ORBSLAM2-ORBextractor

主要参考以下两篇博客：
ORBSLAM2源码学习-ORBextractor
ORB-SLAM2中四叉树管理特征点
1.ORBextractor.h

#ifndef ORBEXTRACTOR_H
#define ORBEXTRACTOR_H

#include 
#include 
#include 

/*
	ORBSLAM使用的是ORB特征，即Oriented FAST and Rotated BRIEF，
	是对FAST特征点与BREIF特征描述子的一种结合与改进
*/
namespace ORB_SLAM2
{


// 分配四叉树时用到的结点类型
//该类中定义了四叉树创建的函数以及树中结点的属性
//bool bNoMore： 根据该结点中被分配的特征点的数目来决定是否继续对其进行分割
//DivisionNode()：实现如何对一个结点进行分割
//vKeys：用来存储被分配到该结点区域内的所有特征点
//UL, UR, BL, BR：四个点定义了一个结点的区域
//lit:list的迭代器，遍历所有生成的节点
class ExtractorNode
{
public:
	//用初始化列表来初始化本类内的成员变量
    ExtractorNode():bNoMore(false){}

    void DivideNode(ExtractorNode &n1, ExtractorNode &n2, ExtractorNode &n3, ExtractorNode &n4);

    std::vector vKeys;
    cv::Point2i UL, UR, BL, BR;
    std::list::iterator lit;
    bool bNoMore;
};

class ORBextractor
{
public:
    
    enum {HARRIS_SCORE=0, FAST_SCORE=1 };

    ORBextractor(int nfeatures, float scaleFactor, int nlevels,
                 int iniThFAST, int minThFAST);

    ~ORBextractor(){}

    // Compute the ORB features and descriptors on an image.
    // ORB are dispersed on the image using an octree.
    // Mask is ignored in the current implementation.
    void operator()( cv::InputArray image, cv::InputArray mask,
      std::vector& keypoints,
      cv::OutputArray descriptors);

    int inline GetLevels(){
        return nlevels;}

    float inline GetScaleFactor(){
        return scaleFactor;}

    std::vector inline GetScaleFactors(){
        return mvScaleFactor;
    }

    std::vector inline GetInverseScaleFactors(){
        return mvInvScaleFactor;
    }

    std::vector inline GetScaleSigmaSquares(){
        return mvLevelSigma2;
    }

    std::vector inline GetInverseScaleSigmaSquares(){
        return mvInvLevelSigma2;
    }
	//图像金字塔 存放各层的图片 
    std::vector mvImagePyramid;

protected:
	//计算高斯金字塔
    void ComputePyramid(cv::Mat image);
	//计算关键点并用四叉树进行存储
    void ComputeKeyPointsOctTree(std::vector >& allKeypoints);    
	//将关键点分配到四叉树
    std::vector DistributeOctTree(const std::vector& vToDistributeKeys, const int &minX,
                                           const int &maxX, const int &minY, const int &maxY, const int &nFeatures, const int &level);
	
    void ComputeKeyPointsOld(std::vector >& allKeypoints);
    //存储关键点附近patch的点对
	std::vector pattern;
	
	//提取特征点的最大数量
    int nfeatures;
	//每层之间的缩放比例
    double scaleFactor;
	//高斯金字塔的层数
    int nlevels;
	//iniThFAST提取FAST角点时初始阈值
    int iniThFAST;
	//minThFAST提取FAST角点时更小的阈值
    int minThFAST;
	//每层的特征数量
    std::vector mnFeaturesPerLevel;
	//Patch圆的每一行最大坐标
    std::vector umax;
	//每层的相对于原始图像的缩放比例
    std::vector mvScaleFactor;
	//每层的相对于原始图像的缩放比例的倒数
    std::vector mvInvScaleFactor;    
    std::vector mvLevelSigma2;
    std::vector mvInvLevelSigma2;
};

} //namespace ORB_SLAM

#endif

2.ORBextractor.cc

#include 
#include 
#include 
#include 
#include 

#include "ORBextractor.h"


using namespace cv;
using namespace std;

namespace ORB_SLAM2
{

const int PATCH_SIZE = 31;//块直径的大小为31
const int HALF_PATCH_SIZE = 15;//块半径的大小为15
const int EDGE_THRESHOLD = 19;//图像边界的阈值为19

