CVRaulMurOrb.cpp

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/**
 * \file      CVRaulMurOrb.cpp
 * \brief     Declares the Raul Mur ORB feature detector and descriptor
 * \details   This File is based on the ORB Implementation of ORB_SLAM
 *            https://github.com/raulmur/ORB_SLAM2
 * \date      Spring 2017
 * \remarks   Please use clangformat to format the code. See more code style on
 *            https://github.com/cpvrlab/SLProject4/wiki/SLProject-Coding-Style
 * \authors   Pascal Zingg, Timon Tschanz, Michael Goettlicher, Marcus Hudritsch
 * \copyright http://opensource.org/licenses/GPL-3.0
*/
 
#include <iterator> // std::back_inserter
#include <CVRaulMurExtNode.h>
#include <CVRaulMurOrb.h>
 
using namespace cv;
using std::pair;
 
const int PATCH_SIZE      = 31;
const int HALF_PATCH_SIZE = 15;
const int EDGE_THRESHOLD  = 19;
 
//-----------------------------------------------------------------------------
//! Returns the angle of the image patch around a keypoint based on the center of gravity.
static float
IC_Angle(const CVMat&       image,
         CVPoint2f          pt,
         const vector<int>& u_max)
{
    int m_01 = 0, m_10 = 0;
 
    const uchar* center = &image.at<uchar>(cvRound(pt.y), cvRound(pt.x));
 
    // 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[(uint)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);
//-----------------------------------------------------------------------------
//! Calculate the Orb descriptor for a keypoint.
static void
computeOrbDescriptor(const CVKeyPoint& kpt,
                     const CVMat&      img,
                     const CVPoint*    pattern,
                     uchar*            desc)
{
 
    float angle = (float)kpt.angle * factorPI;
    float a = (float)cos(angle), b = (float)sin(angle);
 
    const uchar* center = &img.at<uchar>(cvRound(kpt.pt.y), cvRound(kpt.pt.x));
    const int    step   = (int)img.step;
// Define a rotation invariant get_value function which gets the correct pixel for the comparison
#define GET_VALUE(idx) \
    center[cvRound(pattern[idx].x * b + pattern[idx].y * a) * step + \
           cvRound(pattern[idx].x * a - pattern[idx].y * b)]
 
    // clang-format off
    // Do the actual comparisons
    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;
    }
    // clang-format on
 
#undef GET_VALUE
}
//-----------------------------------------------------------------------------
/*! This is the hardcoded comparison pattern which the creators of ORB have
found to give the best results.
*/
// clang-format off
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)*/
};
// clang-format on
 
//-----------------------------------------------------------------------------
CVRaulMurOrb::CVRaulMurOrb(int   _nfeatures,
                           float _scaleFactor,
                           int   _nlevels,
                           int   _iniThFAST,
                           int   _minThFAST)
  : nfeatures(_nfeatures),
    scaleFactor(_scaleFactor),
    nlevels((uint)_nlevels),
    iniThFAST(_iniThFAST),
    minThFAST(_minThFAST)
 
{
    mvScaleFactor.resize(nlevels);
    mvLevelSigma2.resize(nlevels);
    mvScaleFactor[0] = 1.0f;
    mvLevelSigma2[0] = 1.0f;
    for (uint i = 1; i < nlevels; i++)
    {
        mvScaleFactor[i] = mvScaleFactor[i - 1] * (float)scaleFactor;
        mvLevelSigma2[i] = mvScaleFactor[i] * mvScaleFactor[i];
    }
 
    mvInvScaleFactor.resize(nlevels);
    mvInvLevelSigma2.resize(nlevels);
    for (uint i = 0; i < nlevels; i++)
    {
        mvInvScaleFactor[i] = 1.0f / mvScaleFactor[i];
        mvInvLevelSigma2[i] = 1.0f / mvLevelSigma2[i];
    }
 
    mvImagePyramid.resize(nlevels);
 
    mnFeaturesPerLevel.resize(nlevels);
    float factor                   = 1.0f / (float)scaleFactor;
    float nDesiredFeaturesPerScale = nfeatures * (1 - factor) / (1 - (float)pow((double)factor, (double)nlevels));
 
