通过一些简单的算法修改，使ORB的提取效率加速了5.8倍。编译该程序需要CPU支持SSE指令集。
如果我们能够对特征提取部分进一步并行化处理，则算法还可以有加速的空间。
// // Created by xiang on 18-11-25. // #include <opencv2/opencv.hpp> #include <string> #include <nmmintrin.h> #include <chrono> using namespace std; // global variables string first_file = "./1.png"; string second_file = "./2.png"; // 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 cv::Mat first_image = cv::imread(first_file, 0); cv::Mat second_image = cv::imread(second_file, 0); assert(first_image.data != nullptr && second_image.data != nullptr); // detect FAST keypoints1 using threshold=40 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 second vector<cv::KeyPoint> keypoints2; vector<DescType> descriptor2; cv::FAST(second_image, keypoints2, 40); 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; // find matches vector<cv::DMatch> matches; t1 = chrono::steady_clock::now(); BfMatch(descriptor1, descriptor2, matches); 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 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 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) { 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); m01 += dx * pixel; m10 += dy * pixel; } } // 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 * 8 + 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 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; cv::DMatch m{static_cast<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); } } }
