【slam十四讲第二版】【课本例题代码向】【第十一讲~回环检测】【DBoW3的安装】【创建字典】【相似度检测】【增加字典规模】

企业开发 2023-04-09 01:40:33 阅读次数: 0

【slam十四讲第二版】【课本例题代码向】【第十一讲~回环检测】【DBoW3的安装】【创建字典】【相似度检测】【增加字典规模】

0 前言

  • 参考：
  1. 《视觉SLAM十四讲 第二版》笔记及课后习题（第十一讲）
  2. 视觉SLAM十四讲CH11代码解析及课后习题详解

1 DBoW3的安装

  1. 首先获取安装包，事实证明，使用高博的gaoxiang12/slambook第一版里面的安装包才可以，安装包自取：链接: https://pan.baidu.com/s/1CfUpOdoVtaQoaMhcoNOt1w 提取码: 7gvi
  2. 安装过程
cd DBow3/
mkdir build
cd build/

cmake ..
make -j4
sudo make install

2 创建字典

  1. 该工程实现需要一个包含十张图片的数据集，自取：链接: https://pan.baidu.com/s/19I1holzTCyWhzdI1RpOBeg 提取码: khln
  2. 整个工程代码为：链接: https://pan.baidu.com/s/1NV4LZwt7FV-25ef0wNOHIg 提取码: oh59

2.1 feature_training.cpp

#include "DBoW3/DBoW3.h"//词袋支持头文件
#include <opencv2/core/core.hpp>//opencv核心模块
#include <opencv2/highgui/highgui.hpp>//gui模块
#include <opencv2/features2d/features2d.hpp>//特征点头文件
#include <iostream>
#include <vector>
#include <string>

using namespace cv;
using namespace std;

/***************************************************
 * 本节演示了如何根据data/目录下的十张图训练字典
 * ************************************************/

int main( int argc, char** argv )
{
    
    
    // read the image
    cout<<"reading images... "<<endl;//输出reading images...
    vector<Mat> images; //图像
    for ( int i=0; i<10; i++ )//遍历读取十张图像
    {
    
    
        string path = "./data/"+to_string(i+1)+".png";
        images.push_back( imread(path) );
    }
    // detect ORB features
    cout<<"detecting ORB features ... "<<endl;//输出detecting ORB features(正在检测ORB特征) ...
    Ptr< Feature2D > detector = ORB::create();
    vector<Mat> descriptors;//描述子
    for ( Mat& image:images )
    {
    
    
        vector<KeyPoint> keypoints; //关键点
        Mat descriptor;//描述子
        detector->detectAndCompute( image, Mat(), keypoints, descriptor );//检测和计算
        descriptors.push_back( descriptor );
    }

    // create vocabulary (创建字典)
    cout<<"creating vocabulary ... "<<endl;//输出(creating vocabulary ...)创建字典
    DBoW3::Vocabulary vocab;//默认构造函数 k=10,d=5
    vocab.create( descriptors );
    cout<<"vocabulary info: "<<vocab<<endl;//字典信息
    vocab.save( "vocabulary.yml.gz" );//保存字典压缩包
    cout<<"done"<<endl;//输出done

    return 0;
}

2.2 CMakeLists.txt

cmake_minimum_required( VERSION 2.8 )
project( loop_closure )

set( CMAKE_BUILD_TYPE "Release" )
set( CMAKE_CXX_FLAGS "-std=c++14 -O3" )

# opencv
find_package( OpenCV 3.1 REQUIRED )
include_directories( ${
    
    OpenCV_INCLUDE_DIRS} )

# dbow3
# dbow3 is a simple lib so I assume you installed it in default directory
set( DBoW3_INCLUDE_DIRS "/usr/local/include" )
set( DBoW3_LIBS "/usr/local/lib/libDBoW3.a" )

add_executable( feature_training src/feature_training.cpp )
target_link_libraries( feature_training ${
    
    OpenCV_LIBS} ${
    
    DBoW3_LIBS} )

2.3 输出

  1. 会生成一个vocabulary.yml.gz文件，后面会有用，自取：链接: https://pan.baidu.com/s/1i4zAnHM2BeycBi_GwF2fnA 提取码: sbsf
    2.运行指令
 ./feature_training ../data/

