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#include "precomp.hpp"
#include <float.h>
#include <stdio.h>
#include "lkpyramid.hpp"
#include "opencl_kernels.hpp"

#define  CV_DESCALE(x,n)     (((x) + (1 << ((n)-1))) >> (n))

namespace
{
static void calcSharrDeriv(const cv::Mat& src, cv::Mat& dst)
{
    using namespace cv;
    using cv::detail::deriv_type;
    int rows = src.rows, cols = src.cols, cn = src.channels(), colsn = cols*cn, depth = src.depth();
    CV_Assert(depth == CV_8U);
    dst.create(rows, cols, CV_MAKETYPE(DataType<deriv_type>::depth, cn*2));

#ifdef HAVE_TEGRA_OPTIMIZATION
    if (tegra::calcSharrDeriv(src, dst))
        return;
#endif

    int x, y, delta = (int)alignSize((cols + 2)*cn, 16);
    AutoBuffer<deriv_type> _tempBuf(delta*2 + 64);
    deriv_type *trow0 = alignPtr(_tempBuf + cn, 16), *trow1 = alignPtr(trow0 + delta, 16);

#if CV_SSE2
    __m128i z = _mm_setzero_si128(), c3 = _mm_set1_epi16(3), c10 = _mm_set1_epi16(10);
#endif

    for( y = 0; y < rows; y++ )
    {
        const uchar* srow0 = src.ptr<uchar>(y > 0 ? y-1 : rows > 1 ? 1 : 0);
        const uchar* srow1 = src.ptr<uchar>(y);
        const uchar* srow2 = src.ptr<uchar>(y < rows-1 ? y+1 : rows > 1 ? rows-2 : 0);
        deriv_type* drow = dst.ptr<deriv_type>(y);

        // do vertical convolution
        x = 0;
#if CV_SSE2
        for( ; x <= colsn - 8; x += 8 )
        {
            __m128i s0 = _mm_unpacklo_epi8(_mm_loadl_epi64((const __m128i*)(srow0 + x)), z);
            __m128i s1 = _mm_unpacklo_epi8(_mm_loadl_epi64((const __m128i*)(srow1 + x)), z);
            __m128i s2 = _mm_unpacklo_epi8(_mm_loadl_epi64((const __m128i*)(srow2 + x)), z);
            __m128i t0 = _mm_add_epi16(_mm_mullo_epi16(_mm_add_epi16(s0, s2), c3), _mm_mullo_epi16(s1, c10));
            __m128i t1 = _mm_sub_epi16(s2, s0);
            _mm_store_si128((__m128i*)(trow0 + x), t0);
            _mm_store_si128((__m128i*)(trow1 + x), t1);
        }
#endif
        for( ; x < colsn; x++ )
        {
            int t0 = (srow0[x] + srow2[x])*3 + srow1[x]*10;
            int t1 = srow2[x] - srow0[x];
            trow0[x] = (deriv_type)t0;
            trow1[x] = (deriv_type)t1;
        }

        // make border
        int x0 = (cols > 1 ? 1 : 0)*cn, x1 = (cols > 1 ? cols-2 : 0)*cn;
        for( int k = 0; k < cn; k++ )
        {
            trow0[-cn + k] = trow0[x0 + k]; trow0[colsn + k] = trow0[x1 + k];
            trow1[-cn + k] = trow1[x0 + k]; trow1[colsn + k] = trow1[x1 + k];
        }

        // do horizontal convolution, interleave the results and store them to dst
        x = 0;
#if CV_SSE2
        for( ; x <= colsn - 8; x += 8 )
        {
            __m128i s0 = _mm_loadu_si128((const __m128i*)(trow0 + x - cn));
            __m128i s1 = _mm_loadu_si128((const __m128i*)(trow0 + x + cn));
            __m128i s2 = _mm_loadu_si128((const __m128i*)(trow1 + x - cn));
            __m128i s3 = _mm_load_si128((const __m128i*)(trow1 + x));
            __m128i s4 = _mm_loadu_si128((const __m128i*)(trow1 + x + cn));

            __m128i t0 = _mm_sub_epi16(s1, s0);
            __m128i t1 = _mm_add_epi16(_mm_mullo_epi16(_mm_add_epi16(s2, s4), c3), _mm_mullo_epi16(s3, c10));
            __m128i t2 = _mm_unpacklo_epi16(t0, t1);
            t0 = _mm_unpackhi_epi16(t0, t1);
            // this can probably be replaced with aligned stores if we aligned dst properly.
            _mm_storeu_si128((__m128i*)(drow + x*2), t2);
            _mm_storeu_si128((__m128i*)(drow + x*2 + 8), t0);
        }
#endif
        for( ; x < colsn; x++ )
        {
            deriv_type t0 = (deriv_type)(trow0[x+cn] - trow0[x-cn]);
            deriv_type t1 = (deriv_type)((trow1[x+cn] + trow1[x-cn])*3 + trow1[x]*10);
            drow[x*2] = t0; drow[x*2+1] = t1;
        }
    }
}

}//namespace

cv::detail::LKTrackerInvoker::LKTrackerInvoker(
                      const Mat& _prevImg, const Mat& _prevDeriv, const Mat& _nextImg,
                      const Point2f* _prevPts, Point2f* _nextPts,
                      uchar* _status, float* _err,
                      Size _winSize, TermCriteria _criteria,
                      int _level, int _maxLevel, int _flags, float _minEigThreshold )
{
    prevImg = &_prevImg;
    prevDeriv = &_prevDeriv;
    nextImg = &_nextImg;
    prevPts = _prevPts;
    nextPts = _nextPts;
    status = _status;
    err = _err;
    winSize = _winSize;
    criteria = _criteria;
    level = _level;
    maxLevel = _maxLevel;
    flags = _flags;
    minEigThreshold = _minEigThreshold;
}

#if defined __arm__ && !CV_NEON
typedef int64 acctype;
typedef int itemtype;
#else
typedef float acctype;
typedef float itemtype;
#endif

void cv::detail::LKTrackerInvoker::operator()(const Range& range) const
{
    Point2f halfWin((winSize.width-1)*0.5f, (winSize.height-1)*0.5f);
    const Mat& I = *prevImg;
    const Mat& J = *nextImg;
    const Mat& derivI = *prevDeriv;

    int j, cn = I.channels(), cn2 = cn*2;
    cv::AutoBuffer<deriv_type> _buf(winSize.area()*(cn + cn2));
    int derivDepth = DataType<deriv_type>::depth;

    Mat IWinBuf(winSize, CV_MAKETYPE(derivDepth, cn), (deriv_type*)_buf);
    Mat derivIWinBuf(winSize, CV_MAKETYPE(derivDepth, cn2), (deriv_type*)_buf + winSize.area()*cn);

    for( int ptidx = range.start; ptidx < range.end; ptidx++ )
    {
        Point2f prevPt = prevPts[ptidx]*(float)(1./(1 << level));
        Point2f nextPt;
        if( level == maxLevel )
        {
            if( flags & OPTFLOW_USE_INITIAL_FLOW )
                nextPt = nextPts[ptidx]*(float)(1./(1 << level));
            else
                nextPt = prevPt;
        }
        else
            nextPt = nextPts[ptidx]*2.f;
        nextPts[ptidx] = nextPt;

