欢迎您访问程序员文章站本站旨在为大家提供分享程序员计算机编程知识!
您现在的位置是: 首页

关于高博士在《视觉SLAM十四讲》中ch7部分ORB检测算法代码的勘误

程序员文章站 2022-04-19 16:06:07
...
/*源代码的运行过程发现出不来结果,无独有偶,发现网上也有很多大神出现过这种错误,改了之后是可以运行的,因此修改了一下高博士的部分代码,贴出来分享一下!*/

文件名:pose_estimation_3d2d.cpp   此处的错误是在ptr指针(线性方程求解器和矩阵块求解器)

#include <iostream>
#include <opencv2/core/core.hpp>
#include <opencv2/features2d/features2d.hpp>
#include <opencv2/highgui/highgui.hpp>
#include <opencv2/calib3d/calib3d.hpp>
#include <Eigen/Core>
#include <Eigen/Geometry>
#include <g2o/core/base_vertex.h>
#include <g2o/core/base_unary_edge.h>
#include <g2o/core/block_solver.h>
#include <g2o/core/optimization_algorithm_levenberg.h>
#include <g2o/solvers/csparse/linear_solver_csparse.h>
#include <g2o/types/sba/types_six_dof_expmap.h>
#include <chrono>

using namespace std;
using namespace cv;

void find_feature_matches (
    const Mat& img_1, const Mat& img_2,
    std::vector<KeyPoint>& keypoints_1,
    std::vector<KeyPoint>& keypoints_2,
    std::vector< DMatch >& matches );

// 像素坐标转相机归一化坐标
Point2d pixel2cam ( const Point2d& p, const Mat& K );

void bundleAdjustment (
    const vector<Point3f> points_3d,
    const vector<Point2f> points_2d,
    const Mat& K,
    Mat& R, Mat& t
);

int main ( int argc, char** argv )
{
    if ( argc != 5 )
    {
        cout<<"usage: pose_estimation_3d2d img1 img2 depth1 depth2"<<endl;
        return 1;
    }
    //-- 读取图像
    Mat img_1 = imread ( argv[1], CV_LOAD_IMAGE_COLOR );
    Mat img_2 = imread ( argv[2], CV_LOAD_IMAGE_COLOR );

    vector<KeyPoint> keypoints_1, keypoints_2;
    vector<DMatch> matches;
    find_feature_matches ( img_1, img_2, keypoints_1, keypoints_2, matches );
    cout<<"一共找到了"<<matches.size() <<"组匹配点"<<endl;

    // 建立3D点
    Mat d1 = imread ( argv[3], CV_LOAD_IMAGE_UNCHANGED );       // 深度图为16位无符号数,单通道图像
    Mat K = ( Mat_<double> ( 3,3 ) << 520.9, 0, 325.1, 0, 521.0, 249.7, 0, 0, 1 );
    vector<Point3f> pts_3d;
    vector<Point2f> pts_2d;
    for ( DMatch m:matches )
    {
        ushort d = d1.ptr<unsigned short> (int ( keypoints_1[m.queryIdx].pt.y )) [ int ( keypoints_1[m.queryIdx].pt.x ) ];
        if ( d == 0 )   // bad depth
            continue;
        float dd = d/5000.0;
        Point2d p1 = pixel2cam ( keypoints_1[m.queryIdx].pt, K );
        pts_3d.push_back ( Point3f ( p1.x*dd, p1.y*dd, dd ) );
        pts_2d.push_back ( keypoints_2[m.trainIdx].pt );
    }

    cout<<"3d-2d pairs: "<<pts_3d.size() <<endl;