/home/bupo/my_study/slam14/slam14_my/cap11/feature_training/cmake-build-debug/feature_training ./data
reading images... 
detecting ORB features ... 
[ INFO:0] Initialize OpenCL runtime...
creating vocabulary ... 
vocabulary info: Vocabulary: k = 10, L = 5, Weighting = tf-idf, Scoring = L1-norm, Number of words = 4970
done

进程已结束,退出代码0
  • 可以看到：分支数量k为10，深度L为5，单词数量为4983，没有达到最大容量。Weighting是权重，Scoring是评分

3 相似度的计算

  1. 该工程实现需要一个包含十张图片的数据集，自取：链接: https://pan.baidu.com/s/19I1holzTCyWhzdI1RpOBeg 提取码: khln
  2. 前面这个工程会生成一个vocabulary.yml.gz文件，该工程会用到，自取：链接: https://pan.baidu.com/s/1i4zAnHM2BeycBi_GwF2fnA 提取码: sbsf
  3. 该工程自取：链接: 链接: https://pan.baidu.com/s/12E6fvy2DV9-BqOuXtFigYw 提取码: hmp2

3.1 loop_closure.cpp

#include "DBoW3/DBoW3.h"//词袋支持头文件
#include <opencv2/core/core.hpp>//opencv核心模块
#include <opencv2/highgui/highgui.hpp>//gui模块
#include <opencv2/features2d/features2d.hpp>//特征点头文件
#include <iostream>
#include <vector>
#include <string>

using namespace cv;
using namespace std;

/***************************************************
 * 本节演示了如何根据前面训练的字典计算相似性评分
 * ************************************************/
int main(int argc, char **argv) {
    
    
    if (argc != 2) {
    
    
        cout << "Usage: 需要字典" << endl;
        return 1;
    }
    string zidian_file = argv[1];
    DBoW3::Vocabulary vocab(zidian_file);

    // read the images and database(读取图像和数据库)
    cout << "reading database" << endl;//输出reading database(读取数据)
    //DBoW3::Vocabulary vocab("../src/vocabulary.yml.gz");//vocabulary.yml.gz路径
    //DBoW3::Vocabulary vocab("../src/vocab_larger.yml.gz");  // use large vocab if you want:
    if (vocab.empty()) {
    
    
        cerr << "Vocabulary does not exist." << endl;//输出Vocabulary does not exist
        return 1;
    }
    cout << "reading images... " << endl;//输出reading images...
    vector<Mat> images;
    for (int i = 0; i < 10; i++) {
    
    
        string path = "../data/" + to_string(i + 1) + ".png";//图像读取路径
        images.push_back(imread(path));
    }

    // NOTE: in this case we are comparing images with a vocabulary generated by themselves, this may lead to overfit. 这里我们用它们生成的字典比较它们本身的相似性，这可能会产生过拟合
    // detect ORB features
    cout << "detecting ORB features ... " << endl;//输出detecting ORB features ...(正在检测ORB特征)
    Ptr<Feature2D> detector = ORB::create();//默认图像500个特征点
    vector<Mat> descriptors;//描述子  将10张图像提取ORB特征并存放在vector容器里
    for (Mat &image:images) {
    
    
        vector<KeyPoint> keypoints;//关键点
        Mat descriptor;//描述子
        detector->detectAndCompute(image, Mat(), keypoints, descriptor);//检测和计算
        descriptors.push_back(descriptor);
    }

    // we can compare the images directly or we can compare one image to a database
    // images :
    cout << "comparing images with images " << endl;//输出comparing images with images
    for (int i = 0; i < images.size(); i++)
    {
    
    
        DBoW3::BowVector v1;
        //descriptors[i]表示图像i中所有的ORB描述子集合，函数transform()计算出用先前字典来描述的单词向量，每个向量中元素的值要么是0，表示图像i中没有这个单词；要么是该单词的权重
        //BoW描述向量中含有每个单词的ID和权重，两者构成了整个稀疏的向量
        //当比较两个向量时，DBoW3会为我们计算一个分数
        vocab.transform(descriptors[i], v1);
        for (int j = i; j < images.size(); j++)
        {
    
    
            DBoW3::BowVector v2;
            vocab.transform(descriptors[j], v2);
            double score = vocab.score(v1, v2);//p296式(11.9)
            cout << "image " << i << " vs image " << j << " : " << score << endl;//输出一幅图像与另外一幅图像之间的相似度评分
        }
        cout << endl;
    }

