code: def postprocess(
      datas, results, output_dir="output", visualization=False
  ):
      # 检查输出目录
      if visualization:
          if not os.path.exists(output_dir):
              os.mkdir(output_dir)
  
      outputs = []
  
      for img, result, count in zip(datas, results, range(len(datas))):
          # 缩放回原尺寸
          output = cv2.resize(
              result,
              (img.shape[1], img.shape[0]),
              interpolation=cv2.INTER_CUBIC,
          )
  
          # 可视化输出
          if visualization:
              pfm_f, png_f = write_depth(
                  os.path.join(output_dir, str(count)), output, bits=2
              )
  
          outputs.append(output)
  
      return outputs
  
file_path: PaddleHub/modules/image/depth_estimation/MiDaS_Large/module.py
imports: import os
  import cv2
  import numpy
  from paddlehub import Module
  from paddlehub.module.module import moduleinfo
  from paddle.vision.transforms import Compose
  from MiDaS_Large.utils import write_depth
  from MiDaS_Large.inference import InferenceModel
  from MiDaS_Large.transforms import Resize, NormalizeImage, PrepareForNet
label_desc: Use image node_id 4 for calling the MiDaS_Large obj's underlying member method code with example usage: obj.postprocess(datas, results, output_dir, visualization) and returns: outputs