商汤PySot的配置使用（2）---绘制siam跟踪算法与其他跟踪算法的eval结果曲线

前面有人看了看了我的博客商汤PySot的配置使用（1）—siam跟踪算法demo、test、eval，问我怎么用pysot绘制ope曲线，大家可以参考下面的内容。
pysot的官方代码链接https://github.com/STVIR/pysot

一、步骤一：改eval.py的内容

主要是参考https://github.com/StrangerZhang/pysot-toolkit，将里面绘制曲线的代码加进去，大家可以对比一下STVIR/pysot的eval.py和StrangerZhang/pysot-toolkit的eval.py，就能清楚地看到我加了哪些内容。

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
from toolkit.visualization import draw_success_precision, draw_eao, draw_f1
parser = argparse.ArgumentParser(description='tracking evaluation')
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.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 = [os.path.basename(x) for x in trackers]

    assert len(trackers) > 0
    args.num = min(args.num, len(trackers))

    root = os.path.realpath(os.path.join(os.path.dirname(__file__),
                            '../testing_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)
        if args.vis:
            for attr, videos in dataset.attr.items():
                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():
                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():
                draw_success_precision(success_ret,
                            name=dataset.name,
                            video=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 = {}
        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 args.vis:
            draw_f1(f1_result)


if __name__ == '__main__':
    main()

二、 步骤二：修改eval.py的configurations

添加了一行“–vis”，即

--tracker_path
../tools/results
--dataset
OTB100
--num
4
--show_video_level
--vis

三、步骤三：安装MikTex

3.1 安装

按照链接https://miktex.org/download#unx，将里面的代码一行往自己终端里复制粘贴回车，即可

3.2 配置

按照目标跟踪——OTB平台的Python版tracker使用，配置一下。
配置到最后一步，记得点一下update now

然后重启电脑。

四、步骤四：运行eval.py

将你的测试结果放入文件result/OTB100目录下：
（我将SiamFC和SiamRPN在OTB100上的测试结果放入该目录下）

运行eval.py，它会提示你MikTex的相关组件没有安装，你点击安装即可，等待一会，就会出现曲线。

当然，你也可以讲其他的测试结果放进该目录，链接如下

链接：https://pan.baidu.com/s/13jeOhKTxswsJ6kSJzoavVg
提取码：wwmg
（结果来自博客（https://blog.youkuaiyun.com/qq_29894613/article/details/102925068））