After learning the knowledge about visual odometry in Chapter 7 of "Visual Slam Fourteen Lectures", I ran the code for extracting and matching ORB feature points based on opencv library functions. When using the template image that comes with the code, the result is very good, and the feature point matching success rate is very high. On the one hand, the reason is that the shooting angles of the two template photos are not much different, so I took two photos by myself, one is One is a frontal photo, and one is a side photo. When using this code again for feature point matching, the probability of finding a wrong match is a bit high. The following is the difference between the ORB feature point matching based on the opencv library function and the handwritten ORB feature point matching based on the principle.
Table of contents
1. The difference in effect between the two methods
2. The difference between the two methods in the specific code function
(3) BRIEF descriptor matching function
1. The difference in effect between the two methods
Figure 1 Feature point matching based on opencv library function
At the same time, I found a handwritten ORB feature point extraction and matching algorithm in the book. This algorithm is written based on the previous ORB feature point principle. Here is the handwritten ORB code:
#include <opencv2/opencv.hpp>
#include <string>
#include <nmmintrin.h>
#include <chrono>
using namespace std;
// global variables
string first_file = "/home/rxz/桌面/3.jpeg";
string second_file = "/home/rxz/桌面/4.jpeg";
// 32 bit unsigned int, will have 8, 8x32=256
typedef vector<uint32_t> DescType; // Descriptor type
/**
* compute descriptor of orb keypoints
* @param img input image
* @param keypoints detected fast keypoints
* @param descriptors descriptors
*
* NOTE: if a keypoint goes outside the image boundary (8 pixels), descriptors will not be computed and will be left as
* empty
*/
void ComputeORB(const cv::Mat &img, vector<cv::KeyPoint> &keypoints, vector<DescType> &descriptors);
/**
* 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
//加载图片
cv::Mat first_image = cv::imread(first_file, 0);//0为灰度图
cv::Mat second_image = cv::imread(second_file, 0);
assert(first_image.data != nullptr && second_image.data != nullptr);
// detect FAST keypoints1 using threshold=40
//检测FAST关键点并构造描述函数
chrono::steady_clock::time_point t1 = chrono::steady_clock::now();
vector<cv::KeyPoint> keypoints1;
cv::FAST(first_image, keypoints1, 40);//ORB特征点的计算放在keypoints1容器中，利用FAST函数
vector<DescType> descriptor1;
ComputeORB(first_image, keypoints1, descriptor1);//描述子的计算是自定义函数ComputeORB()
// same for the second
//同上
vector<cv::KeyPoint> keypoints2;
vector<DescType> descriptor2;
cv::FAST(second_image, keypoints2, 40,true);//40是中心像素和围绕该像素的圆的像素之间的亮度差阈值
ComputeORB(second_image, keypoints2, descriptor2);
chrono::steady_clock::time_point t2 = chrono::steady_clock::now();
chrono::duration<double> time_used = chrono::duration_cast<chrono::duration<double>>(t2 - t1);
cout << "extract ORB cost = " << time_used.count() << " seconds. " << endl;
// 暴力匹配法找匹配点
vector<cv::DMatch> matches;
t1 = chrono::steady_clock::now();
BfMatch(descriptor1, descriptor2, matches);//寻找相似特征点
t2 = chrono::steady_clock::now();
time_used = chrono::duration_cast<chrono::duration<double>>(t2 - t1);
cout << "match ORB cost = " << time_used.count() << " seconds. " << endl;
cout << "matches: " << matches.size() << endl;
// plot the matches
//连线匹配点，画图
cv::Mat image_show;
cv::drawMatches(first_image, keypoints1, 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;
}
// -------------------------------------------------------------------------------------------------- //
