hog.cpp

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#include <stdio.h>
#include "precomp.hpp"
#include <iterator>
#ifdef HAVE_IPP
#include "ipp.h"
#endif
/****************************************************************************************\
      The code below is implementation of HOG (Histogram-of-Oriented Gradients)
      descriptor and object detection, introduced by Navneet Dalal and Bill Triggs.

      The computed feature vectors are compatible with the
      INRIA Object Detection and Localization Toolkit
      (http://pascal.inrialpes.fr/soft/olt/)
\****************************************************************************************/

namespace cv
{

size_t HOGDescriptor::getDescriptorSize() const
{
    CV_Assert(blockSize.width % cellSize.width == 0 &&
        blockSize.height % cellSize.height == 0);
    CV_Assert((winSize.width - blockSize.width) % blockStride.width == 0 &&
        (winSize.height - blockSize.height) % blockStride.height == 0 );
    return (size_t)nbins*
        (blockSize.width/cellSize.width)*
        (blockSize.height/cellSize.height)*
        ((winSize.width - blockSize.width)/blockStride.width + 1)*
        ((winSize.height - blockSize.height)/blockStride.height + 1);
}

double HOGDescriptor::getWinSigma() const
{
    return winSigma >= 0 ? winSigma : (blockSize.width + blockSize.height)/8.;
}

bool HOGDescriptor::checkDetectorSize() const
{
    size_t detectorSize = svmDetector.size(), descriptorSize = getDescriptorSize();
    return detectorSize == 0 ||
        detectorSize == descriptorSize ||
        detectorSize == descriptorSize + 1;
}

void HOGDescriptor::setSVMDetector(InputArray _svmDetector)
{
    _svmDetector.getMat().convertTo(svmDetector, CV_32F);
    CV_Assert( checkDetectorSize() );
}

#define CV_TYPE_NAME_HOG_DESCRIPTOR "opencv-object-detector-hog"

bool HOGDescriptor::read(FileNode& obj)
{
    if( !obj.isMap() )
        return false;
    FileNodeIterator it = obj["winSize"].begin();
    it >> winSize.width >> winSize.height;
    it = obj["blockSize"].begin();
    it >> blockSize.width >> blockSize.height;
    it = obj["blockStride"].begin();
    it >> blockStride.width >> blockStride.height;
    it = obj["cellSize"].begin();
    it >> cellSize.width >> cellSize.height;
    obj["nbins"] >> nbins;
    obj["derivAperture"] >> derivAperture;
    obj["winSigma"] >> winSigma;
    obj["histogramNormType"] >> histogramNormType;
    obj["L2HysThreshold"] >> L2HysThreshold;
    obj["gammaCorrection"] >> gammaCorrection;
    obj["nlevels"] >> nlevels;

    FileNode vecNode = obj["SVMDetector"];
    if( vecNode.isSeq() )
    {
        vecNode >> svmDetector;
        CV_Assert(checkDetectorSize());
    }
    return true;
}

void HOGDescriptor::write(FileStorage& fs, const String& objName) const
{
    if( !objName.empty() )
        fs << objName;

    fs << "{" CV_TYPE_NAME_HOG_DESCRIPTOR
    << "winSize" << winSize
    << "blockSize" << blockSize
    << "blockStride" << blockStride
    << "cellSize" << cellSize
    << "nbins" << nbins
    << "derivAperture" << derivAperture
    << "winSigma" << getWinSigma()
    << "histogramNormType" << histogramNormType
    << "L2HysThreshold" << L2HysThreshold
    << "gammaCorrection" << gammaCorrection
    << "nlevels" << nlevels;
    if( !svmDetector.empty() )
        fs << "SVMDetector" << "[:" << svmDetector << "]";
    fs << "}";
}

bool HOGDescriptor::load(const String& filename, const String& objname)
{
    FileStorage fs(filename, FileStorage::READ);
    FileNode obj = !objname.empty() ? fs[objname] : fs.getFirstTopLevelNode();
    return read(obj);
}

void HOGDescriptor::save(const String& filename, const String& objName) const
{
    FileStorage fs(filename, FileStorage::WRITE);
    write(fs, !objName.empty() ? objName : FileStorage::getDefaultObjectName(filename));
}

void HOGDescriptor::copyTo(HOGDescriptor& c) const
{
    c.winSize = winSize;
    c.blockSize = blockSize;
    c.blockStride = blockStride;
    c.cellSize = cellSize;
    c.nbins = nbins;
    c.derivAperture = derivAperture;
    c.winSigma = winSigma;
    c.histogramNormType = histogramNormType;
    c.L2HysThreshold = L2HysThreshold;
    c.gammaCorrection = gammaCorrection;
    c.svmDetector = svmDetector;
    c.nlevels = nlevels;
}

void HOGDescriptor::computeGradient(const Mat& img, Mat& grad, Mat& qangle,
                                    Size paddingTL, Size paddingBR) const
{
    CV_Assert( img.type() == CV_8U || img.type() == CV_8UC3 );

    Size gradsize(img.cols + paddingTL.width + paddingBR.width,
                  img.rows + paddingTL.height + paddingBR.height);
    grad.create(gradsize, CV_32FC2);  // <magnitude*(1-alpha), magnitude*alpha>
    qangle.create(gradsize, CV_8UC2); // [0..nbins-1] - quantized gradient orientation
    Size wholeSize;
    Point roiofs;
    img.locateROI(wholeSize, roiofs);

    int i, x, y;
    int cn = img.channels();

    Mat_<float> _lut(1, 256);
    const float* lut = &_lut(0,0);

    if( gammaCorrection )
        for( i = 0; i < 256; i++ )
            _lut(0,i) = std::sqrt((float)i);
    else
        for( i = 0; i < 256; i++ )
            _lut(0,i) = (float)i;

    AutoBuffer<int> mapbuf(gradsize.width + gradsize.height + 4);
    int* xmap = (int*)mapbuf + 1;
    int* ymap = xmap + gradsize.width + 2;

    const int borderType = (int)BORDER_REFLECT_101;

    for( x = -1; x < gradsize.width + 1; x++ )
        xmap[x] = borderInterpolate(x - paddingTL.width + roiofs.x,
                        wholeSize.width, borderType) - roiofs.x;
    for( y = -1; y < gradsize.height + 1; y++ )
        ymap[y] = borderInterpolate(y - paddingTL.height + roiofs.y,
                        wholeSize.height, borderType) - roiofs.y;

