ORBExtractor.cpp

#include <opencv2/core/core.hpp>
#include <opencv2/highgui/highgui.hpp>
#include <opencv2/features2d/features2d.hpp>
#include <opencv2/imgproc/imgproc.hpp>
#include <vector>
#include <iterator>

#include "ORBExtractor.h"
#include <iostream>

using namespace cv;
using namespace std;

namespace Goudan
{
    static float IC_Angle(const Mat &image, Point2f pt, const vector<int> &u_max);
    // ORB-SLAM2 默认参数
    const int PATCH_SIZE = 31;
    const int HALF_PATCH_SIZE = 15;
    const int EDGE_THRESHOLD = 19;

    void ExtractorNode::DivideNode(ExtractorNode &n1, ExtractorNode &n2, ExtractorNode &n3, ExtractorNode &n4)
    {
        // 计算分裂中心点位置
        const int halfX = ceil(static_cast<float>(UR.x - UL.x) / 2);
        const int halfY = ceil(static_cast<float>(BR.y - UL.y) / 2);

        // 确定四个点的边界
        n1.UL = UL;
        n1.UR = cv::Point2i(UL.x + halfX, UL.y);
        n1.BL = cv::Point2i(UL.x, UL.y + halfY);
        n1.BR = cv::Point2i(UL.x + halfX, UL.y + halfY);
        n1.vKeys.reserve(vKeys.size());

        n2.UL = n1.UR;
        n2.UR = UR;
        n2.BL = n1.BR;
        n2.BR = cv::Point2i(UR.x, UL.y + halfY);
        n2.vKeys.reserve(vKeys.size());

        n3.UL = n1.BL;
        n3.UR = n1.BR;
        n3.BL = BL;
        n3.BR = cv::Point2i(n1.BR.x, BL.y);
        n3.vKeys.reserve(vKeys.size());

        n4.UL = n3.UR;
        n4.UR = n2.BR;
        n4.BL = n3.BR;
        n4.BR = BR;
        n4.vKeys.reserve(vKeys.size());

        // 将原来的节点中的提取出来的ORB关键点分配给子节点
        for (size_t i = 0; i < vKeys.size(); ++i)
        {
            const cv::KeyPoint &kp = vKeys[i];
            if (kp.pt.x < n1.UR.x)
            {
                if (kp.pt.y < n1.BR.y)
                    n1.vKeys.push_back(kp);
                else
                    n3.vKeys.push_back(kp);
            }
            else if (kp.pt.y < n1.BR.y)
            {
                n2.vKeys.push_back(kp);
            }
            else
            {
                n4.vKeys.push_back(kp);
            }
        }
        // 将 bNoMore 设置为 true 如果关键点个数为 1
        if (n1.vKeys.size() == 1)
            n1.bNoMore = true;
        if (n2.vKeys.size() == 1)
            n2.bNoMore = true;
        if (n3.vKeys.size() == 1)
            n3.bNoMore = true;
        if (n4.vKeys.size() == 1)
            n4.bNoMore = true;
    }

    static void computeOrientation(const Mat &image, vector<KeyPoint> &keypoints, const vector<int> &umax)
    {
        for (vector<KeyPoint>::iterator keypoint = keypoints.begin(); keypoint != keypoints.end();
             ++keypoint)
        {
            keypoint->angle = IC_Angle(image, keypoint->pt, umax);
        }
    }

    // 描述子匹配点对 模式 数组
    // the 256 pixel pairs around the center pixel p which were the best group comparing with others
    static int bit_pattern_31_[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)y*/,
            -13, 3, -9, -2 /*mean (0.0808592), correlation (0.554y875)*/,
            -9, 8, -6, -3 /*mean (0.0883378), correlation (0.5511y78)*/,
            -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)*/
    };

