视觉SLAM学习【1】-----基于ubuntu16.04的SLAM中ORB特征点提取及暴力匹配

陈一月的编程岁月

发布时间 2021.01.11阅读数 5792 评论数 0

视觉SLAM学习【1】----基于ubuntu16.04的SLAM中ORB特征点提取及暴力匹配目录

一、ORB特征点提取及暴力匹配定义
- 1、什么是ORB特征点提取？
- 2、什么是暴力匹配？
二、进行相关数据准备
- 1、拍摄图像
- 2、特征点提取、匹配的文件夹创建
- 3、在创建的文件夹中新建cpp文件
- 4、图像特征点提取、暴力匹配的代码编写
三、图像特征点匹配完整代码
四、进行图像特征匹配测试
- 1、编译slamORB.cpp文件
- 2、终端运行可执行文件
- 3、运行结果

在之前的ROS嵌入式学习中，我们通过SLAM2的ORB进行相关数据集的演示，对于SLAM有一定的了解，本次博客，林君学长将带大家了解，如何通过SLAM中的ORB特征提取，借助opencv进行图像的暴力匹配首先就让我们了解一下什么是特征提取及暴力匹配吧

一、ORB特征点提取及暴力匹配定义

1、什么是ORB特征点提取？

1）、ORB特征点 ORB(Oriented FAST and BRIEF) 特征是 SLAM 中一种很常用的特征,由于其二进制特性,使得它可以非常快速地提取与计算。匹配的代码。 2）、ORB 提取 ORB 即 Oriented FAST 简称。它实际上是 FAST 特征再加上一个旋转量。本习题将使用 OpenCV 自带的 FAST 提取算法,但是你要完成旋转部分的计算。旋转的计算过程描述如下 : 在一个小图像块中,先计算质心。质心是指以图像块灰度值作为权重的中心。微信图片_20210106162252

实际上只需计算 m 01 和 m 10 即可

2、什么是暴力匹配？

1）、暴力匹配在提取描述之后,我们需要根据描述子进行匹配。暴力匹配是一种简单粗暴的匹配方法,在特征点不多时很有用。 2）、暴力匹配思路所谓暴力匹配思路很简单。给定两组描述子 P = [p 1 , . . . , p M ] 和 Q = [q 1 , . . . , q N ]。那么,对 P 中任意一个点,找到 Q 中对应最小距离点,即算一次匹配。但是这样做会对每个特征点都找到一个匹配,所以我们通常还会限制一个距离阈值 d_max ,即认作匹配的特征点距离不应该大于 d_max 。实践中取 d_max = 50 下面就让我们通过opencv实现对图像特征点的暴力匹配吧

二、进行相关数据准备

1、拍摄图像

1）、通过手机拍摄两张图像，拍摄图像的内容首先应该保持一致，其次，我们需要对拍摄的角度进行调整，但调整不能过大，不然不会匹配到相应的特征点，图像可参考如下：微信图片_20210106162313

2）、将手机拍摄的图像进行压缩，现在大多数智能手机拍摄的图像的大小是非常大的，在ubuntu通过代码读取图像显示的时候，便会非常巨大，不便于我们查看和操作，所以需要在windows上面进行压缩；最好最快的压缩方式就是通过打开该图片，然后通过qq截屏截取这张图片，然后再次保存，这样的图片就很小了，比如如下对比：微信图片_20210106162331

截屏后保存的图片(Ctrl+Alt+A)：微信图片_20210106162345

可以看到，qq截屏压缩后的图片是非常的小的，但这并不影响我们的特征点提取和匹配

2、特征点提取、匹配的文件夹创建

1）、在ubuntu系统的opencv安装目录中创建特征点匹配文件夹

cd ~/lenovo/opencv-3.4.1/
mkdir SLAM_ORB

2）、将手机拍摄图像上传至电脑，然后上传至ubuntu16.04文件系统的对应目录下：微信图片_20210106162408

3、在创建的文件夹中新建cpp文件

1）、创建特征匹配cpp文件

touch slamORB.cpp

2）、打开cpp文件，编写图像特征点提取匹配c++代码

gedit slamORB.cpp

4、图像特征点提取、暴力匹配的代码编写

1）、引入opencv库和定义变量

#include <opencv2/opencv.hpp>
#include <string>
using namespace std;
// global variables
string first_file = "./1.jpg";
string second_file = "./2.jpg"; 
const double pi = 3.1415926;    // 定义圆周率，用于后面求解角度

注意，first_file和second_file需要改为你们自己的图片名字哦！ 2）、计算角度功能函数编写

void computeAngle(const cv::Mat &image, vector<cv::KeyPoint> &keypoints) {
    int half_patch_size = 8;
    for (auto &kp : keypoints) {
        double m10 = 0;
        double m01 = 0;
        int x =cvRound(kp.pt.x);
        int y =cvRound(kp.pt.y);
        if(x-half_patch_size<0||x+half_patch_size>image.cols||
           y-half_patch_size<0||y+half_patch_size>image.rows)
            continue;
        for(int u = x - half_patch_size;u<x + half_patch_size;++u)
        {
            for(int v = y -half_patch_size;v< y + half_patch_size;++v)
            {
                m10 +=  (u-x)*image.at<uchar>(v,u);
                m01 +=  (v-y)*image.at<uchar>(v,u);
            }
        }
 
        double theta = std::atan(m01/m10);
        kp.angle = theta * 180/pi;
        cout<<"kp.angel:"<<kp.angle<<endl;
//         END YOUR CODE HERE
    }
    return;
}