    // x- & y- derivatives for the whole row
    int width = gradsize.width;
    AutoBuffer<float> _dbuf(width*4);
    float* dbuf = _dbuf;
    Mat Dx(1, width, CV_32F, dbuf);
    Mat Dy(1, width, CV_32F, dbuf + width);
    Mat Mag(1, width, CV_32F, dbuf + width*2);
    Mat Angle(1, width, CV_32F, dbuf + width*3);

    int _nbins = nbins;
    float angleScale = (float)(_nbins/CV_PI);
#ifdef HAVE_IPP
    Mat lutimg(img.rows,img.cols,CV_MAKETYPE(CV_32F,cn));
    Mat hidxs(1, width, CV_32F);
    Ipp32f* pHidxs  = (Ipp32f*)hidxs.data;
    Ipp32f* pAngles = (Ipp32f*)Angle.data;

    IppiSize roiSize;
    roiSize.width = img.cols;
    roiSize.height = img.rows;

    for( y = 0; y < roiSize.height; y++ )
    {
       const uchar* imgPtr = img.data + y*img.step;
       float* imglutPtr = (float*)(lutimg.data + y*lutimg.step);

       for( x = 0; x < roiSize.width*cn; x++ )
       {
          imglutPtr[x] = lut[imgPtr[x]];
       }
    }

#endif
    for( y = 0; y < gradsize.height; y++ )
    {
#ifdef HAVE_IPP
        const float* imgPtr  = (float*)(lutimg.data + lutimg.step*ymap[y]);
        const float* prevPtr = (float*)(lutimg.data + lutimg.step*ymap[y-1]);
        const float* nextPtr = (float*)(lutimg.data + lutimg.step*ymap[y+1]);
#else
        const uchar* imgPtr  = img.data + img.step*ymap[y];
        const uchar* prevPtr = img.data + img.step*ymap[y-1];
        const uchar* nextPtr = img.data + img.step*ymap[y+1];
#endif
        float* gradPtr = (float*)grad.ptr(y);
        uchar* qanglePtr = (uchar*)qangle.ptr(y);

        if( cn == 1 )
        {
            for( x = 0; x < width; x++ )
            {
                int x1 = xmap[x];
#ifdef HAVE_IPP
                dbuf[x] = (float)(imgPtr[xmap[x+1]] - imgPtr[xmap[x-1]]);
                dbuf[width + x] = (float)(nextPtr[x1] - prevPtr[x1]);
#else
                dbuf[x] = (float)(lut[imgPtr[xmap[x+1]]] - lut[imgPtr[xmap[x-1]]]);
                dbuf[width + x] = (float)(lut[nextPtr[x1]] - lut[prevPtr[x1]]);
#endif
            }
        }
        else
        {
            for( x = 0; x < width; x++ )
            {
                int x1 = xmap[x]*3;
                float dx0, dy0, dx, dy, mag0, mag;
#ifdef HAVE_IPP
                const float* p2 = imgPtr + xmap[x+1]*3;
                const float* p0 = imgPtr + xmap[x-1]*3;

                dx0 = p2[2] - p0[2];
                dy0 = nextPtr[x1+2] - prevPtr[x1+2];
                mag0 = dx0*dx0 + dy0*dy0;

                dx = p2[1] - p0[1];
                dy = nextPtr[x1+1] - prevPtr[x1+1];
                mag = dx*dx + dy*dy;

                if( mag0 < mag )
                {
                    dx0 = dx;
                    dy0 = dy;
                    mag0 = mag;
                }

                dx = p2[0] - p0[0];
                dy = nextPtr[x1] - prevPtr[x1];
                mag = dx*dx + dy*dy;
#else
                const uchar* p2 = imgPtr + xmap[x+1]*3;
                const uchar* p0 = imgPtr + xmap[x-1]*3;

                dx0 = lut[p2[2]] - lut[p0[2]];
                dy0 = lut[nextPtr[x1+2]] - lut[prevPtr[x1+2]];
                mag0 = dx0*dx0 + dy0*dy0;

                dx = lut[p2[1]] - lut[p0[1]];
                dy = lut[nextPtr[x1+1]] - lut[prevPtr[x1+1]];
                mag = dx*dx + dy*dy;

                if( mag0 < mag )
                {
                    dx0 = dx;
                    dy0 = dy;
                    mag0 = mag;
                }

                dx = lut[p2[0]] - lut[p0[0]];
                dy = lut[nextPtr[x1]] - lut[prevPtr[x1]];
                mag = dx*dx + dy*dy;
 #endif
                if( mag0 < mag )
                {
                    dx0 = dx;
                    dy0 = dy;
                    mag0 = mag;
                }

                dbuf[x] = dx0;
                dbuf[x+width] = dy0;
            }
        }
#ifdef HAVE_IPP
        ippsCartToPolar_32f((const Ipp32f*)Dx.data, (const Ipp32f*)Dy.data, (Ipp32f*)Mag.data, pAngles, width);
        for( x = 0; x < width; x++ )
        {
           if(pAngles[x] < 0.f)
             pAngles[x] += (Ipp32f)(CV_PI*2.);
        }

        ippsNormalize_32f(pAngles, pAngles, width, 0.5f/angleScale, 1.f/angleScale);
        ippsFloor_32f(pAngles,(Ipp32f*)hidxs.data,width);
        ippsSub_32f_I((Ipp32f*)hidxs.data,pAngles,width);
        ippsMul_32f_I((Ipp32f*)Mag.data,pAngles,width);

