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/** Authors: Ethan Rublee, Vincent Rabaud, Gary Bradski */

#include "precomp.hpp"
#include "opencl_kernels_features2d.hpp"
#include <iterator>

#ifndef CV_IMPL_ADD
#define CV_IMPL_ADD(x)
#endif

////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////

namespace cv
{

const float HARRIS_K = 0.04f;

template<typename _Tp> inline void copyVectorToUMat(const std::vector<_Tp>& v, OutputArray um)
{
    if(v.empty())
        um.release();
    else
        Mat(1, (int)(v.size()*sizeof(v[0])), CV_8U, (void*)&v[0]).copyTo(um);
}

#ifdef HAVE_OPENCL
static bool
ocl_HarrisResponses(const UMat& imgbuf,
                    const UMat& layerinfo,
                    const UMat& keypoints,
                    UMat& responses,
                    int nkeypoints, int blockSize, float harris_k)
{
    size_t globalSize[] = {(size_t)nkeypoints};

    float scale = 1.f/((1 << 2) * blockSize * 255.f);
    float scale_sq_sq = scale * scale * scale * scale;

    ocl::Kernel hr_ker("ORB_HarrisResponses", ocl::features2d::orb_oclsrc,
                format("-D ORB_RESPONSES -D blockSize=%d -D scale_sq_sq=%.12ef -D HARRIS_K=%.12ff", blockSize, scale_sq_sq, harris_k));
    if( hr_ker.empty() )
        return false;

    return hr_ker.args(ocl::KernelArg::ReadOnlyNoSize(imgbuf),
                ocl::KernelArg::PtrReadOnly(layerinfo),
                ocl::KernelArg::PtrReadOnly(keypoints),
                ocl::KernelArg::PtrWriteOnly(responses),
                nkeypoints).run(1, globalSize, 0, true);
}

static bool
ocl_ICAngles(const UMat& imgbuf, const UMat& layerinfo,
             const UMat& keypoints, UMat& responses,
             const UMat& umax, int nkeypoints, int half_k)
{
    size_t globalSize[] = {(size_t)nkeypoints};

    ocl::Kernel icangle_ker("ORB_ICAngle", ocl::features2d::orb_oclsrc, "-D ORB_ANGLES");
    if( icangle_ker.empty() )
        return false;

    return icangle_ker.args(ocl::KernelArg::ReadOnlyNoSize(imgbuf),
                ocl::KernelArg::PtrReadOnly(layerinfo),
                ocl::KernelArg::PtrReadOnly(keypoints),
                ocl::KernelArg::PtrWriteOnly(responses),
                ocl::KernelArg::PtrReadOnly(umax),
                nkeypoints, half_k).run(1, globalSize, 0, true);
}


static bool
ocl_computeOrbDescriptors(const UMat& imgbuf, const UMat& layerInfo,
                          const UMat& keypoints, UMat& desc, const UMat& pattern,
                          int nkeypoints, int dsize, int wta_k)
{
    size_t globalSize[] = {(size_t)nkeypoints};

    ocl::Kernel desc_ker("ORB_computeDescriptor", ocl::features2d::orb_oclsrc,
                         format("-D ORB_DESCRIPTORS -D WTA_K=%d", wta_k));
    if( desc_ker.empty() )
        return false;

    return desc_ker.args(ocl::KernelArg::ReadOnlyNoSize(imgbuf),
                         ocl::KernelArg::PtrReadOnly(layerInfo),
                         ocl::KernelArg::PtrReadOnly(keypoints),
                         ocl::KernelArg::PtrWriteOnly(desc),
                         ocl::KernelArg::PtrReadOnly(pattern),
                         nkeypoints, dsize).run(1, globalSize, 0, true);
}
#endif

/**
 * Function that computes the Harris responses in a
 * blockSize x blockSize patch at given points in the image
 */
static void
HarrisResponses(const Mat& img, const std::vector<Rect>& layerinfo,
                std::vector<KeyPoint>& pts, int blockSize, float harris_k)
{
    CV_Assert( img.type() == CV_8UC1 && blockSize*blockSize <= 2048 );

    size_t ptidx, ptsize = pts.size();

    const uchar* ptr00 = img.ptr<uchar>();
    int step = (int)(img.step/img.elemSize1());
    int r = blockSize/2;

    float scale = 1.f/((1 << 2) * blockSize * 255.f);
    float scale_sq_sq = scale * scale * scale * scale;