    Mat r, t;
    solvePnP ( pts_3d, pts_2d, K, Mat(), r, t, false ); // 调用OpenCV 的 PnP 求解,可选择EPNP,DLS等方法
    Mat R;
    cv::Rodrigues ( r, R ); // r为旋转向量形式,用Rodrigues公式转换为矩阵

    cout<<"R="<<endl<<R<<endl;
    cout<<"t="<<endl<<t<<endl;

    cout<<"calling bundle adjustment"<<endl;

    bundleAdjustment ( pts_3d, pts_2d, K, R, t );
}

void find_feature_matches ( const Mat& img_1, const Mat& img_2,
                            std::vector<KeyPoint>& keypoints_1,
                            std::vector<KeyPoint>& keypoints_2,
                            std::vector< DMatch >& matches )
{
    //-- 初始化
    Mat descriptors_1, descriptors_2;
    // used in OpenCV3
    Ptr<FeatureDetector> detector = ORB::create();
    Ptr<DescriptorExtractor> descriptor = ORB::create();
    // use this if you are in OpenCV2
    // Ptr<FeatureDetector> detector = FeatureDetector::create ( "ORB" );
    // Ptr<DescriptorExtractor> descriptor = DescriptorExtractor::create ( "ORB" );
    Ptr<DescriptorMatcher> matcher  = DescriptorMatcher::create ( "BruteForce-Hamming" );
    //-- 第一步:检测 Oriented FAST 角点位置
    detector->detect ( img_1,keypoints_1 );
    detector->detect ( img_2,keypoints_2 );

    //-- 第二步:根据角点位置计算 BRIEF 描述子
    descriptor->compute ( img_1, keypoints_1, descriptors_1 );
    descriptor->compute ( img_2, keypoints_2, descriptors_2 );

    //-- 第三步:对两幅图像中的BRIEF描述子进行匹配,使用 Hamming 距离
    vector<DMatch> match;
    // BFMatcher matcher ( NORM_HAMMING );
    matcher->match ( descriptors_1, descriptors_2, match );

    //-- 第四步:匹配点对筛选
    double min_dist=10000, max_dist=0;

    //找出所有匹配之间的最小距离和最大距离, 即是最相似的和最不相似的两组点之间的距离
    for ( int i = 0; i < descriptors_1.rows; i++ )
    {
        double dist = match[i].distance;
        if ( dist < min_dist ) min_dist = dist;
        if ( dist > max_dist ) max_dist = dist;
    }

    printf ( "-- Max dist : %f \n", max_dist );
    printf ( "-- Min dist : %f \n", min_dist );

    //当描述子之间的距离大于两倍的最小距离时,即认为匹配有误.但有时候最小距离会非常小,设置一个经验值30作为下限.
    for ( int i = 0; i < descriptors_1.rows; i++ )
    {
        if ( match[i].distance <= max ( 2*min_dist, 30.0 ) )
        {
            matches.push_back ( match[i] );
        }
    }
}

Point2d pixel2cam ( const Point2d& p, const Mat& K )
{
    return Point2d
           (
               ( p.x - K.at<double> ( 0,2 ) ) / K.at<double> ( 0,0 ),
               ( p.y - K.at<double> ( 1,2 ) ) / K.at<double> ( 1,1 )
           );
}

void bundleAdjustment (
    const vector< Point3f > points_3d,
    const vector< Point2f > points_2d,
    const Mat& K,
    Mat& R, Mat& t )
{
    // 初始化g2o
    typedef g2o::BlockSolver< g2o::BlockSolverTraits<6,3> > Block;  // pose 维度为 6, landmark 维度为 3
    //Block::LinearSolverType* linearSolver = new g2o::LinearSolverCSparse<Block::PoseMatrixType>(); // 线性方程求解器
    std::unique_ptr<Block::LinearSolverType> linearSolver ( new g2o::LinearSolverCSparse<Block::PoseMatrixType>());
    //Block* solver_ptr = new Block ( linearSolver );     // 矩阵块求解器
    std::unique_ptr<Block> solver_ptr ( new Block ( std::move(linearSolver)));     // 矩阵块求解器
        g2o::OptimizationAlgorithmLevenberg* solver = new g2o::OptimizationAlgorithmLevenberg ( std::move(solver_ptr));

    g2o::SparseOptimizer optimizer;

    optimizer.setAlgorithm ( solver );