        ippsSub_32f_I(pAngles,(Ipp32f*)Mag.data,width);
        ippsRealToCplx_32f((Ipp32f*)Mag.data,pAngles,(Ipp32fc*)gradPtr,width);
#else
        cartToPolar( Dx, Dy, Mag, Angle, false );
#endif
        for( x = 0; x < width; x++ )
        {
#ifdef HAVE_IPP
            int hidx = (int)pHidxs[x];
#else
            float mag = dbuf[x+width*2], angle = dbuf[x+width*3]*angleScale - 0.5f;
            int hidx = cvFloor(angle);
            angle -= hidx;
            gradPtr[x*2] = mag*(1.f - angle);
            gradPtr[x*2+1] = mag*angle;
#endif
            if( hidx < 0 )
                hidx += _nbins;
            else if( hidx >= _nbins )
                hidx -= _nbins;
            assert( (unsigned)hidx < (unsigned)_nbins );

            qanglePtr[x*2] = (uchar)hidx;
            hidx++;
            hidx &= hidx < _nbins ? -1 : 0;
            qanglePtr[x*2+1] = (uchar)hidx;
        }
    }
}


struct HOGCache
{
    struct BlockData
    {
        BlockData() : histOfs(0), imgOffset() {}
        int histOfs;
        Point imgOffset;
    };

    struct PixData
    {
        size_t gradOfs, qangleOfs;
        int histOfs[4];
        float histWeights[4];
        float gradWeight;
    };

    HOGCache();
    HOGCache(const HOGDescriptor* descriptor,
        const Mat& img, Size paddingTL, Size paddingBR,
        bool useCache, Size cacheStride);
    virtual ~HOGCache() {};
    virtual void init(const HOGDescriptor* descriptor,
        const Mat& img, Size paddingTL, Size paddingBR,
        bool useCache, Size cacheStride);

    Size windowsInImage(Size imageSize, Size winStride) const;
    Rect getWindow(Size imageSize, Size winStride, int idx) const;

    const float* getBlock(Point pt, float* buf);
    virtual void normalizeBlockHistogram(float* histogram) const;

    vector<PixData> pixData;
    vector<BlockData> blockData;

    bool useCache;
    vector<int> ymaxCached;
    Size winSize, cacheStride;
    Size nblocks, ncells;
    int blockHistogramSize;
    int count1, count2, count4;
    Point imgoffset;
    Mat_<float> blockCache;
    Mat_<uchar> blockCacheFlags;

    Mat grad, qangle;
    const HOGDescriptor* descriptor;
};


HOGCache::HOGCache()
{
    useCache = false;
    blockHistogramSize = count1 = count2 = count4 = 0;
    descriptor = 0;
}

HOGCache::HOGCache(const HOGDescriptor* _descriptor,
        const Mat& _img, Size _paddingTL, Size _paddingBR,
        bool _useCache, Size _cacheStride)
{
    init(_descriptor, _img, _paddingTL, _paddingBR, _useCache, _cacheStride);
}

void HOGCache::init(const HOGDescriptor* _descriptor,
        const Mat& _img, Size _paddingTL, Size _paddingBR,
        bool _useCache, Size _cacheStride)
{
    descriptor = _descriptor;
    cacheStride = _cacheStride;
    useCache = _useCache;

    descriptor->computeGradient(_img, grad, qangle, _paddingTL, _paddingBR);
    imgoffset = _paddingTL;

    winSize = descriptor->winSize;
    Size blockSize = descriptor->blockSize;
    Size blockStride = descriptor->blockStride;
    Size cellSize = descriptor->cellSize;
    int i, j, nbins = descriptor->nbins;
    int rawBlockSize = blockSize.width*blockSize.height;

    nblocks = Size((winSize.width - blockSize.width)/blockStride.width + 1,
                   (winSize.height - blockSize.height)/blockStride.height + 1);
    ncells = Size(blockSize.width/cellSize.width, blockSize.height/cellSize.height);
    blockHistogramSize = ncells.width*ncells.height*nbins;

    if( useCache )
    {
        Size cacheSize((grad.cols - blockSize.width)/cacheStride.width+1,
                       (winSize.height/cacheStride.height)+1);
        blockCache.create(cacheSize.height, cacheSize.width*blockHistogramSize);
        blockCacheFlags.create(cacheSize);
        size_t cacheRows = blockCache.rows;
        ymaxCached.resize(cacheRows);
        for(size_t ii = 0; ii < cacheRows; ii++ )
            ymaxCached[ii] = -1;
    }

    Mat_<float> weights(blockSize);
    float sigma = (float)descriptor->getWinSigma();
    float scale = 1.f/(sigma*sigma*2);

    for(i = 0; i < blockSize.height; i++)
        for(j = 0; j < blockSize.width; j++)
        {
            float di = i - blockSize.height*0.5f;
            float dj = j - blockSize.width*0.5f;
            weights(i,j) = std::exp(-(di*di + dj*dj)*scale);
        }

    blockData.resize(nblocks.width*nblocks.height);
    pixData.resize(rawBlockSize*3);

    // Initialize 2 lookup tables, pixData & blockData.
    // Here is why:
    //
    // The detection algorithm runs in 4 nested loops (at each pyramid layer):
    //  loop over the windows within the input image
    //    loop over the blocks within each window
    //      loop over the cells within each block
    //        loop over the pixels in each cell
    //
    // As each of the loops runs over a 2-dimensional array,
    // we could get 8(!) nested loops in total, which is very-very slow.
    //
    // To speed the things up, we do the following:
    //   1. loop over windows is unrolled in the HOGDescriptor::{compute|detect} methods;
    //         inside we compute the current search window using getWindow() method.
    //         Yes, it involves some overhead (function call + couple of divisions),
    //         but it's tiny in fact.
    //   2. loop over the blocks is also unrolled. Inside we use pre-computed blockData[j]
    //         to set up gradient and histogram pointers.
    //   3. loops over cells and pixels in each cell are merged
    //       (since there is no overlap between cells, each pixel in the block is processed once)
    //      and also unrolled. Inside we use PixData[k] to access the gradient values and
    //      update the histogram
    //
    count1 = count2 = count4 = 0;
    for( j = 0; j < blockSize.width; j++ )
        for( i = 0; i < blockSize.height; i++ )
        {
            PixData* data = 0;
            float cellX = (j+0.5f)/cellSize.width - 0.5f;
            float cellY = (i+0.5f)/cellSize.height - 0.5f;
            int icellX0 = cvFloor(cellX);
            int icellY0 = cvFloor(cellY);
            int icellX1 = icellX0 + 1, icellY1 = icellY0 + 1;
            cellX -= icellX0;
            cellY -= icellY0;

