text: # Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
  #
  # Licensed under the Apache License, Version 2.0 (the "License");
  # you may not use this file except in compliance with the License.
  # You may obtain a copy of the License at
  #
  #     http://www.apache.org/licenses/LICENSE-2.0
  #
  # Unless required by applicable law or agreed to in writing, software
  # distributed under the License is distributed on an "AS IS" BASIS,
  # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
  # See the License for the specific language governing permissions and
  # limitations under the License.
  
  import os
  import glob
  
  from paddleseg.datasets import Dataset
  from paddleseg.cvlibs import manager
  from paddleseg.transforms import Compose
  
  
  @manager.DATASETS.add_component
  class Cityscapes(Dataset):
      """
      Cityscapes dataset `https://www.cityscapes-dataset.com/`.
      The folder structure is as follow:
  
          cityscapes
          |
          |--leftImg8bit
          |  |--train
          |  |--val
          |  |--test
          |
          |--gtFine
          |  |--train
          |  |--val
          |  |--test
  
      Make sure there are **labelTrainIds.png in gtFine directory. If not, please run the conver_cityscapes.py in tools.
  
      Args:
          transforms (list): Transforms for image.
          dataset_root (str): Cityscapes dataset directory.
          mode (str, optional): Which part of dataset to use. it is one of ('train', 'val', 'test'). Default: 'train'.
          edge (bool, optional): Whether to compute edge while training. Default: False
      """
      NUM_CLASSES = 19
  
      def __init__(self, transforms, dataset_root, mode='train', edge=False):
          self.dataset_root = dataset_root
          self.transforms = Compose(transforms)
          self.file_list = list()
          mode = mode.lower()
          self.mode = mode
          self.num_classes = self.NUM_CLASSES
          self.ignore_index = 255
          self.edge = edge
  
          if mode not in ['train', 'val', 'test']:
              raise ValueError(
                  "mode should be 'train', 'val' or 'test', but got {}.".format(
                      mode))
  
          if self.transforms is None:
              raise ValueError("`transforms` is necessary, but it is None.")
  
          img_dir = os.path.join(self.dataset_root, 'leftImg8bit')
          label_dir = os.path.join(self.dataset_root, 'gtFine')
          if self.dataset_root is None or not os.path.isdir(
                  self.dataset_root) or not os.path.isdir(
                      img_dir) or not os.path.isdir(label_dir):
              raise ValueError(
                  "The dataset is not Found or the folder structure is nonconfoumance."
              )
  
          label_files = sorted(
              glob.glob(
                  os.path.join(label_dir, mode, '*',
                               '*_gtFine_labelTrainIds.png')))
          img_files = sorted(
              glob.glob(os.path.join(img_dir, mode, '*', '*_leftImg8bit.png')))
  
          self.file_list = [
              [img_path, label_path]
              for img_path, label_path in zip(img_files, label_files)
          ]
  