        Point2i iprevPt, inextPt;
        prevPt -= halfWin;
        iprevPt.x = cvFloor(prevPt.x);
        iprevPt.y = cvFloor(prevPt.y);

        if( iprevPt.x < -winSize.width || iprevPt.x >= derivI.cols ||
            iprevPt.y < -winSize.height || iprevPt.y >= derivI.rows )
        {
            if( level == 0 )
            {
                if( status )
                    status[ptidx] = false;
                if( err )
                    err[ptidx] = 0;
            }
            continue;
        }

        float a = prevPt.x - iprevPt.x;
        float b = prevPt.y - iprevPt.y;
        const int W_BITS = 14, W_BITS1 = 14;
        const float FLT_SCALE = 1.f/(1 << 20);
        int iw00 = cvRound((1.f - a)*(1.f - b)*(1 << W_BITS));
        int iw01 = cvRound(a*(1.f - b)*(1 << W_BITS));
        int iw10 = cvRound((1.f - a)*b*(1 << W_BITS));
        int iw11 = (1 << W_BITS) - iw00 - iw01 - iw10;

        int dstep = (int)(derivI.step/derivI.elemSize1());
        int stepI = (int)(I.step/I.elemSize1());
        int stepJ = (int)(J.step/J.elemSize1());
        acctype iA11 = 0, iA12 = 0, iA22 = 0;
        float A11, A12, A22;

#if CV_SSE2
        __m128i qw0 = _mm_set1_epi32(iw00 + (iw01 << 16));
        __m128i qw1 = _mm_set1_epi32(iw10 + (iw11 << 16));
        __m128i z = _mm_setzero_si128();
        __m128i qdelta_d = _mm_set1_epi32(1 << (W_BITS1-1));
        __m128i qdelta = _mm_set1_epi32(1 << (W_BITS1-5-1));
        __m128 qA11 = _mm_setzero_ps(), qA12 = _mm_setzero_ps(), qA22 = _mm_setzero_ps();
#endif

        // extract the patch from the first image, compute covariation matrix of derivatives
        int x, y;
        for( y = 0; y < winSize.height; y++ )
        {
            const uchar* src = (const uchar*)I.data + (y + iprevPt.y)*stepI + iprevPt.x*cn;
            const deriv_type* dsrc = (const deriv_type*)derivI.data + (y + iprevPt.y)*dstep + iprevPt.x*cn2;

            deriv_type* Iptr = (deriv_type*)(IWinBuf.data + y*IWinBuf.step);
            deriv_type* dIptr = (deriv_type*)(derivIWinBuf.data + y*derivIWinBuf.step);

            x = 0;

#if CV_SSE2
            for( ; x <= winSize.width*cn - 4; x += 4, dsrc += 4*2, dIptr += 4*2 )
            {
                __m128i v00, v01, v10, v11, t0, t1;

                v00 = _mm_unpacklo_epi8(_mm_cvtsi32_si128(*(const int*)(src + x)), z);
                v01 = _mm_unpacklo_epi8(_mm_cvtsi32_si128(*(const int*)(src + x + cn)), z);
                v10 = _mm_unpacklo_epi8(_mm_cvtsi32_si128(*(const int*)(src + x + stepI)), z);
                v11 = _mm_unpacklo_epi8(_mm_cvtsi32_si128(*(const int*)(src + x + stepI + cn)), z);

                t0 = _mm_add_epi32(_mm_madd_epi16(_mm_unpacklo_epi16(v00, v01), qw0),
                                   _mm_madd_epi16(_mm_unpacklo_epi16(v10, v11), qw1));
                t0 = _mm_srai_epi32(_mm_add_epi32(t0, qdelta), W_BITS1-5);
                _mm_storel_epi64((__m128i*)(Iptr + x), _mm_packs_epi32(t0,t0));

                v00 = _mm_loadu_si128((const __m128i*)(dsrc));
                v01 = _mm_loadu_si128((const __m128i*)(dsrc + cn2));
                v10 = _mm_loadu_si128((const __m128i*)(dsrc + dstep));
                v11 = _mm_loadu_si128((const __m128i*)(dsrc + dstep + cn2));

                t0 = _mm_add_epi32(_mm_madd_epi16(_mm_unpacklo_epi16(v00, v01), qw0),
                                   _mm_madd_epi16(_mm_unpacklo_epi16(v10, v11), qw1));
                t1 = _mm_add_epi32(_mm_madd_epi16(_mm_unpackhi_epi16(v00, v01), qw0),
                                   _mm_madd_epi16(_mm_unpackhi_epi16(v10, v11), qw1));
                t0 = _mm_srai_epi32(_mm_add_epi32(t0, qdelta_d), W_BITS1);
                t1 = _mm_srai_epi32(_mm_add_epi32(t1, qdelta_d), W_BITS1);
                v00 = _mm_packs_epi32(t0, t1); // Ix0 Iy0 Ix1 Iy1 ...

                _mm_storeu_si128((__m128i*)dIptr, v00);
                t0 = _mm_srai_epi32(v00, 16); // Iy0 Iy1 Iy2 Iy3
                t1 = _mm_srai_epi32(_mm_slli_epi32(v00, 16), 16); // Ix0 Ix1 Ix2 Ix3

                __m128 fy = _mm_cvtepi32_ps(t0);
                __m128 fx = _mm_cvtepi32_ps(t1);

                qA22 = _mm_add_ps(qA22, _mm_mul_ps(fy, fy));
                qA12 = _mm_add_ps(qA12, _mm_mul_ps(fx, fy));
                qA11 = _mm_add_ps(qA11, _mm_mul_ps(fx, fx));
            }
#endif

            for( ; x < winSize.width*cn; x++, dsrc += 2, dIptr += 2 )
            {
                int ival = CV_DESCALE(src[x]*iw00 + src[x+cn]*iw01 +
                                      src[x+stepI]*iw10 + src[x+stepI+cn]*iw11, W_BITS1-5);
                int ixval = CV_DESCALE(dsrc[0]*iw00 + dsrc[cn2]*iw01 +
                                       dsrc[dstep]*iw10 + dsrc[dstep+cn2]*iw11, W_BITS1);
                int iyval = CV_DESCALE(dsrc[1]*iw00 + dsrc[cn2+1]*iw01 + dsrc[dstep+1]*iw10 +
                                       dsrc[dstep+cn2+1]*iw11, W_BITS1);

                Iptr[x] = (short)ival;
                dIptr[0] = (short)ixval;
                dIptr[1] = (short)iyval;

                iA11 += (itemtype)(ixval*ixval);
                iA12 += (itemtype)(ixval*iyval);
                iA22 += (itemtype)(iyval*iyval);
            }
        }