// ORB pattern
//pragma region ORB_pattern[256 * 4]相当于在以关键点为中心[-13,12]的范围内,随机选点对p,q;进行关键点的向量构建
//这个变量里的数字，在ORBSLAM的代码中总共是256行，代表了256个点对儿，也就是每一个都代表了一对点的坐标，
//如第一行表示点q1(8,-3) 和点 q2(9,5), 接下来就是要对比这两个坐标对应的像素值的大小；
//为了保持踩点固定，工程上采用特殊设计的固定的pattern来做
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 ComputeORB(const cv::Mat &img, vector<cv::KeyPoint> &keypoints, vector<DescType> &descriptors)
{
const int half_patch_size = 8;
const int half_boundary = 16;
int bad_points = 0;
for (auto &kp: keypoints)
{//在计算BRIEF描述子时，如果以该keypoints为中心的图像块，超出了图像范围则为坏点
if (kp.pt.x < half_boundary || kp.pt.y < half_boundary ||
kp.pt.x >= img.cols - half_boundary || kp.pt.y >= img.rows - half_boundary)
{
// outside
bad_points++;
descriptors.push_back({});
continue;
}
float m01 = 0, m10 = 0;
for (int dx = -half_patch_size; dx < half_patch_size; ++dx) {
for (int dy = -half_patch_size; dy < half_patch_size; ++dy) {
uchar pixel = img.at<uchar>(kp.pt.y + dy, kp.pt.x + dx);
m10 += dx * pixel;
m01 += dy * pixel;
}
}
// 计算几何中心和质心的角度angle should be arc tan(m01/m10);
float m_sqrt = sqrt(m01 * m01 + m10 * m10) + 1e-18; // avoid divide by zero
float sin_theta = m01 / m_sqrt;
float cos_theta = m10 / m_sqrt;
// compute the angle of this point
DescType desc(8, 0);
for (int i = 0; i < 8; i++) {
uint32_t d = 0;
for (int k = 0; k < 32; k++) {
int idx_pq = i * 32 + k;
cv::Point2f p(ORB_pattern[idx_pq * 4], ORB_pattern[idx_pq * 4 + 1]);
cv::Point2f q(ORB_pattern[idx_pq * 4 + 2], ORB_pattern[idx_pq * 4 + 3]);
// rotate with theta
cv::Point2f pp = cv::Point2f(cos_theta * p.x - sin_theta * p.y, sin_theta * p.x + cos_theta * p.y)
+ kp.pt;
cv::Point2f qq = cv::Point2f(cos_theta * q.x - sin_theta * q.y, sin_theta * q.x + cos_theta * q.y)
+ kp.pt;
if (img.at<uchar>(pp.y, pp.x) < img.at<uchar>(qq.y, qq.x)) {
d |= 1 << k;
}
}
desc[i] = d;
}
descriptors.push_back(desc);
}
cout << "bad/total: " << bad_points << "/" << keypoints.size() << endl;
}
// brute-force matching暴力匹配
void BfMatch(const vector<DescType> &desc1, const vector<DescType> &desc2, vector<cv::DMatch> &matches) {
const int d_max = 40;
for (size_t i1 = 0; i1 < desc1.size(); ++i1)
{
if (desc1[i1].empty()) continue;//如果第i1个描述子为空，则跳出此次循环
cv::DMatch m{i1, 0, 256};//创建DMatch类的变量m，其中前两个分别是第一张图片的索引，queryIdx : 查询点的索引（当前要寻找匹配结果的点在它所在图片上的索引）。trainIdx : 被查询到的点的索引（存储库中的点的在存储库上的索引）。
//第三个参数是设定的汉明距离
for (size_t i2 = 0; i2 < desc2.size(); ++i2)
{
if (desc2[i2].empty()) continue;
int distance = 0;
for (int k = 0; k < 8; k++)
{
//这里的描述子是256位的，对应为8个32位的unsigned int数据，而这里的k表示第几个unsigned int数据
distance += _mm_popcnt_u32(desc1[i1][k] ^ desc2[i2][k]);//利用sse指令集计算unsigned int变量中1的个数，从而计算汉明距离。a^b表示a与b按位异或；
}
if (distance < d_max && distance < m.distance) //在图像1中取一个描述子，然后计算与图像2中的所有描述子的汉明距离，把最近的那个距离找出来
{
m.distance = distance;
m.trainIdx = i2;
}
}
if (m.distance < d_max)
{
matches.push_back(m);
}
}
}The rendering of the handwritten ORB code is as follows:
Figure 2 Handwritten ORB feature point extraction and matching algorithm effect diagram
Also attach the running time of the two methods:
Fig. 3 The running time of handwritten ORB feature point extraction and matching algorithm
Figure 4 The running time of opencv feature point extraction and matching algorithm
It can be found that the handwritten ORB feature point extraction and matching algorithm has a high success rate of feature point matching and fewer matching pairs. In the above figure, there are only 8 matching pairs. Next, analyze and compare the differences between the two codes:
2. The difference between the two methods in the specific code function
(1) FAST corner detection
opencv library function: use the detect function in the ORB feature detector detector.
