text: import os
  
  from PIL import Image
  from tqdm import tqdm
  
  from pspnet import PSPNet
  from utils.utils_metrics import compute_mIoU, show_results
  
  '''
  进行指标评估需要注意以下几点：
  1、该文件生成的图为灰度图，因为值比较小，按照PNG形式的图看是没有显示效果的，所以看到近似全黑的图是正常的。
  2、该文件计算的是验证集的miou，当前该库将测试集当作验证集使用，不单独划分测试集
  '''
  if __name__ == "__main__":
      #---------------------------------------------------------------------------#
      #   miou_mode用于指定该文件运行时计算的内容
      #   miou_mode为0代表整个miou计算流程，包括获得预测结果、计算miou。
      #   miou_mode为1代表仅仅获得预测结果。
      #   miou_mode为2代表仅仅计算miou。
      #---------------------------------------------------------------------------#
      miou_mode       = 0
      #------------------------------#
      #   分类个数+1、如2+1
      #------------------------------#
      num_classes     = 6
      #--------------------------------------------#
      #   区分的种类，和json_to_dataset里面的一样
      #--------------------------------------------#
      #name_classes    = ["background","aeroplane", "bicycle", "bird", "boat", "bottle", "bus", "car", "cat", "chair", "cow", "diningtable", "dog", "horse", "motorbike", "person", "pottedplant", "sheep", "sofa", "train", "tvmonitor"]
      # name_classes    = ["_background_","cat","dog"]
      name_classes = ["impervious surfaces","building","low vegetation","tree","car","clutter/background"]
      #-------------------------------------------------------#
      #   指向VOC数据集所在的文件夹
      #   默认指向根目录下的VOC数据集
      #-------------------------------------------------------#
      VOCdevkit_path  = 'dataset2'
  
      image_ids       = open(os.path.join(VOCdevkit_path, "VOC2007/ImageSets/Segmentation/val.txt"),'r').read().splitlines() 
      gt_dir          = os.path.join(VOCdevkit_path, "VOC2007/SegmentationClass/")
      miou_out_path   = "miou_out"
      pred_dir        = os.path.join(miou_out_path, 'detection-results')
  
      if miou_mode == 0 or miou_mode == 1:
          if not os.path.exists(pred_dir):
              os.makedirs(pred_dir)
              
          print("Load model.")
          pspnet = PSPNet()
          print("Load model done.")
  
          print("Get predict result.")
          for image_id in tqdm(image_ids):
              image_path  = os.path.join(VOCdevkit_path, "VOC2007/JPEGImages/"+image_id+".jpg")
              image       = Image.open(image_path)
              image       = pspnet.get_miou_png(image)
              image.save(os.path.join(pred_dir, image_id + ".png"))
          print("Get predict result done.")
  
      if miou_mode == 0 or miou_mode == 2:
          print("Get miou.")
          hist, IoUs, PA_Recall, Precision = compute_mIoU(gt_dir, pred_dir, image_ids, num_classes, name_classes)  # 执行计算mIoU的函数
          print("Get miou done.")
          show_results(miou_out_path, hist, IoUs, PA_Recall, Precision, name_classes)
  