// 灰度质心法计算特征点方向
static float IC_Angle(const Mat& image, Point2f pt,   const vector & u_max)
{
    int m_01 = 0, m_10 = 0; // P136公式
	// uchar(unsigned char)
    const uchar* center = &image.at (cvRound(pt.y), cvRound(pt.x)); //用指针的方法遍历每一像素的灰度值

    // 对 v=0 这一行单独计算
    for (int u = -HALF_PATCH_SIZE; u <= HALF_PATCH_SIZE; ++u)`
        m_10 += u * center[u];

    int step = (int)image.step1();//Mat的step1与一行数据的通道数之和大小是相同
    for (int v = 1; v <= HALF_PATCH_SIZE; ++v)
    {
        //上下和左右两条线同时计算
        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);
            m_10 += u * (val_plus + val_minus);
        }
        m_01 += v * v_sum;
    }

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

// 计算描述子，按照预定的选取点对的方式
// 选用一种表现还不错的挑选方法——高斯分布，同时定义了描述子长度为256
const float factorPI = (float)(CV_PI/180.f);//CV_PI/180.f弧度制中角度1度对应的值 
static void computeOrbDescriptor(const KeyPoint& kpt,
                                 const Mat& img, const Point* pattern,
                                 uchar* desc)
{
    float angle = (float)kpt.angle*factorPI; // 得到特征点旋转的弧度值
    float a = (float)cos(angle), b = (float)sin(angle); // double cos(double x) x为弧度值(radians)，即180用3.14159..表示

    const uchar* center = &img.at(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 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)*/,
    -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)*/
};
/*
	构造函数中传入基本参数，如提取的特征点数量、高斯金字塔图像之间的比例因子、
	金字塔层数及角点检测阈值等，为了防止出现检测不到角点的情况，特别设置了两个阈值
	在构造函数中，计算了图像金字塔每一层图像的缩放倍数、每一层图像的特征点数量（通
	过等比数列的方式）、接下来计算umax参数，用于后续计算特征点主方向和描述子时用到
*/
ORBextractor::ORBextractor(int _nfeatures, float _scaleFactor, int _nlevels,
         int _iniThFAST, int _minThFAST):
    nfeatures(_nfeatures), scaleFactor(_scaleFactor), nlevels(_nlevels),
    iniThFAST(_iniThFAST), minThFAST(_minThFAST)
{
	/*计算每一层相对于原始图片的放大倍数*/
	// 用resize()函数改变vector的大小
    mvScaleFactor.resize(nlevels);
    mvLevelSigma2.resize(nlevels);
    mvScaleFactor[0]=1.0f;
    mvLevelSigma2[0]=1.0f;
    for(int i=1; i= vmin; --v)
    {
        while (umax[v0] == umax[v0 + 1])
            ++v0;
        umax[v] = v0;
        ++v0;
    }
}

// 关键点提取完之后，对金字塔的每一层图像中的特征点进行特征点方向计算，添加旋转不变性
static void computeOrientation(const Mat& image, vector& keypoints, const vector& umax)
{
    for (vector::iterator keypoint = keypoints.begin(),
         keypointEnd = keypoints.end(); keypoint != keypointEnd; ++keypoint)
    {
        keypoint->angle = IC_Angle(image, keypoint->pt, umax);
    }
}
//结点时如何被分割成四个新的结点的
//这个成员函数的参数是四个引用，所以函数返回值是void，C++中的引用就好比与C语言中的指针
void ExtractorNode::DivideNode(ExtractorNode &n1, ExtractorNode &n2, ExtractorNode &n3, ExtractorNode &n4)
{
	//static_cast就相当于C语言中的强制类型转换
    //在分割结点之前要先计算出每一个新结点的四个角点坐标
    //halfX和halfY分别是已创建结点的x方向的和y方向的中间位置
    const int halfX = ceil(static_cast(UR.x-UL.x)/2);
    const int halfY = ceil(static_cast(BR.y-UL.y)/2);

    //设定四个子结点的边界
    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);
	//将每个新结点的用来存储特征点的向量的capacity设置为母结点中所有的特征点的个数
    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());