    int sumFeatures = 0;
    for (uint level = 0; level < nlevels - 1; level++)
    {
        mnFeaturesPerLevel[level] = cvRound(nDesiredFeaturesPerScale);
        sumFeatures += mnFeaturesPerLevel[level];
        nDesiredFeaturesPerScale *= factor;
    }
    mnFeaturesPerLevel[nlevels - 1] = std::max(nfeatures - sumFeatures, 0);
 
    const int      npoints  = 512;
    const CVPoint* pattern0 = (const CVPoint*)bit_pattern_31_;
    std::copy(pattern0, pattern0 + npoints, std::back_inserter(pattern));
 
    // This is for orientation
    //  pre-compute the end of a row in a circular patch
    umax.resize(HALF_PATCH_SIZE + 1);
 
    int          v, v0, vmax = cvFloor(HALF_PATCH_SIZE * sqrt(2.f) / 2 + 1);
    int          vmin = cvCeil(HALF_PATCH_SIZE * sqrt(2.f) / 2);
    const double hp2  = HALF_PATCH_SIZE * HALF_PATCH_SIZE;
 
    for (v = 0; v <= vmax; ++v)
        umax[(uint)v] = cvRound(sqrt(hp2 - v * v));
 
    // Make sure we are symmetric
    for (v = HALF_PATCH_SIZE, v0 = 0; v >= vmin; --v)
    {
        while (umax[(uint)v0] == umax[(uint)v0 + 1])
            ++v0;
        umax[(uint)v] = v0;
        ++v0;
    }
}
//-----------------------------------------------------------------------------
//! Compute the angle for a keypoint and save it.
static void computeOrientation(const CVMat&       image,
                               CVVKeyPoint&       keypoints,
                               const vector<int>& umax)
{
    for (CVVKeyPoint::iterator keypoint    = keypoints.begin(),
                               keypointEnd = keypoints.end();
         keypoint != keypointEnd;
         ++keypoint)
    {
        keypoint->angle = IC_Angle(image, keypoint->pt, umax);
    }
}
//-----------------------------------------------------------------------------
//! Create The tree and distribute it.
CVVKeyPoint CVRaulMurOrb::DistributeOctTree(const CVVKeyPoint& 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 = (int)round(static_cast<float>(maxX - minX) / (maxY - minY));
 
    const float hX = static_cast<float>(maxX - minX) / nIni;
 
    list<CVRaulMurExtNode> lNodes;
 
    vector<CVRaulMurExtNode*> vpIniNodes;
    vpIniNodes.resize((uint)nIni);
 
    for (int i = 0; i < nIni; i++)
    {
        CVRaulMurExtNode ni;
        // upperleft
        ni.UL = CVPoint2i((int)(hX * (float)(i)), 0);
        // upperright
        ni.UR = CVPoint2i((int)(hX * (float)(i + 1)), 0);
        // bottomleft
        ni.BL = CVPoint2i(ni.UL.x, maxY - minY);
        // bottomright
        ni.BR = CVPoint2i(ni.UR.x, maxY - minY);
        ni.vKeys.reserve(vToDistributeKeys.size());
 
        lNodes.push_back(ni);
        vpIniNodes[(uint)i] = &lNodes.back();
    }
 
    // Associate points to childs
    for (size_t i = 0; i < vToDistributeKeys.size(); i++)
    {
        const CVKeyPoint& kp = vToDistributeKeys[i];
        vpIniNodes[(uint)(kp.pt.x / hX)]->vKeys.push_back(kp);
    }
 
    list<CVRaulMurExtNode>::iterator lit = lNodes.begin();
    //! Check if the nodes are empty and erase them if not
    while (lit != lNodes.end())
    {
        if (lit->vKeys.size() == 1)
        {
            lit->bNoMore = true;
            lit++;
        }
        else if (lit->vKeys.empty())
            lit = lNodes.erase(lit);
        else
            lit++;
    }
 
    bool bFinish = false;
 
    int iteration = 0;
 
    vector<pair<int, CVRaulMurExtNode*>> vSizeAndPointerToNode;
    vSizeAndPointerToNode.reserve(lNodes.size() * 4);
 
    while (!bFinish)
    {
        int prevSize = (int)lNodes.size();
 
        lit = lNodes.begin();
 
        int nToExpand = 0;
 
