orb.cpp

#include "orb.hpp"

void computeAngle(const byte *im, int cols, int rows, int bytes_per_row, vector<KeyPoint> &keypoints)
{
    int half_patch_size = 8;
    int h = 0;
    double temp = 0;
    for (auto &kp : keypoints)
    {

        kp.angle = 0;

        double sum_m10 = 0;
        double sum_m01 = 0;

        for (int i = -half_patch_size; i < half_patch_size; i++)
        {
            for (int j = -half_patch_size; j < half_patch_size; j++)
            {

                if ((kp.x) + i < 0 || (kp.x) + i > rows || (kp.y) + j < 0 || (kp.y) + j > cols)
                    //防止越界，注意x对应cols,y对应rows
                    continue;
                sum_m10 += i * (im[(((kp.y) + j) * bytes_per_row + ((kp.x) + i))]);

                // int cvRound (double value) 对一个double型的数进行四舍五入，并返回一个整型数！
                // 以特征点为中心旋转而不是全局坐标
                sum_m01 += j * (im[(((kp.y) + j) * bytes_per_row + ((kp.x) + i))]);
            }
        }

        kp.angle = std::atan2(sum_m01, sum_m10) * 180 / pi; //角度转换,openCV用角度制保存atan2(y,x)
                                                            //kp.angle = std::atan(temp)*180/pi;            //atan(y/x)
    }

    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)*/
};

void computeORBDesc(const byte *im, int cols, int rows, int bytes_per_row, vector<KeyPoint> &keypoints, vector<DescType> &desc)
{
    for (auto &kp : keypoints)
    {
        DescType d(256, false);                    //typedef vector<bool> DescType;   //int ORB_pattern[256 * 4]
        double Angle = kp.angle * 3.1415926 / 180; //cmath的sin操作的是弧度，而openCV中存的是角度，需要转换
        for (int i = 0; i < 256; i++)
        {

            if (kp.x + ORB_pattern[4 * i] < 0 || kp.x + ORB_pattern[4 * i] > rows || kp.y + ORB_pattern[4 * i + 1] < 0 || kp.y + ORB_pattern[4 * i + 1] > cols || kp.x + ORB_pattern[4 * i + 2] < 0 || kp.x + ORB_pattern[4 * i + 2] > rows || kp.y + ORB_pattern[4 * i + 3] < 0 || kp.y + ORB_pattern[4 * i + 3] > cols) //判断全局坐标pu,pv,qu,qv是否越界
            {
                d.clear(); // 如果kp出界,设置d.clear()
                break;
            }

            double up_1 = ORB_pattern[4 * i] * cos(Angle) - ORB_pattern[4 * i + 1] * sin(Angle);
            double vp_1 = ORB_pattern[4 * i] * sin(Angle) + ORB_pattern[4 * i + 1] * cos(Angle);
            double uq_1 = ORB_pattern[4 * i + 2] * cos(Angle) - ORB_pattern[4 * i + 3] * sin(Angle);
            double vq_1 = ORB_pattern[4 * i + 2] * sin(Angle) + ORB_pattern[4 * i + 3] * cos(Angle); //仅对16*16方块旋转，不是对全局坐标

            int gray_p_1 = im[((kp.y + (int)round(vp_1)) * bytes_per_row + (kp.x + (int)round(up_1)))]; //提取灰度时要用全局坐标
            int gray_q_1 = im[((kp.y + (int)round(vq_1)) * bytes_per_row + (kp.x + (int)round(uq_1)))];

            if (gray_p_1 > gray_q_1)
                d[i] = 0;
            else
                d[i] = 1;
        }

        desc.push_back(d);
    }

    int bad = 0;
    for (auto &d : desc)
    {
        if (d.empty())
            bad++;
    }
    cout << "bad/total: " << bad << "/" << desc.size() << endl;
    return;
}

void bfMatch(const vector<DescType> &desc1, const vector<DescType> &desc2, vector<DMatch> &matches)
{
    int d_max = 50;
    //在信息论中，两个等长字符串之间的汉明距离是两个字符串对应位置的不同字符的个数。换句话说，它就是将一个字符串变换成另外一个字符串所需要替换的字符个数。例如：

    //DescType d(256, false);  //typedef vector<bool> DescType;   //int ORB_pattern[256 * 4]
    int queryIdx = 0;
    for (auto desc1_temp : desc1)
    {
        int trainIdx = 0, min_count = 256, min_trainIdx = -1;
        for (auto desc2_temp : desc2)
        {
            vector<bool> desc1_temp_1 = desc1_temp;
            vector<bool> desc2_temp_2 = desc2_temp;
            if (desc1_temp_1.empty() || desc2_temp_2.empty())
            {
                trainIdx++;
                continue;
            }
            int count = 0;
            for (int i = 0; i < 256; i++)
            {
                if (desc1_temp_1[i] != desc2_temp_2[i])
                    count++;
            }
            if (count < min_count && count <= d_max)
            {
                min_count = count;
                min_trainIdx = trainIdx;
            }
            trainIdx++;
        }
        if (min_trainIdx == -1)
        {
            queryIdx++;
            continue;
        }
        else
        {
            DMatch temp_match;
            temp_match.queryIdx = queryIdx;
            temp_match.trainIdx = min_trainIdx;
            temp_match.distance = min_count;
            temp_match.imgIdx = -1;
            matches.push_back(temp_match);
            queryIdx++;
        }
    }

    // for (auto &m: matches) {
    //     cout << m.queryIdx << ", " << m.trainIdx << ", " << m.distance << endl;
    // }
    return;
}