#if CV_SSE2
        float CV_DECL_ALIGNED(16) A11buf[4], A12buf[4], A22buf[4];
        _mm_store_ps(A11buf, qA11);
        _mm_store_ps(A12buf, qA12);
        _mm_store_ps(A22buf, qA22);
        iA11 += A11buf[0] + A11buf[1] + A11buf[2] + A11buf[3];
        iA12 += A12buf[0] + A12buf[1] + A12buf[2] + A12buf[3];
        iA22 += A22buf[0] + A22buf[1] + A22buf[2] + A22buf[3];
#endif

        A11 = iA11*FLT_SCALE;
        A12 = iA12*FLT_SCALE;
        A22 = iA22*FLT_SCALE;

        float D = A11*A22 - A12*A12;
        float minEig = (A22 + A11 - std::sqrt((A11-A22)*(A11-A22) +
                        4.f*A12*A12))/(2*winSize.width*winSize.height);

        if( err && (flags & OPTFLOW_LK_GET_MIN_EIGENVALS) != 0 )
            err[ptidx] = (float)minEig;

        if( minEig < minEigThreshold || D < FLT_EPSILON )
        {
            if( level == 0 && status )
                status[ptidx] = false;
            continue;
        }

        D = 1.f/D;

        nextPt -= halfWin;
        Point2f prevDelta;

        for( j = 0; j < criteria.maxCount; j++ )
        {
            inextPt.x = cvFloor(nextPt.x);
            inextPt.y = cvFloor(nextPt.y);

            if( inextPt.x < -winSize.width || inextPt.x >= J.cols ||
               inextPt.y < -winSize.height || inextPt.y >= J.rows )
            {
                if( level == 0 && status )
                    status[ptidx] = false;
                break;
            }

            a = nextPt.x - inextPt.x;
            b = nextPt.y - inextPt.y;
            iw00 = cvRound((1.f - a)*(1.f - b)*(1 << W_BITS));
            iw01 = cvRound(a*(1.f - b)*(1 << W_BITS));
            iw10 = cvRound((1.f - a)*b*(1 << W_BITS));
            iw11 = (1 << W_BITS) - iw00 - iw01 - iw10;
            acctype ib1 = 0, ib2 = 0;
            float b1, b2;
#if CV_SSE2
            qw0 = _mm_set1_epi32(iw00 + (iw01 << 16));
            qw1 = _mm_set1_epi32(iw10 + (iw11 << 16));
            __m128 qb0 = _mm_setzero_ps(), qb1 = _mm_setzero_ps();
#endif

            for( y = 0; y < winSize.height; y++ )
            {
                const uchar* Jptr = (const uchar*)J.data + (y + inextPt.y)*stepJ + inextPt.x*cn;
                const deriv_type* Iptr = (const deriv_type*)(IWinBuf.data + y*IWinBuf.step);
                const deriv_type* dIptr = (const deriv_type*)(derivIWinBuf.data + y*derivIWinBuf.step);

                x = 0;

#if CV_SSE2
                for( ; x <= winSize.width*cn - 8; x += 8, dIptr += 8*2 )
                {
                    __m128i diff0 = _mm_loadu_si128((const __m128i*)(Iptr + x)), diff1;
                    __m128i v00 = _mm_unpacklo_epi8(_mm_loadl_epi64((const __m128i*)(Jptr + x)), z);
                    __m128i v01 = _mm_unpacklo_epi8(_mm_loadl_epi64((const __m128i*)(Jptr + x + cn)), z);
                    __m128i v10 = _mm_unpacklo_epi8(_mm_loadl_epi64((const __m128i*)(Jptr + x + stepJ)), z);
                    __m128i v11 = _mm_unpacklo_epi8(_mm_loadl_epi64((const __m128i*)(Jptr + x + stepJ + cn)), z);

                    __m128i t0 = _mm_add_epi32(_mm_madd_epi16(_mm_unpacklo_epi16(v00, v01), qw0),
                                               _mm_madd_epi16(_mm_unpacklo_epi16(v10, v11), qw1));
                    __m128i t1 = _mm_add_epi32(_mm_madd_epi16(_mm_unpackhi_epi16(v00, v01), qw0),
                                               _mm_madd_epi16(_mm_unpackhi_epi16(v10, v11), qw1));
                    t0 = _mm_srai_epi32(_mm_add_epi32(t0, qdelta), W_BITS1-5);
                    t1 = _mm_srai_epi32(_mm_add_epi32(t1, qdelta), W_BITS1-5);
                    diff0 = _mm_subs_epi16(_mm_packs_epi32(t0, t1), diff0);
                    diff1 = _mm_unpackhi_epi16(diff0, diff0);
                    diff0 = _mm_unpacklo_epi16(diff0, diff0); // It0 It0 It1 It1 ...
                    v00 = _mm_loadu_si128((const __m128i*)(dIptr)); // Ix0 Iy0 Ix1 Iy1 ...
                    v01 = _mm_loadu_si128((const __m128i*)(dIptr + 8));
                    v10 = _mm_mullo_epi16(v00, diff0);
                    v11 = _mm_mulhi_epi16(v00, diff0);
                    v00 = _mm_unpacklo_epi16(v10, v11);
                    v10 = _mm_unpackhi_epi16(v10, v11);
                    qb0 = _mm_add_ps(qb0, _mm_cvtepi32_ps(v00));
                    qb1 = _mm_add_ps(qb1, _mm_cvtepi32_ps(v10));
                    v10 = _mm_mullo_epi16(v01, diff1);
                    v11 = _mm_mulhi_epi16(v01, diff1);
                    v00 = _mm_unpacklo_epi16(v10, v11);
                    v10 = _mm_unpackhi_epi16(v10, v11);
                    qb0 = _mm_add_ps(qb0, _mm_cvtepi32_ps(v00));
                    qb1 = _mm_add_ps(qb1, _mm_cvtepi32_ps(v10));
                }
#endif

                for( ; x < winSize.width*cn; x++, dIptr += 2 )
                {
                    int diff = CV_DESCALE(Jptr[x]*iw00 + Jptr[x+cn]*iw01 +
                                          Jptr[x+stepJ]*iw10 + Jptr[x+stepJ+cn]*iw11,
                                          W_BITS1-5) - Iptr[x];
                    ib1 += (itemtype)(diff*dIptr[0]);
                    ib2 += (itemtype)(diff*dIptr[1]);
                }
            }

#if CV_SSE2
            float CV_DECL_ALIGNED(16) bbuf[4];
            _mm_store_ps(bbuf, _mm_add_ps(qb0, qb1));
            ib1 += bbuf[0] + bbuf[2];
            ib2 += bbuf[1] + bbuf[3];
#endif

            b1 = ib1*FLT_SCALE;
            b2 = ib2*FLT_SCALE;

