code: def postprocess(
      self, outputs, visualization=False, output_dir="output"
  ):
      results = []
      if visualization and not os.path.exists(output_dir):
          os.mkdir(output_dir)
  
      for i, image in enumerate(self.imgs):
          # normalization
          pred = outputs[i, 0, :, :]
  
          pred = self.normPRED(pred)
  
          # convert torch tensor to numpy array
          h, w = image.shape[:2]
          mask = cv2.resize(pred, (w, h))
  
          output_img = (
              image * mask[..., np.newaxis]
              + (1 - mask[..., np.newaxis]) * 255
          ).astype(np.uint8)
  
          mask = (mask * 255).astype(np.uint8)
  
          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),
                  output_img,
              )
  
          results.append({"mask": mask, "front": output_img})
  
      return results
  
file_path: PaddleHub/modules/image/semantic_segmentation/U2Netp/processor.py
imports: import os
  import cv2
  import numpy
label_desc: Use image node_id 5 for calling the Processor obj's underlying member method code with example usage: obj.postprocess(outputs, visualization, output_dir) and returns: results