ORB_SLAM3单目代码阅读——ORB特征点提取

ORB特征点坐标为小数：ORB特征提取器默认分8层提取，当把高层图像中的FAST角点坐标转换到原始图像坐标系下时会产生小数，而原始图像所在的最低层因为不需要对坐标进行调整，因此就没有小数部分。

ORBextractor.cpp代码注释

/**
* This file is part of ORB-SLAM3
*
* Copyright (C) 2017-2020 Carlos Campos, Richard Elvira, Juan J. Gómez Rodríguez, José M.M. Montiel and Juan D. Tardós, University of Zaragoza.
* Copyright (C) 2014-2016 Raúl Mur-Artal, José M.M. Montiel and Juan D. Tardós, University of Zaragoza.
*
* ORB-SLAM3 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-SLAM3 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-SLAM3.
* 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"


using namespace cv;
using namespace std;

namespace ORB_SLAM3
{

    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)//计算特征点区域的角度信息
    {
        int m_01 = 0, m_10 = 0;

        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)//m10对该行的灰度值求一个加权平均
            m_10 += u * center[u];

        // Go line by line in the circuI853lar patch
        int step = (int)image.step1();//step[1]:指从当前图像行首到下一行首相距多少字节
        for (int v = 1; v <= HALF_PATCH_SIZE; ++v)
        {
            // Proceed over the two lines
            int v_sum = 0;
            int d = u_max[v];            //umax={15 15 15 15 14 14 14 13 13 12 11 10 9 8 6 3}
            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);//添加旋转角度偏移量，保持旋转不变性

        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,//使用四叉树方法保留特征点
                                                         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));//开始划分node

        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();//输出容器中的最后一个元素
        }

        //Associate points to childs
        for(size_t i=0;ivKeys.push_back(kp);//给四叉树中每个node添加ORB特征点
        }

        list::iterator lit = lNodes.begin();

        while(lit!=lNodes.end())
        {
            if(lit->vKeys.size()==1)
            {
                lit->bNoMore=true;//node中只有一个ORB点就设bNoMore=true，不需要再分裂
                lit++;
            }
            else if(lit->vKeys.empty())
                lit = lNodes.erase(lit);//node中没有点就销毁，释放内存
            else
                lit++;
        }

        bool bFinish = false;//判断node分裂是否完成

        int iteration = 0;

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

        while(!bFinish)
        {
            iteration++;

            int prevSize = lNodes.size();//node未分裂的size

            lit = lNodes.begin();

            int nToExpand = 0;//记录node分裂的数量

            vSizeAndPointerToNode.clear();

            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);

                    // 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);//销毁分裂完成的node，lit迭代器指向lNodes中的下一个node
                    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)//N代表的是该层影像提取特征点的数量
            {
                bFinish = true;
            }
            else if(((int)lNodes.size()+nToExpand*3)>N)//这是为什么是nToExpand*3？ 遍历完一次后node增加的数量
            {

                while(!bFinish)
                {

                    prevSize = lNodes.size();

                    vector > 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->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
        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);//记录node中响应值最大的点，the response by which the most strong keypoints have been selected. Can be used for the further sorting or subsampling
        }

        return vResultKeys;
    }

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

        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 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;

                    vector vKeysCell;

                    FAST(mvImagePyramid[level].rowRange(iniY,maxY).colRange(iniX,maxX),
                         vKeysCell,iniThFAST,true);

                    /*if(bRight && j <= 13){
                        FAST(mvImagePyramid[level].rowRange(iniY,maxY).colRange(iniX,maxX),
                             vKeysCell,10,true);
                    }
                    else if(!bRight && j >= 16){
                        FAST(mvImagePyramid[level].rowRange(iniY,maxY).colRange(iniX,maxX),
                             vKeysCell,10,true);
                    }
                    else{
                        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(bRight && j <= 13){
                            FAST(mvImagePyramid[level].rowRange(iniY,maxY).colRange(iniX,maxX),
                                 vKeysCell,5,true);
                        }
                        else if(!bRight && j >= 16){
                            FAST(mvImagePyramid[level].rowRange(iniY,maxY).colRange(iniX,maxX),
                                 vKeysCell,5,true);
                        }
                        else{
                            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);

            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& 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));//获取长度为256位特征描述符
    }

    int ORBextractor::operator()( InputArray _image, InputArray _mask, vector& _keypoints,
                                  OutputArray _descriptors, std::vector &vLappingArea)
    {
        //cout << "[ORBextractor]: Max Features: " << nfeatures << endl;
        if(_image.empty())
            return -1;

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

        // Pre-compute the scale pyramid
        ComputePyramid(image);

        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);//创建32维的特征nkeypoints*32维的特征描述符矩阵
            descriptors = _descriptors.getMat();
        }

        //_keypoints.clear();
        //_keypoints.reserve(nkeypoints);
        _keypoints = vector(nkeypoints);

        int offset = 0;
        //Modified for speeding up stereo fisheye matching
        int monoIndex = 0, stereoIndex = nkeypoints-1;
        for (int level = 0; level < nlevels; ++level)
        {
            vector& keypoints = allKeypoints[level];
            int nkeypointsLevel = (int)keypoints.size();

            if(nkeypointsLevel==0)
                continue;

            // preprocess the resized image
            Mat workingMat = mvImagePyramid[level].clone();
            GaussianBlur(workingMat, workingMat, Size(7, 7), 2, 2, BORDER_REFLECT_101);//高斯滤波，边缘像素对称分布

            // Compute the descriptors
            //Mat desc = descriptors.rowRange(offset, offset + nkeypointsLevel);
            Mat desc = cv::Mat(nkeypointsLevel, 32, CV_8U);
            computeDescriptors(workingMat, keypoints, desc, pattern);

            offset += nkeypointsLevel;


            float scale = mvScaleFactor[level]; //getScale(level, firstLevel, scaleFactor);
            int i = 0;
            for (vector::iterator keypoint = keypoints.begin(),
                         keypointEnd = keypoints.end(); keypoint != keypointEnd; ++keypoint){

                // Scale keypoint coordinates
                if (level != 0){
                    keypoint->pt *= scale;
                }

                if(keypoint->pt.x >= vLappingArea[0] && keypoint->pt.x <= vLappingArea[1]){
                    _keypoints.at(stereoIndex) = (*keypoint);
                    desc.row(i).copyTo(descriptors.row(stereoIndex));
                    stereoIndex--;
                }
                else{
                    _keypoints.at(monoIndex) = (*keypoint);
                    desc.row(i).copyTo(descriptors.row(monoIndex));
                    monoIndex++;
                }
                i++;
            }
        }
        //cout << "[ORBextractor]: extracted " << _keypoints.size() << " KeyPoints" << endl;
        return monoIndex;
    }

    void ORBextractor::ComputePyramid(cv::Mat image)//影像分层，编号0-7
    {
        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