3）、给ORB模式匹配数组变量赋值

// ORB pattern
int ORB_pattern[256 * 4] = {
        8, -3, 9, 5/*mean (0), correlation (0)*/,
        4, 2, 7, -12/*mean (1.12461e-05), correlation (0.0437584)*/,
        -11, 9, -8, 2/*mean (3.37382e-05), correlation (0.0617409)*/,
        7, -12, 12, -13/*mean (5.62303e-05), correlation (0.0636977)*/,
        2, -13, 2, 12/*mean (0.000134953), correlation (0.085099)*/,
        1, -7, 1, 6/*mean (0.000528565), correlation (0.0857175)*/,
        -2, -10, -2, -4/*mean (0.0188821), correlation (0.0985774)*/,
        -13, -13, -11, -8/*mean (0.0363135), correlation (0.0899616)*/,
        -13, -3, -12, -9/*mean (0.121806), correlation (0.099849)*/,
        10, 4, 11, 9/*mean (0.122065), correlation (0.093285)*/,
        -13, -8, -8, -9/*mean (0.162787), correlation (0.0942748)*/,
        -11, 7, -9, 12/*mean (0.21561), correlation (0.0974438)*/,
        7, 7, 12, 6/*mean (0.160583), correlation (0.130064)*/,
        -4, -5, -3, 0/*mean (0.228171), correlation (0.132998)*/,
        -13, 2, -12, -3/*mean (0.00997526), correlation (0.145926)*/,
        -9, 0, -7, 5/*mean (0.198234), correlation (0.143636)*/,
        12, -6, 12, -1/*mean (0.0676226), correlation (0.16689)*/,
        -3, 6, -2, 12/*mean (0.166847), correlation (0.171682)*/,
        -6, -13, -4, -8/*mean (0.101215), correlation (0.179716)*/,
        11, -13, 12, -8/*mean (0.200641), correlation (0.192279)*/,
        4, 7, 5, 1/*mean (0.205106), correlation (0.186848)*/,
        5, -3, 10, -3/*mean (0.234908), correlation (0.192319)*/,
        3, -7, 6, 12/*mean (0.0709964), correlation (0.210872)*/,
        -8, -7, -6, -2/*mean (0.0939834), correlation (0.212589)*/,
        -2, 11, -1, -10/*mean (0.127778), correlation (0.20866)*/,
        -13, 12, -8, 10/*mean (0.14783), correlation (0.206356)*/,
        -7, 3, -5, -3/*mean (0.182141), correlation (0.198942)*/,
        -4, 2, -3, 7/*mean (0.188237), correlation (0.21384)*/,
        -10, -12, -6, 11/*mean (0.14865), correlation (0.23571)*/,
        5, -12, 6, -7/*mean (0.222312), correlation (0.23324)*/,
        5, -6, 7, -1/*mean (0.229082), correlation (0.23389)*/,
        1, 0, 4, -5/*mean (0.241577), correlation (0.215286)*/,
        9, 11, 11, -13/*mean (0.00338507), correlation (0.251373)*/,
        4, 7, 4, 12/*mean (0.131005), correlation (0.257622)*/,
        2, -1, 4, 4/*mean (0.152755), correlation (0.255205)*/,
        -4, -12, -2, 7/*mean (0.182771), correlation (0.244867)*/,
        -8, -5, -7, -10/*mean (0.186898), correlation (0.23901)*/,
        4, 11, 9, 12/*mean (0.226226), correlation (0.258255)*/,
        0, -8, 1, -13/*mean (0.0897886), correlation (0.274827)*/,
        -13, -2, -8, 2/*mean (0.148774), correlation (0.28065)*/,
        -3, -2, -2, 3/*mean (0.153048), correlation (0.283063)*/,
        -6, 9, -4, -9/*mean (0.169523), correlation (0.278248)*/,
        8, 12, 10, 7/*mean (0.225337), correlation (0.282851)*/,
        0, 9, 1, 3/*mean (0.226687), correlation (0.278734)*/,
        7, -5, 11, -10/*mean (0.00693882), correlation (0.305161)*/,
        -13, -6, -11, 0/*mean (0.0227283), correlation (0.300181)*/,
        10, 7, 12, 1/*mean (0.125517), correlation (0.31089)*/,
        -6, -3, -6, 12/*mean (0.131748), correlation (0.312779)*/,
        10, -9, 12, -4/*mean (0.144827), correlation (0.292797)*/,
        -13, 8, -8, -12/*mean (0.149202), correlation (0.308918)*/,
        -13, 0, -8, -4/*mean (0.160909), correlation (0.310013)*/,
        3, 3, 7, 8/*mean (0.177755), correlation (0.309394)*/,
        5, 7, 10, -7/*mean (0.212337), correlation (0.310315)*/,
        -1, 7, 1, -12/*mean (0.214429), correlation (0.311933)*/,
        3, -10, 5, 6/*mean (0.235807), correlation (0.313104)*/,
        2, -4, 3, -10/*mean (0.00494827), correlation (0.344948)*/,
        -13, 0, -13, 5/*mean (0.0549145), correlation (0.344675)*/,
        -13, -7, -12, 12/*mean (0.103385), correlation (0.342715)*/,
        -13, 3, -11, 8/*mean (0.134222), correlation (0.322922)*/,
        -7, 12, -4, 7/*mean (0.153284), correlation (0.337061)*/,
        6, -10, 12, 8/*mean (0.154881), correlation (0.329257)*/,
        -9, -1, -7, -6/*mean (0.200967), correlation (0.33312)*/,
        -2, -5, 0, 12/*mean (0.201518), correlation (0.340635)*/,
        -12, 5, -7, 5/*mean (0.207805), correlation (0.335631)*/,
        3, -10, 8, -13/*mean (0.224438), correlation (0.34504)*/,
        -7, -7, -4, 5/*mean (0.239361), correlation (0.338053)*/,
        -3, -2, -1, -7/*mean (0.240744), correlation (0.344322)*/,
        2, 9, 5, -11/*mean (0.242949), correlation (0.34145)*/,
        -11, -13, -5, -13/*mean (0.244028), correlation (0.336861)*/,
        -1, 6, 0, -1/*mean (0.247571), correlation (0.343684)*/,
        5, -3, 5, 2/*mean (0.000697256), correlation (0.357265)*/,
        -4, -13, -4, 12/*mean (0.00213675), correlation (0.373827)*/,
        -9, -6, -9, 6/*mean (0.0126856), correlation (0.373938)*/,
        -12, -10, -8, -4/*mean (0.0152497), correlation (0.364237)*/,
        10, 2, 12, -3/*mean (0.0299933), correlation (0.345292)*/,
        7, 12, 12, 12/*mean (0.0307242), correlation (0.366299)*/,
        -7, -13, -6, 5/*mean (0.0534975), correlation (0.368357)*/,
        -4, 9, -3, 4/*mean (0.099865), correlation (0.372276)*/,
        7, -1, 12, 2/*mean (0.117083), correlation (0.364529)*/,
        -7, 6, -5, 1/*mean (0.126125), correlation (0.369606)*/,
        -13, 11, -12, 5/*mean (0.130364), correlation (0.358502)*/,
        -3, 7, -2, -6/*mean (0.131691), correlation (0.375531)*/,
        7, -8, 12, -7/*mean (0.160166), correlation (0.379508)*/,
        -13, -7, -11, -12/*mean (0.167848), correlation (0.353343)*/,
        1, -3, 12, 12/*mean (0.183378), correlation (0.371916)*/,
        2, -6, 3, 0/*mean (0.228711), correlation (0.371761)*/,
        -4, 3, -2, -13/*mean (0.247211), correlation (0.364063)*/,
        -1, -13, 1, 9/*mean (0.249325), correlation (0.378139)*/,
        7, 1, 8, -6/*mean (0.000652272), correlation (0.411682)*/,
        1, -1, 3, 12/*mean (0.00248538), correlation (0.392988)*/,
        9, 1, 12, 6/*mean (0.0206815), correlation (0.386106)*/,
        -1, -9, -1, 3/*mean (0.0364485), correlation (0.410752)*/,
        -13, -13, -10, 5/*mean (0.0376068), correlation (0.398374)*/,
        7, 7, 10, 12/*mean (0.0424202), correlation (0.405663)*/,
        12, -5, 12, 9/*mean (0.0942645), correlation (0.410422)*/,
        6, 3, 7, 11/*mean (0.1074), correlation (0.413224)*/,
        5, -13, 6, 10/*mean (0.109256), correlation (0.408646)*/,
        2, -12, 2, 3/*mean (0.131691), correlation (0.416076)*/,
        3, 8, 4, -6/*mean (0.165081), correlation (0.417569)*/,
        2, 6, 12, -13/*mean (0.171874), correlation (0.408471)*/,
        9, -12, 10, 3/*mean (0.175146), correlation (0.41296)*/,
        -8, 4, -7, 9/*mean (0.183682), correlation (0.402956)*/,
        -11, 12, -4, -6/*mean (0.184672), correlation (0.416125)*/,
        1, 12, 2, -8/*mean (0.191487), correlation (0.386696)*/,
        6, -9, 7, -4/*mean (0.192668), correlation (0.394771)*/,
        2, 3, 3, -2/*mean (0.200157), correlation (0.408303)*/,
        6, 3, 11, 0/*mean (0.204588), correlation (0.411762)*/,
        3, -3, 8, -8/*mean (0.205904), correlation (0.416294)*/,
        7, 8, 9, 3/*mean (0.213237), correlation (0.409306)*/,
        -11, -5, -6, -4/*mean (0.243444), correlation (0.395069)*/,
        -10, 11, -5, 10/*mean (0.247672), correlation (0.413392)*/,
        -5, -8, -3, 12/*mean (0.24774), correlation (0.411416)*/,
        -10, 5, -9, 0/*mean (0.00213675), correlation (0.454003)*/,
        8, -1, 12, -6/*mean (0.0293635), correlation (0.455368)*/,
        4, -6, 6, -11/*mean (0.0404971), correlation (0.457393)*/,
        -10, 12, -8, 7/*mean (0.0481107), correlation (0.448364)*/,
        4, -2, 6, 7/*mean (0.050641), correlation (0.455019)*/,
        -2, 0, -2, 12/*mean (0.0525978), correlation (0.44338)*/,
        -5, -8, -5, 2/*mean (0.0629667), correlation (0.457096)*/,
        7, -6, 10, 12/*mean (0.0653846), correlation (0.445623)*/,
        -9, -13, -8, -8/*mean (0.0858749), correlation (0.449789)*/,
        -5, -13, -5, -2/*mean (0.122402), correlation (0.450201)*/,
        8, -8, 9, -13/*mean (0.125416), correlation (0.453224)*/,
        -9, -11, -9, 0/*mean (0.130128), correlation (0.458724)*/,
        1, -8, 1, -2/*mean (0.132467), correlation (0.440133)*/,
        7, -4, 9, 1/*mean (0.132692), correlation (0.454)*/,
        -2, 1, -1, -4/*mean (0.135695), correlation (0.455739)*/,