            if( (unsigned)icellX0 < (unsigned)ncells.width &&
                (unsigned)icellX1 < (unsigned)ncells.width )
            {
                if( (unsigned)icellY0 < (unsigned)ncells.height &&
                    (unsigned)icellY1 < (unsigned)ncells.height )
                {
                    data = &pixData[rawBlockSize*2 + (count4++)];
                    data->histOfs[0] = (icellX0*ncells.height + icellY0)*nbins;
                    data->histWeights[0] = (1.f - cellX)*(1.f - cellY);
                    data->histOfs[1] = (icellX1*ncells.height + icellY0)*nbins;
                    data->histWeights[1] = cellX*(1.f - cellY);
                    data->histOfs[2] = (icellX0*ncells.height + icellY1)*nbins;
                    data->histWeights[2] = (1.f - cellX)*cellY;
                    data->histOfs[3] = (icellX1*ncells.height + icellY1)*nbins;
                    data->histWeights[3] = cellX*cellY;
                }
                else
                {
                    data = &pixData[rawBlockSize + (count2++)];
                    if( (unsigned)icellY0 < (unsigned)ncells.height )
                    {
                        icellY1 = icellY0;
                        cellY = 1.f - cellY;
                    }
                    data->histOfs[0] = (icellX0*ncells.height + icellY1)*nbins;
                    data->histWeights[0] = (1.f - cellX)*cellY;
                    data->histOfs[1] = (icellX1*ncells.height + icellY1)*nbins;
                    data->histWeights[1] = cellX*cellY;
                    data->histOfs[2] = data->histOfs[3] = 0;
                    data->histWeights[2] = data->histWeights[3] = 0;
                }
            }
            else
            {
                if( (unsigned)icellX0 < (unsigned)ncells.width )
                {
                    icellX1 = icellX0;
                    cellX = 1.f - cellX;
                }

                if( (unsigned)icellY0 < (unsigned)ncells.height &&
                    (unsigned)icellY1 < (unsigned)ncells.height )
                {
                    data = &pixData[rawBlockSize + (count2++)];
                    data->histOfs[0] = (icellX1*ncells.height + icellY0)*nbins;
                    data->histWeights[0] = cellX*(1.f - cellY);
                    data->histOfs[1] = (icellX1*ncells.height + icellY1)*nbins;
                    data->histWeights[1] = cellX*cellY;
                    data->histOfs[2] = data->histOfs[3] = 0;
                    data->histWeights[2] = data->histWeights[3] = 0;
                }
                else
                {
                    data = &pixData[count1++];
                    if( (unsigned)icellY0 < (unsigned)ncells.height )
                    {
                        icellY1 = icellY0;
                        cellY = 1.f - cellY;
                    }
                    data->histOfs[0] = (icellX1*ncells.height + icellY1)*nbins;
                    data->histWeights[0] = cellX*cellY;
                    data->histOfs[1] = data->histOfs[2] = data->histOfs[3] = 0;
                    data->histWeights[1] = data->histWeights[2] = data->histWeights[3] = 0;
                }
            }
            data->gradOfs = (grad.cols*i + j)*2;
            data->qangleOfs = (qangle.cols*i + j)*2;
            data->gradWeight = weights(i,j);
        }

    assert( count1 + count2 + count4 == rawBlockSize );
    // defragment pixData
    for( j = 0; j < count2; j++ )
        pixData[j + count1] = pixData[j + rawBlockSize];
    for( j = 0; j < count4; j++ )
        pixData[j + count1 + count2] = pixData[j + rawBlockSize*2];
    count2 += count1;
    count4 += count2;

    // initialize blockData
    for( j = 0; j < nblocks.width; j++ )
        for( i = 0; i < nblocks.height; i++ )
        {
            BlockData& data = blockData[j*nblocks.height + i];
            data.histOfs = (j*nblocks.height + i)*blockHistogramSize;
            data.imgOffset = Point(j*blockStride.width,i*blockStride.height);
        }
}


const float* HOGCache::getBlock(Point pt, float* buf)
{
    float* blockHist = buf;
    assert(descriptor != 0);

    Size blockSize = descriptor->blockSize;
    pt += imgoffset;

    CV_Assert( (unsigned)pt.x <= (unsigned)(grad.cols - blockSize.width) &&
               (unsigned)pt.y <= (unsigned)(grad.rows - blockSize.height) );

    if( useCache )
    {
        CV_Assert( pt.x % cacheStride.width == 0 &&
                   pt.y % cacheStride.height == 0 );
        Point cacheIdx(pt.x/cacheStride.width,
                      (pt.y/cacheStride.height) % blockCache.rows);
        if( pt.y != ymaxCached[cacheIdx.y] )
        {
            Mat_<uchar> cacheRow = blockCacheFlags.row(cacheIdx.y);
            cacheRow = (uchar)0;
            ymaxCached[cacheIdx.y] = pt.y;
        }

        blockHist = &blockCache[cacheIdx.y][cacheIdx.x*blockHistogramSize];
        uchar& computedFlag = blockCacheFlags(cacheIdx.y, cacheIdx.x);
        if( computedFlag != 0 )
            return blockHist;
        computedFlag = (uchar)1; // set it at once, before actual computing
    }

    int k, C1 = count1, C2 = count2, C4 = count4;
    const float* gradPtr = (const float*)(grad.data + grad.step*pt.y) + pt.x*2;
    const uchar* qanglePtr = qangle.data + qangle.step*pt.y + pt.x*2;