    ORBExtractor::ORBExtractor(int _nfeatures, float _scaleFactor, int _nlevels, int _iniThFAST, int _minThFAST)
        : nfeatures(_nfeatures), scaleFactor(_scaleFactor), nlevels(_nlevels), iniThFAST(_iniThFAST), minThFAST(_minThFAST)
    {
        cout << "ORBExtractor constructor" << endl;
        // 计算每一层相对于原始图片的放大倍数
        mvScaleFactor.resize(nlevels);
        mvLevelSigma2.resize(nlevels);
        mvScaleFactor[0] = 1.0f;
        mvLevelSigma2[0] = 1.0f;
        for (int i = 1; i < nlevels; ++i)
        {
            mvScaleFactor[i] = mvScaleFactor[i - 1] * scaleFactor;
            cout << "Factor "<<i - 1 << ":"<<mvScaleFactor[i - 1];
            mvLevelSigma2[i] = mvScaleFactor[i] * mvScaleFactor[i];
        }

        // 计算每层相对于原始图片放大倍数的逆
        mvInvScaleFactor.resize(nlevels);
        mvInvLevelSigma2.resize(nlevels);
        for (int i = 0; i < nlevels; i++)
        {
            mvInvScaleFactor[i] = 1.0f / mvScaleFactor[i];
            mvInvLevelSigma2[i] = 1.0f / mvLevelSigma2[i];
        }

        mvImagePyramid.resize(nlevels);
        mnFeaturesPerLevel.resize(nlevels);
        float factor = 1.0f / scaleFactor;                                                                             // scaleFactor：1.2   nfeatures：1000   nlevels：8
        float nDesiredFeaturesPerScale = nfeatures * (1 - factor) / (1 - (float)pow((double)factor, (double)nlevels)); // 计算第一层特征点的数量
        // 计算每层特征点的数量
        int sumFeatures = 0;
        for (int level = 0; level < nlevels - 1; level++)
        {
            mnFeaturesPerLevel[level] = cvRound(nDesiredFeaturesPerScale);
            sumFeatures += mnFeaturesPerLevel[level];
            nDesiredFeaturesPerScale *= factor;
        }

        // 复制 模式数组
        const int npoints = 512;
        const Point *pattern0 = (const Point *)bit_pattern_31_;
        std::copy(pattern0, pattern0 + npoints, std::back_inserter(pattern));

        // 计算特征点方向
        // 计算特征方向，每个v坐标对应最大的u坐标
        umax.resize(HALF_PATCH_SIZE + 1);
        // 将v坐标划分为两部分进行计算，主要为了确保计算特征主方向的时候，x,y方向对称
        int v, v0, vmax = cvFloor(HALF_PATCH_SIZE * sqrt(2.f) / 2 + 1); // cvFloor含义是取不大于参数的最大整数值
        int vmin = cvCeil(HALF_PATCH_SIZE * sqrt(2.f) / 2);             // cvCeil含义是取不小于参数的最小整数值
        // 利用勾股定理计算
        const double hp2 = HALF_PATCH_SIZE * HALF_PATCH_SIZE;
        // V坐标的第一部分
        for (v = 0; v <= vmax; ++v)
            umax[v] = cvRound(sqrt(hp2 - v * v));

        // Make sure we are symmetric  确保对称，即保证是一个圆
        // V坐标的第二部分
        for (v = HALF_PATCH_SIZE, v0 = 0; v >= vmin; --v)
        {
            while (umax[v0] == umax[v0 + 1])
                ++v0;
            umax[v] = v0;
            ++v0;
        }
    }
    const float factorPI = ( float )( CV_PI / 180.f );
    static void computeOrbDescriptor( const KeyPoint& kpt, const Mat& img, const Point* pattern, uchar* desc )
	{
		float angle = (float)kpt.angle * factorPI;
		float a = (float)cos(angle), b = (float)sin(angle);

		const uchar* center = &img.at<uchar>( cvRound( kpt.pt.y ), cvRound( kpt.pt.x ) );
		const int step = (int)img.step;

