text: import numpy as np
  
  pred_data_label_filenames = [line.rstrip() for line in open('all_pred_data_label_filelist.txt')]
  gt_label_filenames = [f.rstrip('_pred\.txt') + '_gt.txt' for f in pred_data_label_filenames]
  num_room = len(gt_label_filenames)
  
  
  gt_classes = [0 for _ in range(13)]
  positive_classes = [0 for _ in range(13)]
  true_positive_classes = [0 for _ in range(13)]
  for i in range(num_room):
      print(i)
      data_label = np.loadtxt(pred_data_label_filenames[i])
      pred_label = data_label[:,-1]
      gt_label = np.loadtxt(gt_label_filenames[i])
      print((gt_label.shape))
      for j in range(gt_label.shape[0]):
          gt_l = int(gt_label[j])
          pred_l = int(pred_label[j])
          gt_classes[gt_l] += 1
          positive_classes[pred_l] += 1
          true_positive_classes[gt_l] += int(gt_l==pred_l)
  
  
  print(gt_classes)
  print(positive_classes)
  print(true_positive_classes)
  
  
  print(('Overall accuracy: {0}'.format(sum(true_positive_classes)/float(sum(positive_classes)))))
  
  print('IoU:')
  iou_list = []
  for i in range(13):
      iou = true_positive_classes[i]/float(gt_classes[i]+positive_classes[i]-true_positive_classes[i]) 
      print(iou)
      iou_list.append(iou)
  
  print('Mean IoU:')
  print((sum(iou_list)/13.0))
  