    AutoBuffer<int> ofsbuf(blockSize*blockSize);
    int* ofs = ofsbuf.data();
    for( int i = 0; i < blockSize; i++ )
        for( int j = 0; j < blockSize; j++ )
            ofs[i*blockSize + j] = (int)(i*step + j);

    for( ptidx = 0; ptidx < ptsize; ptidx++ )
    {
        int x0 = cvRound(pts[ptidx].pt.x);
        int y0 = cvRound(pts[ptidx].pt.y);
        int z = pts[ptidx].octave;

        const uchar* ptr0 = ptr00 + (y0 - r + layerinfo[z].y)*step + x0 - r + layerinfo[z].x;
        int a = 0, b = 0, c = 0;

        for( int k = 0; k < blockSize*blockSize; k++ )
        {
            const uchar* ptr = ptr0 + ofs[k];
            int Ix = (ptr[1] - ptr[-1])*2 + (ptr[-step+1] - ptr[-step-1]) + (ptr[step+1] - ptr[step-1]);
            int Iy = (ptr[step] - ptr[-step])*2 + (ptr[step-1] - ptr[-step-1]) + (ptr[step+1] - ptr[-step+1]);
            a += Ix*Ix;
            b += Iy*Iy;
            c += Ix*Iy;
        }
        pts[ptidx].response = ((float)a * b - (float)c * c -
                               harris_k * ((float)a + b) * ((float)a + b))*scale_sq_sq;
    }
}

////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////

static void ICAngles(const Mat& img, const std::vector<Rect>& layerinfo,
                     std::vector<KeyPoint>& pts, const std::vector<int> & u_max, int half_k)
{
    int step = (int)img.step1();
    size_t ptidx, ptsize = pts.size();

    for( ptidx = 0; ptidx < ptsize; ptidx++ )
    {
        const Rect& layer = layerinfo[pts[ptidx].octave];
        const uchar* center = &img.at<uchar>(cvRound(pts[ptidx].pt.y) + layer.y, cvRound(pts[ptidx].pt.x) + layer.x);

        int m_01 = 0, m_10 = 0;

        // Treat the center line differently, v=0
        for (int u = -half_k; u <= half_k; ++u)
            m_10 += u * center[u];

        // Go line by line in the circular patch
        for (int v = 1; v <= half_k; ++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;
        }

        pts[ptidx].angle = fastAtan2((float)m_01, (float)m_10);
    }
}

////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////

static void
computeOrbDescriptors( const Mat& imagePyramid, const std::vector<Rect>& layerInfo,
                       const std::vector<float>& layerScale, std::vector<KeyPoint>& keypoints,
                       Mat& descriptors, const std::vector<Point>& _pattern, int dsize, int wta_k )
{
    int step = (int)imagePyramid.step;
    int j, i, nkeypoints = (int)keypoints.size();

    for( j = 0; j < nkeypoints; j++ )
    {
        const KeyPoint& kpt = keypoints[j];
        const Rect& layer = layerInfo[kpt.octave];
        float scale = 1.f/layerScale[kpt.octave];
        float angle = kpt.angle;

        angle *= (float)(CV_PI/180.f);
        float a = (float)cos(angle), b = (float)sin(angle);

        const uchar* center = &imagePyramid.at<uchar>(cvRound(kpt.pt.y*scale) + layer.y,
                                                      cvRound(kpt.pt.x*scale) + layer.x);
        float x, y;
        int ix, iy;
        const Point* pattern = &_pattern[0];
        uchar* desc = descriptors.ptr<uchar>(j);

    #if 1
        #define GET_VALUE(idx) \
               (x = pattern[idx].x*a - pattern[idx].y*b, \
                y = pattern[idx].x*b + pattern[idx].y*a, \
                ix = cvRound(x), \
                iy = cvRound(y), \
                *(center + iy*step + ix) )
    #else
        #define GET_VALUE(idx) \
            (x = pattern[idx].x*a - pattern[idx].y*b, \
            y = pattern[idx].x*b + pattern[idx].y*a, \
            ix = cvFloor(x), iy = cvFloor(y), \
            x -= ix, y -= iy, \
            cvRound(center[iy*step + ix]*(1-x)*(1-y) + center[(iy+1)*step + ix]*(1-x)*y + \
                    center[iy*step + ix+1]*x*(1-y) + center[(iy+1)*step + ix+1]*x*y))
    #endif