    //Associate points to childs
	//根据特征点的坐标来将特征点分配到不同的新结点区域
    for(size_t i=0;i ORBextractor::DistributeOctTree(const vector& vToDistributeKeys, const int &minX,
                                       const int &maxX, const int &minY, const int &maxY, const int &N, const int &level)
{
    //常用的相机kinect v1的分辨率是：640*480 kinect v2的分辨率是：1920*1080
    //为了尽量使得每一个结点的区域形状接近正方形所以图像的长宽比决定了四叉树根节点的数目
    //如果使用kinect v1那么只有一个根结点，如果使用kinect v2那么就会有两个根结点
    const int nIni = round(static_cast(maxX-minX)/(maxY-minY));
	//hX变量可以理解为一个根节点所占的宽度 
    const float hX = static_cast(maxX-minX)/nIni;
	//lNodes中存储生成的树结点	
    list lNodes;
	//vpIniNodes变量中存储的是结点的地址
    vector vpIniNodes;
	//vpIniNodes的大小先设置成根结点的个数
    vpIniNodes.resize(nIni);

    for(int i=0; i(i),0); //左上角位置坐标
        ni.UR = cv::Point2i(hX*static_cast(i+1),0); //右上角位置坐标
        ni.BL = cv::Point2i(ni.UL.x,maxY-minY); //左下角的位置坐标
        ni.BR = cv::Point2i(ni.UR.x,maxY-minY); //右下角的位置坐标
		//vKeys的大小为在上面的这个根节点范围内总共提取的特征点的个数
        ni.vKeys.reserve(vToDistributeKeys.size());
		//将创建的根节点插入到list lNodes中
        lNodes.push_back(ni);
		//将lNodes变量中的最后存储的那个结点的地址存储到vector变量vpIniNodes中
        //暂时还不知道这个变量做何用
        //vpIniNodes总是把最后插入到lNodes中的结点的地址拿走，然后要为
        //该结点的vKeys成员变量内部添加属于该结点的特征点
        vpIniNodes[i] = &lNodes.back();
    }

    //Associate points to childs
	//要一直记得vToDistributeKeys变量中存储的是该层图像中提取的特征点
    //遍历在该层图像上提取的所有特征点
    for(size_t i=0;ivKeys.push_back(kp);
    }
	
    list::iterator lit = lNodes.begin();
	//遍历已经生成的所有节点
    while(lit!=lNodes.end())
    {
		//如果判断在一个结点里面只有一个特征点
        if(lit->vKeys.size()==1)
        {
			//将该结点的bNoMore属性设置为true，表示不再对这个结点进行分割
            lit->bNoMore=true;
            lit++;
        }
		//如果判断这个结点中没有被分配到任何的特征点那么就将这个结点删除
        else if(lit->vKeys.empty())
            lit = lNodes.erase(lit);
        else
            lit++;
    }
	//lNodes中的结点和 vpIniNodes中的结点指针是同步的，只有在 vpIniNodes中存储的结点中存储了 
    //特征点，才能根据特征点的数目来决定如何处理这个结点
    //那如果在lNodes中删除一个没有特征点的结点，那么在 vpIniNodes中对应的这个地址也会被销毁吗？
    bool bFinish = false;

    int iteration = 0;

    vector > vSizeAndPointerToNode;
    vSizeAndPointerToNode.reserve(lNodes.size()*4);

    while(!bFinish)
    {
        iteration++;
		//lNodes中已经存储的结点的数目
        int prevSize = lNodes.size();

        lit = lNodes.begin();

        int nToExpand = 0;

        vSizeAndPointerToNode.clear();

        while(lit!=lNodes.end())
        {
            if(lit->bNoMore)
            {
                //如果结点内被分配的特征点的数目只有1个则不继续分割这个结点
                lit++;
                continue;
            }
            else
            {
                // 如果结点中被分配到的特征点数大于1则要继续分割
                ExtractorNode n1,n2,n3,n4;
                //DivideNode概括来说就是将上面这个结点分成了四个结点，并且已经完成了特征点的分配，以及特征
                //个数的检测设定好每个节点的bNoMore的值
                lit->DivideNode(n1,n2,n3,n4);