		#define GET_VALUE( idx ) \
			center[ cvRound( pattern[idx].x*b + pattern[idx].y*a ) * step + \
					cvRound( pattern[idx].x*a - pattern[idx].y*b ) ];

		for ( int i = 0; i < 32; ++i, pattern += 16 )
		{
			int t0, t1, val;
			t0 = GET_VALUE(0); t1 = GET_VALUE(1);
			val = t0 < t1;
			t0 = GET_VALUE(2); t1 = GET_VALUE(3);
			val |= (t0 < t1) << 1;
			t0 = GET_VALUE(4); t1 = GET_VALUE(5);
			val |= (t0 < t1) << 2;
			t0 = GET_VALUE(6); t1 = GET_VALUE(7);
			val |= (t0 < t1) << 3;
			t0 = GET_VALUE(8); t1 = GET_VALUE(9);
			val |= (t0 < t1) << 4;
			t0 = GET_VALUE(10); t1 = GET_VALUE(11);
			val |= (t0 < t1) << 5;
			t0 = GET_VALUE(12); t1 = GET_VALUE(13);
			val |= (t0 < t1) << 6;
			t0 = GET_VALUE(14); t1 = GET_VALUE(15);
			val |= (t0 < t1) << 7;

			desc[i] = (uchar)val;
		}

		#undef GET_VALUE
	}

	static void computeDescriptors( const Mat& image, vector< KeyPoint >& keypoints, Mat& descriptors,
		const vector< Point >& pattern )
	{
		descriptors = Mat::zeros( (int)keypoints.size(), 32, CV_8UC1 );

		for ( size_t i = 0; i < keypoints.size(); ++i )
		{
			computeOrbDescriptor( keypoints[i], image, &pattern[0], descriptors.ptr((int)i) );
		}
	}


    // 接口 提取 均匀的 ORB特征点
    void ORBExtractor::operator()(cv::InputArray _image, cv::InputArray _mask,
                                  std::vector<cv::KeyPoint> &_keypoints, cv::OutputArray _desciptors)
    {
        cout << "ORBextractor operator!" << endl;
        if (_image.empty())
            return;

        // 判断图片类型是否符合
        Mat image = _image.getMat();
        assert(image.type() == CV_8UC1);
        // 处理图像金字塔
        ComputePyramid(image);
        // 计算关键点并生成四叉树
        vector<vector<KeyPoint>> allKeypoints;
        ComputeKeyPointsOctTree(allKeypoints);

        Mat desciptors;

        int nkeypoints = 0;
        for (int level = 0; level < nlevels; ++level)
            nkeypoints += (int)allKeypoints[level].size();
        if (nkeypoints == 0)
            _desciptors.release();
        else
        {
            _desciptors.create(nkeypoints, 32, CV_8U);
            desciptors = _desciptors.getMat();
        }

        _keypoints.clear();
        _keypoints.reserve(nkeypoints);
        // 计算每个关键点对应的描述子
        int offset = 0;
        for (int level = 0; level < nlevels; ++level)
        {
            vector<KeyPoint> &keypoints = allKeypoints[level];
            int nkeypointsLevel = (int)keypoints.size();
            if(nkeypointsLevel == 0) continue;
            // 预处理 
            Mat workingMat = mvImagePyramid[level].clone();
            GaussianBlur(workingMat, workingMat, Size(7,7), 2, 2,BORDER_REFLECT_101);
            // 计算描述子
            Mat desc = desciptors.rowRange(offset, offset + nkeypointsLevel);
            computeDescriptors( workingMat, keypoints, desc, pattern);

            offset += nkeypointsLevel;

            // scale keypoint coordinates
			if ( level != 0 )
			{
				float scale = mvScaleFactor[ level ];
				for( vector< KeyPoint >::iterator keypoint = keypoints.begin(), keypointEnd = keypoints.end();
					keypoint != keypointEnd; ++keypoint )
					keypoint->pt *= scale;
			}

            // 插入到输出
            _keypoints.insert(_keypoints.end(), keypoints.begin(), keypoints.end());
        }
    }