    CV_Assert( blockHist != 0 );
#ifdef HAVE_IPP
    ippsZero_32f(blockHist,blockHistogramSize);
#else
    for( k = 0; k < blockHistogramSize; k++ )
        blockHist[k] = 0.f;
#endif

    const PixData* _pixData = &pixData[0];

    for( k = 0; k < C1; k++ )
    {
        const PixData& pk = _pixData[k];
        const float* a = gradPtr + pk.gradOfs;
        float w = pk.gradWeight*pk.histWeights[0];
        const uchar* h = qanglePtr + pk.qangleOfs;
        int h0 = h[0], h1 = h[1];
        float* hist = blockHist + pk.histOfs[0];
        float t0 = hist[h0] + a[0]*w;
        float t1 = hist[h1] + a[1]*w;
        hist[h0] = t0; hist[h1] = t1;
    }

    for( ; k < C2; k++ )
    {
        const PixData& pk = _pixData[k];
        const float* a = gradPtr + pk.gradOfs;
        float w, t0, t1, a0 = a[0], a1 = a[1];
        const uchar* h = qanglePtr + pk.qangleOfs;
        int h0 = h[0], h1 = h[1];

        float* hist = blockHist + pk.histOfs[0];
        w = pk.gradWeight*pk.histWeights[0];
        t0 = hist[h0] + a0*w;
        t1 = hist[h1] + a1*w;
        hist[h0] = t0; hist[h1] = t1;

        hist = blockHist + pk.histOfs[1];
        w = pk.gradWeight*pk.histWeights[1];
        t0 = hist[h0] + a0*w;
        t1 = hist[h1] + a1*w;
        hist[h0] = t0; hist[h1] = t1;
    }

    for( ; k < C4; k++ )
    {
        const PixData& pk = _pixData[k];
        const float* a = gradPtr + pk.gradOfs;
        float w, t0, t1, a0 = a[0], a1 = a[1];
        const uchar* h = qanglePtr + pk.qangleOfs;
        int h0 = h[0], h1 = h[1];

        float* hist = blockHist + pk.histOfs[0];
        w = pk.gradWeight*pk.histWeights[0];
        t0 = hist[h0] + a0*w;
        t1 = hist[h1] + a1*w;
        hist[h0] = t0; hist[h1] = t1;

        hist = blockHist + pk.histOfs[1];
        w = pk.gradWeight*pk.histWeights[1];
        t0 = hist[h0] + a0*w;
        t1 = hist[h1] + a1*w;
        hist[h0] = t0; hist[h1] = t1;

        hist = blockHist + pk.histOfs[2];
        w = pk.gradWeight*pk.histWeights[2];
        t0 = hist[h0] + a0*w;
        t1 = hist[h1] + a1*w;
        hist[h0] = t0; hist[h1] = t1;

        hist = blockHist + pk.histOfs[3];
        w = pk.gradWeight*pk.histWeights[3];
        t0 = hist[h0] + a0*w;
        t1 = hist[h1] + a1*w;
        hist[h0] = t0; hist[h1] = t1;
    }

    normalizeBlockHistogram(blockHist);

    return blockHist;
}


void HOGCache::normalizeBlockHistogram(float* _hist) const
{
    float* hist = &_hist[0];
#ifdef HAVE_IPP
    size_t sz = blockHistogramSize;
#else
    size_t i, sz = blockHistogramSize;
#endif

    float sum = 0;
#ifdef HAVE_IPP
    ippsDotProd_32f(hist,hist,sz,&sum);
#else
    for( i = 0; i < sz; i++ )
        sum += hist[i]*hist[i];
#endif

    float scale = 1.f/(std::sqrt(sum)+sz*0.1f), thresh = (float)descriptor->L2HysThreshold;
#ifdef HAVE_IPP
    ippsMulC_32f_I(scale,hist,sz);
    ippsThreshold_32f_I( hist, sz, thresh, ippCmpGreater );
    ippsDotProd_32f(hist,hist,sz,&sum);
#else
    for( i = 0, sum = 0; i < sz; i++ )
    {
        hist[i] = std::min(hist[i]*scale, thresh);
        sum += hist[i]*hist[i];
    }
#endif

    scale = 1.f/(std::sqrt(sum)+1e-3f);
#ifdef HAVE_IPP
    ippsMulC_32f_I(scale,hist,sz);
#else
    for( i = 0; i < sz; i++ )
        hist[i] *= scale;
#endif
}


Size HOGCache::windowsInImage(Size imageSize, Size winStride) const
{
    return Size((imageSize.width - winSize.width)/winStride.width + 1,
                (imageSize.height - winSize.height)/winStride.height + 1);
}

Rect HOGCache::getWindow(Size imageSize, Size winStride, int idx) const
{
    int nwindowsX = (imageSize.width - winSize.width)/winStride.width + 1;
    int y = idx / nwindowsX;
    int x = idx - nwindowsX*y;
    return Rect( x*winStride.width, y*winStride.height, winSize.width, winSize.height );
}


void HOGDescriptor::compute(const Mat& img, vector<float>& descriptors,
                            Size winStride, Size padding,
                            const vector<Point>& locations) const
{
    if( winStride == Size() )
        winStride = cellSize;
    Size cacheStride(gcd(winStride.width, blockStride.width),
                     gcd(winStride.height, blockStride.height));
    size_t nwindows = locations.size();
    padding.width = (int)alignSize(std::max(padding.width, 0), cacheStride.width);
    padding.height = (int)alignSize(std::max(padding.height, 0), cacheStride.height);
    Size paddedImgSize(img.cols + padding.width*2, img.rows + padding.height*2);

    HOGCache cache(this, img, padding, padding, nwindows == 0, cacheStride);

    if( !nwindows )
        nwindows = cache.windowsInImage(paddedImgSize, winStride).area();

    const HOGCache::BlockData* blockData = &cache.blockData[0];

    int nblocks = cache.nblocks.area();
    int blockHistogramSize = cache.blockHistogramSize;
    size_t dsize = getDescriptorSize();
    descriptors.resize(dsize*nwindows);

    for( size_t i = 0; i < nwindows; i++ )
    {
        float* descriptor = &descriptors[i*dsize];