        if( wta_k == 2 )
        {
            for (i = 0; i < dsize; ++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;
            }
        }
        else if( wta_k == 3 )
        {
            for (i = 0; i < dsize; ++i, pattern += 12)
            {
                int t0, t1, t2, val;
                t0 = GET_VALUE(0); t1 = GET_VALUE(1); t2 = GET_VALUE(2);
                val = t2 > t1 ? (t2 > t0 ? 2 : 0) : (t1 > t0);

                t0 = GET_VALUE(3); t1 = GET_VALUE(4); t2 = GET_VALUE(5);
                val |= (t2 > t1 ? (t2 > t0 ? 2 : 0) : (t1 > t0)) << 2;

                t0 = GET_VALUE(6); t1 = GET_VALUE(7); t2 = GET_VALUE(8);
                val |= (t2 > t1 ? (t2 > t0 ? 2 : 0) : (t1 > t0)) << 4;

                t0 = GET_VALUE(9); t1 = GET_VALUE(10); t2 = GET_VALUE(11);
                val |= (t2 > t1 ? (t2 > t0 ? 2 : 0) : (t1 > t0)) << 6;

                desc[i] = (uchar)val;
            }
        }
        else if( wta_k == 4 )
        {
            for (i = 0; i < dsize; ++i, pattern += 16)
            {
                int t0, t1, t2, t3, u, v, k, val;
                t0 = GET_VALUE(0); t1 = GET_VALUE(1);
                t2 = GET_VALUE(2); t3 = GET_VALUE(3);
                u = 0, v = 2;
                if( t1 > t0 ) t0 = t1, u = 1;
                if( t3 > t2 ) t2 = t3, v = 3;
                k = t0 > t2 ? u : v;
                val = k;

                t0 = GET_VALUE(4); t1 = GET_VALUE(5);
                t2 = GET_VALUE(6); t3 = GET_VALUE(7);
                u = 0, v = 2;
                if( t1 > t0 ) t0 = t1, u = 1;
                if( t3 > t2 ) t2 = t3, v = 3;
                k = t0 > t2 ? u : v;
                val |= k << 2;

                t0 = GET_VALUE(8); t1 = GET_VALUE(9);
                t2 = GET_VALUE(10); t3 = GET_VALUE(11);
                u = 0, v = 2;
                if( t1 > t0 ) t0 = t1, u = 1;
                if( t3 > t2 ) t2 = t3, v = 3;
                k = t0 > t2 ? u : v;
                val |= k << 4;

                t0 = GET_VALUE(12); t1 = GET_VALUE(13);
                t2 = GET_VALUE(14); t3 = GET_VALUE(15);
                u = 0, v = 2;
                if( t1 > t0 ) t0 = t1, u = 1;
                if( t3 > t2 ) t2 = t3, v = 3;
                k = t0 > t2 ? u : v;
                val |= k << 6;

                desc[i] = (uchar)val;
            }
        }
        else
            CV_Error( Error::StsBadSize, "Wrong wta_k. It can be only 2, 3 or 4." );
        #undef GET_VALUE
    }
}


static void initializeOrbPattern( const Point* pattern0, std::vector<Point>& pattern, int ntuples, int tupleSize, int poolSize )
{
    RNG rng(0x12345678);
    int i, k, k1;
    pattern.resize(ntuples*tupleSize);

    for( i = 0; i < ntuples; i++ )
    {
        for( k = 0; k < tupleSize; k++ )
        {
            for(;;)
            {
                int idx = rng.uniform(0, poolSize);
                Point pt = pattern0[idx];
                for( k1 = 0; k1 < k; k1++ )
                    if( pattern[tupleSize*i + k1] == pt )
                        break;
                if( k1 == k )
                {
                    pattern[tupleSize*i + k] = pt;
                    break;
                }
            }
        }
    }
}