Handwriting: With the improved FAST algorithm, the intensity difference threshold between the center pixel and the pixels of the circle surrounding this pixel and non-maximum suppression are increased.
The FAST() function structure is as follows:
1. Detect FAST key points using the cv::FAST() function
CV_EXPORTS void FAST( InputArray image, CV_OUT std::vector<KeyPoint>& keypoints,
int threshold, bool nonmaxSuppression=true );
image: detected grayscale image
keypoints: keypoints detected on the image
threshold: The intensity difference threshold between the central pixel and the pixels of the circle surrounding this pixel
nonmaxSuppression: parameter non-maximum suppression, the default is true, apply non-maximum suppression to the detected corners.
(2) Descriptor calculation
opencv library function: construct the compute function in the ORB feature descriptor descriptor
Handwriting: Customize the function ComputeORB() to calculate the descriptor. In this function, some feature points near the edge will be excluded first. The method of judging the excluded bad pixels is as follows:
1. When kp.pt.x < half_boundary, the image block whose side length is boundary will exceed the image on the -x axis.
2. When kp.pt.y < half_boundary, the image block whose side length is boundary will exceed the image on the -y axis.
3. When kp.pt.x >= img.cols - half_boundary (kp.pt.x>= 640-16), the image block whose side length is boundary(32) will exceed the image on the +x axis.
4. When kp.pt.y >= img.rows - half_boundary (kp.pt.y >= 480-16), the image block whose side length is boundary(32) will exceed the image on the +y axis.
At the same time, in the handwritten code, the moment and centroid of the image block are defined according to the gray-scale centroid method, and finally the angle of the feature point is obtained.
When finding the descriptor, 256*4 data sets are prepared in advance, which represent the key point as the center, within the range of [-13,12], randomly select point pairs p, q. Select two points p, q, the coordinates of these two points are selected from the data set, and then multiply the previously calculated angle and the key point to find two random pixels near the key point, and then compare the pixel values. Finally, a descriptor is formed.
(3) BRIEF descriptor matching function
Under opencv: Use the built-in match function to compare the Hamming distance of the descriptors of the two images, and sort them in the matches container from small to large, and then select good descriptors in the container, and these descriptors meet the requirements between the descriptors. The distance is less than twice the minimum distance and the minimum value of the empirical threshold, because the minimum distance may be 0;
In the handwritten code: the descriptor is described in 256-bit binary, corresponding to 8 32-bit unsigned int data, and the number of 1s in each unsigned int variable is calculated using the SSE instruction set, thereby calculating the Hamming distance. Input three parameters in the handwritten brute force matching code, which are the descriptors of the first and second images, and the container for storing the output matching pairs; the idea of brute force matching here is: take a description in the first image , respectively calculate the Hamming distance with each descriptor of the second picture, then select the closest distance and the corresponding matching pair, and then select the descriptor in picture 1 to repeat the above operation to find the shortest distance and corresponding matching pairs. Finally, compare the minimum distance obtained from the comparison with the set experience threshold, and if it is smaller than the experience threshold, the matching pair is retained and output.
3. Amway Moment
(1) How to change the window size of the displayed image
If the image shows part of the code is just:
cv::imshow("matches", image_show);
Then the image window that comes out cannot be resized. So how to change the image window size at will? Just enter the following code:
namedWindow("窗口名",0);//创建窗口
imshow("窗口名",要显示的图片);//在创建的窗口中显示图片
默认情况下，namedWindow里的参数Flags=1，为自动调整窗口大小模式，如果在图片高清情况下，显示图片窗口很大，电脑屏幕无法显示全部，并且窗口还不能通过拖动鼠标来调整大小。令Flags=0，转为WINDOW_NORMAL，在这个模式下可以调整窗口的大小。
（2）如何通过终端打开系统中的某张指定的图片
eog + picture name
or
eog + the path of the image
The full name of eog: eye of gmone, is a built-in image viewer under linux.
Reference link:
(1) Visual SLAM ch7 code summary (1)_Rain screen,'s blog-CSDN blog
(2) ORB feature matching, detailed explanation of handwritten ORB feature codes-Knowledge
(4) Common interface for feature descriptor matching — OpenCV 2.3.2 documentation