        11, -6, 12, -11/*mean (0.142904), correlation (0.446114)*/,
        -12, -9, -6, 4/*mean (0.146165), correlation (0.451473)*/,
        3, 7, 7, 12/*mean (0.147627), correlation (0.456643)*/,
        5, 5, 10, 8/*mean (0.152901), correlation (0.455036)*/,
        0, -4, 2, 8/*mean (0.167083), correlation (0.459315)*/,
        -9, 12, -5, -13/*mean (0.173234), correlation (0.454706)*/,
        0, 7, 2, 12/*mean (0.18312), correlation (0.433855)*/,
        -1, 2, 1, 7/*mean (0.185504), correlation (0.443838)*/,
        5, 11, 7, -9/*mean (0.185706), correlation (0.451123)*/,
        3, 5, 6, -8/*mean (0.188968), correlation (0.455808)*/,
        -13, -4, -8, 9/*mean (0.191667), correlation (0.459128)*/,
        -5, 9, -3, -3/*mean (0.193196), correlation (0.458364)*/,
        -4, -7, -3, -12/*mean (0.196536), correlation (0.455782)*/,
        6, 5, 8, 0/*mean (0.1972), correlation (0.450481)*/,
        -7, 6, -6, 12/*mean (0.199438), correlation (0.458156)*/,
        -13, 6, -5, -2/*mean (0.211224), correlation (0.449548)*/,
        1, -10, 3, 10/*mean (0.211718), correlation (0.440606)*/,
        4, 1, 8, -4/*mean (0.213034), correlation (0.443177)*/,
        -2, -2, 2, -13/*mean (0.234334), correlation (0.455304)*/,
        2, -12, 12, 12/*mean (0.235684), correlation (0.443436)*/,
        -2, -13, 0, -6/*mean (0.237674), correlation (0.452525)*/,
        4, 1, 9, 3/*mean (0.23962), correlation (0.444824)*/,
        -6, -10, -3, -5/*mean (0.248459), correlation (0.439621)*/,
        -3, -13, -1, 1/*mean (0.249505), correlation (0.456666)*/,
        7, 5, 12, -11/*mean (0.00119208), correlation (0.495466)*/,
        4, -2, 5, -7/*mean (0.00372245), correlation (0.484214)*/,
        -13, 9, -9, -5/*mean (0.00741116), correlation (0.499854)*/,
        7, 1, 8, 6/*mean (0.0208952), correlation (0.499773)*/,
        7, -8, 7, 6/*mean (0.0220085), correlation (0.501609)*/,
        -7, -4, -7, 1/*mean (0.0233806), correlation (0.496568)*/,
        -8, 11, -7, -8/*mean (0.0236505), correlation (0.489719)*/,
        -13, 6, -12, -8/*mean (0.0268781), correlation (0.503487)*/,
        2, 4, 3, 9/*mean (0.0323324), correlation (0.501938)*/,
        10, -5, 12, 3/*mean (0.0399235), correlation (0.494029)*/,
        -6, -5, -6, 7/*mean (0.0420153), correlation (0.486579)*/,
        8, -3, 9, -8/*mean (0.0548021), correlation (0.484237)*/,
        2, -12, 2, 8/*mean (0.0616622), correlation (0.496642)*/,
        -11, -2, -10, 3/*mean (0.0627755), correlation (0.498563)*/,
        -12, -13, -7, -9/*mean (0.0829622), correlation (0.495491)*/,
        -11, 0, -10, -5/*mean (0.0843342), correlation (0.487146)*/,
        5, -3, 11, 8/*mean (0.0929937), correlation (0.502315)*/,
        -2, -13, -1, 12/*mean (0.113327), correlation (0.48941)*/,
        -1, -8, 0, 9/*mean (0.132119), correlation (0.467268)*/,
        -13, -11, -12, -5/*mean (0.136269), correlation (0.498771)*/,
        -10, -2, -10, 11/*mean (0.142173), correlation (0.498714)*/,
        -3, 9, -2, -13/*mean (0.144141), correlation (0.491973)*/,
        2, -3, 3, 2/*mean (0.14892), correlation (0.500782)*/,
        -9, -13, -4, 0/*mean (0.150371), correlation (0.498211)*/,
        -4, 6, -3, -10/*mean (0.152159), correlation (0.495547)*/,
        -4, 12, -2, -7/*mean (0.156152), correlation (0.496925)*/,
        -6, -11, -4, 9/*mean (0.15749), correlation (0.499222)*/,
        6, -3, 6, 11/*mean (0.159211), correlation (0.503821)*/,
        -13, 11, -5, 5/*mean (0.162427), correlation (0.501907)*/,
        11, 11, 12, 6/*mean (0.16652), correlation (0.497632)*/,
        7, -5, 12, -2/*mean (0.169141), correlation (0.484474)*/,
        -1, 12, 0, 7/*mean (0.169456), correlation (0.495339)*/,
        -4, -8, -3, -2/*mean (0.171457), correlation (0.487251)*/,
        -7, 1, -6, 7/*mean (0.175), correlation (0.500024)*/,
        -13, -12, -8, -13/*mean (0.175866), correlation (0.497523)*/,
        -7, -2, -6, -8/*mean (0.178273), correlation (0.501854)*/,
        -8, 5, -6, -9/*mean (0.181107), correlation (0.494888)*/,
        -5, -1, -4, 5/*mean (0.190227), correlation (0.482557)*/,
        -13, 7, -8, 10/*mean (0.196739), correlation (0.496503)*/,
        1, 5, 5, -13/*mean (0.19973), correlation (0.499759)*/,
        1, 0, 10, -13/*mean (0.204465), correlation (0.49873)*/,
        9, 12, 10, -1/*mean (0.209334), correlation (0.49063)*/,
        5, -8, 10, -9/*mean (0.211134), correlation (0.503011)*/,
        -1, 11, 1, -13/*mean (0.212), correlation (0.499414)*/,
        -9, -3, -6, 2/*mean (0.212168), correlation (0.480739)*/,
        -1, -10, 1, 12/*mean (0.212731), correlation (0.502523)*/,
        -13, 1, -8, -10/*mean (0.21327), correlation (0.489786)*/,
        8, -11, 10, -6/*mean (0.214159), correlation (0.488246)*/,
        2, -13, 3, -6/*mean (0.216993), correlation (0.50287)*/,
        7, -13, 12, -9/*mean (0.223639), correlation (0.470502)*/,
        -10, -10, -5, -7/*mean (0.224089), correlation (0.500852)*/,
        -10, -8, -8, -13/*mean (0.228666), correlation (0.502629)*/,
        4, -6, 8, 5/*mean (0.22906), correlation (0.498305)*/,
        3, 12, 8, -13/*mean (0.233378), correlation (0.503825)*/,
        -4, 2, -3, -3/*mean (0.234323), correlation (0.476692)*/,
        5, -13, 10, -12/*mean (0.236392), correlation (0.475462)*/,
        4, -13, 5, -1/*mean (0.236842), correlation (0.504132)*/,
        -9, 9, -4, 3/*mean (0.236977), correlation (0.497739)*/,
        0, 3, 3, -9/*mean (0.24314), correlation (0.499398)*/,
        -12, 1, -6, 1/*mean (0.243297), correlation (0.489447)*/,
        3, 2, 4, -8/*mean (0.00155196), correlation (0.553496)*/,
        -10, -10, -10, 9/*mean (0.00239541), correlation (0.54297)*/,
        8, -13, 12, 12/*mean (0.0034413), correlation (0.544361)*/,
        -8, -12, -6, -5/*mean (0.003565), correlation (0.551225)*/,
        2, 2, 3, 7/*mean (0.00835583), correlation (0.55285)*/,
        10, 6, 11, -8/*mean (0.00885065), correlation (0.540913)*/,
        6, 8, 8, -12/*mean (0.0101552), correlation (0.551085)*/,
        -7, 10, -6, 5/*mean (0.0102227), correlation (0.533635)*/,
        -3, -9, -3, 9/*mean (0.0110211), correlation (0.543121)*/,
        -1, -13, -1, 5/*mean (0.0113473), correlation (0.550173)*/,
        -3, -7, -3, 4/*mean (0.0140913), correlation (0.554774)*/,
        -8, -2, -8, 3/*mean (0.017049), correlation (0.55461)*/,
        4, 2, 12, 12/*mean (0.01778), correlation (0.546921)*/,
        2, -5, 3, 11/*mean (0.0224022), correlation (0.549667)*/,
        6, -9, 11, -13/*mean (0.029161), correlation (0.546295)*/,
        3, -1, 7, 12/*mean (0.0303081), correlation (0.548599)*/,
        11, -1, 12, 4/*mean (0.0355151), correlation (0.523943)*/,
        -3, 0, -3, 6/*mean (0.0417904), correlation (0.543395)*/,
        4, -11, 4, 12/*mean (0.0487292), correlation (0.542818)*/,
        2, -4, 2, 1/*mean (0.0575124), correlation (0.554888)*/,
        -10, -6, -8, 1/*mean (0.0594242), correlation (0.544026)*/,
        -13, 7, -11, 1/*mean (0.0597391), correlation (0.550524)*/,
        -13, 12, -11, -13/*mean (0.0608974), correlation (0.55383)*/,
        6, 0, 11, -13/*mean (0.065126), correlation (0.552006)*/,
        0, -1, 1, 4/*mean (0.074224), correlation (0.546372)*/,
        -13, 3, -9, -2/*mean (0.0808592), correlation (0.554875)*/,
        -9, 8, -6, -3/*mean (0.0883378), correlation (0.551178)*/,
        -13, -6, -8, -2/*mean (0.0901035), correlation (0.548446)*/,
        5, -9, 8, 10/*mean (0.0949843), correlation (0.554694)*/,
        2, 7, 3, -9/*mean (0.0994152), correlation (0.550979)*/,
        -1, -6, -1, -1/*mean (0.10045), correlation (0.552714)*/,
        9, 5, 11, -2/*mean (0.100686), correlation (0.552594)*/,
        11, -3, 12, -8/*mean (0.101091), correlation (0.532394)*/,
        3, 0, 3, 5/*mean (0.101147), correlation (0.525576)*/,
        -1, 4, 0, 10/*mean (0.105263), correlation (0.531498)*/,
        3, -6, 4, 5/*mean (0.110785), correlation (0.540491)*/,
        -13, 0, -10, 5/*mean (0.112798), correlation (0.536582)*/,
        5, 8, 12, 11/*mean (0.114181), correlation (0.555793)*/,
        8, 9, 9, -6/*mean (0.117431), correlation (0.553763)*/,
        7, -4, 8, -12/*mean (0.118522), correlation (0.553452)*/,
        -10, 4, -10, 9/*mean (0.12094), correlation (0.554785)*/,
        7, 3, 12, 4/*mean (0.122582), correlation (0.555825)*/,
        9, -7, 10, -2/*mean (0.124978), correlation (0.549846)*/,
        7, 0, 12, -2/*mean (0.127002), correlation (0.537452)*/,
        -1, -6, 0, -11/*mean (0.127148), correlation (0.547401)*/
};