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)*/
};


static void makeRandomPattern(int patchSize, Point* pattern, int npoints)
{
    RNG rng(0x34985739); // we always start with a fixed seed,
                         // to make patterns the same on each run
    for( int i = 0; i < npoints; i++ )
    {
        pattern[i].x = rng.uniform(-patchSize/2, patchSize/2+1);
        pattern[i].y = rng.uniform(-patchSize/2, patchSize/2+1);
    }
}


static inline float getScale(int level, int firstLevel, double scaleFactor)
{
    return (float)std::pow(scaleFactor, (double)(level - firstLevel));
}


class ORB_Impl CV_FINAL : public ORB
{
public:
    explicit ORB_Impl(int _nfeatures, float _scaleFactor, int _nlevels, int _edgeThreshold,
             int _firstLevel, int _WTA_K, int _scoreType, int _patchSize, int _fastThreshold) :
        nfeatures(_nfeatures), scaleFactor(_scaleFactor), nlevels(_nlevels),
        edgeThreshold(_edgeThreshold), firstLevel(_firstLevel), wta_k(_WTA_K),
        scoreType(_scoreType), patchSize(_patchSize), fastThreshold(_fastThreshold)
    {}

    void setMaxFeatures(int maxFeatures) CV_OVERRIDE { nfeatures = maxFeatures; }
    int getMaxFeatures() const CV_OVERRIDE { return nfeatures; }

    void setScaleFactor(double scaleFactor_) CV_OVERRIDE { scaleFactor = scaleFactor_; }
    double getScaleFactor() const CV_OVERRIDE { return scaleFactor; }

    void setNLevels(int nlevels_) CV_OVERRIDE { nlevels = nlevels_; }
    int getNLevels() const CV_OVERRIDE { return nlevels; }

    void setEdgeThreshold(int edgeThreshold_) CV_OVERRIDE { edgeThreshold = edgeThreshold_; }
    int getEdgeThreshold() const CV_OVERRIDE { return edgeThreshold; }

    void setFirstLevel(int firstLevel_) CV_OVERRIDE { CV_Assert(firstLevel_ >= 0);  firstLevel = firstLevel_; }
    int getFirstLevel() const CV_OVERRIDE { return firstLevel; }

    void setWTA_K(int wta_k_) CV_OVERRIDE { wta_k = wta_k_; }
    int getWTA_K() const CV_OVERRIDE { return wta_k; }

    void setScoreType(int scoreType_) CV_OVERRIDE { scoreType = scoreType_; }
    int getScoreType() const CV_OVERRIDE { return scoreType; }

    void setPatchSize(int patchSize_) CV_OVERRIDE { patchSize = patchSize_; }
    int getPatchSize() const CV_OVERRIDE { return patchSize; }

    void setFastThreshold(int fastThreshold_) CV_OVERRIDE { fastThreshold = fastThreshold_; }
    int getFastThreshold() const CV_OVERRIDE { return fastThreshold; }

    // returns the descriptor size in bytes
    int descriptorSize() const CV_OVERRIDE;
    // returns the descriptor type
    int descriptorType() const CV_OVERRIDE;
    // returns the default norm type
    int defaultNorm() const CV_OVERRIDE;

    // Compute the ORB_Impl features and descriptors on an image
    void detectAndCompute( InputArray image, InputArray mask, std::vector<KeyPoint>& keypoints,
                     OutputArray descriptors, bool useProvidedKeypoints=false ) CV_OVERRIDE;

protected:

    int nfeatures;
    double scaleFactor;
    int nlevels;
    int edgeThreshold;
    int firstLevel;
    int wta_k;
    int scoreType;
    int patchSize;
    int fastThreshold;
};

int ORB_Impl::descriptorSize() const
{
    return kBytes;
}

int ORB_Impl::descriptorType() const
{
    return CV_8U;
}

int ORB_Impl::defaultNorm() const
{
    switch (wta_k)
    {
    case 2:
        return NORM_HAMMING;
    case 3:
    case 4:
        return NORM_HAMMING2;
    default:
        return -1;
    }
}

#ifdef HAVE_OPENCL
static void uploadORBKeypoints(const std::vector<KeyPoint>& src, std::vector<Vec3i>& buf, OutputArray dst)
{
    size_t i, n = src.size();
    buf.resize(std::max(buf.size(), n));
    for( i = 0; i < n; i++ )
        buf[i] = Vec3i(cvRound(src[i].pt.x), cvRound(src[i].pt.y), src[i].octave);
    copyVectorToUMat(buf, dst);
}

typedef union if32_t
{
    int i;
    float f;
}
if32_t;

static void uploadORBKeypoints(const std::vector<KeyPoint>& src,
                               const std::vector<float>& layerScale,
                               std::vector<Vec4i>& buf, OutputArray dst)
{
    size_t i, n = src.size();
    buf.resize(std::max(buf.size(), n));
    for( i = 0; i < n; i++ )
    {
        int z = src[i].octave;
        float scale = 1.f/layerScale[z];
        if32_t angle;
        angle.f = src[i].angle;
        buf[i] = Vec4i(cvRound(src[i].pt.x*scale), cvRound(src[i].pt.y*scale), z, angle.i);
    }
    copyVectorToUMat(buf, dst);
}
#endif