    // vertex
    g2o::VertexSE3Expmap* pose = new g2o::VertexSE3Expmap(); // camera pose
    Eigen::Matrix3d R_mat;
    R_mat <<
          R.at<double> ( 0,0 ), R.at<double> ( 0,1 ), R.at<double> ( 0,2 ),
               R.at<double> ( 1,0 ), R.at<double> ( 1,1 ), R.at<double> ( 1,2 ),
               R.at<double> ( 2,0 ), R.at<double> ( 2,1 ), R.at<double> ( 2,2 );
    pose->setId ( 0 );
    pose->setEstimate ( g2o::SE3Quat (
                            R_mat,
                            Eigen::Vector3d ( t.at<double> ( 0,0 ), t.at<double> ( 1,0 ), t.at<double> ( 2,0 ) )
                        ) );
    optimizer.addVertex ( pose );

    int index = 1;
    for ( const Point3f p:points_3d )   // landmarks
    {
        g2o::VertexSBAPointXYZ* point = new g2o::VertexSBAPointXYZ();
        point->setId ( index++ );
        point->setEstimate ( Eigen::Vector3d ( p.x, p.y, p.z ) );
        point->setMarginalized ( true ); // g2o 中必须设置 marg 参见第十讲内容
        optimizer.addVertex ( point );
    }

    // parameter: camera intrinsics
    g2o::CameraParameters* camera = new g2o::CameraParameters (
        K.at<double> ( 0,0 ), Eigen::Vector2d ( K.at<double> ( 0,2 ), K.at<double> ( 1,2 ) ), 0
    );
    camera->setId ( 0 );
    optimizer.addParameter ( camera );

    // edges
    index = 1;
    for ( const Point2f p:points_2d )
    {
        g2o::EdgeProjectXYZ2UV* edge = new g2o::EdgeProjectXYZ2UV();
        edge->setId ( index );
        edge->setVertex ( 0, dynamic_cast<g2o::VertexSBAPointXYZ*> ( optimizer.vertex ( index ) ) );
        edge->setVertex ( 1, pose );
        edge->setMeasurement ( Eigen::Vector2d ( p.x, p.y ) );
        edge->setParameterId ( 0,0 );
        edge->setInformation ( Eigen::Matrix2d::Identity() );
        optimizer.addEdge ( edge );
        index++;
    }

    chrono::steady_clock::time_point t1 = chrono::steady_clock::now();
    optimizer.setVerbose ( true );
    optimizer.initializeOptimization();
    optimizer.optimize ( 100 );
    chrono::steady_clock::time_point t2 = chrono::steady_clock::now();
    chrono::duration<double> time_used = chrono::duration_cast<chrono::duration<double>> ( t2-t1 );
    cout<<"optimization costs time: "<<time_used.count() <<" seconds."<<endl;

    cout<<endl<<"after optimization:"<<endl;
    cout<<"T="<<endl<<Eigen::Isometry3d ( pose->estimate() ).matrix() <<endl;
}

文件名:pose_estimation_3d3d.cpp

#include <iostream>
#include <opencv2/core/core.hpp>
#include <opencv2/features2d/features2d.hpp>
#include <opencv2/highgui/highgui.hpp>
#include <opencv2/calib3d/calib3d.hpp>
#include <Eigen/Core>
#include <Eigen/Geometry>
#include <Eigen/SVD>
#include <g2o/core/base_vertex.h>
#include <g2o/core/base_unary_edge.h>
#include <g2o/core/block_solver.h>
#include <g2o/core/optimization_algorithm_gauss_newton.h>
#include <g2o/solvers/eigen/linear_solver_eigen.h>
#include <g2o/types/sba/types_six_dof_expmap.h>
#include <chrono>

using namespace std;
using namespace cv;

void find_feature_matches (
    const Mat& img_1, const Mat& img_2,
    std::vector<KeyPoint>& keypoints_1,
    std::vector<KeyPoint>& keypoints_2,
    std::vector< DMatch >& matches );

// 像素坐标转相机归一化坐标
Point2d pixel2cam ( const Point2d& p, const Mat& K );

void pose_estimation_3d3d (
    const vector<Point3f>& pts1,
    const vector<Point3f>& pts2,
    Mat& R, Mat& t
);

void bundleAdjustment(
    const vector<Point3f>& points_3d,
    const vector<Point3f>& points_2d,
    Mat& R, Mat& t
);