            Point2f delta( (float)((A12*b2 - A22*b1) * D),
                          (float)((A12*b1 - A11*b2) * D));
            //delta = -delta;

            nextPt += delta;
            nextPts[ptidx] = nextPt + halfWin;

            if( delta.ddot(delta) <= criteria.epsilon )
                break;

            if( j > 0 && std::abs(delta.x + prevDelta.x) < 0.01 &&
               std::abs(delta.y + prevDelta.y) < 0.01 )
            {
                nextPts[ptidx] -= delta*0.5f;
                break;
            }
            prevDelta = delta;
        }

        if( status[ptidx] && err && level == 0 && (flags & OPTFLOW_LK_GET_MIN_EIGENVALS) == 0 )
        {
            Point2f nextPoint = nextPts[ptidx] - halfWin;
            Point inextPoint;

            inextPoint.x = cvFloor(nextPoint.x);
            inextPoint.y = cvFloor(nextPoint.y);

            if( inextPoint.x < -winSize.width || inextPoint.x >= J.cols ||
                inextPoint.y < -winSize.height || inextPoint.y >= J.rows )
            {
                if( status )
                    status[ptidx] = false;
                continue;
            }

            float aa = nextPoint.x - inextPoint.x;
            float bb = nextPoint.y - inextPoint.y;
            iw00 = cvRound((1.f - aa)*(1.f - bb)*(1 << W_BITS));
            iw01 = cvRound(aa*(1.f - bb)*(1 << W_BITS));
            iw10 = cvRound((1.f - aa)*bb*(1 << W_BITS));
            iw11 = (1 << W_BITS) - iw00 - iw01 - iw10;
            float errval = 0.f;

            for( y = 0; y < winSize.height; y++ )
            {
                const uchar* Jptr = (const uchar*)J.data + (y + inextPoint.y)*stepJ + inextPoint.x*cn;
                const deriv_type* Iptr = (const deriv_type*)(IWinBuf.data + y*IWinBuf.step);

                for( x = 0; x < winSize.width*cn; x++ )
                {
                    int diff = CV_DESCALE(Jptr[x]*iw00 + Jptr[x+cn]*iw01 +
                                          Jptr[x+stepJ]*iw10 + Jptr[x+stepJ+cn]*iw11,
                                          W_BITS1-5) - Iptr[x];
                    errval += std::abs((float)diff);
                }
            }
            err[ptidx] = errval * 1.f/(32*winSize.width*cn*winSize.height);
        }
    }
}

int cv::buildOpticalFlowPyramid(InputArray _img, OutputArrayOfArrays pyramid, Size winSize, int maxLevel, bool withDerivatives,
                                int pyrBorder, int derivBorder, bool tryReuseInputImage)
{
    Mat img = _img.getMat();
    CV_Assert(img.depth() == CV_8U && winSize.width > 2 && winSize.height > 2 );
    int pyrstep = withDerivatives ? 2 : 1;

    pyramid.create(1, (maxLevel + 1) * pyrstep, 0 /*type*/, -1, true, 0);

    int derivType = CV_MAKETYPE(DataType<cv::detail::deriv_type>::depth, img.channels() * 2);

    //level 0
    bool lvl0IsSet = false;
    if(tryReuseInputImage && img.isSubmatrix() && (pyrBorder & BORDER_ISOLATED) == 0)
    {
        Size wholeSize;
        Point ofs;
        img.locateROI(wholeSize, ofs);
        if (ofs.x >= winSize.width && ofs.y >= winSize.height
              && ofs.x + img.cols + winSize.width <= wholeSize.width
              && ofs.y + img.rows + winSize.height <= wholeSize.height)
        {
            pyramid.getMatRef(0) = img;
            lvl0IsSet = true;
        }
    }

    if(!lvl0IsSet)
    {
        Mat& temp = pyramid.getMatRef(0);

        if(!temp.empty())
            temp.adjustROI(winSize.height, winSize.height, winSize.width, winSize.width);
        if(temp.type() != img.type() || temp.cols != winSize.width*2 + img.cols || temp.rows != winSize.height * 2 + img.rows)
            temp.create(img.rows + winSize.height*2, img.cols + winSize.width*2, img.type());

        if(pyrBorder == BORDER_TRANSPARENT)
            img.copyTo(temp(Rect(winSize.width, winSize.height, img.cols, img.rows)));
        else
            copyMakeBorder(img, temp, winSize.height, winSize.height, winSize.width, winSize.width, pyrBorder);
        temp.adjustROI(-winSize.height, -winSize.height, -winSize.width, -winSize.width);
    }

    Size sz = img.size();
    Mat prevLevel = pyramid.getMatRef(0);
    Mat thisLevel = prevLevel;

    for(int level = 0; level <= maxLevel; ++level)
    {
        if (level != 0)
        {
            Mat& temp = pyramid.getMatRef(level * pyrstep);

            if(!temp.empty())
                temp.adjustROI(winSize.height, winSize.height, winSize.width, winSize.width);
            if(temp.type() != img.type() || temp.cols != winSize.width*2 + sz.width || temp.rows != winSize.height * 2 + sz.height)
                temp.create(sz.height + winSize.height*2, sz.width + winSize.width*2, img.type());

            thisLevel = temp(Rect(winSize.width, winSize.height, sz.width, sz.height));
            pyrDown(prevLevel, thisLevel, sz);

            if(pyrBorder != BORDER_TRANSPARENT)
                copyMakeBorder(thisLevel, temp, winSize.height, winSize.height, winSize.width, winSize.width, pyrBorder|BORDER_ISOLATED);
            temp.adjustROI(-winSize.height, -winSize.height, -winSize.width, -winSize.width);
        }

        if(withDerivatives)
        {
            Mat& deriv = pyramid.getMatRef(level * pyrstep + 1);

            if(!deriv.empty())
                deriv.adjustROI(winSize.height, winSize.height, winSize.width, winSize.width);
            if(deriv.type() != derivType || deriv.cols != winSize.width*2 + sz.width || deriv.rows != winSize.height * 2 + sz.height)
                deriv.create(sz.height + winSize.height*2, sz.width + winSize.width*2, derivType);