/** Compute the ORB_Impl keypoints on an image
 * @param image_pyramid the image pyramid to compute the features and descriptors on
 * @param mask_pyramid the masks to apply at every level
 * @param keypoints the resulting keypoints, clustered per level
 */
static void computeKeyPoints(const Mat& imagePyramid,
                             const UMat& uimagePyramid,
                             const Mat& maskPyramid,
                             const std::vector<Rect>& layerInfo,
                             const UMat& ulayerInfo,
                             const std::vector<float>& layerScale,
                             std::vector<KeyPoint>& allKeypoints,
                             int nfeatures, double scaleFactor,
                             int edgeThreshold, int patchSize, int scoreType,
                             bool useOCL, int fastThreshold  )
{
#ifndef HAVE_OPENCL
    CV_UNUSED(uimagePyramid);CV_UNUSED(ulayerInfo);CV_UNUSED(useOCL);
#endif

    int i, nkeypoints, level, nlevels = (int)layerInfo.size();
    std::vector<int> nfeaturesPerLevel(nlevels);

    // fill the extractors and descriptors for the corresponding scales
    float factor = (float)(1.0 / scaleFactor);
    float ndesiredFeaturesPerScale = nfeatures*(1 - factor)/(1 - (float)std::pow((double)factor, (double)nlevels));

    int sumFeatures = 0;
    for( level = 0; level < nlevels-1; level++ )
    {
        nfeaturesPerLevel[level] = cvRound(ndesiredFeaturesPerScale);
        sumFeatures += nfeaturesPerLevel[level];
        ndesiredFeaturesPerScale *= factor;
    }
    nfeaturesPerLevel[nlevels-1] = std::max(nfeatures - sumFeatures, 0);

    // Make sure we forget about what is too close to the boundary
    //edge_threshold_ = std::max(edge_threshold_, patch_size_/2 + kKernelWidth / 2 + 2);

    // pre-compute the end of a row in a circular patch
    int halfPatchSize = patchSize / 2;
    std::vector<int> umax(halfPatchSize + 2);

    int v, v0, vmax = cvFloor(halfPatchSize * std::sqrt(2.f) / 2 + 1);
    int vmin = cvCeil(halfPatchSize * std::sqrt(2.f) / 2);
    for (v = 0; v <= vmax; ++v)
        umax[v] = cvRound(std::sqrt((double)halfPatchSize * halfPatchSize - v * v));

    // Make sure we are symmetric
    for (v = halfPatchSize, v0 = 0; v >= vmin; --v)
    {
        while (umax[v0] == umax[v0 + 1])
            ++v0;
        umax[v] = v0;
        ++v0;
    }

    allKeypoints.clear();
    std::vector<KeyPoint> keypoints;
    std::vector<int> counters(nlevels);
    keypoints.reserve(nfeaturesPerLevel[0]*2);

    for( level = 0; level < nlevels; level++ )
    {
        int featuresNum = nfeaturesPerLevel[level];
        Mat img = imagePyramid(layerInfo[level]);
        Mat mask = maskPyramid.empty() ? Mat() : maskPyramid(layerInfo[level]);

        // Detect FAST features, 20 is a good threshold
        {
        Ptr<FastFeatureDetector> fd = FastFeatureDetector::create(fastThreshold, true);
        fd->detect(img, keypoints, mask);
        }

        // Remove keypoints very close to the border
        KeyPointsFilter::runByImageBorder(keypoints, img.size(), edgeThreshold);

        // Keep more points than necessary as FAST does not give amazing corners
        KeyPointsFilter::retainBest(keypoints, scoreType == ORB_Impl::HARRIS_SCORE ? 2 * featuresNum : featuresNum);

        nkeypoints = (int)keypoints.size();
        counters[level] = nkeypoints;

        float sf = layerScale[level];
        for( i = 0; i < nkeypoints; i++ )
        {
            keypoints[i].octave = level;
            keypoints[i].size = patchSize*sf;
        }

        std::copy(keypoints.begin(), keypoints.end(), std::back_inserter(allKeypoints));
    }

    std::vector<Vec3i> ukeypoints_buf;

    nkeypoints = (int)allKeypoints.size();
    if(nkeypoints == 0)
    {
        return;
    }
    Mat responses;
    UMat ukeypoints, uresponses(1, nkeypoints, CV_32F);