                // Add childs if they contain points
                if(n1.vKeys.size()>0)
                {
					//如果新分割出来的第一个结点中被分配的特征点的个数大于0那么就将这个结点
                    //插入到list的头部
                    lNodes.push_front(n1);   
					//如果这个新结点中被分配的特征点的个数大于1，那么接下来要被分割的结点的数目
                    //就得加1了
                    if(n1.vKeys.size()>1)
                    {
                        nToExpand++;
						//变量vSizeAndPointerToNode中存储的是每一个结点的地址以及该结点中被分配到的特征点的个数
                        vSizeAndPointerToNode.push_back(make_pair(n1.vKeys.size(),&lNodes.front()));
                        lNodes.front().lit = lNodes.begin();
                    }
                }
				//对新分配出的第二个结点进行同上面相同的测试和操作
                if(n2.vKeys.size()>0)
                {
					//在list的头部插入元素
                    lNodes.push_front(n2);
                    if(n2.vKeys.size()>1)
                    {
                        nToExpand++;
                        vSizeAndPointerToNode.push_back(make_pair(n2.vKeys.size(),&lNodes.front()));
						//每插入一个结点就要更新list的开始结点的位置
                        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;
            }
        }       

        // Finish if there are more nodes than required features
        // or all nodes contain just one point
		//当创建的结点的数目比要求的特征点还要多或者每个结点中都只有一个特征点的时候就停止分割
        if((int)lNodes.size()>=N || (int)lNodes.size()==prevSize)
        {
            bFinish = true;
        }
		//如果现在生成的结点全部进行分割后生成的结点满足大于需求的特征点的数目，但是不继续分割又
        //不能满足大于N的要求时
        //这里为什么是乘以三，这里也正好印证了上面所说的当一个结点被分割成四个新的结点时，
        //这个结点时要被删除的，其实总的结点时增加了三个
        else if(((int)lNodes.size()+nToExpand*3)>N)
        {

            while(!bFinish)
            {

                prevSize = lNodes.size();
				//这里将已经创建好的结点放到一个新的容器中
                vector > vPrevSizeAndPointerToNode = vSizeAndPointerToNode;
                vSizeAndPointerToNode.clear();
				//根据结点中被分配都的特征点的数目对结点进行排序
                //这里为何要排序，我们想要的结果是想让尽可能多的特征点均匀的分布在图像上
                //如果前面的特征分布相对均匀的结点中的特征点数目已经达到了指标那么就可以将
                //后面那些分布密集的特征点去掉了
                sort(vPrevSizeAndPointerToNode.begin(),vPrevSizeAndPointerToNode.end());
                for(int j=vPrevSizeAndPointerToNode.size()-1;j>=0;j--)
                {
                    ExtractorNode n1,n2,n3,n4;
                    vPrevSizeAndPointerToNode[j].second->DivideNode(n1,n2,n3,n4);

                    // Add childs 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);
					//如果多有的结点还没有被分割完就已经达到了大于N的要求那么就直接跳出循环
                    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 vResultKeys;
    vResultKeys.reserve(nfeatures);
	//遍历创建的所有结点
    for(list::iterator lit=lNodes.begin(); lit!=lNodes.end(); lit++)
    {
        vector &vNodeKeys = lit->vKeys;
        cv::KeyPoint* pKP = &vNodeKeys[0];
        float maxResponse = pKP->response;
		//在每个结点中找到那个最强壮的特征点进行保存
        for(size_t k=1;kmaxResponse)
            {
                pKP = &vNodeKeys[k];
                maxResponse = vNodeKeys[k].response;
            }
        }
		//只将每个结点下最强壮的的特征点保存
        vResultKeys.push_back(*pKP);
    }

    return vResultKeys;
}

/*
	提取特征点，作者将金字塔中每一张图像按照尺寸分成一定大小的格子cell，然后在每一个cell内提取FAST
	特征点，目的是保证特征点的均匀分布，不至于过度集中，之后将一层中的特征点分配到四叉树中，所有层
	的特征点提取完毕后，统一计算特征点的主方向
*/
void ORBextractor::ComputeKeyPointsOctTree(vector >& allKeypoints)
{
    allKeypoints.resize(nlevels);
	//设定每个格子的大小
    const float W = 30;
	//每层分别提取特征点
    for (int level = 0; level < nlevels; ++level)
    {
		//设定该层图像中检测的X，Y最大最小坐标
        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;
		// 用于分配的关键点
        vector vToDistributeKeys;
        vToDistributeKeys.reserve(nfeatures*10);