// g2o edge
class EdgeProjectXYZRGBDPoseOnly : public g2o::BaseUnaryEdge<3, Eigen::Vector3d, g2o::VertexSE3Expmap>
{
public:
    EIGEN_MAKE_ALIGNED_OPERATOR_NEW;
    EdgeProjectXYZRGBDPoseOnly( const Eigen::Vector3d& point ) : _point(point) {}

    virtual void computeError()
    {
        const g2o::VertexSE3Expmap* pose = static_cast<const g2o::VertexSE3Expmap*> ( _vertices[0] );
        // measurement is p, point is p'
        _error = _measurement - pose->estimate().map( _point );
    }

    virtual void linearizeOplus()
    {
        g2o::VertexSE3Expmap* pose = static_cast<g2o::VertexSE3Expmap *>(_vertices[0]);
        g2o::SE3Quat T(pose->estimate());
        Eigen::Vector3d xyz_trans = T.map(_point);
        double x = xyz_trans[0];
        double y = xyz_trans[1];
        double z = xyz_trans[2];

        _jacobianOplusXi(0,0) = 0;
        _jacobianOplusXi(0,1) = -z;
        _jacobianOplusXi(0,2) = y;
        _jacobianOplusXi(0,3) = -1;
        _jacobianOplusXi(0,4) = 0;
        _jacobianOplusXi(0,5) = 0;

        _jacobianOplusXi(1,0) = z;
        _jacobianOplusXi(1,1) = 0;
        _jacobianOplusXi(1,2) = -x;
        _jacobianOplusXi(1,3) = 0;
        _jacobianOplusXi(1,4) = -1;
        _jacobianOplusXi(1,5) = 0;

        _jacobianOplusXi(2,0) = -y;
        _jacobianOplusXi(2,1) = x;
        _jacobianOplusXi(2,2) = 0;
        _jacobianOplusXi(2,3) = 0;
        _jacobianOplusXi(2,4) = 0;
        _jacobianOplusXi(2,5) = -1;
    }

    bool read ( istream& in ) {}
    bool write ( ostream& out ) const {}
protected:
    Eigen::Vector3d _point;
};

int main ( int argc, char** argv )
{
    if ( argc != 5 )
    {
        cout<<"usage: pose_estimation_3d3d img1 img2 depth1 depth2"<<endl;
        return 1;
    }
    //-- 读取图像
    Mat img_1 = imread ( argv[1], CV_LOAD_IMAGE_COLOR );
    Mat img_2 = imread ( argv[2], CV_LOAD_IMAGE_COLOR );

    vector<KeyPoint> keypoints_1, keypoints_2;
    vector<DMatch> matches;
    find_feature_matches ( img_1, img_2, keypoints_1, keypoints_2, matches );
    cout<<"一共找到了"<<matches.size() <<"组匹配点"<<endl;

    // 建立3D点
    Mat depth1 = imread ( argv[3], CV_LOAD_IMAGE_UNCHANGED );       // 深度图为16位无符号数,单通道图像
    Mat depth2 = imread ( argv[4], CV_LOAD_IMAGE_UNCHANGED );       // 深度图为16位无符号数,单通道图像
    Mat K = ( Mat_<double> ( 3,3 ) << 520.9, 0, 325.1, 0, 521.0, 249.7, 0, 0, 1 );
    vector<Point3f> pts1, pts2;

    for ( DMatch m:matches )
    {
        ushort d1 = depth1.ptr<unsigned short> ( int ( keypoints_1[m.queryIdx].pt.y ) ) [ int ( keypoints_1[m.queryIdx].pt.x ) ];
        ushort d2 = depth2.ptr<unsigned short> ( int ( keypoints_2[m.trainIdx].pt.y ) ) [ int ( keypoints_2[m.trainIdx].pt.x ) ];
        if ( d1==0 || d2==0 )   // bad depth
            continue;
        Point2d p1 = pixel2cam ( keypoints_1[m.queryIdx].pt, K );
        Point2d p2 = pixel2cam ( keypoints_2[m.trainIdx].pt, K );
        float dd1 = float ( d1 ) /5000.0;
        float dd2 = float ( d2 ) /5000.0;
        pts1.push_back ( Point3f ( p1.x*dd1, p1.y*dd1, dd1 ) );
        pts2.push_back ( Point3f ( p2.x*dd2, p2.y*dd2, dd2 ) );
    }