    // 直接使用resize进行构建金字塔
    void ORBExtractor::ComputePyramid(cv::Mat image)
    {
        // cv::imshow("ComputePyramid", image);
        for (int level = 0; level < nlevels; ++level)
        {
            float scale = mvInvScaleFactor[level];
            Size sz(cvRound((float)image.cols * scale), cvRound((float)image.rows * scale));
            // Compute the resized image
            if (level != 0)
            {
                resize(mvImagePyramid[level - 1], mvImagePyramid[level], sz, 0, 0, INTER_LINEAR);
            }
            else
            {
                // 此函数将图像转化为更大的图像，并用BORDER_REFLECT_101的方式来处理边缘图像
                mvImagePyramid[level] = image;
            }
        }
    }

    void ORBExtractor::ComputeKeyPointsOctTree(std::vector<std::vector<cv::KeyPoint>> &allKeypoints)
    {
        allKeypoints.resize(nlevels);
        // 单元大小为30*30;
        const float W = 30;
        for (int level = 0; level < nlevels; ++level)
        {
            const int minBorderX = EDGE_THRESHOLD - 3;
            const int minBorderY = minBorderX;
            const int maxBorderX = mvImagePyramid[level].cols - EDGE_THRESHOLD + 3;
            const int maxBorderY = mvImagePyramid[level].rows - EDGE_THRESHOLD + 3;

            vector<KeyPoint> vToDistributeKeys;
            vToDistributeKeys.reserve(nfeatures * 10);

            const float width = (maxBorderX - minBorderX);
            const float height = (maxBorderY - minBorderY);

            const int nCols = width / W;
            const int nRows = height / W;
            const int wCell = ceil(width / nCols);
            const int hCell = ceil(height / nRows);

            for (int i = 0; i < nRows; ++i)
            {
                const float iniY = minBorderY + i * hCell;
                float maxY = iniY + hCell + 6;

                if (iniY >= maxBorderY - 3)
                    continue;
                if (maxY > maxBorderY)
                    maxY = maxBorderY;

                for (int j = 0; j < nCols; ++j)
                {
                    const float iniX = minBorderX + j * wCell;
                    float maxX = iniX + wCell + 6;
                    if (iniX >= maxBorderX - 6)
                        continue;
                    if (maxX > maxBorderX)
                        maxX = maxBorderX;
                    vector<cv::KeyPoint> vKeyCell;
                    FAST(mvImagePyramid[level].rowRange(iniY, maxY).colRange(iniX, maxX), vKeyCell, iniThFAST, true);
                    // 如果检测为空就降低阈值再进行检测
                    if (vKeyCell.empty())
                    {
                        FAST(mvImagePyramid[level].rowRange(iniY, maxY).colRange(iniX, maxX), vKeyCell, minThFAST, true);
                    }
                    // 如果检测不为空就将检测到的特征点放到vToDistributeKeys中
                    if (!vKeyCell.empty())
                    {
                        for (vector<cv::KeyPoint>::iterator vit = vKeyCell.begin(); vit != vKeyCell.end(); vit++)
                        {
                            (*vit).pt.x += j * wCell;
                            (*vit).pt.y += i * hCell;
                            vToDistributeKeys.push_back(*vit);
                        }
                    }
                }
            }
            vector<KeyPoint> &keypoints = allKeypoints[level];
            keypoints.reserve(nfeatures);

            keypoints = DistributeOctTree(vToDistributeKeys, minBorderX, maxBorderX, minBorderY, maxBorderY, mnFeaturesPerLevel[level], level);
            // 根据尺度的不同计算特征点Patch的大小
            const int scaledPatchSize = PATCH_SIZE * mvScaleFactor[level];

            // 将边界信息考虑进去计算特征点的位置
            const int nkps = keypoints.size();
            for (int i = 0; i < nkps; i++)
            {
                keypoints[i].pt.x += minBorderX;     // 特征点的x坐标
                keypoints[i].pt.y += minBorderY;     // 特征点的y坐标
                keypoints[i].octave = level;         // 特征点所在的层数
                keypoints[i].size = scaledPatchSize; // 特征点Patch的大小将来计算描述子时使用
            }
        }
        // 计算特征点方向
        for (int level = 0; level < nlevels; ++level)
        {
            computeOrientation(mvImagePyramid[level], allKeypoints[level], umax);
        }
    }

    // 灰度质心法计算关键点方向
    static float IC_Angle(const Mat &image, Point2f pt, const vector<int> &u_max)
    {
        int m_01 = 0, m_10 = 0;
        const uchar *center = &image.at<uchar>(cvRound(pt.y), cvRound(pt.x));

        // 中心水平线计算
        for (int u = -HALF_PATCH_SIZE; u <= HALF_PATCH_SIZE; ++u)
        {
            m_10 += u * center[u];
        }

