text: # encoding: utf-8
  
  import h5py
  import os.path as osp
  from scipy.io import loadmat
  from scipy.misc import imsave
  
  from utils.iotools import mkdir_if_missing, write_json, read_json
  from .bases import BaseImageDataset
  
  
  class CUHK03(BaseImageDataset):
      """
      CUHK03
      Reference:
      Li et al. DeepReID: Deep Filter Pairing Neural Network for Person Re-identification. CVPR 2014.
      URL: http://www.ee.cuhk.edu.hk/~xgwang/CUHK_identification.html#!
  
      Dataset statistics:
      # identities: 1360
      # images: 13164
      # cameras: 6
      # splits: 20 (classic)
      Args:
          split_id (int): split index (default: 0)
          cuhk03_labeled (bool): whether to load labeled images; if false, detected images are loaded (default: False)
      """
      dataset_dir = 'cuhk03'
  
      def __init__(self, root='./toDataset', split_id=0, cuhk03_labeled=False,
                   cuhk03_classic_split=False, verbose=True,
                   **kwargs):
          super(CUHK03, self).__init__()
          self.dataset_dir = osp.join(root, self.dataset_dir)
          self.data_dir = osp.join(self.dataset_dir, 'cuhk03_release')
          self.raw_mat_path = osp.join(self.data_dir, 'cuhk-03.mat')
  
          self.imgs_detected_dir = osp.join(self.dataset_dir, 'images_detected')
          self.imgs_labeled_dir = osp.join(self.dataset_dir, 'images_labeled')
  
          self.split_classic_det_json_path = osp.join(self.dataset_dir, 'splits_classic_detected.json')
          self.split_classic_lab_json_path = osp.join(self.dataset_dir, 'splits_classic_labeled.json')
  
          self.split_new_det_json_path = osp.join(self.dataset_dir, 'splits_new_detected.json')
          self.split_new_lab_json_path = osp.join(self.dataset_dir, 'splits_new_labeled.json')
  
          self.split_new_det_mat_path = osp.join(self.dataset_dir, 'cuhk03_new_protocol_config_detected.mat')
          self.split_new_lab_mat_path = osp.join(self.dataset_dir, 'cuhk03_new_protocol_config_labeled.mat')
  
          self._check_before_run()
          self._preprocess()
  
          if cuhk03_labeled:
              image_type = 'labeled'
              split_path = self.split_classic_lab_json_path if cuhk03_classic_split else self.split_new_lab_json_path
          else:
              image_type = 'detected'
              split_path = self.split_classic_det_json_path if cuhk03_classic_split else self.split_new_det_json_path
  
          splits = read_json(split_path)
          assert split_id < len(splits), "Condition split_id ({}) < len(splits) ({}) is false".format(split_id,
                                                                                                      len(splits))
          split = splits[split_id]
          print("Split index = {}".format(split_id))
  
          train = split['train']
          query = split['query']
          gallery = split['gallery']
  
          if verbose:
              print("=> CUHK03 ({}) loaded".format(image_type))
              self.print_dataset_statistics(train, query, gallery)
  
          self.train = train
          self.query = query
          self.gallery = gallery
  
          self.num_train_pids, self.num_train_imgs, self.num_train_cams = self.get_imagedata_info(self.train)
          self.num_query_pids, self.num_query_imgs, self.num_query_cams = self.get_imagedata_info(self.query)
          self.num_gallery_pids, self.num_gallery_imgs, self.num_gallery_cams = self.get_imagedata_info(self.gallery)
  
      def _check_before_run(self):
          """Check if all files are available before going deeper"""
          if not osp.exists(self.dataset_dir):
              raise RuntimeError("'{}' is not available".format(self.dataset_dir))
          if not osp.exists(self.data_dir):
              raise RuntimeError("'{}' is not available".format(self.data_dir))
          if not osp.exists(self.raw_mat_path):
              raise RuntimeError("'{}' is not available".format(self.raw_mat_path))
          if not osp.exists(self.split_new_det_mat_path):
              raise RuntimeError("'{}' is not available".format(self.split_new_det_mat_path))
          if not osp.exists(self.split_new_lab_mat_path):
              raise RuntimeError("'{}' is not available".format(self.split_new_lab_mat_path))
  