4）、计算描述符的功能函数编写

// compute the descriptor
void computeORBDesc(const cv::Mat &image, vector<cv::KeyPoint> &keypoints, vector<DescType> &desc) {
    for (auto &kp: keypoints) {
        DescType d(256, false);
        double un_up,un_vp,un_uq,un_vq; // 一对点；
        double up,vp,uq,vq; // 旋转后的点；
        for (int i = 0; i < 256; i++) {
            // START YOUR CODE HERE (~7 lines)
            d[i] = 0;  // if kp goes outside, set d.clear()
 
            un_up = ORB_pattern[i*4];
            un_vp = ORB_pattern[i*4+1];
            un_uq = ORB_pattern[i*4+2];
            un_vq = ORB_pattern[i*4+3];           // 比较两组点的灰度值大小；
 
            // 旋转到主方向上；
            double angle = kp.angle * (pi/180);
 
            up =kp.pt.x+  cos(angle)*un_up-sin(angle)*un_vp;
            vp =kp.pt.y+ sin(angle)*un_up + cos(angle)*un_vp;
            uq =kp.pt.x+  cos(angle)*un_uq-sin(angle)*un_vq;
            vq =kp.pt.y+ sin(angle)*un_uq + cos(angle)*un_vq;
 
            //边界约束；
            if(up>image.cols||up<0 || vp <0||vp>image.rows||uq>image.cols||uq<0 || vq <0||vq>image.rows)
            {
                d.clear();//超出边界，特征点描述子清零；
                break;
            }
            else if(image.at<uchar>(vp,up)<image.at<uchar>(vq,uq))
            {
                d[i]=1;
            }
 
        // END YOUR CODE HERE
        }
        desc.push_back(d);
    }
 
    int bad = 0;
    for (auto &d: desc) {
        if (d.empty()) bad++;
    }
    cout << "bad/total: " << bad << "/" << desc.size() << endl;
    return;
}

5）、暴力匹配功能函数编写

// brute-force matching
void bfMatch(const vector<DescType> &desc1, const vector<DescType> &desc2, vector<cv::DMatch> &matches) {
    int d_max = 50;
    int d = 0;
    int queryIdx,trainIdx;
    int dis;
    // START YOUR CODE HERE (~12 lines)
    // find matches between desc1 and desc2.
    for(int i =0;i<desc1.size();++i)
    {
        if(desc1[i].empty())
            continue;
        d=0;
        dis = 0;
        for(int j =0;j<desc2.size();++j)
        {
         if(desc2[j].empty())
             continue;
             d=0;
            for(int k = 0;k<256;++k)
            {
                if(desc1[i][k] != desc2[j][k])
                    d += 1;
            }
//            cout<<"d: "<<d<<endl;
            if (d<d_max&&(dis==0||d<dis))
            {
                dis = d;
                queryIdx = i;
                trainIdx = j;
            }
        }
        if(dis != 0) //匹配到了；
        {
            matches.push_back(cv::DMatch(queryIdx,trainIdx,dis));
        }
 
    }
    // END YOUR CODE HERE
 
    for (auto &m: matches) {
        cout << m.queryIdx << ", " << m.trainIdx << ", " << m.distance << endl;
    }
    return;
}

6）、主函数相关调用如下所示：

int main(int argc, char **argv) {
    // load image
    cv::Mat first_image = cv::imread(first_file, 0);    // load grayscale image
    cv::Mat second_image = cv::imread(second_file, 0);  // load grayscale image
 
    // plot the image
    cv::imshow("first image", first_image);
    cv::imshow("second image", second_image);
    cv::waitKey(0);
 
    // detect FAST keypoints using threshold=40
    vector<cv::KeyPoint> keypoints;
    cv::FAST(first_image, keypoints, 40);
    cout << "keypoints: " << keypoints.size() << endl;
 
    // compute angle for each keypoint
    computeAngle(first_image, keypoints);
 
    // compute ORB descriptors
    vector<DescType> descriptors;
    computeORBDesc(first_image, keypoints, descriptors);
 
    // plot the keypoints
    cv::Mat image_show;
    cv::drawKeypoints(first_image, keypoints, image_show, cv::Scalar::all(-1),
                      cv::DrawMatchesFlags::DRAW_RICH_KEYPOINTS);
    cv::imshow("features", image_show);
    cv::imwrite("feat1.png", image_show);
    cv::waitKey(0);
 
    // we can also match descriptors between images
    // same for the second
    vector<cv::KeyPoint> keypoints2;
    cv::FAST(second_image, keypoints2, 40);
    cout << "keypoints: " << keypoints2.size() << endl;
 
    // compute angle for each keypoint
    computeAngle(second_image, keypoints2);
 
    // compute ORB descriptors
    vector<DescType> descriptors2;
    computeORBDesc(second_image, keypoints2, descriptors2);
 
    // find matches
    vector<cv::DMatch> matches;
    bfMatch(descriptors, descriptors2, matches);
    cout << "matches: " << matches.size() << endl;
 