        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
                CVRaulMurExtNode n1, n2, n3, n4;
                lit->DivideNode(n1, n2, n3, n4);
 
                // Add children if they contain points
                if (!n1.vKeys.empty())
                {
                    lNodes.push_front(n1);
                    if (!n1.vKeys.empty())
                    {
                        nToExpand++;
                        vSizeAndPointerToNode.push_back(std::make_pair(n1.vKeys.size(), &lNodes.front()));
                        lNodes.front().lit = lNodes.begin();
                    }
                }
                if (!n2.vKeys.empty())
                {
                    lNodes.push_front(n2);
                    if (n2.vKeys.size() > 1)
                    {
                        nToExpand++;
                        vSizeAndPointerToNode.push_back(std::make_pair(n2.vKeys.size(), &lNodes.front()));
                        lNodes.front().lit = lNodes.begin();
                    }
                }
                if (!n3.vKeys.empty())
                {
                    lNodes.push_front(n3);
                    if (n3.vKeys.size() > 1)
                    {
                        nToExpand++;
                        vSizeAndPointerToNode.push_back(std::make_pair(n3.vKeys.size(), &lNodes.front()));
                        lNodes.front().lit = lNodes.begin();
                    }
                }
                if (!n4.vKeys.empty())
                {
                    lNodes.push_front(n4);
                    if (n4.vKeys.size() > 1)
                    {
                        nToExpand++;
                        vSizeAndPointerToNode.push_back(std::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;
        }
        else if (((int)lNodes.size() + nToExpand * 3) > N)
        {
            while (!bFinish)
            {
                prevSize = (int)lNodes.size();
 
                vector<pair<int, CVRaulMurExtNode*>> vPrevSizeAndPointerToNode = vSizeAndPointerToNode;
                vSizeAndPointerToNode.clear();
 
                sort(vPrevSizeAndPointerToNode.begin(), vPrevSizeAndPointerToNode.end());
                for (int j = (int)vPrevSizeAndPointerToNode.size() - 1; j >= 0; j--)
                {
                    CVRaulMurExtNode n1, n2, n3, n4;
                    vPrevSizeAndPointerToNode[(uint)j].second->DivideNode(n1, n2, n3, n4);
 
                    // Add childs if they contain points
                    if (!n1.vKeys.empty())
                    {
                        lNodes.push_front(n1);
                        if (n1.vKeys.size() > 1)
                        {
                            vSizeAndPointerToNode.push_back(std::make_pair(n1.vKeys.size(), &lNodes.front()));
                            lNodes.front().lit = lNodes.begin();
                        }
                    }
                    if (!n2.vKeys.empty())
                    {
                        lNodes.push_front(n2);
                        if (n2.vKeys.size() > 1)
                        {
                            vSizeAndPointerToNode.push_back(std::make_pair(n2.vKeys.size(), &lNodes.front()));
                            lNodes.front().lit = lNodes.begin();
                        }
                    }
                    if (!n3.vKeys.empty())
                    {
                        lNodes.push_front(n3);
                        if (n3.vKeys.size() > 1)
                        {
                            vSizeAndPointerToNode.push_back(std::make_pair(n3.vKeys.size(), &lNodes.front()));
                            lNodes.front().lit = lNodes.begin();
                        }
                    }
                    if (!n4.vKeys.empty())
                    {
                        lNodes.push_front(n4);
                        if (n4.vKeys.size() > 1)
                        {
                            vSizeAndPointerToNode.push_back(std::make_pair(n4.vKeys.size(), &lNodes.front()));
                            lNodes.front().lit = lNodes.begin();
                        }
                    }
 
                    lNodes.erase(vPrevSizeAndPointerToNode[(uint)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
    CVVKeyPoint vResultKeys;
    vResultKeys.reserve((uint)nfeatures);
    for (list<CVRaulMurExtNode>::iterator lit = lNodes.begin(); lit != lNodes.end(); lit++)
    {
        CVVKeyPoint& vNodeKeys   = lit->vKeys;
        CVKeyPoint*  pKP         = &vNodeKeys[0];
        float        maxResponse = pKP->response;
 
