = ((dl_cfd > 0.9) + (dl_cfd < 0.1)) * is_track
      index_less01 = (dl_weights < 0.1) * index_certain
      fusion_cfd[index_less01] = (
          0.3 * dl_cfd[index_less01] + 0.7 * track_cfd[index_less01]
      )
      index_larger09 = (dl_weights >= 0.1) * index_certain
      fusion_cfd[index_larger09] = (
          0.4 * dl_cfd[index_larger09] + 0.6 * track_cfd[index_larger09]
      )
      return fusion_cfd
  
imports: import numpy
label_desc: Use image node_id 2 for calling a global function with example usage: human_seg_track_fuse(track_cfd, dl_cfd, dl_weights, is_track) and returns: fusion_cfd