    cout<<"3d-3d pairs: "<<pts1.size() <<endl;
    Mat R, t;
    pose_estimation_3d3d ( pts1, pts2, R, t );
    cout<<"ICP via SVD results: "<<endl;
    cout<<"R = "<<R<<endl;
    cout<<"t = "<<t<<endl;
    cout<<"R_inv = "<<R.t() <<endl;
    cout<<"t_inv = "<<-R.t() *t<<endl;

    cout<<"calling bundle adjustment"<<endl;

    bundleAdjustment( pts1, pts2, R, t );

    // verify p1 = R*p2 + t
    for ( int i=0; i<5; i++ )
    {
        cout<<"p1 = "<<pts1[i]<<endl;
        cout<<"p2 = "<<pts2[i]<<endl;
        cout<<"(R*p2+t) = "<<
            R * (Mat_<double>(3,1)<<pts2[i].x, pts2[i].y, pts2[i].z) + t
            <<endl;
        cout<<endl;
    }
}

void find_feature_matches ( const Mat& img_1, const Mat& img_2,
                            std::vector<KeyPoint>& keypoints_1,
                            std::vector<KeyPoint>& keypoints_2,
                            std::vector< DMatch >& matches )
{
    //-- 初始化
    Mat descriptors_1, descriptors_2;
    // used in OpenCV3
    Ptr<FeatureDetector> detector = ORB::create();
    Ptr<DescriptorExtractor> descriptor = ORB::create();
    // use this if you are in OpenCV2
    // Ptr<FeatureDetector> detector = FeatureDetector::create ( "ORB" );
    // Ptr<DescriptorExtractor> descriptor = DescriptorExtractor::create ( "ORB" );
    Ptr<DescriptorMatcher> matcher  = DescriptorMatcher::create("BruteForce-Hamming");
    //-- 第一步:检测 Oriented FAST 角点位置
    detector->detect ( img_1,keypoints_1 );
    detector->detect ( img_2,keypoints_2 );

    //-- 第二步:根据角点位置计算 BRIEF 描述子
    descriptor->compute ( img_1, keypoints_1, descriptors_1 );
    descriptor->compute ( img_2, keypoints_2, descriptors_2 );

    //-- 第三步:对两幅图像中的BRIEF描述子进行匹配,使用 Hamming 距离
    vector<DMatch> match;
   // BFMatcher matcher ( NORM_HAMMING );
    matcher->match ( descriptors_1, descriptors_2, match );

    //-- 第四步:匹配点对筛选
    double min_dist=10000, max_dist=0;

    //找出所有匹配之间的最小距离和最大距离, 即是最相似的和最不相似的两组点之间的距离
    for ( int i = 0; i < descriptors_1.rows; i++ )
    {
        double dist = match[i].distance;
        if ( dist < min_dist ) min_dist = dist;
        if ( dist > max_dist ) max_dist = dist;
    }

    printf ( "-- Max dist : %f \n", max_dist );
    printf ( "-- Min dist : %f \n", min_dist );

    //当描述子之间的距离大于两倍的最小距离时,即认为匹配有误.但有时候最小距离会非常小,设置一个经验值30作为下限.
    for ( int i = 0; i < descriptors_1.rows; i++ )
    {
        if ( match[i].distance <= max ( 2*min_dist, 30.0 ) )
        {
            matches.push_back ( match[i] );
        }
    }
}