    // or with database
    //在进行数据库查询时，DBoW对上面的分数进行排序，给出最相似的结果
    cout << "comparing images with database " << endl;
    DBoW3::Database db(vocab, false, 0);
    for (int i = 0; i < descriptors.size(); i++)
        db.add(descriptors[i]);
    cout << "database info: " << db << endl;//输出database info(数据库信息)为
    for (int i = 0; i < descriptors.size(); i++)
    {
    
    
        DBoW3::QueryResults ret;
        db.query(descriptors[i], ret, 4);      // max result=4
        cout << "searching for image " << i << " returns " << ret << endl << endl;
    }
    cout << "done." << endl;
}

3.2 CMakeLists.txt

cmake_minimum_required( VERSION 2.8 )
project( loop_closure )

set( CMAKE_BUILD_TYPE "Release" )
set( CMAKE_CXX_FLAGS "-std=c++14 -O3" )

# opencv
find_package( OpenCV 3.1 REQUIRED )
include_directories( ${
    
    OpenCV_INCLUDE_DIRS} )

# dbow3
# dbow3 is a simple lib so I assume you installed it in default directory
set( DBoW3_INCLUDE_DIRS "/usr/local/include" )
set( DBoW3_LIBS "/usr/local/lib/libDBoW3.a" )

add_executable( loop_closure src/loop_closure.cpp )
target_link_libraries( loop_closure ${
    
    OpenCV_LIBS} ${
    
    DBoW3_LIBS} )

3.3 输出

  1. 运行指令
./loop_closure 

/home/bupo/my_study/slam14/slam14_my/cap11/loop_closure/cmake-build-debug/loop_closure
reading database
reading images... 
detecting ORB features ... 
[ INFO:0] Initialize OpenCL runtime...
comparing images with images 
image 0 vs image 0 : 1
image 0 vs image 1 : 0.0367529
image 0 vs image 2 : 0.0277822
image 0 vs image 3 : 0.0281337
image 0 vs image 4 : 0.0335461
image 0 vs image 5 : 0.0427682
image 0 vs image 6 : 0.038458
image 0 vs image 7 : 0.0305787
image 0 vs image 8 : 0.0295247
image 0 vs image 9 : 0.0621202

image 1 vs image 1 : 1
image 1 vs image 2 : 0.0371357
image 1 vs image 3 : 0.0300564
image 1 vs image 4 : 0.0339359
image 1 vs image 5 : 0.0412664
image 1 vs image 6 : 0.0226204
image 1 vs image 7 : 0.0304568
image 1 vs image 8 : 0.0426928
image 1 vs image 9 : 0.033033

image 2 vs image 2 : 1
image 2 vs image 3 : 0.0318231
image 2 vs image 4 : 0.0271078
image 2 vs image 5 : 0.0269704
image 2 vs image 6 : 0.0233219
image 2 vs image 7 : 0.0497462
image 2 vs image 8 : 0.0374033
image 2 vs image 9 : 0.0292551

image 3 vs image 3 : 1
image 3 vs image 4 : 0.0348777
image 3 vs image 5 : 0.0366132
image 3 vs image 6 : 0.0424612
image 3 vs image 7 : 0.0172669
image 3 vs image 8 : 0.032024
image 3 vs image 9 : 0.042369

image 4 vs image 4 : 1
image 4 vs image 5 : 0.0627885
image 4 vs image 6 : 0.041152
image 4 vs image 7 : 0.0233412
image 4 vs image 8 : 0.0198614
image 4 vs image 9 : 0.0288873

image 5 vs image 5 : 1
image 5 vs image 6 : 0.0319993
image 5 vs image 7 : 0.0236407
image 5 vs image 8 : 0.0263738
image 5 vs image 9 : 0.0306995

image 6 vs image 6 : 1
image 6 vs image 7 : 0.0345444
image 6 vs image 8 : 0.0376772
image 6 vs image 9 : 0.0297798

image 7 vs image 7 : 1
image 7 vs image 8 : 0.0315193
image 7 vs image 9 : 0.0284877

