code: def preprocess(self, datas):
      input_datas = []
  
      for img in datas:
          # 归一化
          img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB) / 255.0
  
          # 图像变换
          img = self.transform({"image": img})["image"]
  
          # 新增维度
          input_data = img[np.newaxis, ...]
  
          input_datas.append(input_data)
  
      # 拼接数据
      input_datas = np.concatenate(input_datas, 0)
  
      return input_datas
  
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 3 for calling the MiDaS_Large obj's underlying member method code with example usage: obj.preprocess(datas) and returns: input_datas