    // plot the matches
    cv::drawMatches(first_image, keypoints, second_image, keypoints2, matches, image_show);
    cv::imshow("matches", image_show);
    cv::imwrite("matches.png", image_show);
    cv::waitKey(0);
 
    cout << "done." << endl;
    return 0;
}

三、图像特征点匹配完整代码

#include <opencv2/opencv.hpp>
#include <string>
using namespace std;
// global variables
string first_file = "./1.jpg";
string second_file = "./2.jpg"; 
const double pi = 3.1415926;    // pi
 
 
// TODO implement this function
/**
 * compute the angle for ORB descriptor
 * @param [in] image input image
 * @param [in|out] detected keypoints
 */
void computeAngle(const cv::Mat &image, vector<cv::KeyPoint> &keypoints);
 
// TODO implement this function
/**
 * compute ORB descriptor
 * @param [in] image the input image
 * @param [in] keypoints detected keypoints
 * @param [out] desc descriptor
 */
typedef vector<bool> DescType;  // type of descriptor, 256 bools
void computeORBDesc(const cv::Mat &image, vector<cv::KeyPoint> &keypoints, vector<DescType> &desc);
 
// TODO implement this function
/**
 * brute-force match two sets of descriptors
 * @param desc1 the first descriptor
 * @param desc2 the second descriptor
 * @param matches matches of two images
 */
void bfMatch(const vector<DescType> &desc1, const vector<DescType> &desc2, vector<cv::DMatch> &matches);
 
int main(int argc, char **argv) {
 
    // load image
    cv::Mat first_image = cv::imread(first_file, 0);    // load grayscale image
    cv::Mat second_image = cv::imread(second_file, 0);  // load grayscale image
 
    // plot the image
    cv::imshow("first image", first_image);
    cv::imshow("second image", second_image);
    cv::waitKey(0);
 
    // detect FAST keypoints using threshold=40
    vector<cv::KeyPoint> keypoints;
    cv::FAST(first_image, keypoints, 40);
    cout << "keypoints: " << keypoints.size() << endl;
 
    // compute angle for each keypoint
    computeAngle(first_image, keypoints);
 
    // compute ORB descriptors
    vector<DescType> descriptors;
    computeORBDesc(first_image, keypoints, descriptors);
 
    // plot the keypoints
    cv::Mat image_show;
    cv::drawKeypoints(first_image, keypoints, image_show, cv::Scalar::all(-1),
                      cv::DrawMatchesFlags::DRAW_RICH_KEYPOINTS);
    cv::imshow("features", image_show);
    cv::imwrite("feat1.png", image_show);
    cv::waitKey(0);
 
    // we can also match descriptors between images
    // same for the second
    vector<cv::KeyPoint> keypoints2;
    cv::FAST(second_image, keypoints2, 40);
    cout << "keypoints: " << keypoints2.size() << endl;
 
    // compute angle for each keypoint
    computeAngle(second_image, keypoints2);
 
    // compute ORB descriptors
    vector<DescType> descriptors2;
    computeORBDesc(second_image, keypoints2, descriptors2);
 
    // find matches
    vector<cv::DMatch> matches;
    bfMatch(descriptors, descriptors2, matches);
    cout << "matches: " << matches.size() << endl;
 
    // plot the matches
    cv::drawMatches(first_image, keypoints, second_image, keypoints2, matches, image_show);
    cv::imshow("matches", image_show);
    cv::imwrite("matches.png", image_show);
    cv::waitKey(0);
 
    cout << "done." << endl;
    return 0;
}
 
// -------------------------------------------------------------------------------------------------- //
 
// compute the angle
void computeAngle(const cv::Mat &image, vector<cv::KeyPoint> &keypoints) {
    int half_patch_size = 8;
    for (auto &kp : keypoints) {
    // START YOUR CODE HERE (~7 lines)
//        kp.angle = 0; // compute kp.angle
        double m10 = 0;
        double m01 = 0;
        int x =cvRound(kp.pt.x);
        int y =cvRound(kp.pt.y);
        if(x-half_patch_size<0||x+half_patch_size>image.cols||
           y-half_patch_size<0||y+half_patch_size>image.rows)
            continue;
        for(int u = x - half_patch_size;u<x + half_patch_size;++u)
        {
            for(int v = y -half_patch_size;v< y + half_patch_size;++v)
            {
                m10 +=  (u-x)*image.at<uchar>(v,u);
                m01 +=  (v-y)*image.at<uchar>(v,u);
            }
        }
 
        double theta = std::atan(m01/m10);
        kp.angle = theta * 180/pi;
        cout<<"kp.angel:"<<kp.angle<<endl;
//         END YOUR CODE HERE
    }
    return;
}
 
