text: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  from __future__ import unicode_literals
  
  import os
  import argparse
  
  from glob import glob
  from tqdm import tqdm
  from multiprocessing import Pool
  from toolkit.datasets import OTBDataset, UAVDataset, LaSOTDataset, \
          VOTDataset, NFSDataset, VOTLTDataset
  from toolkit.evaluation import OPEBenchmark, AccuracyRobustnessBenchmark, \
          EAOBenchmark, F1Benchmark
  
  parser = argparse.ArgumentParser(description='tracking evaluation')
  <<<<<<< HEAD
  parser.add_argument('--tracker_path', '-p', type=str, help='tracker result path')
  parser.add_argument('--dataset', '-d', type=str, help='dataset name')
  parser.add_argument('--num', '-n', default=1, type=int, help='number of thread to eval')
  parser.add_argument('--tracker_prefix', '-t', default='', type=str, help='tracker name')
  parser.add_argument('--show_video_level', '-s', dest='show_video_level', action='store_true')
  parser.set_defaults(show_video_level=True)
  =======
  parser.add_argument('--tracker_path', '-p', type=str,
                      help='tracker result path')
  parser.add_argument('--dataset', '-d', type=str,
                      help='dataset name')
  parser.add_argument('--num', '-n', default=1, type=int,
                      help='number of thread to eval')
  parser.add_argument('--tracker_prefix', '-t', default='',
                      type=str, help='tracker name')
  parser.add_argument('--show_video_level', '-s', dest='show_video_level',
                      action='store_true')
  parser.set_defaults(show_video_level=False)
  >>>>>>> b899d80c8a641ef636a99e55a52bf508292c5e84
  args = parser.parse_args()
  
  
  def main():
      tracker_dir = os.path.join(args.tracker_path, args.dataset)
      trackers = glob(os.path.join(args.tracker_path,
                                   args.dataset,
                                   args.tracker_prefix+'*'))
      trackers = [x.split('/')[-1] for x in trackers]
  
      assert len(trackers) > 0
      args.num = min(args.num, len(trackers))
  
      root = os.path.realpath('D:\\Phd\\dataset')
      root = os.path.join(root, args.dataset)
      if 'OTB' in args.dataset:
          dataset = OTBDataset(args.dataset, root)
          dataset.set_tracker(tracker_dir, trackers)
          benchmark = OPEBenchmark(dataset)
          success_ret = {}
          with Pool(processes=args.num) as pool:
              for ret in tqdm(pool.imap_unordered(benchmark.eval_success,
                  trackers), desc='eval success', total=len(trackers), ncols=100):
                  success_ret.update(ret)
          precision_ret = {}
          with Pool(processes=args.num) as pool:
              for ret in tqdm(pool.imap_unordered(benchmark.eval_precision,
                  trackers), desc='eval precision', total=len(trackers), ncols=100):
                  precision_ret.update(ret)
          benchmark.show_result(success_ret, precision_ret,
                  show_video_level=args.show_video_level)
      elif 'LaSOT' == args.dataset:
          dataset = LaSOTDataset(args.dataset, root)
          dataset.set_tracker(tracker_dir, trackers)
          benchmark = OPEBenchmark(dataset)
          success_ret = {}
          with Pool(processes=args.num) as pool:
              for ret in tqdm(pool.imap_unordered(benchmark.eval_success,
                  trackers), desc='eval success', total=len(trackers), ncols=100):
                  success_ret.update(ret)
          precision_ret = {}
          with Pool(processes=args.num) as pool:
              for ret in tqdm(pool.imap_unordered(benchmark.eval_precision,
                  trackers), desc='eval precision', total=len(trackers), ncols=100):
                  precision_ret.update(ret)
          norm_precision_ret = {}
          with Pool(processes=args.num) as pool:
              for ret in tqdm(pool.imap_unordered(benchmark.eval_norm_precision,
                  trackers), desc='eval norm precision', total=len(trackers), ncols=100):
                  norm_precision_ret.update(ret)
          benchmark.show_result(success_ret, precision_ret, norm_precision_ret,
                  show_video_level=args.show_video_level)
      elif 'UAV' in args.dataset:
          dataset = UAVDataset(args.dataset, root)
          dataset.set_tracker(tracker_dir, trackers)
          benchmark = OPEBenchmark(dataset)
          success_ret = {}
          with Pool(processes=args.num) as pool:
              for ret in tqdm(pool.imap_unordered(benchmark.eval_success,
                  trackers), desc='eval success', total=len(trackers), ncols=100):
                  success_ret.update(ret)
          precision_ret = {}
          with Pool(processes=args.num) as pool:
              for ret in tqdm(pool.imap_unordered(benchmark.eval_precision,
                  trackers), desc='eval precision', total=len(trackers), ncols=100):
                  precision_ret.update(ret)
          benchmark.show_result(success_ret, precision_ret,
                  show_video_level=args.show_video_level)
      elif 'NFS' in args.dataset:
          dataset = NFSDataset(args.dataset, root)
          dataset.set_tracker(tracker_dir, trackers)
          benchmark = OPEBenchmark(dataset)
          success_ret = {}
          with Pool(processes=args.num) as pool:
              for ret in tqdm(pool.imap_unordered(benchmark.eval_success,
                  trackers), desc='eval success', total=len(trackers), ncols=100):
                  success_ret.update(ret)
          precision_ret = {}
          with Pool(processes=args.num) as pool:
              for ret in tqdm(pool.imap_unordered(benchmark.eval_precision,
                  trackers), desc='eval precision', total=len(trackers), ncols=100):
                  precision_ret.update(ret)
          benchmark.show_result(success_ret, precision_ret,
                  show_video_level=args.show_video_level)
      elif args.dataset in ['VOT2016', 'VOT2017', 'VOT2018', 'VOT2019']:
          dataset = VOTDataset(args.dataset, root)
          dataset.set_tracker(tracker_dir, trackers)
          ar_benchmark = AccuracyRobustnessBenchmark(dataset)
          ar_result = {}
          with Pool(processes=args.num) as pool:
              for ret in tqdm(pool.imap_unordered(ar_benchmark.eval,
                  trackers), desc='eval ar', total=len(trackers), ncols=100):
                  ar_result.update(ret)
  
          benchmark = EAOBenchmark(dataset)
          eao_result = {}
          with Pool(processes=args.num) as pool:
              for ret in tqdm(pool.imap_unordered(benchmark.eval,
                  trackers), desc='eval eao', total=len(trackers), ncols=100):
                  eao_result.update(ret)
          ar_benchmark.show_result(ar_result, eao_result,
                  show_video_level=args.show_video_level)
      elif 'VOT2018-LT' == args.dataset:
          dataset = VOTLTDataset(args.dataset, root)
          dataset.set_tracker(tracker_dir, trackers)
          benchmark = F1Benchmark(dataset)
          f1_result = {}
          with Pool(processes=args.num) as pool:
              for ret in tqdm(pool.imap_unordered(benchmark.eval,
                  trackers), desc='eval f1', total=len(trackers), ncols=100):
                  f1_result.update(ret)
          benchmark.show_result(f1_result,
                  show_video_level=args.show_video_level)
  
  
  if __name__ == '__main__':
      main()
  