        Point pt0;
        if( !locations.empty() )
        {
            pt0 = locations[i];
            if( pt0.x < -padding.width || pt0.x > img.cols + padding.width - winSize.width ||
                pt0.y < -padding.height || pt0.y > img.rows + padding.height - winSize.height )
                continue;
        }
        else
        {
            pt0 = cache.getWindow(paddedImgSize, winStride, (int)i).tl() - Point(padding);
            CV_Assert(pt0.x % cacheStride.width == 0 && pt0.y % cacheStride.height == 0);
        }

        for( int j = 0; j < nblocks; j++ )
        {
            const HOGCache::BlockData& bj = blockData[j];
            Point pt = pt0 + bj.imgOffset;

            float* dst = descriptor + bj.histOfs;
            const float* src = cache.getBlock(pt, dst);
            if( src != dst )
#ifdef HAVE_IPP
               ippsCopy_32f(src,dst,blockHistogramSize);
#else
                for( int k = 0; k < blockHistogramSize; k++ )
                    dst[k] = src[k];
#endif
        }
    }
}


void HOGDescriptor::detect(const Mat& img,
    vector<Point>& hits, vector<double>& weights, double hitThreshold,
    Size winStride, Size padding, const vector<Point>& locations) const
{
    hits.clear();
    if( svmDetector.empty() )
        return;

    if( winStride == Size() )
        winStride = cellSize;
    Size cacheStride(gcd(winStride.width, blockStride.width),
                     gcd(winStride.height, blockStride.height));
    size_t nwindows = locations.size();
    padding.width = (int)alignSize(std::max(padding.width, 0), cacheStride.width);
    padding.height = (int)alignSize(std::max(padding.height, 0), cacheStride.height);
    Size paddedImgSize(img.cols + padding.width*2, img.rows + padding.height*2);

    HOGCache cache(this, img, padding, padding, nwindows == 0, cacheStride);

    if( !nwindows )
        nwindows = cache.windowsInImage(paddedImgSize, winStride).area();

    const HOGCache::BlockData* blockData = &cache.blockData[0];

    int nblocks = cache.nblocks.area();
    int blockHistogramSize = cache.blockHistogramSize;
    size_t dsize = getDescriptorSize();

    double rho = svmDetector.size() > dsize ? svmDetector[dsize] : 0;
    vector<float> blockHist(blockHistogramSize);

    for( size_t i = 0; i < nwindows; i++ )
    {
        Point pt0;
        if( !locations.empty() )
        {
            pt0 = locations[i];
            if( pt0.x < -padding.width || pt0.x > img.cols + padding.width - winSize.width ||
                pt0.y < -padding.height || pt0.y > img.rows + padding.height - winSize.height )
                continue;
        }
        else
        {
            pt0 = cache.getWindow(paddedImgSize, winStride, (int)i).tl() - Point(padding);
            CV_Assert(pt0.x % cacheStride.width == 0 && pt0.y % cacheStride.height == 0);
        }
        double s = rho;
        const float* svmVec = &svmDetector[0];
#ifdef HAVE_IPP
        int j;
#else
        int j, k;
#endif
        for( j = 0; j < nblocks; j++, svmVec += blockHistogramSize )
        {
            const HOGCache::BlockData& bj = blockData[j];
            Point pt = pt0 + bj.imgOffset;

            const float* vec = cache.getBlock(pt, &blockHist[0]);
#ifdef HAVE_IPP
            Ipp32f partSum;
            ippsDotProd_32f(vec,svmVec,blockHistogramSize,&partSum);
            s += (double)partSum;
#else
            for( k = 0; k <= blockHistogramSize - 4; k += 4 )
                s += vec[k]*svmVec[k] + vec[k+1]*svmVec[k+1] +
                    vec[k+2]*svmVec[k+2] + vec[k+3]*svmVec[k+3];
            for( ; k < blockHistogramSize; k++ )
                s += vec[k]*svmVec[k];
#endif
        }
        if( s >= hitThreshold )
        {
            hits.push_back(pt0);
            weights.push_back(s);
        }
    }
}

void HOGDescriptor::detect(const Mat& img, vector<Point>& hits, double hitThreshold,
                           Size winStride, Size padding, const vector<Point>& locations) const
{
    vector<double> weightsV;
    detect(img, hits, weightsV, hitThreshold, winStride, padding, locations);
}

class HOGInvoker : public ParallelLoopBody
{
public:
    HOGInvoker( const HOGDescriptor* _hog, const Mat& _img,
                double _hitThreshold, Size _winStride, Size _padding,
                const double* _levelScale, std::vector<Rect> * _vec, Mutex* _mtx,
                std::vector<double>* _weights=0, std::vector<double>* _scales=0 )
    {
        hog = _hog;
        img = _img;
        hitThreshold = _hitThreshold;
        winStride = _winStride;
        padding = _padding;
        levelScale = _levelScale;
        vec = _vec;
        weights = _weights;
        scales = _scales;
        mtx = _mtx;
    }

    void operator()( const Range& range ) const
    {
        int i, i1 = range.start, i2 = range.end;
        double minScale = i1 > 0 ? levelScale[i1] : i2 > 1 ? levelScale[i1+1] : std::max(img.cols, img.rows);
        Size maxSz(cvCeil(img.cols/minScale), cvCeil(img.rows/minScale));
        Mat smallerImgBuf(maxSz, img.type());
        vector<Point> locations;
        vector<double> hitsWeights;

        for( i = i1; i < i2; i++ )
        {
            double scale = levelScale[i];
            Size sz(cvRound(img.cols/scale), cvRound(img.rows/scale));
            Mat smallerImg(sz, img.type(), smallerImgBuf.data);
            if( sz == img.size() )
                smallerImg = Mat(sz, img.type(), img.data, img.step);
            else
                resize(img, smallerImg, sz);
            hog->detect(smallerImg, locations, hitsWeights, hitThreshold, winStride, padding);
            Size scaledWinSize = Size(cvRound(hog->winSize.width*scale), cvRound(hog->winSize.height*scale));

            mtx->lock();
            for( size_t j = 0; j < locations.size(); j++ )
            {
                vec->push_back(Rect(cvRound(locations[j].x*scale),
                                    cvRound(locations[j].y*scale),
                                    scaledWinSize.width, scaledWinSize.height));
                if (scales)
                {
                    scales->push_back(scale);
                }
            }
            mtx->unlock();

            if (weights && (!hitsWeights.empty()))
            {
                mtx->lock();
                for (size_t j = 0; j < locations.size(); j++)
                {
                    weights->push_back(hitsWeights[j]);
                }
                mtx->unlock();
            }
        }
    }

    const HOGDescriptor* hog;
    Mat img;
    double hitThreshold;
    Size winStride;
    Size padding;
    const double* levelScale;
    std::vector<Rect>* vec;
    std::vector<double>* weights;
    std::vector<double>* scales;
    Mutex* mtx;
};