            Mat derivI = deriv(Rect(winSize.width, winSize.height, sz.width, sz.height));
            calcSharrDeriv(thisLevel, derivI);

            if(derivBorder != BORDER_TRANSPARENT)
                copyMakeBorder(derivI, deriv, winSize.height, winSize.height, winSize.width, winSize.width, derivBorder|BORDER_ISOLATED);
            deriv.adjustROI(-winSize.height, -winSize.height, -winSize.width, -winSize.width);
        }

        sz = Size((sz.width+1)/2, (sz.height+1)/2);
        if( sz.width <= winSize.width || sz.height <= winSize.height )
        {
            pyramid.create(1, (level + 1) * pyrstep, 0 /*type*/, -1, true, 0);//check this
            return level;
        }

        prevLevel = thisLevel;
    }

    return maxLevel;
}

namespace cv
{
    class PyrLKOpticalFlow
    {
        struct dim3
        {
            unsigned int x, y, z;
        };
    public:
        PyrLKOpticalFlow()
        {
            winSize = Size(21, 21);
            maxLevel = 3;
            iters = 30;
            derivLambda = 0.5;
            useInitialFlow = false;
            //minEigThreshold = 1e-4f;
            //getMinEigenVals = false;
        }

        bool checkParam()
        {
            iters = std::min(std::max(iters, 0), 100);

            derivLambda = std::min(std::max(derivLambda, 0.0), 1.0);
            if (derivLambda < 0)
                return false;
            if (maxLevel < 0 || winSize.width <= 2 || winSize.height <= 2)
                return false;
            calcPatchSize();
            if (patch.x <= 0 || patch.x >= 6 || patch.y <= 0 || patch.y >= 6)
                return false;
            if (!initWaveSize())
                return false;
            return true;
        }

        bool sparse(const UMat &prevImg, const UMat &nextImg, const UMat &prevPts, UMat &nextPts, UMat &status, UMat &err)
        {
            if (!checkParam())
                return false;

            UMat temp1 = (useInitialFlow ? nextPts : prevPts).reshape(1);
            UMat temp2 = nextPts.reshape(1);
            multiply(1.0f / (1 << maxLevel) /2.0f, temp1, temp2);

            status.setTo(Scalar::all(1));

            // build the image pyramids.
            std::vector<UMat> prevPyr; prevPyr.resize(maxLevel + 1);
            std::vector<UMat> nextPyr; nextPyr.resize(maxLevel + 1);

            prevImg.convertTo(prevPyr[0], CV_32F);
            nextImg.convertTo(nextPyr[0], CV_32F);

            for (int level = 1; level <= maxLevel; ++level)
            {
                pyrDown(prevPyr[level - 1], prevPyr[level]);
                pyrDown(nextPyr[level - 1], nextPyr[level]);
            }

            // dI/dx ~ Ix, dI/dy ~ Iy
            for (int level = maxLevel; level >= 0; level--)
            {
                if (!lkSparse_run(prevPyr[level], nextPyr[level], prevPts,
                                  nextPts, status, err,
                                  prevPts.cols, level))
                    return false;
            }
            return true;
        }

        Size winSize;
        int maxLevel;
        int iters;
        double derivLambda;
        bool useInitialFlow;
        //float minEigThreshold;
        //bool getMinEigenVals;

    private:
        int waveSize;
        bool initWaveSize()
        {
            waveSize = 1;
            if (isDeviceCPU())
                return true;

            ocl::Kernel kernel;
            if (!kernel.create("lkSparse", cv::ocl::video::pyrlk_oclsrc, ""))
                return false;
            waveSize = (int)kernel.preferedWorkGroupSizeMultiple();
            return true;
        }
        dim3 patch;
        void calcPatchSize()
        {
            dim3 block;
            //winSize.width *= cn;

            if (winSize.width > 32 && winSize.width > 2 * winSize.height)
            {
                block.x = 32;
                block.y = 8;
            }
            else
            {
                block.x = 16;
                block.y = 16;
            }

            patch.x = (winSize.width  + block.x - 1) / block.x;
            patch.y = (winSize.height + block.y - 1) / block.y;

            block.z = patch.z = 1;
        }

        #define SAFE_KERNEL_SET_ARG(idx, arg) \
        {\
            int idxNew = kernel.set(idx, arg);\
            if (-1 == idxNew)\
            {\
                printf("lkSparse_run can't setup argument index = %d to kernel\n", idx);\
                return false;\
            }\
            idx = idxNew;\
        }
        bool lkSparse_run(UMat &I, UMat &J, const UMat &prevPts, UMat &nextPts, UMat &status, UMat& err,
            int ptcount, int level)
        {
            size_t localThreads[3]  = { 8, 8};
            size_t globalThreads[3] = { 8 * ptcount, 8};
            char calcErr = (0 == level) ? 1 : 0;

            cv::String build_options;
            if (isDeviceCPU())
                build_options = " -D CPU";
            else
                build_options = cv::format("-D WAVE_SIZE=%d", waveSize);

            ocl::Kernel kernel;
            if (!kernel.create("lkSparse", cv::ocl::video::pyrlk_oclsrc, build_options))
                return false;

            ocl::Image2D imageI(I);
            ocl::Image2D imageJ(J);
            int idxArg = 0;
#if 0
            idxArg = kernel.set(idxArg, imageI); //image2d_t I
            idxArg = kernel.set(idxArg, imageJ); //image2d_t J
            idxArg = kernel.set(idxArg, ocl::KernelArg::PtrReadOnly(prevPts)); // __global const float2* prevPts
            idxArg = kernel.set(idxArg, (int)prevPts.step); // int prevPtsStep
            idxArg = kernel.set(idxArg, ocl::KernelArg::PtrReadWrite(nextPts)); // __global const float2* nextPts
            idxArg = kernel.set(idxArg, (int)nextPts.step); //  int nextPtsStep
            idxArg = kernel.set(idxArg, ocl::KernelArg::PtrReadWrite(status)); // __global uchar* status
            idxArg = kernel.set(idxArg, ocl::KernelArg::PtrReadWrite(err)); // __global float* err
            idxArg = kernel.set(idxArg, (int)level); // const int level
            idxArg = kernel.set(idxArg, (int)I.rows); // const int rows
            idxArg = kernel.set(idxArg, (int)I.cols); // const int cols
            idxArg = kernel.set(idxArg, (int)patch.x); // int PATCH_X
            idxArg = kernel.set(idxArg, (int)patch.y); // int PATCH_Y
            idxArg = kernel.set(idxArg, (int)winSize.width); // int c_winSize_x
            idxArg = kernel.set(idxArg, (int)winSize.height); // int c_winSize_y
            idxArg = kernel.set(idxArg, (int)iters); // int c_iters
            idxArg = kernel.set(idxArg, (char)calcErr); //char calcErr
#else
            SAFE_KERNEL_SET_ARG(idxArg, imageI); //image2d_t I
            SAFE_KERNEL_SET_ARG(idxArg, imageJ); //image2d_t J
            SAFE_KERNEL_SET_ARG(idxArg, ocl::KernelArg::PtrReadOnly(prevPts)); // __global const float2* prevPts
            SAFE_KERNEL_SET_ARG(idxArg, (int)prevPts.step); // int prevPtsStep
            SAFE_KERNEL_SET_ARG(idxArg, ocl::KernelArg::PtrReadWrite(nextPts)); // __global const float2* nextPts
            SAFE_KERNEL_SET_ARG(idxArg, (int)nextPts.step); //  int nextPtsStep
            SAFE_KERNEL_SET_ARG(idxArg, ocl::KernelArg::PtrReadWrite(status)); // __global uchar* status
            SAFE_KERNEL_SET_ARG(idxArg, ocl::KernelArg::PtrReadWrite(err)); // __global float* err
            SAFE_KERNEL_SET_ARG(idxArg, (int)level); // const int level
            SAFE_KERNEL_SET_ARG(idxArg, (int)I.rows); // const int rows
            SAFE_KERNEL_SET_ARG(idxArg, (int)I.cols); // const int cols
            SAFE_KERNEL_SET_ARG(idxArg, (int)patch.x); // int PATCH_X
            SAFE_KERNEL_SET_ARG(idxArg, (int)patch.y); // int PATCH_Y
            SAFE_KERNEL_SET_ARG(idxArg, (int)winSize.width); // int c_winSize_x
            SAFE_KERNEL_SET_ARG(idxArg, (int)winSize.height); // int c_winSize_y
            SAFE_KERNEL_SET_ARG(idxArg, (int)iters); // int c_iters
            SAFE_KERNEL_SET_ARG(idxArg, (char)calcErr); //char calcErr
#endif

