ORBSLAM理论与实战（9） 特征点提取 ORBextractor类

ORBSLAM2看了一段时间啦，看大佬博客，抽时间总结一下。欢迎指正

目录

1.基础知识

2.特征点理论

 3.ORBextractor类

3.1 ORBextractor.h

3.1 ORBextractor.cpp

---

1.基础知识

    特征点由关键点（Key-point）和描述子（Descriptor）两部分组成。ORB特征点（Oriented FAST and Rotated BRIEF）是由Oriented FAST角点和 BRIEF （Binary Robust Independent Elementary Features）描述子构成，其计算速度是sift特征点的100倍，是surf特征点的10倍。

2.特征点理论

可参考博客

1. http://www.cnblogs.com/ronny/p/4083537.html
2. http://www.cnblogs.com/ronny/p/4078710.html
3. http://www.cnblogs.com/ronny/p/4081362.html

或者参考高博14讲 视觉里程计相应的章节

 3.ORBextractor类

参考博文：

1.  认真的虎ORBSLAM2源码解读(七)：图解ORB特征提取ORBextractor
2. ORBSLAM2源码学习（1）ORBextractor
3. ORB_SLAM2 源码阅读 ORB_SLAM2::ORBextractor
4. ORB-SLAM（七）ORBextractor 特征提取

5. ORB-SLAM2从理论到代码实现（三）：ORB特征提取和匹配理论和代码详解

构造函数进行初始化，传入设定几个重要的成员变量。nfeatures（特征点的个数）、nlevels（构造金字塔的层数）、scaleFactor（金字塔中相邻层图像的比例系数）、iniThFAST（检测 FAST 角点的阈值）、minThFAST（在 iniThFAST 没有检测到角点的前提下，降低的阈值）。

括号运算符对输入的图像进行角点检测。

1. ComputePyramid 函数构造金字塔。

2. ComputeKeyPointsOctTree 对金字塔图像进行角点检测。

3. 计算角点的描述子，输出。

3.1 ORBextractor.h

#ifndef ORBEXTRACTOR_H
#define ORBEXTRACTOR_H

#include 
#include 
#include 


namespace ORB_SLAM2
{

// 分配四叉树时用到的结点类型
class ExtractorNode
{
public:
    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 };

   //设置两个阈值的原因是在FAST提取角点进行分块后有可能在某个块中在原始阈值情况下提取不到角点，使用更小的阈值进一步提取
    //构造函数
    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.
    //对外接口，重载了()运算符
    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，存放各层的图像
    std::vector mvImagePyramid;

protected:

    //计算图像金字塔
    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);

    //作者遗留下旧的提取特征点方法
    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;

    //存放相邻两层的比例因子
    double scaleFactor;

    //图形金字塔的层数
    int nlevels;

    //iniThFAST提取FAST角点时初始阈值
    int iniThFAST;

    //若提取不到iniThFAST，取minThFAST提取FAST角点时更小的阈值
    int minThFAST;

    //图像金字塔每层提取的特征点数
    std::vector mnFeaturesPerLevel;

    //Patch圆的最大坐标
    std::vector umax;

    //每层的相对于原始图像的缩放比例
    std::vector mvScaleFactor;

    //mvScaleFactor的倒数
    std::vector mvInvScaleFactor;   
   
    //mvScaleFactor的平方 
    std::vector mvLevelSigma2;

    //mvScaleFactor的平方的倒数
    std::vector mvInvLevelSigma2;
};

} //namespace ORB_SLAM

#endif

3.1 ORBextractor.cpp

/**
* This file is part of ORB-SLAM2.
* This file is based on the file orb.cpp from the OpenCV library (see BSD license below).
*
* Copyright (C) 2014-2016 Raúl Mur-Artal  (University of Zaragoza)
* For more information see 
*
* ORB-SLAM2 is free software: you can redistribute it and/or modify
* it under the terms of the GNU General Public License as published by
* the Free Software Foundation, either version 3 of the License, or
* (at your option) any later version.
*
* ORB-SLAM2 is distributed in the hope that it will be useful,
* but WITHOUT ANY WARRANTY; without even the implied warranty of
* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
* GNU General Public License for more details.
*
* You should have received a copy of the GNU General Public License
* along with ORB-SLAM2. If not, see .
*/
/**
* Software License Agreement (BSD License)
*
*  Copyright (c) 2009, Willow Garage, Inc.
*  All rights reserved.
*
*  Redistribution and use in source and binary forms, with or without
*  modification, are permitted provided that the following conditions
*  are met:
*
*   * Redistributions of source code must retain the above copyright
*     notice, this list of conditions and the following disclaimer.
*   * Redistributions in binary form must reproduce the above
*     copyright notice, this list of conditions and the following
*     disclaimer in the documentation and/or other materials provided
*     with the distribution.
*   * Neither the name of the Willow Garage nor the names of its
*     contributors may be used to endorse or promote products derived
*     from this software without specific prior written permission.
*
*  THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
*  "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
*  LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS
*  FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE
*  COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT,
*  INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING,
*  BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
*  LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
*  CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT
*  LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN
*  ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
*  POSSIBILITY OF SUCH DAMAGE.
*
*/