void HOGDescriptor::detectMultiScale(
    const Mat& img, vector<Rect>& foundLocations, vector<double>& foundWeights,
    double hitThreshold, Size winStride, Size padding,
    double scale0, double finalThreshold, bool useMeanshiftGrouping) const
{
    double scale = 1.;
    int levels = 0;

    vector<double> levelScale;
    for( levels = 0; levels < nlevels; levels++ )
    {
        levelScale.push_back(scale);
        if( cvRound(img.cols/scale) < winSize.width ||
            cvRound(img.rows/scale) < winSize.height ||
            scale0 <= 1 )
            break;
        scale *= scale0;
    }
    levels = std::max(levels, 1);
    levelScale.resize(levels);

    std::vector<Rect> allCandidates;
    std::vector<double> tempScales;
    std::vector<double> tempWeights;
    std::vector<double> foundScales;
    Mutex mtx;

    parallel_for_(Range(0, (int)levelScale.size()),
                 HOGInvoker(this, img, hitThreshold, winStride, padding, &levelScale[0], &allCandidates, &mtx, &tempWeights, &tempScales));

    std::copy(tempScales.begin(), tempScales.end(), back_inserter(foundScales));
    foundLocations.clear();
    std::copy(allCandidates.begin(), allCandidates.end(), back_inserter(foundLocations));
    foundWeights.clear();
    std::copy(tempWeights.begin(), tempWeights.end(), back_inserter(foundWeights));

    if ( useMeanshiftGrouping )
    {
        groupRectangles_meanshift(foundLocations, foundWeights, foundScales, finalThreshold, winSize);
    }
    else
    {
        groupRectangles(foundLocations, (int)finalThreshold, 0.2);
    }
}

void HOGDescriptor::detectMultiScale(const Mat& img, vector<Rect>& foundLocations,
                                     double hitThreshold, Size winStride, Size padding,
                                     double scale0, double finalThreshold, bool useMeanshiftGrouping) const
{
    vector<double> foundWeights;
    detectMultiScale(img, foundLocations, foundWeights, hitThreshold, winStride,
                     padding, scale0, finalThreshold, useMeanshiftGrouping);
}

typedef RTTIImpl<HOGDescriptor> HOGRTTI;

CvType hog_type( CV_TYPE_NAME_HOG_DESCRIPTOR, HOGRTTI::isInstance,
                 HOGRTTI::release, HOGRTTI::read, HOGRTTI::write, HOGRTTI::clone);

