text: import numpy as np
  import argparse
  import json
  from PIL import Image
  from os.path import join
  
  
  def fast_hist(a, b, n):
      k = (a >= 0) & (a < n)
      return np.bincount(n * a[k].astype(int) + b[k], minlength=n ** 2).reshape(n, n)
  
  
  def per_class_iu(hist):
      return np.diag(hist) / (hist.sum(1) + hist.sum(0) - np.diag(hist))
  
  
  def label_mapping(input, mapping):
      output = np.copy(input)
      for ind in range(len(mapping)):
          output[input == mapping[ind][0]] = mapping[ind][1]
      return np.array(output, dtype=np.int64)
  
  
  def compute_mIoU(gt_dir, pred_dir, devkit_dir=''):
      """
      Compute IoU given the predicted colorized images and 
      """
      with open(join(devkit_dir, 'info.json'), 'r') as fp:
        info = json.load(fp)
      num_classes = np.int(info['classes'])
      print(('Num classes', num_classes))
      name_classes = np.array(info['label'], dtype=np.str)
      mapping = np.array(info['label2train'], dtype=np.int)
      hist = np.zeros((num_classes, num_classes))
  
      image_path_list = join(devkit_dir, 'val.txt')
      label_path_list = join(devkit_dir, 'label.txt')
      gt_imgs = open(label_path_list, 'r').read().splitlines()
      gt_imgs = [join(gt_dir, x) for x in gt_imgs]
      pred_imgs = open(image_path_list, 'r').read().splitlines()
      pred_imgs = [join(pred_dir, x.split('/')[-1]) for x in pred_imgs]
  
      for ind in range(len(gt_imgs)):
          pred = np.array(Image.open(pred_imgs[ind]))
          label = np.array(Image.open(gt_imgs[ind]))
          label = label_mapping(label, mapping)
          if len(label.shape) == 3 and label.shape[2]==4:
              label = label[:,:,0]
          if len(label.flatten()) != len(pred.flatten()):
              print(('Skipping: len(gt) = {:d}, len(pred) = {:d}, {:s}, {:s}'.format(len(label.flatten()), len(pred.flatten()), gt_imgs[ind], pred_imgs[ind])))
              continue
          hist += fast_hist(label.flatten(), pred.flatten(), num_classes)
          if ind > 0 and ind % 10 == 0:
              print(('{:d} / {:d}: {:0.2f}'.format(ind, len(gt_imgs), 100*np.mean(per_class_iu(hist)))))
      
      mIoUs = per_class_iu(hist)
      for ind_class in range(num_classes):
          print(('===>' + name_classes[ind_class] + ':\t' + str(round(mIoUs[ind_class] * 100, 2))))
      print(('===> mIoU: ' + str(round(np.nanmean(mIoUs) * 100, 2))))
      return mIoUs
  
  
  def main(args):
     compute_mIoU(args.gt_dir, args.pred_dir, args.devkit_dir)
  
  
  if __name__ == "__main__":
      parser = argparse.ArgumentParser()
      parser.add_argument('gt_dir', type=str, help='directory which stores CityScapes val gt images')
      parser.add_argument('pred_dir', type=str, help='directory which stores CityScapes val pred images')
      parser.add_argument('--devkit_dir', default='dataset/cityscapes_list', help='base directory of cityscapes')
      args = parser.parse_args()
      main(args)
  