        const float width = (maxBorderX-minBorderX);
        const float height = (maxBorderY-minBorderY);
		// 将待检测区域划分为格子的行列数
        const int nCols = width/W;
        const int nRows = height/W;
		// 重新计算每个格子的大小
        const int wCell = ceil(width/nCols);
        const int hCell = ceil(height/nRows);
		// 在每个格子内进行fast特征检测
        for(int i=0; i=maxBorderY-3)
                continue;
            if(maxY>maxBorderY)
                maxY = maxBorderY;

            for(int j=0; j=maxBorderX-6)
                    continue;
                if(maxX>maxBorderX)
                    maxX = maxBorderX;

                vector vKeysCell;
				// FAST 关键点检测
                FAST(mvImagePyramid[level].rowRange(iniY,maxY).colRange(iniX,maxX),
                     vKeysCell,iniThFAST,true);
				// 如果检测为空就降低阈值再进行检测
                if(vKeysCell.empty())
                {
                    FAST(mvImagePyramid[level].rowRange(iniY,maxY).colRange(iniX,maxX),
                         vKeysCell,minThFAST,true);
                }
				//如果检测不为空就将检测到的特征点放到vToDistributeKeys中
                if(!vKeysCell.empty())
                {
                    for(vector::iterator vit=vKeysCell.begin(); vit!=vKeysCell.end();vit++)
                    {
                        (*vit).pt.x+=j*wCell;
                        (*vit).pt.y+=i*hCell;
                        vToDistributeKeys.push_back(*vit);
                    }
                }

            }
        }

        vector & keypoints = allKeypoints[level];
        keypoints.reserve(nfeatures);

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

        // Add border to coordinates and scale information
        const int nkps = keypoints.size();
		//考虑边界信息进去计算特征点的位置
        for(int i=0; i > &allKeypoints)
{
    allKeypoints.resize(nlevels);

    float imageRatio = (float)mvImagePyramid[0].cols/mvImagePyramid[0].rows;

    for (int level = 0; level < nlevels; ++level)
    {
        const int nDesiredFeatures = mnFeaturesPerLevel[level];

        const int levelCols = sqrt((float)nDesiredFeatures/(5*imageRatio));
        const int levelRows = imageRatio*levelCols;

        const int minBorderX = EDGE_THRESHOLD;
        const int minBorderY = minBorderX;
        const int maxBorderX = mvImagePyramid[level].cols-EDGE_THRESHOLD;
        const int maxBorderY = mvImagePyramid[level].rows-EDGE_THRESHOLD;

        const int W = maxBorderX - minBorderX;
        const int H = maxBorderY - minBorderY;
        const int cellW = ceil((float)W/levelCols);
        const int cellH = ceil((float)H/levelRows);

        const int nCells = levelRows*levelCols;
        const int nfeaturesCell = ceil((float)nDesiredFeatures/nCells);

        vector > > cellKeyPoints(levelRows, vector >(levelCols));

        vector > nToRetain(levelRows,vector(levelCols,0));
        vector > nTotal(levelRows,vector(levelCols,0));
        vector > bNoMore(levelRows,vector(levelCols,false));
        vector iniXCol(levelCols);
        vector iniYRow(levelRows);
        int nNoMore = 0;
        int nToDistribute = 0;


        float hY = cellH + 6;

        for(int i=0; infeaturesCell)
                {
                    nToRetain[i][j] = nfeaturesCell;
                    bNoMore[i][j] = false;
                }
                else
                {
                    nToRetain[i][j] = nKeys;
                    nToDistribute += nfeaturesCell-nKeys;
                    bNoMore[i][j] = true;
                    nNoMore++;
                }

            }
        }


        // Retain by score

        while(nToDistribute>0 && nNoMorenNewFeaturesCell)
                        {
                            nToRetain[i][j] = nNewFeaturesCell;
                            bNoMore[i][j] = false;
                        }
                        else
                        {
                            nToRetain[i][j] = nTotal[i][j];
                            nToDistribute += nNewFeaturesCell-nTotal[i][j];
                            bNoMore[i][j] = true;
                            nNoMore++;
                        }
                    }
                }
            }
        }

        vector & keypoints = allKeypoints[level];
        keypoints.reserve(nDesiredFeatures*2);

        const int scaledPatchSize = PATCH_SIZE*mvScaleFactor[level];

