orb 算法源码实现

代码在git
link

├── 1.png
├── 2.png
├── build
├── CMakeLists.txt
└── orb_self.cpp

cd build
cmake ..
make
./orb_cv ../1.png ../2.png

//
// Created by wcm on 2020/6/1.
////nmmintrin与SSE指令集有关
//SSE指令集提供标量和包裹式浮点运算
#include <opencv2/opencv.hpp>
#include <string>
#include <nmmintrin.h>
#include <chrono>using namespace std;//global variables
string first_file= "/home/ledi/桌面/clion/makfile_learn/slambook2/ch7/orb_self/1.png";
string second_file= "/home/ledi/桌面/clion/makfile_learn/slambook2/ch7/orb_self/2.png";//Descriptor type
//32 bit unsigned int, will have 8, 8*32=256
typedef vector<uint32_t> DescType;/*** compute descriptors 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 compute and leave 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 descriptors* @param desc2 the second descriptors* @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 imagecv::Mat first_image = cv::imread(first_file, CV_LOAD_IMAGE_COLOR);cv::Mat second_image = cv::imread(second_file, CV_LOAD_IMAGE_COLOR);assert(first_image.data != nullptr && second_image.data != nullptr );//detect fast keypoints using threshold=40//ORB使用FAST算法检测特征点//OpenCV中的ORB采用了图像金字塔来解决尺度变换一致性//自定义ComputeORB函数来描述ORB特征点，并旋转使其具备旋转尺度不变性chrono::steady_clock::time_point t1 = chrono::steady_clock::now();vector<cv::KeyPoint> keypoints1;cv::FAST(first_image, keypoints1, 40);vector<DescType> descriptor1;ComputeORB(first_image, keypoints1, descriptor1);//same for the secondvector<cv::KeyPoint> keypoints2;cv::FAST(second_image, keypoints2, 40);vector<DescType> descriptor2;ComputeORB(second_image, keypoints2, descriptor2);chrono::steady_clock::time_point t2 = chrono::steady_clock::now();chrono::duration<double> time_used_extract = chrono::duration_cast<chrono::duration<double>>(t2 - t1);cout << " extract ORB cost = " << time_used_extract.count() << " seconds. "<< endl;//find matches//自定义BfMatch函数，匹配特征点vector<cv::DMatch> matches;chrono::steady_clock::time_point t3 = chrono::steady_clock::now();BfMatch(descriptor1, descriptor2, matches);chrono::steady_clock::time_point t4 = chrono::steady_clock::now();chrono::duration<double> time_used_match = chrono::duration_cast<chrono::duration<double>>(t4 - t3);cout << " match ORB cost " << time_used_match.count() << " seconds" <<endl;chrono::duration<double> ORB_total_time_used = chrono::duration_cast<chrono::duration<double>>(time_used_extract + time_used_match);cout << " extract and match ORB cost = " << ORB_total_time_used.count() << " seconds" <<endl;cout << " matches: " << matches.size() <<endl;//plot the matchescv::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
//特征点附近256次像素比较，每次比较两个像素点
//patter选取的两个像素点的分布方式，如高斯分布，随机分布，每种分布的结果应该差不多
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
//将特征点领域看成patch
//patch size of comparison
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) {    //剔除边缘关键点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) {//outsidebad_points++;descriptors.push_back({});continue;}//计算局部区域内所有像素点//以关键点为圆心，patch为特征点领域，m10、m01为领域像素灰度质心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;}}//angle should be arc tan(m01/m10)float m_sqrt = sqrt(m01 * m01 + m10 * m10) + 1e-18;//avoid divided by zero;float sin_theta = m01 / m_sqrt;float cos_theta = m10 / m_sqrt;//compute the angle of the point//计算局部区域内所有像素点角度//描述子为二进制描述子，p大于q取1，小于取0DescType 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,关键点邻域需要旋转//p,q为关键点附近两个比较的像素，像素分布为pattern//pp、qq表达式为图像中任意点(x1,y1)绕另一坐标点(x2,y2)旋转坐标表达式//旋转角度为特征点到质心构成的向量的角度//p.x=x1-x2、p.y=y1-y2cv::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.y + 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//每个描述子DescType均为32*8=256位二进制//desc1、desc2为32*8的二维向量//desc.size()为图像的描述子的数量//每个特征点x(t)与所有x(t+1)测量描述子的距离//比较排序，取最近的作为匹配点，记录ℹi匹配的i2void BfMatch(const vector<DescType> &desc1, const vector<DescType> &desc2, vector<cv::DMatch> &matches) {const int d_max = 20;for(size_t i1 = 0; i1 < desc1.size(); ++i1){if (desc1[i1].empty()) continue;cv::DMatch m{int(i1), 0, 256};for (size_t i2 = 0; i2 < desc2.size(); ++i2){if (desc2[i2].empty()) continue;int distance = 0;for (int k = 0; k < 8; k++){distance += _mm_popcnt_u32(desc1[i1][k] ^ desc2[i2][k]);}if (distance < d_max && distance < m.distance){m.distance = distance;m.trainIdx = i2;}}if (m.distance < d_max){matches.push_back(m);}}
}

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