Point2d pixel2cam ( const Point2d& p, const Mat& K )
{
    return Point2d
           (
               ( p.x - K.at<double> ( 0,2 ) ) / K.at<double> ( 0,0 ),
               ( p.y - K.at<double> ( 1,2 ) ) / K.at<double> ( 1,1 )
           );
}

void pose_estimation_3d3d (
    const vector<Point3f>& pts1,
    const vector<Point3f>& pts2,
    Mat& R, Mat& t
)
{
    Point3f p1, p2;     // center of mass
    int N = pts1.size();
    for ( int i=0; i<N; i++ )
    {
        p1 += pts1[i];
        p2 += pts2[i];
    }
    p1 = Point3f( Vec3f(p1) /  N);
    p2 = Point3f( Vec3f(p2) / N);
    vector<Point3f>     q1 ( N ), q2 ( N ); // remove the center
    for ( int i=0; i<N; i++ )
    {
        q1[i] = pts1[i] - p1;
        q2[i] = pts2[i] - p2;
    }

    // compute q1*q2^T
    Eigen::Matrix3d W = Eigen::Matrix3d::Zero();
    for ( int i=0; i<N; i++ )
    {
        W += Eigen::Vector3d ( q1[i].x, q1[i].y, q1[i].z ) * Eigen::Vector3d ( q2[i].x, q2[i].y, q2[i].z ).transpose();
    }
    cout<<"W="<<W<<endl;

    // SVD on W
    Eigen::JacobiSVD<Eigen::Matrix3d> svd ( W, Eigen::ComputeFullU|Eigen::ComputeFullV );
    Eigen::Matrix3d U = svd.matrixU();
    Eigen::Matrix3d V = svd.matrixV();
    cout<<"U="<<U<<endl;
    cout<<"V="<<V<<endl;

    Eigen::Matrix3d R_ = U* ( V.transpose() );
    Eigen::Vector3d t_ = Eigen::Vector3d ( p1.x, p1.y, p1.z ) - R_ * Eigen::Vector3d ( p2.x, p2.y, p2.z );

    // convert to cv::Mat
    R = ( Mat_<double> ( 3,3 ) <<
          R_ ( 0,0 ), R_ ( 0,1 ), R_ ( 0,2 ),
          R_ ( 1,0 ), R_ ( 1,1 ), R_ ( 1,2 ),
          R_ ( 2,0 ), R_ ( 2,1 ), R_ ( 2,2 )
        );
    t = ( Mat_<double> ( 3,1 ) << t_ ( 0,0 ), t_ ( 1,0 ), t_ ( 2,0 ) );
}

void bundleAdjustment (
    const vector< Point3f >& pts1,
    const vector< Point3f >& pts2,
    Mat& R, Mat& t )
{
    // 初始化g2o
    typedef g2o::BlockSolver< g2o::BlockSolverTraits<6,3> > Block;  // pose维度为 6, landmark 维度为 3
    Block::LinearSolverType* linearSolver = new g2o::LinearSolverEigen<Block::PoseMatrixType>(); // 线性方程求解器
    Block* solver_ptr = new Block( std::unique_ptr<Block::LinearSolverType>(linearSolver));      // 矩阵块求解器
    g2o::OptimizationAlgorithmGaussNewton* solver = new g2o::OptimizationAlgorithmGaussNewton(std::unique_ptr<Block>(solver_ptr));
    g2o::SparseOptimizer optimizer;
    optimizer.setAlgorithm( solver );

    // vertex
    g2o::VertexSE3Expmap* pose = new g2o::VertexSE3Expmap(); // camera pose
    pose->setId(0);
    pose->setEstimate( g2o::SE3Quat(
        Eigen::Matrix3d::Identity(),
        Eigen::Vector3d( 0,0,0 )
    ) );
    optimizer.addVertex( pose );