#include 
#include 
#include 
#include 
#include 
#include 

#include "ORBextractor.h"
#include 


using namespace cv;
using namespace std;

namespace ORB_SLAM2
{

const int PATCH_SIZE = 31;
const int HALF_PATCH_SIZE = 15;
const int EDGE_THRESHOLD = 19;


static float IC_Angle(const Mat& image, Point2f pt,  const vector & u_max)
{
    int m_01 = 0, m_10 = 0;
    
    //使用时注意point2f(x,y)在图像坐标系的，在图像上的image(y,x)
    const uchar* center = &image.at (cvRound(pt.y), cvRound(pt.x));
    
    // Treat the center line differently, v=0
    for (int u = -HALF_PATCH_SIZE; u <= HALF_PATCH_SIZE; ++u)
        m_10 += u * center[u];

    // Go line by line in the circuI853lar patch
    int step = (int)image.step1();
    for (int v = 1; v <= HALF_PATCH_SIZE; ++v)
    {
        // Proceed over the two lines
        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);
}


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(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)*/
};

ORBextractor::ORBextractor(int _nfeatures, float _scaleFactor, int _nlevels,
         int _iniThFAST, int _minThFAST):
    nfeatures(_nfeatures), scaleFactor(_scaleFactor), nlevels(_nlevels),
    iniThFAST(_iniThFAST), minThFAST(_minThFAST)
{
    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;
    }
}

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);
    }
}

void ExtractorNode::DivideNode(ExtractorNode &n1, ExtractorNode &n2, ExtractorNode &n3, ExtractorNode &n4)
{
    const int halfX = ceil(static_cast(UR.x-UL.x)/2);
    const int halfY = ceil(static_cast(BR.y-UL.y)/2);

    //Define boundaries of childs
    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());

    //Associate points to childs
    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)
{
    // Compute how many initial nodes   
    const int nIni = round(static_cast(maxX-minX)/(maxY-minY));

    const float hX = static_cast(maxX-minX)/nIni;

    list lNodes;

    vector vpIniNodes;
    vpIniNodes.resize(nIni);

    for(int i=0; i(i),0);
        ni.UR = cv::Point2i(hX*static_cast(i+1),0);
        ni.BL = cv::Point2i(ni.UL.x,maxY-minY);
        ni.BR = cv::Point2i(ni.UR.x,maxY-minY);
        ni.vKeys.reserve(vToDistributeKeys.size());

        lNodes.push_back(ni);
        vpIniNodes[i] = &lNodes.back();
    }

    //Associate points to childs
    for(size_t i=0;ivKeys.push_back(kp);
    }

    list::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 > vSizeAndPointerToNode;
    vSizeAndPointerToNode.reserve(lNodes.size()*4);

    // 根据兴趣点分布,利用N叉树方法对图像进行划分区域
    while(!bFinish)
    {
        iteration++;

        int prevSize = lNodes.size();

        lit = lNodes.begin();

        int nToExpand = 0;

        vSizeAndPointerToNode.clear();

        // 将目前的子区域经行划分
        while(lit!=lNodes.end())
        {
            if(lit->bNoMore)
            {
                // If node only contains one point do not subdivide and continue
                lit++;
                continue;
            }
            else
            {
                // If more than one point, subdivide
                ExtractorNode n1,n2,n3,n4;
                lit->DivideNode(n1,n2,n3,n4); // 再细分成四个子区域

                // Add childs if they contain points
                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;
            }
        }       

        // Finish if there are more nodes than required features
        // or all nodes contain just one point
        if((int)lNodes.size()>=N || (int)lNodes.size()==prevSize)
        {
            bFinish = true;
        }
        // 当再划分之后所有的Node数大于要求数目时
        else if(((int)lNodes.size()+nToExpand*3)>N)
        {

            while(!bFinish)
            {

                prevSize = lNodes.size();

                vector > vPrevSizeAndPointerToNode = vSizeAndPointerToNode;
                vSizeAndPointerToNode.clear();

                // 对需要划分的部分进行排序, 即对兴趣点数较多的区域进行划分
                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);

                    if((int)lNodes.size()>=N)
                        break;
                }

                if((int)lNodes.size()>=N || (int)lNodes.size()==prevSize)
                    bFinish = true;