        for (size_t k = 1; k < vNodeKeys.size(); k++)
        {
            if (vNodeKeys[k].response > maxResponse)
            {
                pKP         = &vNodeKeys[k];
                maxResponse = vNodeKeys[k].response;
            }
        }
 
        vResultKeys.push_back(*pKP);
    }
 
    return vResultKeys;
}
//-----------------------------------------------------------------------------
//! Get the Keypoints and distribute them.
void CVRaulMurOrb::ComputeKeyPointsOctTree(CVVVKeyPoint& allKeypoints)
{
    const float W = 30;
 
    for (uint 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;
 
        CVVKeyPoint vToDistributeKeys;
        vToDistributeKeys.reserve((uint)nfeatures * 10);
 
        const float width  = (float)(maxBorderX - minBorderX);
        const float height = (float)(maxBorderY - minBorderY);
 
        // generate the Cells to look for features in
        const int nCols = (int)(width / W);
        const int nRows = (int)(height / W);
        const int wCell = (int)(ceil(width / nCols));
        const int hCell = (int)(ceil(height / nRows));
 
        for (int i = 0; i < nRows; i++)
        {
            const float iniY = (float)(minBorderY + i * hCell);
            float       maxY = iniY + hCell + 6;
 
            if (iniY >= maxBorderY - 3)
                continue;
            if (maxY > maxBorderY)
                maxY = (float)maxBorderY;
 
            for (int j = 0; j < nCols; j++)
            {
                const float iniX = (float)(minBorderX + j * wCell);
                float       maxX = iniX + wCell + 6;
                if (iniX >= maxBorderX - 6)
                    continue;
                if (maxX > maxBorderX)
                    maxX = (float)maxBorderX;
 
                CVVKeyPoint vKeysCell;
 
                // Try to get Keypoints with initial Threshold
 
                FAST(mvImagePyramid[level]
                       .rowRange((int)iniY, (int)maxY)
                       .colRange((int)iniX, (int)maxX),
                     vKeysCell,
                     iniThFAST,
                     true);
 
                // If no Keypoints are found try again with a lower Threshold
                if (vKeysCell.empty())
                {
                    FAST(mvImagePyramid[level]
                           .rowRange((int)iniY, (int)maxY)
                           .colRange((int)iniX, (int)maxX),
                         vKeysCell,
                         minThFAST,
                         true);
                }
 
                if (!vKeysCell.empty())
                {
                    for (CVVKeyPoint::iterator vit = vKeysCell.begin(); vit != vKeysCell.end(); vit++)
                    {
                        (*vit).pt.x += j * wCell;
                        (*vit).pt.y += i * hCell;
                        vToDistributeKeys.push_back(*vit);
                    }
                }
            }
        }
 
        CVVKeyPoint& keypoints = allKeypoints[level];
        keypoints.reserve((uint)nfeatures);
 
        keypoints = DistributeOctTree(vToDistributeKeys,
                                      minBorderX,
                                      maxBorderX,
                                      minBorderY,
                                      maxBorderY,
                                      mnFeaturesPerLevel[level],
                                      (int)level);
 
        const int scaledPatchSize = (int)(PATCH_SIZE * mvScaleFactor[level]);
 
        // Add border to coordinates and scale information
        const int nkps = (int)keypoints.size();
        for (uint i = 0; i < (uint)nkps; i++)
        {
            keypoints[i].pt.x += minBorderX;
            keypoints[i].pt.y += minBorderY;
            keypoints[i].octave = (int)level;
            keypoints[i].size   = (float)scaledPatchSize;
        }
    }
 
    // compute orientations
 
    for (uint level = 0; level < nlevels; ++level)
        computeOrientation(mvImagePyramid[level], allKeypoints[level], umax);
}
 
//-----------------------------------------------------------------------------
 
//! Computes the descriptors for all passed keypoints
static void
computeDescriptors(const CVMat& image,
                   CVVKeyPoint& keypoints,
                   CVMat&       descriptors,
                   CVVPoint&    pattern)
{
    descriptors = CVMat::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));
}
 
