text: """Compute depth maps for images in the input folder.
  """
  import os
  import glob
  import torch
  # from monodepth_net import MonoDepthNet
  # import utils
  import numpy as np
  import cv2
  import imageio
  
  
  def run_depth(img_names, input_path, output_path, model_path, Net, utils, target_w=None):
      """Run MonoDepthNN to compute depth maps.
  
      Args:
          input_path (str): path to input folder
          output_path (str): path to output folder
          model_path (str): path to saved model
      """
      print("initialize")
  
      # select device
      device = torch.device("cpu")
      print("device: %s" % device)
  
      # load network
      model = Net(model_path)
      model.to(device)
      model.eval()
  
      # get input
      # img_names = glob.glob(os.path.join(input_path, "*"))
      num_images = len(img_names)
  
      # create output folder
      os.makedirs(output_path, exist_ok=True)
  
      print("start processing")
  
      for ind, img_name in enumerate(img_names):
  
          print("  processing {} ({}/{})".format(img_name, ind + 1, num_images))
  
          # input
          img = utils.read_image(img_name)
          w = img.shape[1]
          scale = 640. / max(img.shape[0], img.shape[1])
          target_height, target_width = int(round(img.shape[0] * scale)), int(round(img.shape[1] * scale))
          img_input = utils.resize_image(img)
          print(img_input.shape)
          img_input = img_input.to(device)
          # compute
          with torch.no_grad():
              out = model.forward(img_input)
          
          depth = utils.resize_depth(out, target_width, target_height)
          img = cv2.resize((img * 255).astype(np.uint8), (target_width, target_height), interpolation=cv2.INTER_AREA)
  
          filename = os.path.join(
              output_path, os.path.splitext(os.path.basename(img_name))[0]
          )
          np.save(filename + '.npy', depth)
          utils.write_depth(filename, depth, bits=2)
  
      print("finished")
  
  
  # if __name__ == "__main__":
  #     # set paths
  #     INPUT_PATH = "image"
  #     OUTPUT_PATH = "output"
  #     MODEL_PATH = "model.pt"
  
  #     # set torch options
  #     torch.backends.cudnn.enabled = True
  #     torch.backends.cudnn.benchmark = True
  
  #     # compute depth maps
  #     run_depth(INPUT_PATH, OUTPUT_PATH, MODEL_PATH, Net, target_w=640)
  