code: def postprocess(self, pred_img, origin_img, data, bg):
      trans_info = data["trans_info"]
      score_map = pred_img[0, 1, :, :]
  
      # post process
      if self.args.use_post_process:
          mask_original = score_map.copy()
          mask_original = (mask_original * 255).astype("uint8")
          _, mask_thr = cv2.threshold(
              mask_original, 240, 1, cv2.THRESH_BINARY
          )
          kernel_erode = cv2.getStructuringElement(
              cv2.MORPH_CROSS, (5, 5)
          )
          kernel_dilate = cv2.getStructuringElement(
              cv2.MORPH_CROSS, (25, 25)
          )
          mask_erode = cv2.erode(mask_thr, kernel_erode)
          mask_dilate = cv2.dilate(mask_erode, kernel_dilate)
          score_map *= mask_dilate
  
      # optical flow
      if self.args.use_optic_flow:
          score_map = 255 * score_map
          cur_gray = cv2.cvtColor(origin_img, cv2.COLOR_BGR2GRAY)
          cur_gray = cv2.resize(
              cur_gray, (pred_img.shape[-1], pred_img.shape[-2])
          )
          optflow_map = optic_flow_process(
              cur_gray,
              score_map,
              self.prev_gray,
              self.prev_cfd,
              self.disflow,
              self.is_first_frame,
          )
          self.prev_gray = cur_gray.copy()
          self.prev_cfd = optflow_map.copy()
          self.is_first_frame = False
          score_map = optflow_map / 255.0
  
      score_map = score_map[np.newaxis, np.newaxis, ...]
      score_map = reverse_transform(
          paddle.to_tensor(score_map), trans_info, mode="bilinear"
      )
      alpha = np.transpose(score_map.numpy().squeeze(1), [1, 2, 0])
  
      h, w, _ = origin_img.shape
      bg = cv2.resize(bg, (w, h))
      if bg.ndim == 2:
          bg = bg[..., np.newaxis]
  
      out = (alpha * origin_img + (1 - alpha) * bg).astype(np.uint8)
      return out
  
imports: import codecs
  import os
  import sys
  import time
  import yaml
  import numpy
  import cv2
  import paddle
  from paddle.inference import create_predictor, PrecisionType
  from paddle.inference import Config
  import paddleseg.transforms
  from paddleseg.core.infer import reverse_transform
  from paddleseg.cvlibs import manager
  from paddleseg.utils import TimeAverager
  from optic_flow_process import optic_flow_process
label_desc: Use image node_id 3 for calling the Predictor obj's underlying member method code with example usage: obj.postprocess(pred_img, origin_img, data, bg) and returns: out