text: from __future__ import division, print_function, absolute_import
  import glob
  import os.path as osp
  from scipy.io import loadmat
  
  from torchreid.utils import read_json, write_json
  
  from ..dataset import ImageDataset
  
  
  class GRID(ImageDataset):
      """GRID.
  
      Reference:
          Loy et al. Multi-camera activity correlation analysis. CVPR 2009.
  
      URL: `<http://personal.ie.cuhk.edu.hk/~ccloy/downloads_qmul_underground_reid.html>`_
      
      Dataset statistics:
          - identities: 250.
          - images: 1275.
          - cameras: 8.
      """
      dataset_dir = 'grid'
      dataset_url = 'http://personal.ie.cuhk.edu.hk/~ccloy/files/datasets/underground_reid.zip'
      _junk_pids = [0]
  
      def __init__(self, root='', split_id=0, **kwargs):
          self.root = osp.abspath(osp.expanduser(root))
          self.dataset_dir = osp.join(self.root, self.dataset_dir)
          self.download_dataset(self.dataset_dir, self.dataset_url)
  
          self.probe_path = osp.join(
              self.dataset_dir, 'underground_reid', 'probe'
          )
          self.gallery_path = osp.join(
              self.dataset_dir, 'underground_reid', 'gallery'
          )
          self.split_mat_path = osp.join(
              self.dataset_dir, 'underground_reid', 'features_and_partitions.mat'
          )
          self.split_path = osp.join(self.dataset_dir, 'splits.json')
  
          required_files = [
              self.dataset_dir, self.probe_path, self.gallery_path,
              self.split_mat_path
          ]
          self.check_before_run(required_files)
  
          self.prepare_split()
          splits = read_json(self.split_path)
          if split_id >= len(splits):
              raise ValueError(
                  'split_id exceeds range, received {}, '
                  'but expected between 0 and {}'.format(
                      split_id,
                      len(splits) - 1
                  )
              )
          split = splits[split_id]
  
          train = split['train']
          query = split['query']
          gallery = split['gallery']
  
          train = [tuple(item) for item in train]
          query = [tuple(item) for item in query]
          gallery = [tuple(item) for item in gallery]
  
          super(GRID, self).__init__(train, query, gallery, **kwargs)
  
      def prepare_split(self):
          if not osp.exists(self.split_path):
              print('Creating 10 random splits')
              split_mat = loadmat(self.split_mat_path)
              trainIdxAll = split_mat['trainIdxAll'][0] # length = 10
              probe_img_paths = sorted(
                  glob.glob(osp.join(self.probe_path, '*.jpeg'))
              )
              gallery_img_paths = sorted(
                  glob.glob(osp.join(self.gallery_path, '*.jpeg'))
              )
  
              splits = []
              for split_idx in range(10):
                  train_idxs = trainIdxAll[split_idx][0][0][2][0].tolist()
                  assert len(train_idxs) == 125
                  idx2label = {
                      idx: label
                      for label, idx in enumerate(train_idxs)
                  }
  
                  train, query, gallery = [], [], []
  
                  # processing probe folder
                  for img_path in probe_img_paths:
                      img_name = osp.basename(img_path)
                      img_idx = int(img_name.split('_')[0])
                      camid = int(
                          img_name.split('_')[1]
                      ) - 1 # index starts from 0
                      if img_idx in train_idxs:
                          train.append((img_path, idx2label[img_idx], camid))
                      else:
                          query.append((img_path, img_idx, camid))
  
                  # process gallery folder
                  for img_path in gallery_img_paths:
                      img_name = osp.basename(img_path)
                      img_idx = int(img_name.split('_')[0])
                      camid = int(
                          img_name.split('_')[1]
                      ) - 1 # index starts from 0
                      if img_idx in train_idxs:
                          train.append((img_path, idx2label[img_idx], camid))
                      else:
                          gallery.append((img_path, img_idx, camid))
  
                  split = {
                      'train': train,
                      'query': query,
                      'gallery': gallery,
                      'num_train_pids': 125,
                      'num_query_pids': 125,
                      'num_gallery_pids': 900
                  }
                  splits.append(split)
  
              print('Totally {} splits are created'.format(len(splits)))
              write_json(splits, self.split_path)
              print('Split file saved to {}'.format(self.split_path))
  