//-----------------------------------------------------------------------------
/*! Main detection function. Can be seperated if predefined keypoints are given
or no descriptor array is given.
*/
void CVRaulMurOrb::detectAndCompute(CVInputArray  _image,
                                    CVInputArray  _mask,
                                    CVVKeyPoint&  _keypoints,
                                    CVOutputArray _descriptors,
                                    bool          useProvidedKeypoints)
{
    if (_image.empty())
        return;
 
    CVMat image = _image.getMat();
    assert(image.type() == CV_8UC1);
 
    // Pre-compute the scale pyramid
    ComputePyramid(image);
    CVMat        descriptors;
    CVVVKeyPoint allKeypoints;
    allKeypoints.resize(nlevels);
    int nkeypoints = 0;
    if (!useProvidedKeypoints)
    {
        ComputeKeyPointsOctTree(allKeypoints);
        // ComputeKeyPointsOld(allKeypoints);
        for (uint level = 0; level < nlevels; ++level)
            nkeypoints += (int)allKeypoints[level].size();
        _keypoints.clear();
        _keypoints.reserve((uint)nkeypoints);
    }
    else
    {
        //! Remove Points from image border. Ensures that ORB_SLAM and ORB
        //! generate the same descriptors from the same keypoints.
        KeyPointsFilter::runByImageBorder(_keypoints, _image.size(), 31);
        nkeypoints     = (int)_keypoints.size();
        int last_level = 0;
        for (uint index = 0; index < _keypoints.size(); index++)
        {
            if (_keypoints[index].octave > last_level)
                last_level = _keypoints[index].octave;
 
            _keypoints[index].pt /= mvScaleFactor[(uint)_keypoints[index].octave];
            allKeypoints[(uint)_keypoints[index].octave].push_back(_keypoints[index]);
        }
    }
    if (nkeypoints == 0)
        _descriptors.release();
    else if (_descriptors.needed())
    {
        _descriptors.create(nkeypoints, 32, CV_8U);
        descriptors = _descriptors.getMat();
    }
 
    int offset = 0;
    for (uint level = 0; level < nlevels; ++level)
    {
        CVVKeyPoint& keypoints       = allKeypoints[level];
        int          nkeypointsLevel = (int)keypoints.size();
 
        if (nkeypointsLevel == 0)
            continue;
        if (_descriptors.needed())
        {
            // preprocess the resized image
            CVMat workingMat = mvImagePyramid[level].clone();
            GaussianBlur(workingMat, workingMat, cv::Size(7, 7), 2, 2, BORDER_REFLECT_101);
 
            // Compute the descriptors
            CVMat 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 (CVVKeyPoint::iterator keypoint    = keypoints.begin(),
                                       keypointEnd = keypoints.end();
                 keypoint != keypointEnd;
                 ++keypoint)
                keypoint->pt *= scale;
        }
        // And add the keypoints to the output
        if (!useProvidedKeypoints)
        {
            _keypoints.insert(_keypoints.end(), keypoints.begin(), keypoints.end());
        }
    }
}
//-----------------------------------------------------------------------------
void CVRaulMurOrb::ComputePyramid(CVMat image)
{
    for (uint level = 0; level < (uint)nlevels; ++level)
    {
        float  scale = mvInvScaleFactor[level];
        CVSize sz(cvRound((float)image.cols * scale),
                  cvRound((float)image.rows * scale));
        CVSize wholeSize(sz.width + EDGE_THRESHOLD * 2,
                         sz.height + EDGE_THRESHOLD * 2);
        CVMat  temp(wholeSize, image.type()), masktemp;
 
        mvImagePyramid[level] = temp(CVRect(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);
        }
    }
 
    // save image pyriamid
    /*for (int level = 0; level < nlevels; ++level) {
        string filename = "D:/Development/SLProject/debug_ouput/imagePyriamid" + to_string(level) + ".jpg";
        cv::imwrite(filename, mvImagePyramid[level]);
    }*/
}
//-----------------------------------------------------------------------------