image 8 vs image 8 : 1
image 8 vs image 9 : 0.040886

image 9 vs image 9 : 1

comparing images with database 
database info: Database: Entries = 10, Using direct index = no. Vocabulary: k = 10, L = 5, Weighting = tf-idf, Scoring = L1-norm, Number of words = 4983
searching for image 0 returns 4 results:
<EntryId: 0, Score: 1>
<EntryId: 9, Score: 0.0621202>
<EntryId: 5, Score: 0.0427682>
<EntryId: 6, Score: 0.038458>

searching for image 1 returns 4 results:
<EntryId: 1, Score: 1>
<EntryId: 8, Score: 0.0426928>
<EntryId: 5, Score: 0.0412664>
<EntryId: 2, Score: 0.0371357>

searching for image 2 returns 4 results:
<EntryId: 2, Score: 1>
<EntryId: 7, Score: 0.0497462>
<EntryId: 8, Score: 0.0374033>
<EntryId: 1, Score: 0.0371357>

searching for image 3 returns 4 results:
<EntryId: 3, Score: 1>
<EntryId: 6, Score: 0.0424612>
<EntryId: 9, Score: 0.042369>
<EntryId: 5, Score: 0.0366132>

searching for image 4 returns 4 results:
<EntryId: 4, Score: 1>
<EntryId: 5, Score: 0.0627885>
<EntryId: 6, Score: 0.041152>
<EntryId: 3, Score: 0.0348777>

searching for image 5 returns 4 results:
<EntryId: 5, Score: 1>
<EntryId: 4, Score: 0.0627885>
<EntryId: 0, Score: 0.0427682>
<EntryId: 1, Score: 0.0412664>

searching for image 6 returns 4 results:
<EntryId: 6, Score: 1>
<EntryId: 3, Score: 0.0424612>
<EntryId: 4, Score: 0.041152>
<EntryId: 0, Score: 0.038458>

searching for image 7 returns 4 results:
<EntryId: 7, Score: 1>
<EntryId: 2, Score: 0.0497462>
<EntryId: 6, Score: 0.0345444>
<EntryId: 8, Score: 0.0315193>

searching for image 8 returns 4 results:
<EntryId: 8, Score: 1>
<EntryId: 1, Score: 0.0426928>
<EntryId: 9, Score: 0.040886>
<EntryId: 6, Score: 0.0376772>

searching for image 9 returns 4 results:
<EntryId: 9, Score: 1>
<EntryId: 0, Score: 0.0621202>
<EntryId: 3, Score: 0.042369>
<EntryId: 8, Score: 0.040886>

done.

进程已结束,退出代码0
  • 可以看到不同的图像与相似图像的评分有多大差异。我们看到，明显相似的图1和图10（在C++中下标为0和9）其相似度评分约为0.0525，而其他图像约为0.02。

4 增加字典规模

  1. 这里需要下载对应的数据集，可以去官网下载：https://vision.in.tum.de/data/datasets/rgbd-dataset/download#，也可以在网盘自取链接: https://pan.baidu.com/s/1bg6L8u9RF0YtpkzYX38MAw 提取码: 6dj4
  2. 整个工程除去数据集自取:链接: https://pan.baidu.com/s/1LIhMVq8FlUsOA0Sk1IlD5g 提取码: kv9b

4.1 gen_vocab_large.cpp

#include "DBoW3/DBoW3.h"//词袋支持头文件
#include <opencv2/core/core.hpp>//opencv核心模块
#include <opencv2/highgui/highgui.hpp>//gui模块
#include <opencv2/features2d/features2d.hpp>//特征点头文件
#include <iostream>
#include <vector>
#include <string>

using namespace cv;
using namespace std;


int main( int argc, char** argv )
{
    
    
    String directoryPath = "/home/bupo/my_study/slam14/slam14_my/cap11/gen_vocab_large/rgbd_dataset_freiburg1_desk2/rgb";//图像路径
    vector<String> imagesPath;
    glob(directoryPath, imagesPath);
    // string dataset_dir = argv[1];
    // ifstream fin ( dataset_dir+"/home/liqiang/slambook2/ch11/rgbd_dataset_freiburg1_desk2/rgb" );
    // if ( !fin )
    // {
    
    
    //     cout<<"please generate the associate file called associate.txt!"<<endl;
    //     return 1;
    // }

    // vector<string> rgb_files, depth_files;
    // vector<double> rgb_times, depth_times;
    // while ( !fin.eof() )
    // {
    