        //
        int step = (int)image.step1();
        for (int v = 1; v <= HALF_PATCH_SIZE; ++v)
        {
            int v_sum = 0;
            int d = u_max[v];
            for (int u = -d; u <= d; ++u)
            {
                int val_plus = center[u + v * step], val_minus = center[u - v * step];
                v_sum += (val_plus - val_minus);    // 计算上下的时候是有符号的，所以这边是减
                m_10 += u * (val_plus + val_minus); // 将(u,v)和(u,-v)两个点的像素一起计算   这边加是由于u已经确定好了符号
            }
            m_01 += v * v_sum; // 将x=v这条直线上所有的坐标点的像素值求和在进行计算
        }

        return fastAtan2((float)m_01, (float)m_10);
    }

    std::vector<cv::KeyPoint> ORBExtractor::DistributeOctTree(const std::vector<cv::KeyPoint> &vToDistributeKeys,
                                                              const int &minX, const int &maxX, const int &minY, const int &maxY, const int &N, const int &level)
    {
        // 计算有多少需要初始化的node
        const int nIni = round(static_cast<float>(maxX - minX) / (maxY - minY));
        const float hX = static_cast<float>(maxX - minX) / nIni;

        list<ExtractorNode> lNodes;
        vector<ExtractorNode *> vpInitNodes;
        vpInitNodes.resize(nIni);

        for (int i = 0; i < nIni; ++i)
        {
            ExtractorNode ni;
            ni.UL = cv::Point2i(hX * static_cast<float>(i), 0);
            ni.UR = cv::Point2i(hX * static_cast<float>(i + 1), 0);
            ni.BL = cv::Point2i(ni.UL.x, maxY - minY);
            ni.BR = cv::Point2i(ni.UR.x, maxY - minY);

            lNodes.push_back(ni);
            vpInitNodes[i] = &lNodes.back();
        }
        // 分配关键点
        for (size_t i = 0; i < vToDistributeKeys.size(); ++i)
        {
            const cv::KeyPoint &kp = vToDistributeKeys[i];
            vpInitNodes[kp.pt.x / hX]->vKeys.push_back(kp);
        }
        list<ExtractorNode>::iterator lit = lNodes.begin();

        while (lit != lNodes.end())
        {
            if (lit->vKeys.size() == 1)
            {
                lit->bNoMore = true;
                ++lit;
            }
            else if (lit->vKeys.empty())
            {
                lit = lNodes.erase(lit);
            }
            else
            {
                ++lit;
            }
        }

        bool bFinish = false;
        int iteration = 0;

        vector<pair<int, ExtractorNode *>> vSizeAndPointerToNode;
        vSizeAndPointerToNode.reserve(lNodes.size() * 4);

        while (!bFinish)
        {
            iteration++;
            int prevSize = lNodes.size();
            lit = lNodes.begin();
            int nToExpand = 0;
            vSizeAndPointerToNode.clear();
            while (lit != lNodes.end())
            {
                // 如果某个节点只包括一个点就不用分裂并继续
                if (lit->bNoMore)
                {
                    ++lit;
                    continue;
                }
                else
                {
                    ExtractorNode n1, n2, n3, n4;
                    lit->DivideNode(n1, n2, n3, n4);

                    // 如果他们包括关键点添加子节点
                    if (n1.vKeys.size() > 0)
                    {
                        lNodes.push_front(n1);
                        if (n1.vKeys.size() > 1)
                        {
                            nToExpand++;
                            vSizeAndPointerToNode.push_back(make_pair(n1.vKeys.size(), &lNodes.front()));
                            lNodes.front().lit = lNodes.begin();
                        }
                    }
                    if (n2.vKeys.size() > 0)
                    {
                        lNodes.push_front(n2);
                        if (n2.vKeys.size() > 1)
                        {
                            nToExpand++;
                            vSizeAndPointerToNode.push_back(make_pair(n2.vKeys.size(), &lNodes.front()));
                            lNodes.front().lit = lNodes.begin();
                        }
                    }
                    if (n3.vKeys.size() > 0)
                    {
                        lNodes.push_front(n3);
                        if (n3.vKeys.size() > 1)
                        {
                            nToExpand++;
                            vSizeAndPointerToNode.push_back(make_pair(n3.vKeys.size(), &lNodes.front()));
                            lNodes.front().lit = lNodes.begin();
                        }
                    }
                    if (n4.vKeys.size() > 0)
                    {
                        lNodes.push_front(n4);
                        if (n4.vKeys.size() > 1)
                        {
                            nToExpand++;
                            vSizeAndPointerToNode.push_back(make_pair(n4.vKeys.size(), &lNodes.front()));
                            lNodes.front().lit = lNodes.begin();
                        }
                    }