// -------------------------------------------------------------------------------------------------- //
// ORB pattern
int ORB_pattern[256 * 4] = {
        8, -3, 9, 5/*mean (0), correlation (0)*/,
        4, 2, 7, -12/*mean (1.12461e-05), correlation (0.0437584)*/,
        -11, 9, -8, 2/*mean (3.37382e-05), correlation (0.0617409)*/,
        7, -12, 12, -13/*mean (5.62303e-05), correlation (0.0636977)*/,
        2, -13, 2, 12/*mean (0.000134953), correlation (0.085099)*/,
        1, -7, 1, 6/*mean (0.000528565), correlation (0.0857175)*/,
        -2, -10, -2, -4/*mean (0.0188821), correlation (0.0985774)*/,
        -13, -13, -11, -8/*mean (0.0363135), correlation (0.0899616)*/,
        -13, -3, -12, -9/*mean (0.121806), correlation (0.099849)*/,
        10, 4, 11, 9/*mean (0.122065), correlation (0.093285)*/,
        -13, -8, -8, -9/*mean (0.162787), correlation (0.0942748)*/,
        -11, 7, -9, 12/*mean (0.21561), correlation (0.0974438)*/,
        7, 7, 12, 6/*mean (0.160583), correlation (0.130064)*/,
        -4, -5, -3, 0/*mean (0.228171), correlation (0.132998)*/,
        -13, 2, -12, -3/*mean (0.00997526), correlation (0.145926)*/,
        -9, 0, -7, 5/*mean (0.198234), correlation (0.143636)*/,
        12, -6, 12, -1/*mean (0.0676226), correlation (0.16689)*/,
        -3, 6, -2, 12/*mean (0.166847), correlation (0.171682)*/,
        -6, -13, -4, -8/*mean (0.101215), correlation (0.179716)*/,
        11, -13, 12, -8/*mean (0.200641), correlation (0.192279)*/,
        4, 7, 5, 1/*mean (0.205106), correlation (0.186848)*/,
        5, -3, 10, -3/*mean (0.234908), correlation (0.192319)*/,
        3, -7, 6, 12/*mean (0.0709964), correlation (0.210872)*/,
        -8, -7, -6, -2/*mean (0.0939834), correlation (0.212589)*/,
        -2, 11, -1, -10/*mean (0.127778), correlation (0.20866)*/,
        -13, 12, -8, 10/*mean (0.14783), correlation (0.206356)*/,
        -7, 3, -5, -3/*mean (0.182141), correlation (0.198942)*/,
        -4, 2, -3, 7/*mean (0.188237), correlation (0.21384)*/,
        -10, -12, -6, 11/*mean (0.14865), correlation (0.23571)*/,
        5, -12, 6, -7/*mean (0.222312), correlation (0.23324)*/,
        5, -6, 7, -1/*mean (0.229082), correlation (0.23389)*/,
        1, 0, 4, -5/*mean (0.241577), correlation (0.215286)*/,
        9, 11, 11, -13/*mean (0.00338507), correlation (0.251373)*/,
        4, 7, 4, 12/*mean (0.131005), correlation (0.257622)*/,
        2, -1, 4, 4/*mean (0.152755), correlation (0.255205)*/,
        -4, -12, -2, 7/*mean (0.182771), correlation (0.244867)*/,
        -8, -5, -7, -10/*mean (0.186898), correlation (0.23901)*/,
        4, 11, 9, 12/*mean (0.226226), correlation (0.258255)*/,
        0, -8, 1, -13/*mean (0.0897886), correlation (0.274827)*/,
        -13, -2, -8, 2/*mean (0.148774), correlation (0.28065)*/,
        -3, -2, -2, 3/*mean (0.153048), correlation (0.283063)*/,
        -6, 9, -4, -9/*mean (0.169523), correlation (0.278248)*/,
        8, 12, 10, 7/*mean (0.225337), correlation (0.282851)*/,
        0, 9, 1, 3/*mean (0.226687), correlation (0.278734)*/,
        7, -5, 11, -10/*mean (0.00693882), correlation (0.305161)*/,
        -13, -6, -11, 0/*mean (0.0227283), correlation (0.300181)*/,
        10, 7, 12, 1/*mean (0.125517), correlation (0.31089)*/,
        -6, -3, -6, 12/*mean (0.131748), correlation (0.312779)*/,
        10, -9, 12, -4/*mean (0.144827), correlation (0.292797)*/,
        -13, 8, -8, -12/*mean (0.149202), correlation (0.308918)*/,
        -13, 0, -8, -4/*mean (0.160909), correlation (0.310013)*/,
        3, 3, 7, 8/*mean (0.177755), correlation (0.309394)*/,
        5, 7, 10, -7/*mean (0.212337), correlation (0.310315)*/,
        -1, 7, 1, -12/*mean (0.214429), correlation (0.311933)*/,
        3, -10, 5, 6/*mean (0.235807), correlation (0.313104)*/,
        2, -4, 3, -10/*mean (0.00494827), correlation (0.344948)*/,
        -13, 0, -13, 5/*mean (0.0549145), correlation (0.344675)*/,
        -13, -7, -12, 12/*mean (0.103385), correlation (0.342715)*/,
        -13, 3, -11, 8/*mean (0.134222), correlation (0.322922)*/,
        -7, 12, -4, 7/*mean (0.153284), correlation (0.337061)*/,
        6, -10, 12, 8/*mean (0.154881), correlation (0.329257)*/,
        -9, -1, -7, -6/*mean (0.200967), correlation (0.33312)*/,
        -2, -5, 0, 12/*mean (0.201518), correlation (0.340635)*/,
        -12, 5, -7, 5/*mean (0.207805), correlation (0.335631)*/,
        3, -10, 8, -13/*mean (0.224438), correlation (0.34504)*/,
        -7, -7, -4, 5/*mean (0.239361), correlation (0.338053)*/,
        -3, -2, -1, -7/*mean (0.240744), correlation (0.344322)*/,
        2, 9, 5, -11/*mean (0.242949), correlation (0.34145)*/,
        -11, -13, -5, -13/*mean (0.244028), correlation (0.336861)*/,
        -1, 6, 0, -1/*mean (0.247571), correlation (0.343684)*/,
        5, -3, 5, 2/*mean (0.000697256), correlation (0.357265)*/,
        -4, -13, -4, 12/*mean (0.00213675), correlation (0.373827)*/,
        -9, -6, -9, 6/*mean (0.0126856), correlation (0.373938)*/,
        -12, -10, -8, -4/*mean (0.0152497), correlation (0.364237)*/,
        10, 2, 12, -3/*mean (0.0299933), correlation (0.345292)*/,
        7, 12, 12, 12/*mean (0.0307242), correlation (0.366299)*/,
        -7, -13, -6, 5/*mean (0.0534975), correlation (0.368357)*/,
        -4, 9, -3, 4/*mean (0.099865), correlation (0.372276)*/,
        7, -1, 12, 2/*mean (0.117083), correlation (0.364529)*/,
        -7, 6, -5, 1/*mean (0.126125), correlation (0.369606)*/,
        -13, 11, -12, 5/*mean (0.130364), correlation (0.358502)*/,
        -3, 7, -2, -6/*mean (0.131691), correlation (0.375531)*/,
        7, -8, 12, -7/*mean (0.160166), correlation (0.379508)*/,
        -13, -7, -11, -12/*mean (0.167848), correlation (0.353343)*/,
        1, -3, 12, 12/*mean (0.183378), correlation (0.371916)*/,
        2, -6, 3, 0/*mean (0.228711), correlation (0.371761)*/,
        -4, 3, -2, -13/*mean (0.247211), correlation (0.364063)*/,
        -1, -13, 1, 9/*mean (0.249325), correlation (0.378139)*/,
        7, 1, 8, -6/*mean (0.000652272), correlation (0.411682)*/,
        1, -1, 3, 12/*mean (0.00248538), correlation (0.392988)*/,
        9, 1, 12, 6/*mean (0.0206815), correlation (0.386106)*/,
        -1, -9, -1, 3/*mean (0.0364485), correlation (0.410752)*/,
        -13, -13, -10, 5/*mean (0.0376068), correlation (0.398374)*/,
        7, 7, 10, 12/*mean (0.0424202), correlation (0.405663)*/,
        12, -5, 12, 9/*mean (0.0942645), correlation (0.410422)*/,
        6, 3, 7, 11/*mean (0.1074), correlation (0.413224)*/,
        5, -13, 6, 10/*mean (0.109256), correlation (0.408646)*/,
        2, -12, 2, 3/*mean (0.131691), correlation (0.416076)*/,
        3, 8, 4, -6/*mean (0.165081), correlation (0.417569)*/,
        2, 6, 12, -13/*mean (0.171874), correlation (0.408471)*/,
        9, -12, 10, 3/*mean (0.175146), correlation (0.41296)*/,
        -8, 4, -7, 9/*mean (0.183682), correlation (0.402956)*/,
        -11, 12, -4, -6/*mean (0.184672), correlation (0.416125)*/,
        1, 12, 2, -8/*mean (0.191487), correlation (0.386696)*/,
        6, -9, 7, -4/*mean (0.192668), correlation (0.394771)*/,
        2, 3, 3, -2/*mean (0.200157), correlation (0.408303)*/,
        6, 3, 11, 0/*mean (0.204588), correlation (0.411762)*/,
        3, -3, 8, -8/*mean (0.205904), correlation (0.416294)*/,
        7, 8, 9, 3/*mean (0.213237), correlation (0.409306)*/,
        -11, -5, -6, -4/*mean (0.243444), correlation (0.395069)*/,
        -10, 11, -5, 10/*mean (0.247672), correlation (0.413392)*/,
        -5, -8, -3, 12/*mean (0.24774), correlation (0.411416)*/,
        -10, 5, -9, 0/*mean (0.00213675), correlation (0.454003)*/,
        8, -1, 12, -6/*mean (0.0293635), correlation (0.455368)*/,
        4, -6, 6, -11/*mean (0.0404971), correlation (0.457393)*/,
        -10, 12, -8, 7/*mean (0.0481107), correlation (0.448364)*/,
        4, -2, 6, 7/*mean (0.050641), correlation (0.455019)*/,
        -2, 0, -2, 12/*mean (0.0525978), correlation (0.44338)*/,
        -5, -8, -5, 2/*mean (0.0629667), correlation (0.457096)*/,
        7, -6, 10, 12/*mean (0.0653846), correlation (0.445623)*/,
        -9, -13, -8, -8/*mean (0.0858749), correlation (0.449789)*/,
        -5, -13, -5, -2/*mean (0.122402), correlation (0.450201)*/,
        8, -8, 9, -13/*mean (0.125416), correlation (0.453224)*/,
        -9, -11, -9, 0/*mean (0.130128), correlation (0.458724)*/,
        1, -8, 1, -2/*mean (0.132467), correlation (0.440133)*/,
        7, -4, 9, 1/*mean (0.132692), correlation (0.454)*/,
        -2, 1, -1, -4/*mean (0.135695), correlation (0.455739)*/,