            return kernel.run(2, globalThreads, localThreads, true);
        }
    private:
        inline static bool isDeviceCPU()
        {
            return (cv::ocl::Device::TYPE_CPU == cv::ocl::Device::getDefault().type());
        }
    };


    static bool ocl_calcOpticalFlowPyrLK(InputArray _prevImg, InputArray _nextImg,
                                  InputArray _prevPts, InputOutputArray _nextPts,
                                  OutputArray _status, OutputArray _err,
                                  Size winSize, int maxLevel,
                                  TermCriteria criteria,
                                  int flags/*, double minEigThreshold*/ )
    {
        if (0 != (OPTFLOW_LK_GET_MIN_EIGENVALS & flags))
            return false;
        if (!cv::ocl::Device::getDefault().imageSupport())
            return false;
        if (_nextImg.size() != _prevImg.size())
            return false;
        int typePrev = _prevImg.type();
        int typeNext = _nextImg.type();
        if ((1 != CV_MAT_CN(typePrev)) || (1 != CV_MAT_CN(typeNext)))
            return false;
        if ((0 != CV_MAT_DEPTH(typePrev)) || (0 != CV_MAT_DEPTH(typeNext)))
            return false;

        if (_prevPts.empty() || _prevPts.size().height != 1 || _prevPts.type() != CV_32FC2)
            return false;
        bool useInitialFlow  = (0 != (flags & OPTFLOW_USE_INITIAL_FLOW));
        if (useInitialFlow)
        {
            if (_nextPts.size() != _prevPts.size() || _nextPts.type() != CV_32FC2)
                return false;
        }

        PyrLKOpticalFlow opticalFlow;
        opticalFlow.winSize     = winSize;
        opticalFlow.maxLevel    = maxLevel;
        opticalFlow.iters       = criteria.maxCount;
        opticalFlow.derivLambda = criteria.epsilon;
        opticalFlow.useInitialFlow  = useInitialFlow;

        if (!opticalFlow.checkParam())
            return false;

        UMat umatErr;
        if (_err.needed())
        {
            _err.create(_prevPts.size(), CV_32FC1);
            umatErr = _err.getUMat();
        }
        else
            umatErr.create(_prevPts.size(), CV_32FC1);

        _nextPts.create(_prevPts.size(), _prevPts.type());
        _status.create(_prevPts.size(), CV_8UC1);
        UMat umatNextPts = _nextPts.getUMat();
        UMat umatStatus = _status.getUMat();
        return opticalFlow.sparse(_prevImg.getUMat(), _nextImg.getUMat(), _prevPts.getUMat(), umatNextPts, umatStatus, umatErr);
    }
};

void cv::calcOpticalFlowPyrLK( InputArray _prevImg, InputArray _nextImg,
                           InputArray _prevPts, InputOutputArray _nextPts,
                           OutputArray _status, OutputArray _err,
                           Size winSize, int maxLevel,
                           TermCriteria criteria,
                           int flags, double minEigThreshold )
{
    bool use_opencl = ocl::useOpenCL() && (_prevImg.isUMat() || _nextImg.isUMat());
    if ( use_opencl && ocl_calcOpticalFlowPyrLK(_prevImg, _nextImg, _prevPts, _nextPts, _status, _err, winSize, maxLevel, criteria, flags/*, minEigThreshold*/))
        return;

    Mat prevPtsMat = _prevPts.getMat();
    const int derivDepth = DataType<cv::detail::deriv_type>::depth;

    CV_Assert( maxLevel >= 0 && winSize.width > 2 && winSize.height > 2 );

    int level=0, i, npoints;
    CV_Assert( (npoints = prevPtsMat.checkVector(2, CV_32F, true)) >= 0 );

    if( npoints == 0 )
    {
        _nextPts.release();
        _status.release();
        _err.release();
        return;
    }

    if( !(flags & OPTFLOW_USE_INITIAL_FLOW) )
        _nextPts.create(prevPtsMat.size(), prevPtsMat.type(), -1, true);

    Mat nextPtsMat = _nextPts.getMat();
    CV_Assert( nextPtsMat.checkVector(2, CV_32F, true) == npoints );

    const Point2f* prevPts = (const Point2f*)prevPtsMat.data;
    Point2f* nextPts = (Point2f*)nextPtsMat.data;

    _status.create((int)npoints, 1, CV_8U, -1, true);
    Mat statusMat = _status.getMat(), errMat;
    CV_Assert( statusMat.isContinuous() );
    uchar* status = statusMat.data;
    float* err = 0;

    for( i = 0; i < npoints; i++ )
        status[i] = true;

    if( _err.needed() )
    {
        _err.create((int)npoints, 1, CV_32F, -1, true);
        errMat = _err.getMat();
        CV_Assert( errMat.isContinuous() );
        err = (float*)errMat.data;
    }

    std::vector<Mat> prevPyr, nextPyr;
    int levels1 = -1;
    int lvlStep1 = 1;
    int levels2 = -1;
    int lvlStep2 = 1;

    if(_prevImg.kind() == _InputArray::STD_VECTOR_MAT)
    {
        _prevImg.getMatVector(prevPyr);

        levels1 = int(prevPyr.size()) - 1;
        CV_Assert(levels1 >= 0);

        if (levels1 % 2 == 1 && prevPyr[0].channels() * 2 == prevPyr[1].channels() && prevPyr[1].depth() == derivDepth)
        {
            lvlStep1 = 2;
            levels1 /= 2;
        }