            }
        }
    }

    // Retain the best point in each node
    // 保留每个区域响应值最大的一个兴趣点
    vector vResultKeys;
    vResultKeys.reserve(nfeatures);
    for(list::iterator lit=lNodes.begin(); lit!=lNodes.end(); lit++)
    {
        vector &vNodeKeys = lit->vKeys;
        cv::KeyPoint* pKP = &vNodeKeys[0];
        float maxResponse = pKP->response;

        for(size_t k=1;kmaxResponse)
            {
                pKP = &vNodeKeys[k];
                maxResponse = vNodeKeys[k].response;
            }
        }

        vResultKeys.push_back(*pKP);
    }

    return vResultKeys;
}

void ORBextractor::ComputeKeyPointsOctTree(vector >& allKeypoints)
{
    allKeypoints.resize(nlevels);

    const float W = 30;

    // 对每一层图像做处理
    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 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=maxBorderY-3)
                continue;
            if(maxY>maxBorderY)
                maxY = maxBorderY;

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

                // FAST提取兴趣点, 自适应阈值
                vector vKeysCell;
                FAST(mvImagePyramid[level].rowRange(iniY,maxY).colRange(iniX,maxX),
                     vKeysCell,iniThFAST,true);

                if(vKeysCell.empty())
                {
                    FAST(mvImagePyramid[level].rowRange(iniY,maxY).colRange(iniX,maxX),
                         vKeysCell,minThFAST,true);
                }

                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);

        // 根据mnFeaturesPerLevel,即该层的兴趣点数,对特征点进行剔除
        keypoints = DistributeOctTree(vToDistributeKeys, minBorderX, maxBorderX,
                                      minBorderY, maxBorderY,mnFeaturesPerLevel[level], level);

        const int scaledPatchSize = PATCH_SIZE*mvScaleFactor[level];

        // Add border to coordinates and scale information
        const int nkps = keypoints.size();
        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));
}

/*
 * https://www.cnblogs.com/yangxudong/p/3872053.html
 * 操作符重载，重载()使得该对象成为一个函数对象，即该对象有类似函数的功能，在很多场合下可以当成函数指针使用，在STL的很多算法模板里广泛使用。
 *
 * 2019.05.17  lishuwei
 */

void ORBextractor::operator()( InputArray _image, InputArray _mask, vector& _keypoints,
                      OutputArray _descriptors)
{ 
    if(_image.empty())
        return;

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

    // Pre-compute the scale pyramid
    // 构建图像金字塔
    ComputePyramid(image);

    // 计算每层图像的兴趣点
    vector < vector > allKeypoints; // vector>
    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);

    int offset = 0;
    for (int level = 0; level < nlevels; ++level)
    {
        vector& keypoints = allKeypoints[level];
        int nkeypointsLevel = (int)keypoints.size();

        if(nkeypointsLevel==0)
            continue;

        // preprocess the resized image 对图像进行高斯模糊
        Mat workingMat = mvImagePyramid[level].clone();
        GaussianBlur(workingMat, workingMat, Size(7, 7), 2, 2, BORDER_REFLECT_101);

        // Compute the descriptors 计算描述子
        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;
        }
        // And add the keypoints to the output
        _keypoints.insert(_keypoints.end(), keypoints.begin(), keypoints.end());
    }
}

/**
 * 构建图像金字塔
 * @param image 输入图像
 */
void ORBextractor::ComputePyramid(cv::Mat image)
{
    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 )
        {
            //https://www.cnblogs.com/korbin/p/5612427.html 和 https://blog.csdn.net/qq_32095699/article/details/80689145  resize oepncv  2019.05.15  lishuwei
            resize(mvImagePyramid[level-1], mvImagePyramid[level], sz, 0, 0, cv::INTER_LINEAR);

            //感觉没啥用    2019.05.15  lishuwei
            copyMakeBorder(mvImagePyramid[level], temp, EDGE_THRESHOLD, EDGE_THRESHOLD, EDGE_THRESHOLD, EDGE_THRESHOLD,
                           BORDER_REFLECT_101+BORDER_ISOLATED);            
        }
        else
        {
            //https://blog.csdn.net/qq_22764813/article/details/52787553   OpenCV：copyMakeBorder的用法    opencv   2019.05.15 lishuwei
            //这个设计到深copy 和浅copy
            copyMakeBorder(image, temp, EDGE_THRESHOLD, EDGE_THRESHOLD, EDGE_THRESHOLD, EDGE_THRESHOLD,
                           BORDER_REFLECT_101);            
        }
    }

}

} //namespace ORB_SLAM

/*
 参考博客
1.https://www.cnblogs.com/JingeTU/p/6438968.html
2.https://blog.csdn.net/u012936940/article/details/81124152
 */