    // edges
    int index = 1;
    vector<EdgeProjectXYZRGBDPoseOnly*> edges;
    for ( size_t i=0; i<pts1.size(); i++ )
    {
        EdgeProjectXYZRGBDPoseOnly* edge = new EdgeProjectXYZRGBDPoseOnly(
            Eigen::Vector3d(pts2[i].x, pts2[i].y, pts2[i].z) );
        edge->setId( index );
        edge->setVertex( 0, dynamic_cast<g2o::VertexSE3Expmap*> (pose) );
        edge->setMeasurement( Eigen::Vector3d(
            pts1[i].x, pts1[i].y, pts1[i].z) );
        edge->setInformation( Eigen::Matrix3d::Identity()*1e4 );
        optimizer.addEdge(edge);
        index++;
        edges.push_back(edge);
    }

    chrono::steady_clock::time_point t1 = chrono::steady_clock::now();
    optimizer.setVerbose( true );
    optimizer.initializeOptimization();
    optimizer.optimize(10);
    chrono::steady_clock::time_point t2 = chrono::steady_clock::now();
    chrono::duration<double> time_used = chrono::duration_cast<chrono::duration<double>>(t2-t1);
    cout<<"optimization costs time: "<<time_used.count()<<" seconds."<<endl;

    cout<<endl<<"after optimization:"<<endl;
    cout<<"T="<<endl<<Eigen::Isometry3d( pose->estimate() ).matrix()<<endl;

}

文件名:feature_extraction.cpp

#include <iostream>
#include <opencv2/core/core.hpp>
#include <opencv2/features2d/features2d.hpp>
#include <opencv2/highgui/highgui.hpp>
using namespace std;
using namespace cv;

int main(int argc, char** argv)
{

    if(argc!=3)//判断命令行输入对错
    {
        cout<<"usage: feature_extraction img1 img2"<<endl;
        return 1;
    }

    //读取要匹配的两张图像
    Mat img_1 = imread(argv[1], CV_LOAD_IMAGE_COLOR);
    Mat img_2 = imread(argv[2], CV_LOAD_IMAGE_COLOR);
    imshow("img_1",img_1);
    imshow("img_2",img_2);

    //初始化
    //首先创建两个关键点数组,用于存放两张图像的关键点,数组元素是KeyPoint类型
    std::vector<KeyPoint> keypoints_1, keypoints_2;

    //创建两张图像的描述子,类型是Mat类型
    Mat descriptors_1, descriptors_2;

    //创建一个ORB类型指针orb,ORB类是继承自Feature2D类
    //class CV_EXPORTS_W ORB : public Feature2D
    //这里看一下create()源码:参数较多,不介绍。
    //creat()方法所有参数都有默认值,返回static Ptr<ORB>类型。
    /*
    CV_WRAP static Ptr<ORB> create(int nfeatures=500,
                                   float scaleFactor=1.2f,
                                   int nlevels=8,
                                   int edgeThreshold=31,
                                   int firstLevel=0,
                                   int WTA_K=2,
                                   int scoreType=ORB::HARRIS_SCORE,
                                   int patchSize=31,
                                   int fastThreshold=20);
    */
    //所以这里的语句就是创建一个Ptr<ORB>类型的orb,用于接收ORB类中create()函数的返回值
    Ptr<ORB> orb = ORB::create();

    //第一步:检测Oriented FAST角点位置.
    //detect是Feature2D中的方法,orb是子类指针,可以调用
    //看一下detect()方法的原型参数:需要检测的图像,关键点数组,第三个参数为默认值
    /*
    CV_WRAP virtual void detect( InputArray image,
                                 CV_OUT std::vector<KeyPoint>& keypoints,
                                 InputArray mask=noArray() );
    */
    orb->detect(img_1, keypoints_1);
    orb->detect(img_2, keypoints_2);


    //第二步:根据角点位置计算BRIEF描述子
    //compute是Feature2D中的方法,orb是子类指针,可以调用
    //看一下compute()原型参数:图像,图像的关键点数组,Mat类型的描述子
    /*
    CV_WRAP virtual void compute( InputArray image,
                                  CV_OUT CV_IN_OUT std::vector<KeyPoint>& keypoints,
                                  OutputArray descriptors );
    */
    orb->compute(img_1, keypoints_1, descriptors_1);
    orb->compute(img_2, keypoints_2, descriptors_2);