    // Select best features using the Harris cornerness (better scoring than FAST)
    if( scoreType == ORB_Impl::HARRIS_SCORE )
    {
#ifdef HAVE_OPENCL
        if( useOCL )
        {
            uploadORBKeypoints(allKeypoints, ukeypoints_buf, ukeypoints);
            useOCL = ocl_HarrisResponses( uimagePyramid, ulayerInfo, ukeypoints,
                                          uresponses, nkeypoints, 7, HARRIS_K );
            if( useOCL )
            {
                CV_IMPL_ADD(CV_IMPL_OCL);
                uresponses.copyTo(responses);
                for( i = 0; i < nkeypoints; i++ )
                    allKeypoints[i].response = responses.at<float>(i);
            }
        }

        if( !useOCL )
#endif
            HarrisResponses(imagePyramid, layerInfo, allKeypoints, 7, HARRIS_K);

        std::vector<KeyPoint> newAllKeypoints;
        newAllKeypoints.reserve(nfeaturesPerLevel[0]*nlevels);

        int offset = 0;
        for( level = 0; level < nlevels; level++ )
        {
            int featuresNum = nfeaturesPerLevel[level];
            nkeypoints = counters[level];
            keypoints.resize(nkeypoints);
            std::copy(allKeypoints.begin() + offset,
                      allKeypoints.begin() + offset + nkeypoints,
                      keypoints.begin());
            offset += nkeypoints;

            //cull to the final desired level, using the new Harris scores.
            KeyPointsFilter::retainBest(keypoints, featuresNum);

            std::copy(keypoints.begin(), keypoints.end(), std::back_inserter(newAllKeypoints));
        }
        std::swap(allKeypoints, newAllKeypoints);
    }

    nkeypoints = (int)allKeypoints.size();

#ifdef HAVE_OPENCL
    if( useOCL )
    {
        UMat uumax;
        if( useOCL )
            copyVectorToUMat(umax, uumax);

        uploadORBKeypoints(allKeypoints, ukeypoints_buf, ukeypoints);
        useOCL = ocl_ICAngles(uimagePyramid, ulayerInfo, ukeypoints, uresponses, uumax,
                              nkeypoints, halfPatchSize);

        if( useOCL )
        {
            CV_IMPL_ADD(CV_IMPL_OCL);
            uresponses.copyTo(responses);
            for( i = 0; i < nkeypoints; i++ )
                allKeypoints[i].angle = responses.at<float>(i);
        }
    }

    if( !useOCL )
#endif
    {
        ICAngles(imagePyramid, layerInfo, allKeypoints, umax, halfPatchSize);
    }

    for( i = 0; i < nkeypoints; i++ )
    {
        float scale = layerScale[allKeypoints[i].octave];
        allKeypoints[i].pt *= scale;
    }
}


/** Compute the ORB_Impl features and descriptors on an image
 * @param img the image to compute the features and descriptors on
 * @param mask the mask to apply
 * @param keypoints the resulting keypoints
 * @param descriptors the resulting descriptors
 * @param do_keypoints if true, the keypoints are computed, otherwise used as an input
 * @param do_descriptors if true, also computes the descriptors
 */
void ORB_Impl::detectAndCompute( InputArray _image, InputArray _mask,
                                 std::vector<KeyPoint>& keypoints,
                                 OutputArray _descriptors, bool useProvidedKeypoints )
{
    CV_INSTRUMENT_REGION();

    CV_Assert(patchSize >= 2);

    bool do_keypoints = !useProvidedKeypoints;
    bool do_descriptors = _descriptors.needed();

    if( (!do_keypoints && !do_descriptors) || _image.empty() )
        return;

    //ROI handling
    const int HARRIS_BLOCK_SIZE = 9;
    int halfPatchSize = patchSize / 2;
    // sqrt(2.0) is for handling patch rotation
    int descPatchSize = cvCeil(halfPatchSize*sqrt(2.0));
    int border = std::max(edgeThreshold, std::max(descPatchSize, HARRIS_BLOCK_SIZE/2))+1;

#ifdef HAVE_OPENCL
    bool useOCL = ocl::isOpenCLActivated() && OCL_FORCE_CHECK(_image.isUMat() || _descriptors.isUMat());
#else
    bool useOCL = false;
#endif