    
    //     string rgb_time, rgb_file, depth_time, depth_file;
    //     fin>>rgb_time>>rgb_file>>depth_time>>depth_file;
    //     rgb_times.push_back ( atof ( rgb_time.c_str() ) );
    //     depth_times.push_back ( atof ( depth_time.c_str() ) );
    //     rgb_files.push_back ( dataset_dir+"/"+rgb_file );
    //     depth_files.push_back ( dataset_dir+"/"+depth_file );

    //     if ( fin.good() == false )
    //         break;
    // }
    // fin.close();

    cout<<"generating features ... "<<endl;//输出generating features (正在检测ORB特征)...
    vector<Mat> descriptors;//描述子
    Ptr< Feature2D > detector = ORB::create();
    int index = 1;
    for ( String path : imagesPath )
    {
    
    
        Mat image = imread(path);
        vector<KeyPoint> keypoints; //关键点
        Mat descriptor;//描述子
        detector->detectAndCompute( image, Mat(), keypoints, descriptor );
        descriptors.push_back( descriptor );
        cout<<"extracting features from image " << index++ <<endl;//输出extracting features from image(从图像中提取特征)
    }
    cout<<"extract total "<<descriptors.size()*500<<" features."<<endl;

    // create vocabulary
    cout<<"creating vocabulary, please wait ... "<<endl;//输出creating vocabulary, please wait (创建词典，请稍等)...
    DBoW3::Vocabulary vocab;
    vocab.create( descriptors );
    cout<<"vocabulary info: "<<vocab<<endl;
    vocab.save( "vocab_larger.yml.gz" );//保存词典
    cout<<"done"<<endl;

    return 0;
}

4.2 CMakeLists.txt

cmake_minimum_required( VERSION 2.8 )
project( loop_closure )

set( CMAKE_BUILD_TYPE "Release" )
set( CMAKE_CXX_FLAGS "-std=c++14 -O3" )

# opencv
find_package( OpenCV 3.1 REQUIRED )
include_directories( ${
    
    OpenCV_INCLUDE_DIRS} )

# dbow3
# dbow3 is a simple lib so I assume you installed it in default directory
set( DBoW3_INCLUDE_DIRS "/usr/local/include" )
set( DBoW3_LIBS "/usr/local/lib/libDBoW3.a" )

add_executable( gen_vocab src/gen_vocab_large.cpp )
target_link_libraries( gen_vocab ${
    
    OpenCV_LIBS} ${
    
    DBoW3_LIBS} )

4.3 输出

/home/bupo/my_study/slam14/slam14_my/cap11/gen_vocab_large/cmake-build-debug/gen_vocab
generating features ... 
[ INFO:0] Initialize OpenCL runtime...
extracting features from image 1
extracting features from image 2

extracting features from image 639
extracting features from image 640
extract total 320000 features.
creating vocabulary, please wait ... 
vocabulary info: Vocabulary: k = 10, L = 5, Weighting = tf-idf, Scoring = L1-norm, Number of words = 89849
done

进程已结束,退出代码0
  • 同时会生成文件vocab_larger.yml.gz，这个文件，是通过640个图片训练得到的字典，可以在loop_closure中，当然，如果你不想训练，可以直接自取我训练得到的结果:链接: https://pan.baidu.com/s/1-L7uYoCKmzrLRqmTxwxxMg 提取码: 66kc

4.4 使用该字典进行回环

  • 对loop_closure修改字典文件，然后回环
/home/bupo/my_study/slam14/slam14_my/cap11/loop_closure/cmake-build-debug/loop_closure
reading database
reading images... 
detecting ORB features ... 
[ INFO:0] Initialize OpenCL runtime...
comparing images with images 
image 0 vs image 0 : 1
image 0 vs image 1 : 0.0264319
image 0 vs image 2 : 0.0192686
image 0 vs image 3 : 0.0174829
image 0 vs image 4 : 0.00919418
image 0 vs image 5 : 0.0196796
image 0 vs image 6 : 0.0195631
image 0 vs image 7 : 0.0156169
image 0 vs image 8 : 0.0225417
image 0 vs image 9 : 0.0543705