    //定义输出检测特征点的图片。
    Mat outimg1;
    //drawKeypoints()函数原型参数:原图,原图关键点,带有关键点的输出图像,后面两个为默认值
    /*
    CV_EXPORTS_W void drawKeypoints( InputArray image,
                                     const std::vector<KeyPoint>& keypoints,
                                     InputOutputArray outImage,
                                     const Scalar& color=Scalar::all(-1),
                                     int flags=DrawMatchesFlags::DEFAULT );
    */
    //注意看,这里并没有用到描述子,描述子的作用是用于后面的关键点筛选。
    drawKeypoints(img_1, keypoints_1, outimg1, Scalar::all(-1), DrawMatchesFlags::DEFAULT);

    imshow("ORB detectors",outimg1);


    //第三步:对两幅图像中的BRIEF描述子进行匹配,使用 Hamming 距离

    //创建一个匹配点数组,用于承接匹配出的DMatch,其实叫match_points_array更为贴切。matches类型为数组,元素类型为DMatch
    vector<DMatch> matches;

    //创建一个BFMatcher匹配器,BFMatcher类构造函数如下:两个参数都有默认值,但是第一个距离类型下面使用的并不是默认值,而是汉明距离
    //CV_WRAP BFMatcher( int normType=NORM_L2, bool crossCheck=false );
    BFMatcher matcher (NORM_HAMMING);

    //调用matcher的match方法进行匹配,这里用到了描述子,没有用关键点。
    //匹配出来的结果写入上方定义的matches[]数组中
    matcher.match(descriptors_1, descriptors_2, matches);

    //第四步:遍历matches[]数组,找出匹配点的最大距离和最小距离,用于后面的匹配点筛选。
    //这里的距离是上方求出的汉明距离数组,汉明距离表征了两个匹配的相似程度,所以也就找出了最相似和最不相似的两组点之间的距离。
    double min_dist=0, max_dist=0;//定义距离

    for (int i = 0; i < descriptors_1.rows; ++i)//遍历
    {
        double dist = matches[i].distance;
        if(dist<min_dist) min_dist = dist;
        if(dist>max_dist) max_dist = dist;
    }

    printf("Max dist: %f\n", max_dist);
    printf("Min dist: %f\n", min_dist);

    //第五步:根据最小距离,对匹配点进行筛选,
    //当描述自之间的距离大于两倍的min_dist,即认为匹配有误,舍弃掉。
    //但是有时最小距离非常小,比如趋近于0了,所以这样就会导致min_dist到2*min_dist之间没有几个匹配。
    // 所以,在2*min_dist小于30的时候,就取30当上限值,小于30即可,不用2*min_dist这个值了
    std::vector<DMatch> good_matches;
    for (int j = 0; j < descriptors_1.rows; ++j)
    {
        if (matches[j].distance <= max(2*min_dist, 30.0))
            good_matches.push_back(matches[j]);
    }

    //第六步:绘制匹配结果

    Mat img_match;//所有匹配点图
    //这里看一下drawMatches()原型参数,简单用法就是:图1,图1关键点,图2,图2关键点,匹配数组,承接图像,后面的有默认值
    /*
    CV_EXPORTS_W void drawMatches( InputArray img1,
                                   const std::vector<KeyPoint>& keypoints1,
                                   InputArray img2,
                                   const std::vector<KeyPoint>& keypoints2,
                                   const std::vector<DMatch>& matches1to2,
                                   InputOutputArray outImg,
                                   const Scalar& matchColor=Scalar::all(-1),
                                   const Scalar& singlePointColor=Scalar::all(-1),
                                   const std::vector<char>& matchesMask=std::vector<char>(),
                                   int flags=DrawMatchesFlags::DEFAULT );
    */

    drawMatches(img_1, keypoints_1, img_2, keypoints_2, matches, img_match);
    imshow("all th matches", img_match);

    Mat img_goodmatch;//筛选后的匹配点图
    drawMatches(img_1, keypoints_1, img_2, keypoints_2, good_matches, img_goodmatch);
    imshow("matches", img_goodmatch);

    waitKey(0);

    return 0;
}

最后附两张结果图!加油!!

关于高博士在《视觉SLAM十四讲》中ch7部分ORB检测算法代码的勘误

关于高博士在《视觉SLAM十四讲》中ch7部分ORB检测算法代码的勘误