    Mat image = _image.getMat(), mask = _mask.getMat();
    if( image.type() != CV_8UC1 )
        cvtColor(_image, image, COLOR_BGR2GRAY);

    int i, level, nLevels = this->nlevels, nkeypoints = (int)keypoints.size();
    bool sortedByLevel = true;

    if( !do_keypoints )
    {
        // if we have pre-computed keypoints, they may use more levels than it is set in parameters
        // !!!TODO!!! implement more correct method, independent from the used keypoint detector.
        // Namely, the detector should provide correct size of each keypoint. Based on the keypoint size
        // and the algorithm used (i.e. BRIEF, running on 31x31 patches) we should compute the approximate
        // scale-factor that we need to apply. Then we should cluster all the computed scale-factors and
        // for each cluster compute the corresponding image.
        //
        // In short, ultimately the descriptor should
        // ignore octave parameter and deal only with the keypoint size.
        nLevels = 0;
        for( i = 0; i < nkeypoints; i++ )
        {
            level = keypoints[i].octave;
            CV_Assert(level >= 0);
            if( i > 0 && level < keypoints[i-1].octave )
                sortedByLevel = false;
            nLevels = std::max(nLevels, level);
        }
        nLevels++;
    }

    std::vector<Rect> layerInfo(nLevels);
    std::vector<int> layerOfs(nLevels);
    std::vector<float> layerScale(nLevels);
    Mat imagePyramid, maskPyramid;
    UMat uimagePyramid, ulayerInfo;

    int level_dy = image.rows + border*2;
    Point level_ofs(0,0);
    Size bufSize((cvRound(image.cols/getScale(0, firstLevel, scaleFactor)) + border*2 + 15) & -16, 0);

    for( level = 0; level < nLevels; level++ )
    {
        float scale = getScale(level, firstLevel, scaleFactor);
        layerScale[level] = scale;
        Size sz(cvRound(image.cols/scale), cvRound(image.rows/scale));
        Size wholeSize(sz.width + border*2, sz.height + border*2);
        if( level_ofs.x + wholeSize.width > bufSize.width )
        {
            level_ofs = Point(0, level_ofs.y + level_dy);
            level_dy = wholeSize.height;
        }

        Rect linfo(level_ofs.x + border, level_ofs.y + border, sz.width, sz.height);
        layerInfo[level] = linfo;
        layerOfs[level] = linfo.y*bufSize.width + linfo.x;
        level_ofs.x += wholeSize.width;
    }
    bufSize.height = level_ofs.y + level_dy;

    imagePyramid.create(bufSize, CV_8U);
    if( !mask.empty() )
        maskPyramid.create(bufSize, CV_8U);

    Mat prevImg = image, prevMask = mask;

    // Pre-compute the scale pyramids
    for (level = 0; level < nLevels; ++level)
    {
        Rect linfo = layerInfo[level];
        Size sz(linfo.width, linfo.height);
        Size wholeSize(sz.width + border*2, sz.height + border*2);
        Rect wholeLinfo = Rect(linfo.x - border, linfo.y - border, wholeSize.width, wholeSize.height);
        Mat extImg = imagePyramid(wholeLinfo), extMask;
        Mat currImg = extImg(Rect(border, border, sz.width, sz.height)), currMask;

        if( !mask.empty() )
        {
            extMask = maskPyramid(wholeLinfo);
            currMask = extMask(Rect(border, border, sz.width, sz.height));
        }

        // Compute the resized image
        if( level != firstLevel )
        {
            resize(prevImg, currImg, sz, 0, 0, INTER_LINEAR_EXACT);
            if( !mask.empty() )
            {
                resize(prevMask, currMask, sz, 0, 0, INTER_LINEAR_EXACT);
                if( level > firstLevel )
                    threshold(currMask, currMask, 254, 0, THRESH_TOZERO);
            }

            copyMakeBorder(currImg, extImg, border, border, border, border,
                           BORDER_REFLECT_101+BORDER_ISOLATED);
            if (!mask.empty())
                copyMakeBorder(currMask, extMask, border, border, border, border,
                               BORDER_CONSTANT+BORDER_ISOLATED);
        }
        else
        {
            copyMakeBorder(image, extImg, border, border, border, border,
                           BORDER_REFLECT_101);
            if( !mask.empty() )
                copyMakeBorder(mask, extMask, border, border, border, border,
                               BORDER_CONSTANT+BORDER_ISOLATED);
        }
        if (level > firstLevel)
        {
            prevImg = currImg;
            prevMask = currMask;
        }
    }

    if( useOCL )
        copyVectorToUMat(layerOfs, ulayerInfo);

    if( do_keypoints )
    {
        if( useOCL )
            imagePyramid.copyTo(uimagePyramid);