image 1 vs image 1 : 1
image 1 vs image 2 : 0.0305082
image 1 vs image 3 : 0.0226657
image 1 vs image 4 : 0.0116976
image 1 vs image 5 : 0.0178306
image 1 vs image 6 : 0.0277621
image 1 vs image 7 : 0.0198653
image 1 vs image 8 : 0.0267385
image 1 vs image 9 : 0.0262276

image 2 vs image 2 : 1
image 2 vs image 3 : 0.0210716
image 2 vs image 4 : 0.0280933
image 2 vs image 5 : 0.0342187
image 2 vs image 6 : 0.0247903
image 2 vs image 7 : 0.0233946
image 2 vs image 8 : 0.0221119
image 2 vs image 9 : 0.0238046

image 3 vs image 3 : 1
image 3 vs image 4 : 0.0189549
image 3 vs image 5 : 0.0257821
image 3 vs image 6 : 0.0244571
image 3 vs image 7 : 0.0196989
image 3 vs image 8 : 0.0260922
image 3 vs image 9 : 0.0240474

image 4 vs image 4 : 1
image 4 vs image 5 : 0.0398126
image 4 vs image 6 : 0.0276248
image 4 vs image 7 : 0.014187
image 4 vs image 8 : 0.014109
image 4 vs image 9 : 0.0167629

image 5 vs image 5 : 1
image 5 vs image 6 : 0.0176884
image 5 vs image 7 : 0.0227182
image 5 vs image 8 : 0.0249955
image 5 vs image 9 : 0.0248106

image 6 vs image 6 : 1
image 6 vs image 7 : 0.0155511
image 6 vs image 8 : 0.0323909
image 6 vs image 9 : 0.0206828

image 7 vs image 7 : 1
image 7 vs image 8 : 0.0309826
image 7 vs image 9 : 0.0322799

image 8 vs image 8 : 1
image 8 vs image 9 : 0.0163077

image 9 vs image 9 : 1

comparing images with database 
database info: Database: Entries = 10, Using direct index = no. Vocabulary: k = 10, L = 5, Weighting = tf-idf, Scoring = L1-norm, Number of words = 89849
searching for image 0 returns 4 results:
<EntryId: 0, Score: 1>
<EntryId: 9, Score: 0.0543705>
<EntryId: 1, Score: 0.0264319>
<EntryId: 8, Score: 0.0225417>

searching for image 1 returns 4 results:
<EntryId: 1, Score: 1>
<EntryId: 2, Score: 0.0305082>
<EntryId: 6, Score: 0.0277621>
<EntryId: 8, Score: 0.0267385>

searching for image 2 returns 4 results:
<EntryId: 2, Score: 1>
<EntryId: 5, Score: 0.0342187>
<EntryId: 1, Score: 0.0305082>
<EntryId: 4, Score: 0.0280933>

searching for image 3 returns 4 results:
<EntryId: 3, Score: 1>
<EntryId: 8, Score: 0.0260922>
<EntryId: 5, Score: 0.0257821>
<EntryId: 6, Score: 0.0244571>

searching for image 4 returns 4 results:
<EntryId: 4, Score: 1>
<EntryId: 5, Score: 0.0398126>
<EntryId: 2, Score: 0.0280933>
<EntryId: 6, Score: 0.0276248>

searching for image 5 returns 4 results:
<EntryId: 5, Score: 1>
<EntryId: 4, Score: 0.0398126>
<EntryId: 2, Score: 0.0342187>
<EntryId: 3, Score: 0.0257821>

searching for image 6 returns 4 results:
<EntryId: 6, Score: 1>
<EntryId: 8, Score: 0.0323909>
<EntryId: 1, Score: 0.0277621>
<EntryId: 4, Score: 0.0276248>

searching for image 7 returns 4 results:
<EntryId: 7, Score: 1>
<EntryId: 9, Score: 0.0322799>
<EntryId: 8, Score: 0.0309826>
<EntryId: 2, Score: 0.0233946>

searching for image 8 returns 4 results:
<EntryId: 8, Score: 1>
<EntryId: 6, Score: 0.0323909>
<EntryId: 7, Score: 0.0309826>
<EntryId: 1, Score: 0.0267385>

searching for image 9 returns 4 results:
<EntryId: 9, Score: 1>
<EntryId: 0, Score: 0.0543705>
<EntryId: 7, Score: 0.0322799>
<EntryId: 1, Score: 0.0262276>
done.
进程已结束,退出代码0

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