Spaces:
Running
on
T4
Running
on
T4
File size: 2,346 Bytes
d4b77ac |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 |
# --------------------------------------------------------
# Python Single Object Tracking Evaluation
# Licensed under The MIT License [see LICENSE for details]
# Written by Fangyi Zhang
# @author fangyi.zhang@vipl.ict.ac.cn
# @project https://github.com/StrangerZhang/pysot-toolkit.git
# Revised for SiamMask by foolwood
# --------------------------------------------------------
import argparse
import glob
from os.path import join, realpath, dirname
from tqdm import tqdm
from multiprocessing import Pool
from utils.pysot.datasets import VOTDataset
from utils.pysot.evaluation import AccuracyRobustnessBenchmark, EAOBenchmark
if __name__ == '__main__':
parser = argparse.ArgumentParser(description='VOT Evaluation')
parser.add_argument('--dataset', type=str, help='dataset name')
parser.add_argument('--result_dir', type=str, help='tracker result root')
parser.add_argument('--tracker_prefix', type=str, help='tracker prefix')
parser.add_argument('--show_video_level', action='store_true')
parser.add_argument('--num', type=int, help='number of processes to eval', default=1)
args = parser.parse_args()
root = join(realpath(dirname(__file__)), '../data')
tracker_dir = args.result_dir
trackers = glob.glob(join(tracker_dir, args.tracker_prefix+'*'))
trackers = [t.split('/')[-1] for t in trackers]
assert len(trackers) > 0
args.num = min(args.num, len(trackers))
if args.dataset in ['VOT2016', '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)
|