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eval.py
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from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from __future__ import unicode_literals
import os
import argparse
import sys
env_path = os.path.join(os.path.dirname(__file__), '..')
if env_path not in sys.path:
sys.path.append(env_path)
from glob import glob
from tqdm import tqdm
from multiprocessing import Pool
from pysot_toolkit.toolkit.datasets import OTBDataset, UAVDataset, LaSOTDataset, VOTDataset, NFSDataset, VOTLTDataset
from pysot_toolkit.toolkit.evaluation import OPEBenchmark, AccuracyRobustnessBenchmark, EAOBenchmark, F1Benchmark
from pysot_toolkit.toolkit.visualization import draw_success_precision
import numpy as np
parser = argparse.ArgumentParser(description='tracking evaluation')
parser.add_argument('--tracker_path', '-p', type=str, default='',
help='tracker result path')
parser.add_argument('--dataset', '-d', type=str, default='LaSOT',
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.add_argument('--vis', dest='vis', action='store_true')
parser.set_defaults(show_video_level=False)
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 = '/home/cx/cx2/LaSOTBenchmark'
# 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)
if args.vis:
for attr, videos in dataset.attr.items():
if attr == 'ALL':
draw_success_precision(success_ret,
name=dataset.name,
videos=videos,
attr=attr,
precision_ret=precision_ret)
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)
if args.vis:
draw_success_precision(success_ret,
name=dataset.name,
videos=dataset.attr['ALL'],
attr='ALL',
precision_ret=precision_ret,
norm_precision_ret=norm_precision_ret)
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)
if args.vis:
for attr, videos in dataset.attr.items():
if attr == 'ALL':
draw_success_precision(success_ret,
name=dataset.name,
videos=videos,
attr=attr,
precision_ret=precision_ret)
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)
if args.vis:
for attr, videos in dataset.attr.items():
if attr == 'ALL':
draw_success_precision(success_ret,
name=dataset.name,
videos=videos,
attr=attr,
precision_ret=precision_ret)
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 = {}
EAO_list = [] # newly added (2020.07.05)
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)
for name in eao_result:
EAO_list.append(eao_result[name]['all'])
mean_eao = np.mean(np.array(EAO_list))
print('Mean EAO = ',mean_eao)
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()