        // ensure that pyramid has reqired padding
        if(levels1 > 0)
        {
            Size fullSize;
            Point ofs;
            prevPyr[lvlStep1].locateROI(fullSize, ofs);
            CV_Assert(ofs.x >= winSize.width && ofs.y >= winSize.height
                && ofs.x + prevPyr[lvlStep1].cols + winSize.width <= fullSize.width
                && ofs.y + prevPyr[lvlStep1].rows + winSize.height <= fullSize.height);
        }

        if(levels1 < maxLevel)
            maxLevel = levels1;
    }

    if(_nextImg.kind() == _InputArray::STD_VECTOR_MAT)
    {
        _nextImg.getMatVector(nextPyr);

        levels2 = int(nextPyr.size()) - 1;
        CV_Assert(levels2 >= 0);

        if (levels2 % 2 == 1 && nextPyr[0].channels() * 2 == nextPyr[1].channels() && nextPyr[1].depth() == derivDepth)
        {
            lvlStep2 = 2;
            levels2 /= 2;
        }

        // ensure that pyramid has reqired padding
        if(levels2 > 0)
        {
            Size fullSize;
            Point ofs;
            nextPyr[lvlStep2].locateROI(fullSize, ofs);
            CV_Assert(ofs.x >= winSize.width && ofs.y >= winSize.height
                && ofs.x + nextPyr[lvlStep2].cols + winSize.width <= fullSize.width
                && ofs.y + nextPyr[lvlStep2].rows + winSize.height <= fullSize.height);
        }

        if(levels2 < maxLevel)
            maxLevel = levels2;
    }

    if (levels1 < 0)
        maxLevel = buildOpticalFlowPyramid(_prevImg, prevPyr, winSize, maxLevel, false);

    if (levels2 < 0)
        maxLevel = buildOpticalFlowPyramid(_nextImg, nextPyr, winSize, maxLevel, false);

    if( (criteria.type & TermCriteria::COUNT) == 0 )
        criteria.maxCount = 30;
    else
        criteria.maxCount = std::min(std::max(criteria.maxCount, 0), 100);
    if( (criteria.type & TermCriteria::EPS) == 0 )
        criteria.epsilon = 0.01;
    else
        criteria.epsilon = std::min(std::max(criteria.epsilon, 0.), 10.);
    criteria.epsilon *= criteria.epsilon;

    // dI/dx ~ Ix, dI/dy ~ Iy
    Mat derivIBuf;
    if(lvlStep1 == 1)
        derivIBuf.create(prevPyr[0].rows + winSize.height*2, prevPyr[0].cols + winSize.width*2, CV_MAKETYPE(derivDepth, prevPyr[0].channels() * 2));

    for( level = maxLevel; level >= 0; level-- )
    {
        Mat derivI;
        if(lvlStep1 == 1)
        {
            Size imgSize = prevPyr[level * lvlStep1].size();
            Mat _derivI( imgSize.height + winSize.height*2,
                imgSize.width + winSize.width*2, derivIBuf.type(), derivIBuf.data );
            derivI = _derivI(Rect(winSize.width, winSize.height, imgSize.width, imgSize.height));
            calcSharrDeriv(prevPyr[level * lvlStep1], derivI);
            copyMakeBorder(derivI, _derivI, winSize.height, winSize.height, winSize.width, winSize.width, BORDER_CONSTANT|BORDER_ISOLATED);
        }
        else
            derivI = prevPyr[level * lvlStep1 + 1];

        CV_Assert(prevPyr[level * lvlStep1].size() == nextPyr[level * lvlStep2].size());
        CV_Assert(prevPyr[level * lvlStep1].type() == nextPyr[level * lvlStep2].type());

#ifdef HAVE_TEGRA_OPTIMIZATION
        typedef tegra::LKTrackerInvoker<cv::detail::LKTrackerInvoker> LKTrackerInvoker;
#else
        typedef cv::detail::LKTrackerInvoker LKTrackerInvoker;
#endif

        parallel_for_(Range(0, npoints), LKTrackerInvoker(prevPyr[level * lvlStep1], derivI,
                                                          nextPyr[level * lvlStep2], prevPts, nextPts,
                                                          status, err,
                                                          winSize, criteria, level, maxLevel,
                                                          flags, (float)minEigThreshold));
    }
}

namespace cv
{

static void
getRTMatrix( const Point2f* a, const Point2f* b,
             int count, Mat& M, bool fullAffine )
{
    CV_Assert( M.isContinuous() );

    if( fullAffine )
    {
        double sa[6][6]={{0.}}, sb[6]={0.};
        Mat A( 6, 6, CV_64F, &sa[0][0] ), B( 6, 1, CV_64F, sb );
        Mat MM = M.reshape(1, 6);

        for( int i = 0; i < count; i++ )
        {
            sa[0][0] += a[i].x*a[i].x;
            sa[0][1] += a[i].y*a[i].x;
            sa[0][2] += a[i].x;

            sa[1][1] += a[i].y*a[i].y;
            sa[1][2] += a[i].y;

            sa[2][2] += 1;

            sb[0] += a[i].x*b[i].x;
            sb[1] += a[i].y*b[i].x;
            sb[2] += b[i].x;
            sb[3] += a[i].x*b[i].y;
            sb[4] += a[i].y*b[i].y;
            sb[5] += b[i].y;
        }

        sa[3][4] = sa[4][3] = sa[1][0] = sa[0][1];
        sa[3][5] = sa[5][3] = sa[2][0] = sa[0][2];
        sa[4][5] = sa[5][4] = sa[2][1] = sa[1][2];

        sa[3][3] = sa[0][0];
        sa[4][4] = sa[1][1];
        sa[5][5] = sa[2][2];

        solve( A, B, MM, DECOMP_EIG );
    }
    else
    {
        double sa[4][4]={{0.}}, sb[4]={0.}, m[4];
        Mat A( 4, 4, CV_64F, sa ), B( 4, 1, CV_64F, sb );
        Mat MM( 4, 1, CV_64F, m );

        for( int i = 0; i < count; i++ )
        {
            sa[0][0] += a[i].x*a[i].x + a[i].y*a[i].y;
            sa[0][2] += a[i].x;
            sa[0][3] += a[i].y;


            sa[2][1] += -a[i].y;
            sa[2][2] += 1;

            sa[3][0] += a[i].y;
            sa[3][1] += a[i].x;
            sa[3][3] += 1;

            sb[0] += a[i].x*b[i].x + a[i].y*b[i].y;
            sb[1] += a[i].x*b[i].y - a[i].y*b[i].x;
            sb[2] += b[i].x;
            sb[3] += b[i].y;
        }

        sa[1][1] = sa[0][0];
        sa[2][1] = sa[1][2] = -sa[0][3];
        sa[3][1] = sa[1][3] = sa[2][0] = sa[0][2];
        sa[2][2] = sa[3][3] = count;
        sa[3][0] = sa[0][3];

        solve( A, B, MM, DECOMP_EIG );

        double* om = M.ptr<double>();
        om[0] = om[4] = m[0];
        om[1] = -m[1];
        om[3] = m[1];
        om[2] = m[2];
        om[5] = m[3];
    }
}

