code: def postprocess(self, outputs, datas, output_dir, visualization):
      # 检查输出目录
      if visualization:
          if not os.path.exists(output_dir):
              os.mkdir(output_dir)
  
      # 拼接输出
      outputs = paddle.concat(outputs)
  
      results = []
      for output, img, i in zip(outputs, datas, range(len(datas))):
          # 计算MASK
          mask = 1 - (output[0] > 0).numpy().astype("float32")
  
          # 缩放
          h, w = img.shape[:2]
          mask = cv2.resize(mask, (w, h))
  
          # 计算输出图像
          result = (
              img * mask[..., np.newaxis]
              + (1 - mask[..., np.newaxis]) * 255
          ).astype(np.uint8)
  
          # 格式还原
          mask = (mask * 255).astype(np.uint8)
  
          # 可视化
          if visualization:
              cv2.imwrite(
                  os.path.join(output_dir, "result_mask_%d.png" % i),
                  mask,
              )
              cv2.imwrite(
                  os.path.join(output_dir, "result_%d.png" % i), result
              )
  
          results.append({"mask": mask, "result": result})
  
      return results
  
file_path: PaddleHub/modules/image/semantic_segmentation/SINet_Portrait_Segmentation/module.py
imports: import os
  import cv2
  import paddle
  import numpy
  from paddle.nn import Layer
  from paddlehub.module.module import moduleinfo
label_desc: Use image node_id 4 for calling the SINet_Portrait_Segmentation obj's underlying member method code with example usage: obj.postprocess(outputs, datas, output_dir, visualization) and returns: results