        11, -6, 12, -11/*mean (0.142904), correlation (0.446114)*/,
        -12, -9, -6, 4/*mean (0.146165), correlation (0.451473)*/,
        3, 7, 7, 12/*mean (0.147627), correlation (0.456643)*/,
        5, 5, 10, 8/*mean (0.152901), correlation (0.455036)*/,
        0, -4, 2, 8/*mean (0.167083), correlation (0.459315)*/,
        -9, 12, -5, -13/*mean (0.173234), correlation (0.454706)*/,
        0, 7, 2, 12/*mean (0.18312), correlation (0.433855)*/,
        -1, 2, 1, 7/*mean (0.185504), correlation (0.443838)*/,
        5, 11, 7, -9/*mean (0.185706), correlation (0.451123)*/,
        3, 5, 6, -8/*mean (0.188968), correlation (0.455808)*/,
        -13, -4, -8, 9/*mean (0.191667), correlation (0.459128)*/,
        -5, 9, -3, -3/*mean (0.193196), correlation (0.458364)*/,
        -4, -7, -3, -12/*mean (0.196536), correlation (0.455782)*/,
        6, 5, 8, 0/*mean (0.1972), correlation (0.450481)*/,
        -7, 6, -6, 12/*mean (0.199438), correlation (0.458156)*/,
        -13, 6, -5, -2/*mean (0.211224), correlation (0.449548)*/,
        1, -10, 3, 10/*mean (0.211718), correlation (0.440606)*/,
        4, 1, 8, -4/*mean (0.213034), correlation (0.443177)*/,
        -2, -2, 2, -13/*mean (0.234334), correlation (0.455304)*/,
        2, -12, 12, 12/*mean (0.235684), correlation (0.443436)*/,
        -2, -13, 0, -6/*mean (0.237674), correlation (0.452525)*/,
        4, 1, 9, 3/*mean (0.23962), correlation (0.444824)*/,
        -6, -10, -3, -5/*mean (0.248459), correlation (0.439621)*/,
        -3, -13, -1, 1/*mean (0.249505), correlation (0.456666)*/,
        7, 5, 12, -11/*mean (0.00119208), correlation (0.495466)*/,
        4, -2, 5, -7/*mean (0.00372245), correlation (0.484214)*/,
        -13, 9, -9, -5/*mean (0.00741116), correlation (0.499854)*/,
        7, 1, 8, 6/*mean (0.0208952), correlation (0.499773)*/,
        7, -8, 7, 6/*mean (0.0220085), correlation (0.501609)*/,
        -7, -4, -7, 1/*mean (0.0233806), correlation (0.496568)*/,
        -8, 11, -7, -8/*mean (0.0236505), correlation (0.489719)*/,
        -13, 6, -12, -8/*mean (0.0268781), correlation (0.503487)*/,
        2, 4, 3, 9/*mean (0.0323324), correlation (0.501938)*/,
        10, -5, 12, 3/*mean (0.0399235), correlation (0.494029)*/,
        -6, -5, -6, 7/*mean (0.0420153), correlation (0.486579)*/,
        8, -3, 9, -8/*mean (0.0548021), correlation (0.484237)*/,
        2, -12, 2, 8/*mean (0.0616622), correlation (0.496642)*/,
        -11, -2, -10, 3/*mean (0.0627755), correlation (0.498563)*/,
        -12, -13, -7, -9/*mean (0.0829622), correlation (0.495491)*/,
        -11, 0, -10, -5/*mean (0.0843342), correlation (0.487146)*/,
        5, -3, 11, 8/*mean (0.0929937), correlation (0.502315)*/,
        -2, -13, -1, 12/*mean (0.113327), correlation (0.48941)*/,
        -1, -8, 0, 9/*mean (0.132119), correlation (0.467268)*/,
        -13, -11, -12, -5/*mean (0.136269), correlation (0.498771)*/,
        -10, -2, -10, 11/*mean (0.142173), correlation (0.498714)*/,
        -3, 9, -2, -13/*mean (0.144141), correlation (0.491973)*/,
        2, -3, 3, 2/*mean (0.14892), correlation (0.500782)*/,
        -9, -13, -4, 0/*mean (0.150371), correlation (0.498211)*/,
        -4, 6, -3, -10/*mean (0.152159), correlation (0.495547)*/,
        -4, 12, -2, -7/*mean (0.156152), correlation (0.496925)*/,
        -6, -11, -4, 9/*mean (0.15749), correlation (0.499222)*/,
        6, -3, 6, 11/*mean (0.159211), correlation (0.503821)*/,
        -13, 11, -5, 5/*mean (0.162427), correlation (0.501907)*/,
        11, 11, 12, 6/*mean (0.16652), correlation (0.497632)*/,
        7, -5, 12, -2/*mean (0.169141), correlation (0.484474)*/,
        -1, 12, 0, 7/*mean (0.169456), correlation (0.495339)*/,
        -4, -8, -3, -2/*mean (0.171457), correlation (0.487251)*/,
        -7, 1, -6, 7/*mean (0.175), correlation (0.500024)*/,
        -13, -12, -8, -13/*mean (0.175866), correlation (0.497523)*/,
        -7, -2, -6, -8/*mean (0.178273), correlation (0.501854)*/,
        -8, 5, -6, -9/*mean (0.181107), correlation (0.494888)*/,
        -5, -1, -4, 5/*mean (0.190227), correlation (0.482557)*/,
        -13, 7, -8, 10/*mean (0.196739), correlation (0.496503)*/,
        1, 5, 5, -13/*mean (0.19973), correlation (0.499759)*/,
        1, 0, 10, -13/*mean (0.204465), correlation (0.49873)*/,
        9, 12, 10, -1/*mean (0.209334), correlation (0.49063)*/,
        5, -8, 10, -9/*mean (0.211134), correlation (0.503011)*/,
        -1, 11, 1, -13/*mean (0.212), correlation (0.499414)*/,
        -9, -3, -6, 2/*mean (0.212168), correlation (0.480739)*/,
        -1, -10, 1, 12/*mean (0.212731), correlation (0.502523)*/,
        -13, 1, -8, -10/*mean (0.21327), correlation (0.489786)*/,
        8, -11, 10, -6/*mean (0.214159), correlation (0.488246)*/,
        2, -13, 3, -6/*mean (0.216993), correlation (0.50287)*/,
        7, -13, 12, -9/*mean (0.223639), correlation (0.470502)*/,
        -10, -10, -5, -7/*mean (0.224089), correlation (0.500852)*/,
        -10, -8, -8, -13/*mean (0.228666), correlation (0.502629)*/,
        4, -6, 8, 5/*mean (0.22906), correlation (0.498305)*/,
        3, 12, 8, -13/*mean (0.233378), correlation (0.503825)*/,
        -4, 2, -3, -3/*mean (0.234323), correlation (0.476692)*/,
        5, -13, 10, -12/*mean (0.236392), correlation (0.475462)*/,
        4, -13, 5, -1/*mean (0.236842), correlation (0.504132)*/,
        -9, 9, -4, 3/*mean (0.236977), correlation (0.497739)*/,
        0, 3, 3, -9/*mean (0.24314), correlation (0.499398)*/,
        -12, 1, -6, 1/*mean (0.243297), correlation (0.489447)*/,
        3, 2, 4, -8/*mean (0.00155196), correlation (0.553496)*/,
        -10, -10, -10, 9/*mean (0.00239541), correlation (0.54297)*/,
        8, -13, 12, 12/*mean (0.0034413), correlation (0.544361)*/,
        -8, -12, -6, -5/*mean (0.003565), correlation (0.551225)*/,
        2, 2, 3, 7/*mean (0.00835583), correlation (0.55285)*/,
        10, 6, 11, -8/*mean (0.00885065), correlation (0.540913)*/,
        6, 8, 8, -12/*mean (0.0101552), correlation (0.551085)*/,
        -7, 10, -6, 5/*mean (0.0102227), correlation (0.533635)*/,
        -3, -9, -3, 9/*mean (0.0110211), correlation (0.543121)*/,
        -1, -13, -1, 5/*mean (0.0113473), correlation (0.550173)*/,
        -3, -7, -3, 4/*mean (0.0140913), correlation (0.554774)*/,
        -8, -2, -8, 3/*mean (0.017049), correlation (0.55461)*/,
        4, 2, 12, 12/*mean (0.01778), correlation (0.546921)*/,
        2, -5, 3, 11/*mean (0.0224022), correlation (0.549667)*/,
        6, -9, 11, -13/*mean (0.029161), correlation (0.546295)*/,
        3, -1, 7, 12/*mean (0.0303081), correlation (0.548599)*/,
        11, -1, 12, 4/*mean (0.0355151), correlation (0.523943)*/,
        -3, 0, -3, 6/*mean (0.0417904), correlation (0.543395)*/,
        4, -11, 4, 12/*mean (0.0487292), correlation (0.542818)*/,
        2, -4, 2, 1/*mean (0.0575124), correlation (0.554888)*/,
        -10, -6, -8, 1/*mean (0.0594242), correlation (0.544026)*/,
        -13, 7, -11, 1/*mean (0.0597391), correlation (0.550524)*/,
        -13, 12, -11, -13/*mean (0.0608974), correlation (0.55383)*/,
        6, 0, 11, -13/*mean (0.065126), correlation (0.552006)*/,
        0, -1, 1, 4/*mean (0.074224), correlation (0.546372)*/,
        -13, 3, -9, -2/*mean (0.0808592), correlation (0.554875)*/,
        -9, 8, -6, -3/*mean (0.0883378), correlation (0.551178)*/,
        -13, -6, -8, -2/*mean (0.0901035), correlation (0.548446)*/,
        5, -9, 8, 10/*mean (0.0949843), correlation (0.554694)*/,
        2, 7, 3, -9/*mean (0.0994152), correlation (0.550979)*/,
        -1, -6, -1, -1/*mean (0.10045), correlation (0.552714)*/,
        9, 5, 11, -2/*mean (0.100686), correlation (0.552594)*/,
        11, -3, 12, -8/*mean (0.101091), correlation (0.532394)*/,
        3, 0, 3, 5/*mean (0.101147), correlation (0.525576)*/,
        -1, 4, 0, 10/*mean (0.105263), correlation (0.531498)*/,
        3, -6, 4, 5/*mean (0.110785), correlation (0.540491)*/,
        -13, 0, -10, 5/*mean (0.112798), correlation (0.536582)*/,
        5, 8, 12, 11/*mean (0.114181), correlation (0.555793)*/,
        8, 9, 9, -6/*mean (0.117431), correlation (0.553763)*/,
        7, -4, 8, -12/*mean (0.118522), correlation (0.553452)*/,
        -10, 4, -10, 9/*mean (0.12094), correlation (0.554785)*/,
        7, 3, 12, 4/*mean (0.122582), correlation (0.555825)*/,
        9, -7, 10, -2/*mean (0.124978), correlation (0.549846)*/,
        7, 0, 12, -2/*mean (0.127002), correlation (0.537452)*/,
        -1, -6, 0, -11/*mean (0.127148), correlation (0.547401)*/
};
 