}

cv::Mat cv::estimateRigidTransform( InputArray src1, InputArray src2, bool fullAffine )
{
    Mat M(2, 3, CV_64F), A = src1.getMat(), B = src2.getMat();

    const int COUNT = 15;
    const int WIDTH = 160, HEIGHT = 120;
    const int RANSAC_MAX_ITERS = 500;
    const int RANSAC_SIZE0 = 3;
    const double RANSAC_GOOD_RATIO = 0.5;

    std::vector<Point2f> pA, pB;
    std::vector<int> good_idx;
    std::vector<uchar> status;

    double scale = 1.;
    int i, j, k, k1;

    RNG rng((uint64)-1);
    int good_count = 0;

    if( A.size() != B.size() )
        CV_Error( Error::StsUnmatchedSizes, "Both input images must have the same size" );

    if( A.type() != B.type() )
        CV_Error( Error::StsUnmatchedFormats, "Both input images must have the same data type" );

    int count = A.checkVector(2);

    if( count > 0 )
    {
        A.reshape(2, count).convertTo(pA, CV_32F);
        B.reshape(2, count).convertTo(pB, CV_32F);
    }
    else if( A.depth() == CV_8U )
    {
        int cn = A.channels();
        CV_Assert( cn == 1 || cn == 3 || cn == 4 );
        Size sz0 = A.size();
        Size sz1(WIDTH, HEIGHT);

        scale = std::max(1., std::max( (double)sz1.width/sz0.width, (double)sz1.height/sz0.height ));

        sz1.width = cvRound( sz0.width * scale );
        sz1.height = cvRound( sz0.height * scale );

        bool equalSizes = sz1.width == sz0.width && sz1.height == sz0.height;

        if( !equalSizes || cn != 1 )
        {
            Mat sA, sB;

            if( cn != 1 )
            {
                Mat gray;
                cvtColor(A, gray, COLOR_BGR2GRAY);
                resize(gray, sA, sz1, 0., 0., INTER_AREA);
                cvtColor(B, gray, COLOR_BGR2GRAY);
                resize(gray, sB, sz1, 0., 0., INTER_AREA);
            }
            else
            {
                resize(A, sA, sz1, 0., 0., INTER_AREA);
                resize(B, sB, sz1, 0., 0., INTER_AREA);
            }

            A = sA;
            B = sB;
        }

        int count_y = COUNT;
        int count_x = cvRound((double)COUNT*sz1.width/sz1.height);
        count = count_x * count_y;

        pA.resize(count);
        pB.resize(count);
        status.resize(count);

        for( i = 0, k = 0; i < count_y; i++ )
            for( j = 0; j < count_x; j++, k++ )
            {
                pA[k].x = (j+0.5f)*sz1.width/count_x;
                pA[k].y = (i+0.5f)*sz1.height/count_y;
            }

        // find the corresponding points in B
        calcOpticalFlowPyrLK(A, B, pA, pB, status, noArray(), Size(21, 21), 3,
                             TermCriteria(TermCriteria::MAX_ITER,40,0.1));

        // repack the remained points
        for( i = 0, k = 0; i < count; i++ )
            if( status[i] )
            {
                if( i > k )
                {
                    pA[k] = pA[i];
                    pB[k] = pB[i];
                }
                k++;
            }
        count = k;
        pA.resize(count);
        pB.resize(count);
    }
    else
        CV_Error( Error::StsUnsupportedFormat, "Both input images must have either 8uC1 or 8uC3 type" );

    good_idx.resize(count);

    if( count < RANSAC_SIZE0 )
        return Mat();

    Rect brect = boundingRect(pB);

    // RANSAC stuff:
    // 1. find the consensus
    for( k = 0; k < RANSAC_MAX_ITERS; k++ )
    {
        int idx[RANSAC_SIZE0];
        Point2f a[RANSAC_SIZE0];
        Point2f b[RANSAC_SIZE0];

        // choose random 3 non-complanar points from A & B
        for( i = 0; i < RANSAC_SIZE0; i++ )
        {
            for( k1 = 0; k1 < RANSAC_MAX_ITERS; k1++ )
            {
                idx[i] = rng.uniform(0, count);

                for( j = 0; j < i; j++ )
                {
                    if( idx[j] == idx[i] )
                        break;
                    // check that the points are not very close one each other
                    if( fabs(pA[idx[i]].x - pA[idx[j]].x) +
                        fabs(pA[idx[i]].y - pA[idx[j]].y) < FLT_EPSILON )
                        break;
                    if( fabs(pB[idx[i]].x - pB[idx[j]].x) +
                        fabs(pB[idx[i]].y - pB[idx[j]].y) < FLT_EPSILON )
                        break;
                }

                if( j < i )
                    continue;

                if( i+1 == RANSAC_SIZE0 )
                {
                    // additional check for non-complanar vectors
                    a[0] = pA[idx[0]];
                    a[1] = pA[idx[1]];
                    a[2] = pA[idx[2]];

                    b[0] = pB[idx[0]];
                    b[1] = pB[idx[1]];
                    b[2] = pB[idx[2]];

                    double dax1 = a[1].x - a[0].x, day1 = a[1].y - a[0].y;
                    double dax2 = a[2].x - a[0].x, day2 = a[2].y - a[0].y;
                    double dbx1 = b[1].x - b[0].x, dby1 = b[1].y - b[0].y;
                    double dbx2 = b[2].x - b[0].x, dby2 = b[2].y - b[0].y;
                    const double eps = 0.01;

                    if( fabs(dax1*day2 - day1*dax2) < eps*std::sqrt(dax1*dax1+day1*day1)*std::sqrt(dax2*dax2+day2*day2) ||
                        fabs(dbx1*dby2 - dby1*dbx2) < eps*std::sqrt(dbx1*dbx1+dby1*dby1)*std::sqrt(dbx2*dbx2+dby2*dby2) )
                        continue;
                }
                break;
            }

            if( k1 >= RANSAC_MAX_ITERS )
                break;
        }

        if( i < RANSAC_SIZE0 )
            continue;

        // estimate the transformation using 3 points
        getRTMatrix( a, b, 3, M, fullAffine );

        const double* m = M.ptr<double>();
        for( i = 0, good_count = 0; i < count; i++ )
        {
            if( std::abs( m[0]*pA[i].x + m[1]*pA[i].y + m[2] - pB[i].x ) +
                std::abs( m[3]*pA[i].x + m[4]*pA[i].y + m[5] - pB[i].y ) < std::max(brect.width,brect.height)*0.05 )
                good_idx[good_count++] = i;
        }

        if( good_count >= count*RANSAC_GOOD_RATIO )
            break;
    }

    if( k >= RANSAC_MAX_ITERS )
        return Mat();

    if( good_count < count )
    {
        for( i = 0; i < good_count; i++ )
        {
            j = good_idx[i];
            pA[i] = pA[j];
            pB[i] = pB[j];
        }
    }

    getRTMatrix( &pA[0], &pB[0], good_count, M, fullAffine );
    M.at<double>(0, 2) /= scale;
    M.at<double>(1, 2) /= scale;

    return M;
}

/* End of file. */