        // Retain by score and transform coordinates
        for(int i=0; i &keysCell = cellKeyPoints[i][j];
                KeyPointsFilter::retainBest(keysCell,nToRetain[i][j]);
                if((int)keysCell.size()>nToRetain[i][j])
                    keysCell.resize(nToRetain[i][j]);


                for(size_t k=0, kend=keysCell.size(); knDesiredFeatures)
        {
            KeyPointsFilter::retainBest(keypoints,nDesiredFeatures);
            keypoints.resize(nDesiredFeatures);
        }
    }

    // and compute orientations
    for (int level = 0; level < nlevels; ++level)
        computeOrientation(mvImagePyramid[level], allKeypoints[level], umax);
}

static void computeDescriptors(const Mat& image, vector& keypoints, Mat& descriptors,
                               const vector& 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));
}
/*对外的特征提取接口，即重载的operator()运算符*/
/*
	计算高斯图像金字塔
	提取特征点并生成四叉树
	逐层计算特征点描述子
*/
void ORBextractor::operator()( InputArray _image, InputArray _mask, vector& _keypoints,
                      OutputArray _descriptors)
{ 
    if(_image.empty())
        return;

    Mat image = _image.getMat();
    assert(image.type() == CV_8UC1 );

    //构建高斯金字塔
    ComputePyramid(image);
	//计算关键点并生成四叉树
    vector < vector > allKeypoints;
    ComputeKeyPointsOctTree(allKeypoints);
    //ComputeKeyPointsOld(allKeypoints);

    Mat descriptors;

    int nkeypoints = 0;
    for (int level = 0; level < nlevels; ++level)
        nkeypoints += (int)allKeypoints[level].size();
    if( nkeypoints == 0 )
        _descriptors.release();
    else
    {
        _descriptors.create(nkeypoints, 32, CV_8U);
        descriptors = _descriptors.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)//逐层计算
    {
        vector& keypoints = allKeypoints[level];
        int nkeypointsLevel = (int)keypoints.size();

        if(nkeypointsLevel==0)
            continue;

        // preprocess the resized image
        Mat workingMat = mvImagePyramid[level].clone();
		// 进行高斯模糊，用BORDER_REFLECT_101方法处理边缘
        GaussianBlur(workingMat, workingMat, Size(7, 7), 2, 2, BORDER_REFLECT_101);

        // Compute the descriptors
        Mat desc = descriptors.rowRange(offset, offset + nkeypointsLevel);
        computeDescriptors(workingMat, keypoints, desc, pattern);

        offset += nkeypointsLevel;

        // Scale keypoint coordinates
        if (level != 0)
        {
            float scale = mvScaleFactor[level]; //getScale(level, firstLevel, scaleFactor);
            for (vector::iterator keypoint = keypoints.begin(),
                 keypointEnd = keypoints.end(); keypoint != keypointEnd; ++keypoint)
                keypoint->pt *= scale;// 按缩放因子进行坐标缩放
        }
        // And add the keypoints to the output
        _keypoints.insert(_keypoints.end(), keypoints.begin(), keypoints.end());
    }
}
//构建高斯金字塔
void ORBextractor::ComputePyramid(cv::Mat image)
{
	// 每一层根据尺度因子重新计算图像大小，之后调用resize函数
	// 计算结果存入std::vector mvImagePyramid 中
    for (int level = 0; level < nlevels; ++level)
    {
        float scale = mvInvScaleFactor[level];
        Size sz(cvRound((float)image.cols*scale), cvRound((float)image.rows*scale));
        Size wholeSize(sz.width + EDGE_THRESHOLD*2, sz.height + EDGE_THRESHOLD*2);
        Mat temp(wholeSize, image.type()), masktemp;
        mvImagePyramid[level] = temp(Rect(EDGE_THRESHOLD, EDGE_THRESHOLD, sz.width, sz.height));

        // Compute the resized image
        if( level != 0 )
        {
            resize(mvImagePyramid[level-1], mvImagePyramid[level], sz, 0, 0, INTER_LINEAR);                                            

            copyMakeBorder(mvImagePyramid[level], temp, EDGE_THRESHOLD, EDGE_THRESHOLD, EDGE_THRESHOLD, EDGE_THRESHOLD,
                           BORDER_REFLECT_101+BORDER_ISOLATED);            
        }
        else
        {
            copyMakeBorder(image, temp, EDGE_THRESHOLD, EDGE_THRESHOLD, EDGE_THRESHOLD, EDGE_THRESHOLD,
                           BORDER_REFLECT_101);            
        }
    }

}

} //namespace ORB_SLAM