text: # encoding: utf-8
  """
  @author:  sherlock
  @contact: sherlockliao01@gmail.com
  """
  
  import glob
  import re
  
  import os.path as osp
  
  from .bases import BaseImageDataset
  import json
  
  
  class Market1501(BaseImageDataset):
      """
      Market1501
      Reference:
      Zheng et al. Scalable Person Re-identification: A Benchmark. ICCV 2015.
      URL: http://www.liangzheng.org/Project/project_reid.html
  
      Dataset statistics:
      # identities: 1501 (+1 for background)
      # images: 12936 (train) + 3368 (query) + 15913 (gallery)
      """
      dataset_dir = 'market1501'
  
      def __init__(self, root='/home/haoluo/data', train_anno=1, verbose=True, **kwargs):
          super(Market1501, self).__init__()
          self.dataset_dir = osp.join(root, self.dataset_dir)
          self.train_dir = osp.join(self.dataset_dir, 'bounding_box_train')
          self.query_dir = osp.join(self.dataset_dir, 'query')
          self.gallery_dir = osp.join(self.dataset_dir, 'bounding_box_test')
  
          # self._check_before_run()
  
          self.root = "/raid/home/henrayzhao/person_search/dataset/multi_person/prw"
          # self.multi_person_training_info2()
          self.train_anno = train_anno
  
  
          train = self.process_dir_train(relabel=True)
          query = self._process_dir(self.query_dir, relabel=False)
          gallery = self._process_dir(self.gallery_dir, relabel=False)
  
          # if verbose:
          #     print("=> Market1501 loaded")
          #     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_train(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.train_dir):
      #         raise RuntimeError("'{}' is not available".format(self.train_dir))
      #     if not osp.exists(self.query_dir):
      #         raise RuntimeError("'{}' is not available".format(self.query_dir))
      #     if not osp.exists(self.gallery_dir):
      #         raise RuntimeError("'{}' is not available".format(self.gallery_dir))
  
  
      def get_imagedata_info_train(self, data):
  
          pids, cams = [], []
          for _, _, pid, camid, pid2, pos_neg in data:
              pids += [pid]
              pids += [pid2]
              cams += [camid]
          pids = set(pids)
          cams = set(cams)
          num_pids = len(pids)
          num_cams = len(cams)
          num_imgs = len(data)
          return num_pids, num_imgs, num_cams
  
      def _process_dir(self, dir_path, relabel=False):
          img_paths = glob.glob(osp.join(dir_path, '*.jpg'))
          pattern = re.compile(r'([-\d]+)_c(\d)')
  
          pid_container = set()
          for img_path in img_paths:
              pid, _ = map(int, pattern.search(img_path).groups())
              if pid == -1: continue  # junk images are just ignored
              pid_container.add(pid)
          pid2label = {pid: label for label, pid in enumerate(pid_container)}
  
          dataset = []
          for img_path in img_paths:
              pid, camid = map(int, pattern.search(img_path).groups())
              if pid == -1: continue  # junk images are just ignored
              assert 0 <= pid <= 1501  # pid == 0 means background
              assert 1 <= camid <= 6
              camid -= 1  # index starts from 0
              if relabel: pid = pid2label[pid]
              dataset.append((img_path, pid, camid))
              if 'query' in dir_path and len(dataset) >= 300:
                  break
  
          return dataset
  
      def process_dir_train(self, relabel=True):
          root = "/raid/home/henrayzhao/person_search/dataset/person_search/prw"
          anno_path = osp.join(root, "training_box", "training_box.json")
          with open(anno_path, 'r+') as f:
              all_anno = json.load(f)
  
          pid_container = set()
          for img_name, pid in all_anno.items():
              pid_container.add(pid)
          pid2label = {int(pid): label for label, pid in enumerate(pid_container)}
  
          new_anno_path = osp.join(self.root, "pair_pos_unary" + str(self.train_anno) + ".json")
          with open(new_anno_path, 'r+') as f:
              all_anno = json.load(f)
          data = []
  
          img_root1 = "/raid/home/henrayzhao/person_search/dataset/multi_person/prw/hard_gallery_train/image"
          img_root2 = "/raid/home/henrayzhao/person_search/dataset/multi_person/prw/train_gt/image"
  
          for one_pair in all_anno:
              # print(one_pair)
              hard_imgname = one_pair[0]
              query_train_imgname1 = one_pair[1]
              pid1 = one_pair[2]
              query_train_imgname2 = one_pair[3]
              pid2 = one_pair[4]
              camera_id = one_pair[5]
              if relabel:
                  pid1 = pid2label[pid1]
                  pid2 = pid2label[pid2]
              hard_imgname_path = osp.join(img_root1, hard_imgname)
              query_train_path1 = osp.join(img_root2, query_train_imgname1)
              query_train_path2 = osp.join(img_root2, query_train_imgname2)
              new_anno = [hard_imgname_path, query_train_path1, pid1, query_train_path2, pid2, camera_id]
              data.append(new_anno)
          return data
  