      def _preprocess(self):
          """
          This function is a bit complex and ugly, what it does is
          1. Extract data from cuhk-03.mat and save as png images.
          2. Create 20 classic splits. (Li et al. CVPR'14)
          3. Create new split. (Zhong et al. CVPR'17)
          """
          print(
              "Note: if root path is changed, the previously generated json files need to be re-generated (delete them first)")
          if osp.exists(self.imgs_labeled_dir) and \
                  osp.exists(self.imgs_detected_dir) and \
                  osp.exists(self.split_classic_det_json_path) and \
                  osp.exists(self.split_classic_lab_json_path) and \
                  osp.exists(self.split_new_det_json_path) and \
                  osp.exists(self.split_new_lab_json_path):
              return
  
          mkdir_if_missing(self.imgs_detected_dir)
          mkdir_if_missing(self.imgs_labeled_dir)
  
          print("Extract image data from {} and save as png".format(self.raw_mat_path))
          mat = h5py.File(self.raw_mat_path, 'r')
  
          def _deref(ref):
              return mat[ref][:].T
  
          def _process_images(img_refs, campid, pid, save_dir):
              img_paths = []  # Note: some persons only have images for one view
              for imgid, img_ref in enumerate(img_refs):
                  img = _deref(img_ref)
                  # skip empty cell
                  if img.size == 0 or img.ndim < 3: continue
                  # images are saved with the following format, index-1 (ensure uniqueness)
                  # campid: index of camera pair (1-5)
                  # pid: index of person in 'campid'-th camera pair
                  # viewid: index of view, {1, 2}
                  # imgid: index of image, (1-10)
                  viewid = 1 if imgid < 5 else 2
                  img_name = '{:01d}_{:03d}_{:01d}_{:02d}.png'.format(campid + 1, pid + 1, viewid, imgid + 1)
                  img_path = osp.join(save_dir, img_name)
                  if not osp.isfile(img_path):
                      imsave(img_path, img)
                  img_paths.append(img_path)
              return img_paths
  
          def _extract_img(name):
              print("Processing {} images (extract and save) ...".format(name))
              meta_data = []
              imgs_dir = self.imgs_detected_dir if name == 'detected' else self.imgs_labeled_dir
              for campid, camp_ref in enumerate(mat[name][0]):
                  camp = _deref(camp_ref)
                  num_pids = camp.shape[0]
                  for pid in range(num_pids):
                      img_paths = _process_images(camp[pid, :], campid, pid, imgs_dir)
                      assert len(img_paths) > 0, "campid{}-pid{} has no images".format(campid, pid)
                      meta_data.append((campid + 1, pid + 1, img_paths))
                  print("- done camera pair {} with {} identities".format(campid + 1, num_pids))
              return meta_data
  
          meta_detected = _extract_img('detected')
          meta_labeled = _extract_img('labeled')
  
          def _extract_classic_split(meta_data, test_split):
              train, test = [], []
              num_train_pids, num_test_pids = 0, 0
              num_train_imgs, num_test_imgs = 0, 0
              for i, (campid, pid, img_paths) in enumerate(meta_data):
  
                  if [campid, pid] in test_split:
                      for img_path in img_paths:
                          camid = int(osp.basename(img_path).split('_')[2]) - 1  # make it 0-based
                          test.append((img_path, num_test_pids, camid))
                      num_test_pids += 1
                      num_test_imgs += len(img_paths)
                  else:
                      for img_path in img_paths:
                          camid = int(osp.basename(img_path).split('_')[2]) - 1  # make it 0-based
                          train.append((img_path, num_train_pids, camid))
                      num_train_pids += 1
                      num_train_imgs += len(img_paths)
              return train, num_train_pids, num_train_imgs, test, num_test_pids, num_test_imgs
  
          print("Creating classic splits (# = 20) ...")
          splits_classic_det, splits_classic_lab = [], []
          for split_ref in mat['testsets'][0]:
              test_split = _deref(split_ref).tolist()
  