        // Get keypoints, those will be far enough from the border that no check will be required for the descriptor
        computeKeyPoints(imagePyramid, uimagePyramid, maskPyramid,
                         layerInfo, ulayerInfo, layerScale, keypoints,
                         nfeatures, scaleFactor, edgeThreshold, patchSize, scoreType, useOCL, fastThreshold);
    }
    else
    {
        KeyPointsFilter::runByImageBorder(keypoints, image.size(), edgeThreshold);

        if( !sortedByLevel )
        {
            std::vector<std::vector<KeyPoint> > allKeypoints(nLevels);
            nkeypoints = (int)keypoints.size();
            for( i = 0; i < nkeypoints; i++ )
            {
                level = keypoints[i].octave;
                CV_Assert(0 <= level);
                allKeypoints[level].push_back(keypoints[i]);
            }
            keypoints.clear();
            for( level = 0; level < nLevels; level++ )
                std::copy(allKeypoints[level].begin(), allKeypoints[level].end(), std::back_inserter(keypoints));
        }
    }

    if( do_descriptors )
    {
        int dsize = descriptorSize();

        nkeypoints = (int)keypoints.size();
        if( nkeypoints == 0 )
        {
            _descriptors.release();
            return;
        }

        _descriptors.create(nkeypoints, dsize, CV_8U);
        std::vector<Point> pattern;

        const int npoints = 512;
        Point patternbuf[npoints];
        const Point* pattern0 = (const Point*)bit_pattern_31_;

        if( patchSize != 31 )
        {
            pattern0 = patternbuf;
            makeRandomPattern(patchSize, patternbuf, npoints);
        }

        CV_Assert( wta_k == 2 || wta_k == 3 || wta_k == 4 );

        if( wta_k == 2 )
            std::copy(pattern0, pattern0 + npoints, std::back_inserter(pattern));
        else
        {
            int ntuples = descriptorSize()*4;
            initializeOrbPattern(pattern0, pattern, ntuples, wta_k, npoints);
        }

        for( level = 0; level < nLevels; level++ )
        {
            // preprocess the resized image
            Mat workingMat = imagePyramid(layerInfo[level]);

            //boxFilter(working_mat, working_mat, working_mat.depth(), Size(5,5), Point(-1,-1), true, BORDER_REFLECT_101);
            GaussianBlur(workingMat, workingMat, Size(7, 7), 2, 2, BORDER_REFLECT_101);
        }

#ifdef HAVE_OPENCL
        if( useOCL )
        {
            imagePyramid.copyTo(uimagePyramid);
            std::vector<Vec4i> kptbuf;
            UMat ukeypoints, upattern;
            copyVectorToUMat(pattern, upattern);
            uploadORBKeypoints(keypoints, layerScale, kptbuf, ukeypoints);

            UMat udescriptors = _descriptors.getUMat();
            useOCL = ocl_computeOrbDescriptors(uimagePyramid, ulayerInfo,
                                               ukeypoints, udescriptors, upattern,
                                               nkeypoints, dsize, wta_k);
            if(useOCL)
            {
                CV_IMPL_ADD(CV_IMPL_OCL);
            }
        }

        if( !useOCL )
#endif
        {
            Mat descriptors = _descriptors.getMat();
            computeOrbDescriptors(imagePyramid, layerInfo, layerScale,
                                  keypoints, descriptors, pattern, dsize, wta_k);
        }
    }
}

Ptr<ORB> ORB::create(int nfeatures, float scaleFactor, int nlevels, int edgeThreshold,
           int firstLevel, int wta_k, int scoreType, int patchSize, int fastThreshold)
{
    CV_Assert(firstLevel >= 0);
    return makePtr<ORB_Impl>(nfeatures, scaleFactor, nlevels, edgeThreshold,
                             firstLevel, wta_k, scoreType, patchSize, fastThreshold);
}

String ORB::getDefaultName() const
{
    return (Feature2D::getDefaultName() + ".ORB");
}

}