// compute the descriptor
void computeORBDesc(const cv::Mat &image, vector<cv::KeyPoint> &keypoints, vector<DescType> &desc) {
    for (auto &kp: keypoints) {
        DescType d(256, false);
        double un_up,un_vp,un_uq,un_vq; // 一对点；
        double up,vp,uq,vq; // 旋转后的点；
        for (int i = 0; i < 256; i++) {
            // START YOUR CODE HERE (~7 lines)
            d[i] = 0;  // if kp goes outside, set d.clear()
 
            un_up = ORB_pattern[i*4];
            un_vp = ORB_pattern[i*4+1];
            un_uq = ORB_pattern[i*4+2];
            un_vq = ORB_pattern[i*4+3];           // 比较两组点的灰度值大小；
 
            // 旋转到主方向上；
            double angle = kp.angle * (pi/180);
 
            up =kp.pt.x+  cos(angle)*un_up-sin(angle)*un_vp;
            vp =kp.pt.y+ sin(angle)*un_up + cos(angle)*un_vp;
            uq =kp.pt.x+  cos(angle)*un_uq-sin(angle)*un_vq;
            vq =kp.pt.y+ sin(angle)*un_uq + cos(angle)*un_vq;
 
            //边界约束；
            if(up>image.cols||up<0 || vp <0||vp>image.rows||uq>image.cols||uq<0 || vq <0||vq>image.rows)
            {
                d.clear();//超出边界，特征点描述子清零；
                break;
            }
            else if(image.at<uchar>(vp,up)<image.at<uchar>(vq,uq))
            {
                d[i]=1;
            }
 
        // END YOUR CODE HERE
        }
        desc.push_back(d);
    }
 
    int bad = 0;
    for (auto &d: desc) {
        if (d.empty()) bad++;
    }
    cout << "bad/total: " << bad << "/" << desc.size() << endl;
    return;
}
 
 
 
// brute-force matching
void bfMatch(const vector<DescType> &desc1, const vector<DescType> &desc2, vector<cv::DMatch> &matches) {
    int d_max = 50;
    int d = 0;
    int queryIdx,trainIdx;
    int dis;
    // START YOUR CODE HERE (~12 lines)
    // find matches between desc1 and desc2.
    for(int i =0;i<desc1.size();++i)
    {
        if(desc1[i].empty())
            continue;
        d=0;
        dis = 0;
        for(int j =0;j<desc2.size();++j)
        {
         if(desc2[j].empty())
             continue;
             d=0;
            for(int k = 0;k<256;++k)
            {
                if(desc1[i][k] != desc2[j][k])
                    d += 1;
            }
//            cout<<"d: "<<d<<endl;
            if (d<d_max&&(dis==0||d<dis))
            {
                dis = d;
                queryIdx = i;
                trainIdx = j;
            }
        }
        if(dis != 0) //匹配到了；
        {
            matches.push_back(cv::DMatch(queryIdx,trainIdx,dis));
        }
 
    }
    // END YOUR CODE HERE
 
    for (auto &m: matches) {
        cout << m.queryIdx << ", " << m.trainIdx << ", " << m.distance << endl;
    }
    return;
}

四、进行图像特征匹配测试

1、编译slamORB.cpp文件

1）、在终端输入如下命令进行编译

g++ slamORB.cpp -o slamORB `pkg-config --cflags --libs opencv` -std=c++11

2）、编译之后，该文件中会生成可执行文件，如下所示：微信图片_20210106162613

2、终端运行可执行文件

1）、终端输入如下命令运行可执行文件

./slamORB

2）、可执行文件运行之后，便会出现相应图像了，接下来我们看看运行结果吧！

3、运行结果

1）、两张图像的读取显示微信图片_20210106162711

2）、关闭上面出现的两张图片后，特征点提取的图片将会显示，如下：微信图片_20210106162733

3）、关闭特征点图像之后，便会出现暴力匹配的图像显示，如下所示：微信图片_20210106162749

4）、依次关闭之后，文件中将会保存我们的体征点提取图像和暴力匹配结果图像，如下：微信图片_20210106162808

到这里我们的SLAM中ORB特征点提取及暴力匹配就完完整整的做完啦，过程还是很简单，主要是对代码的理解，和对特征点提取及暴力匹配的理解！以上就是本次博客的全部内容啦，希望小伙伴们对本次博客的阅读可以帮助大家理解SLAM中ORB特征点提取及暴力匹配，了解匹配机制，并知道如何编写相关程序哦！ 遇到问题的小伙伴记得评论区留言，林君学长看到会为大家解答的，这个学长不太冷！ 陈一月的又一天编程岁月^ _ ^

openCV VSLAM 特征点提取

原创文章作者：陈一月的编程岁月。如若转载，请注明出处：古月居 https://www.guyuehome.com/22932

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下一篇：视觉SLAM学习【2】-----ubuntu16.04基于kitti数据集的SLAM中ORB特征点提取及暴力匹配

视觉SLAM学习【1】-----基于ubuntu16.04的SLAM中ORB特征点提取及暴力匹配

陈一月的编程岁月

视觉SLAM学习【1】----基于ubuntu16.04的SLAM中ORB特征点提取及暴力匹配目录

一、ORB特征点提取及暴力匹配定义

1、什么是ORB特征点提取？

2、什么是暴力匹配？

二、进行相关数据准备

1、拍摄图像

2、特征点提取、匹配的文件夹创建

3、在创建的文件夹中新建cpp文件

4、图像特征点提取、暴力匹配的代码编写

三、图像特征点匹配完整代码

四、进行图像特征匹配测试

1、编译slamORB.cpp文件

2、终端运行可执行文件

3、运行结果

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