              # create split for detected images
              train, num_train_pids, num_train_imgs, test, num_test_pids, num_test_imgs = \
                  _extract_classic_split(meta_detected, test_split)
              splits_classic_det.append({
                  'train': train, 'query': test, 'gallery': test,
                  'num_train_pids': num_train_pids, 'num_train_imgs': num_train_imgs,
                  'num_query_pids': num_test_pids, 'num_query_imgs': num_test_imgs,
                  'num_gallery_pids': num_test_pids, 'num_gallery_imgs': num_test_imgs,
              })
  
              # create split for labeled images
              train, num_train_pids, num_train_imgs, test, num_test_pids, num_test_imgs = \
                  _extract_classic_split(meta_labeled, test_split)
              splits_classic_lab.append({
                  'train': train, 'query': test, 'gallery': test,
                  'num_train_pids': num_train_pids, 'num_train_imgs': num_train_imgs,
                  'num_query_pids': num_test_pids, 'num_query_imgs': num_test_imgs,
                  'num_gallery_pids': num_test_pids, 'num_gallery_imgs': num_test_imgs,
              })
  
          write_json(splits_classic_det, self.split_classic_det_json_path)
          write_json(splits_classic_lab, self.split_classic_lab_json_path)
  
          def _extract_set(filelist, pids, pid2label, idxs, img_dir, relabel):
              tmp_set = []
              unique_pids = set()
              for idx in idxs:
                  img_name = filelist[idx][0]
                  camid = int(img_name.split('_')[2]) - 1  # make it 0-based
                  pid = pids[idx]
                  if relabel: pid = pid2label[pid]
                  img_path = osp.join(img_dir, img_name)
                  tmp_set.append((img_path, int(pid), camid))
                  unique_pids.add(pid)
              return tmp_set, len(unique_pids), len(idxs)
  
          def _extract_new_split(split_dict, img_dir):
              train_idxs = split_dict['train_idx'].flatten() - 1  # index-0
              pids = split_dict['labels'].flatten()
              train_pids = set(pids[train_idxs])
              pid2label = {pid: label for label, pid in enumerate(train_pids)}
              query_idxs = split_dict['query_idx'].flatten() - 1
              gallery_idxs = split_dict['gallery_idx'].flatten() - 1
              filelist = split_dict['filelist'].flatten()
              train_info = _extract_set(filelist, pids, pid2label, train_idxs, img_dir, relabel=True)
              query_info = _extract_set(filelist, pids, pid2label, query_idxs, img_dir, relabel=False)
              gallery_info = _extract_set(filelist, pids, pid2label, gallery_idxs, img_dir, relabel=False)
              return train_info, query_info, gallery_info
  
          print("Creating new splits for detected images (767/700) ...")
          train_info, query_info, gallery_info = _extract_new_split(
              loadmat(self.split_new_det_mat_path),
              self.imgs_detected_dir,
          )
          splits = [{
              'train': train_info[0], 'query': query_info[0], 'gallery': gallery_info[0],
              'num_train_pids': train_info[1], 'num_train_imgs': train_info[2],
              'num_query_pids': query_info[1], 'num_query_imgs': query_info[2],
              'num_gallery_pids': gallery_info[1], 'num_gallery_imgs': gallery_info[2],
          }]
          write_json(splits, self.split_new_det_json_path)
  
          print("Creating new splits for labeled images (767/700) ...")
          train_info, query_info, gallery_info = _extract_new_split(
              loadmat(self.split_new_lab_mat_path),
              self.imgs_labeled_dir,
          )
          splits = [{
              'train': train_info[0], 'query': query_info[0], 'gallery': gallery_info[0],
              'num_train_pids': train_info[1], 'num_train_imgs': train_info[2],
              'num_query_pids': query_info[1], 'num_query_imgs': query_info[2],
              'num_gallery_pids': gallery_info[1], 'num_gallery_imgs': gallery_info[2],
          }]
          write_json(splits, self.split_new_lab_json_path)
  