                    lit = lNodes.erase(lit);
                    continue;
                }
            }
            // 如果现在已经有超过所需要的特征点个数的特征点，或者所有节点都只包含一个特征点
            // 那就结束
            if ((int)lNodes.size() >= N || (int)lNodes.size() == prevSize)
            {
                bFinish = true;
            }
            else if (((int)lNodes.size() + nToExpand * 3) > N)
            {
                while (!bFinish)
                {
                    prevSize = lNodes.size();
                    vector<pair<int, ExtractorNode *>> vPrevSizeAndPointerToNode = vSizeAndPointerToNode;
                    vSizeAndPointerToNode.clear();
                    sort(vPrevSizeAndPointerToNode.begin(), vPrevSizeAndPointerToNode.end());
                    for (int j = vPrevSizeAndPointerToNode.size() - 1; j >= 0; --j)
                    {
                        ExtractorNode n1, n2, n3, n4;
                        vPrevSizeAndPointerToNode[j].second->lit->DivideNode(n1, n2, n3, n4);

                        // add child if they contain points
                        if (n1.vKeys.size() > 0)
                        {
                            lNodes.push_front(n1);
                            if (n1.vKeys.size() > 1)
                            {
                                vSizeAndPointerToNode.push_back(make_pair(n1.vKeys.size(), &lNodes.front()));
                                lNodes.front().lit = lNodes.begin();
                            }
                        }
                        if (n2.vKeys.size() > 0)
                        {
                            lNodes.push_front(n2);
                            if (n2.vKeys.size() > 1)
                            {
                                vSizeAndPointerToNode.push_back(make_pair(n2.vKeys.size(), &lNodes.front()));
                                lNodes.front().lit = lNodes.begin();
                            }
                        }
                        if (n3.vKeys.size() > 0)
                        {
                            lNodes.push_front(n3);
                            if (n3.vKeys.size() > 1)
                            {
                                vSizeAndPointerToNode.push_back(make_pair(n3.vKeys.size(), &lNodes.front()));
                                lNodes.front().lit = lNodes.begin();
                            }
                        }
                        if (n4.vKeys.size() > 0)
                        {
                            lNodes.push_front(n4);
                            if (n4.vKeys.size() > 1)
                            {
                                vSizeAndPointerToNode.push_back(make_pair(n4.vKeys.size(), &lNodes.front()));
                                lNodes.front().lit = lNodes.begin();
                            }
                        }

                        lNodes.erase(vPrevSizeAndPointerToNode[j].second->lit);

                        if ((int)lNodes.size() >= N)
                        {
                            break;
                        }
                    }
                    if ((int)lNodes.size() >= N || (int)lNodes.size() == prevSize)
                    {
                        bFinish = true;
                    }
                }
            }
        }
        // retain the best point in each node
        vector<cv::KeyPoint> vResultKeys;
        vResultKeys.reserve(nfeatures);
        for (list<ExtractorNode>::iterator lit = lNodes.begin(); lit != lNodes.end(); ++lit)
        {
            vector<cv::KeyPoint> &vNodeKeys = lit->vKeys;
            cv::KeyPoint *pKP = &vNodeKeys[0];
            float maxResponse = pKP->response;

            for (size_t k = 1; k < vNodeKeys.size(); ++k)
            {
                if (vNodeKeys[k].response > maxResponse)
                {
                    pKP = &vNodeKeys[k];
                    maxResponse = pKP->response;
                }
            }

            vResultKeys.push_back(*pKP);
        }
        return vResultKeys;
    }
}