vector<float> HOGDescriptor::getDefaultPeopleDetector()
{
    static const float detector[] = {
       0.05359386f, -0.14721455f, -0.05532170f, 0.05077307f,
       0.11547081f, -0.04268804f, 0.04635834f, -0.05468199f, 0.08232084f,
       0.10424068f, -0.02294518f, 0.01108519f, 0.01378693f, 0.11193510f,
       0.01268418f, 0.08528346f, -0.06309239f, 0.13054633f, 0.08100729f,
       -0.05209739f, -0.04315529f, 0.09341384f, 0.11035026f, -0.07596218f,
       -0.05517511f, -0.04465296f, 0.02947334f, 0.04555536f,
       -3.55954492e-003f, 0.07818956f, 0.07730991f, 0.07890715f, 0.06222893f,
       0.09001380f, -0.03574381f, 0.03414327f, 0.05677258f, -0.04773581f,
       0.03746637f, -0.03521175f, 0.06955440f, -0.03849038f, 0.01052293f,
       0.01736112f, 0.10867710f, 0.08748853f, 3.29739624e-003f, 0.10907028f,
       0.07913758f, 0.10393070f, 0.02091867f, 0.11594022f, 0.13182420f,
       0.09879354f, 0.05362710f, -0.06745391f, -7.01260753e-003f,
       5.24702156e-003f, 0.03236255f, 0.01407916f, 0.02207983f, 0.02537322f,
       0.04547948f, 0.07200756f, 0.03129894f, -0.06274468f, 0.02107014f,
       0.06035208f, 0.08636236f, 4.53164103e-003f, 0.02193363f, 0.02309801f,
       0.05568166f, -0.02645093f, 0.04448695f, 0.02837519f, 0.08975694f,
       0.04461516f, 0.08975355f, 0.07514391f, 0.02306982f, 0.10410084f,
       0.06368385f, 0.05943464f, 4.58420580e-003f, 0.05220337f, 0.06675851f,
       0.08358569f, 0.06712101f, 0.06559004f, -0.03930482f, -9.15936660e-003f,
       -0.05897915f, 0.02816453f, 0.05032348f, 0.06780671f, 0.03377650f,
       -6.09417039e-004f, -0.01795146f, -0.03083684f, -0.01302475f,
       -0.02972313f, 7.88706727e-003f, -0.03525961f, -2.50397739e-003f,
       0.05245084f, 0.11791293f, -0.02167498f, 0.05299332f, 0.06640524f,
       0.05190265f, -8.27316567e-003f, 0.03033127f, 0.05842173f,
       -4.01050318e-003f, -6.25105947e-003f, 0.05862958f, -0.02465461f,
       0.05546781f, -0.08228195f, -0.07234028f, 0.04640540f, -0.01308254f,
       -0.02506191f, 0.03100746f, -0.04665651f, -0.04591486f, 0.02949927f,
       0.06035462f, 0.02244646f, -0.01698639f, 0.01040041f, 0.01131170f,
       0.05419579f, -0.02130277f, -0.04321722f, -0.03665198f, 0.01126490f,
       -0.02606488f, -0.02228328f, -0.02255680f, -0.03427236f,
       -7.75165204e-003f, -0.06195229f, 8.21638294e-003f, 0.09535975f,
       -0.03709979f, -0.06942501f, 0.14579427f, -0.05448192f, -0.02055904f,
       0.05747357f, 0.02781788f, -0.07077577f, -0.05178314f, -0.10429011f,
       -0.11235505f, 0.07529039f, -0.07559302f, -0.08786739f, 0.02983843f,
       0.02667585f, 0.01382199f, -0.01797496f, -0.03141199f, -0.02098101f,
       0.09029204f, 0.04955018f, 0.13718739f, 0.11379953f, 1.80019124e-003f,
       -0.04577610f, -1.11108483e-003f, -0.09470536f, -0.11596080f,
       0.04489342f, 0.01784211f, 3.06850672e-003f, 0.10781866f,
       3.36498418e-003f, -0.10842580f, -0.07436839f, -0.10535070f,
       -0.01866805f, 0.16057891f, -5.07316366e-003f, -0.04295658f,
       -5.90488780e-003f, 8.82003549e-003f, -0.01492646f, -0.05029279f,
       -0.12875880f, 8.78831954e-004f, -0.01297184f, -0.07592774f,
       -0.02668831f, -6.93787413e-004f, 0.02406698f, -0.01773298f,
       -0.03855745f, -0.05877856f, 0.03259695f, 0.12826584f, 0.06292590f,
       -4.10733931e-003f, 0.10996531f, 0.01332991f, 0.02088735f, 0.04037504f,
       -0.05210760f, 0.07760046f, 0.06399347f, -0.05751930f, -0.10053057f,
       0.07505023f, -0.02139782f, 0.01796176f, 2.34400877e-003f, -0.04208319f,
       0.07355055f, 0.05093350f, -0.02996780f, -0.02219072f, 0.03355330f,
       0.04418742f, -0.05580705f, -0.05037573f, -0.04548179f, 0.01379514f,
       0.02150671f, -0.02194211f, -0.13682702f, 0.05464972f, 0.01608082f,
       0.05309116f, 0.04701022f, 1.33690401e-003f, 0.07575664f, 0.09625306f,
       8.92647635e-003f, -0.02819123f, 0.10866830f, -0.03439325f,
       -0.07092371f, -0.06004780f, -0.02712298f, -7.07467366e-003f,
       -0.01637020f, 0.01336790f, -0.10313606f, 0.04906582f, -0.05732445f,
       -0.02731079f, 0.01042235f, -0.08340668f, 0.03686501f, 0.06108340f,
       0.01322748f, -0.07809529f, 0.03774724f, -0.03413248f, -0.06096525f,
       -0.04212124f, -0.07982176f, -1.25973229e-003f, -0.03045501f,
       -0.01236493f, -0.06312395f, 0.04789570f, -0.04602066f, 0.08576570f,
       0.02521080f, 0.02988098f, 0.10314583f, 0.07060035f, 0.04520544f,
       -0.04426654f, 0.13146530f, 0.08386490f, 0.02164590f, -2.12280243e-003f,
       -0.03686353f, -0.02074944f, -0.03829959f, -0.01530596f, 0.02689708f,
       0.11867401f, -0.06043470f, -0.02785023f, -0.04775074f, 0.04878745f,
       0.06350956f, 0.03494788f, 0.01467400f, 1.17890188e-003f, 0.04379614f,
       2.03681854e-003f, -0.03958609f, -0.01072688f, 6.43705716e-003f,
       0.02996500f, -0.03418507f, -0.01960307f, -0.01219154f,
       -4.37000440e-003f, -0.02549453f, 0.02646318f, -0.01632513f,
       6.46516960e-003f, -0.01929734f, 4.78711911e-003f, 0.04962371f,
       0.03809111f, 0.07265724f, 0.05758125f, -0.03741554f, 0.01648608f,
       -8.45285598e-003f, 0.03996826f, -0.08185477f, 0.02638875f,
       -0.04026615f, -0.02744674f, -0.04071517f, 1.05096330e-003f,
       -0.04741232f, -0.06733172f, 8.70434940e-003f, -0.02192543f,
       1.35350740e-003f, -0.03056974f, -0.02975521f, -0.02887780f,
       -0.01210713f, -0.04828526f, -0.09066251f, -0.09969629f, -0.03665164f,
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       -0.02607902f, -0.02938001f, 0.02720689f, -0.06228397f, -0.02970936f,
       -0.03426210f, -0.10280876f, -0.06739304f, -0.05227850f, 0.03360292f,
       -0.11278441f, -0.06966180f, -0.13937433f, 9.10932291e-003f,
       2.52020749e-004f, -4.07359656e-003f, 0.12310639f, 0.09343060f,
       0.07302511f, 0.03222093f, 0.07532879f, 0.03792387f, -0.04985180f,
       0.01804602f, 0.02694195f, 0.13481498f, 0.04601225f, 0.04106982f,
       0.08511057f, 0.12314661f, 0.01320830f, 0.05044121f, -5.52943908e-003f,
       -0.08992624f, -0.02249301f, -0.08181777f, 0.06165213f, -0.03256603f,
       -0.01068920f, -0.01323473f, -0.11970232f, -0.04616347f, -0.12088681f,
       -0.06762606f, -0.08676834f, -0.06434575f, 0.01772529f, 0.03469615f,
       -0.10926618f, 0.03013873f, 0.14030397f, 0.16130108f, 0.17985588f,
       0.11281928f, 0.10530639f, 0.08905948f, 0.07733764f, 0.06695238f,
       0.02142088f, 0.06438877f, 0.09794453f, 0.05745072f, 0.02788557f,
       0.02632830f, 0.07985807f, 4.24902979e-003f, 8.47890321e-003f,
       -0.02679466f, -5.28812688e-003f, -0.02162580f, -0.07490715f,
       -0.08251337f, -0.02056576f, -0.01026194f, -1.15492963e-003f,
       -5.75720915e-004f, -0.07210591f, -0.07320981f, -0.04883312f,
       -0.10897151f, -0.07477258f, -0.08867134f, -0.09222437f, -0.10924666f,
       -0.10430276f, 0.07953499f, 0.02767959f, 0.11393359f, 0.18779543f,
       0.03313421f, 0.02143700f, 0.05852016f, -2.12067598e-003f,
       -3.76984011e-003f, 0.02774167f, -0.03124610f, 0.01465141f, 0.01616004f,
       -0.01391913f, -0.04404102f, -0.05444227f, -0.14684731f, -0.15016587f,
       0.04509468f, 1.29563001e-003f, 0.01398350f, 0.05610404f, -0.04868806f,
       -0.04776716f, -8.16873740e-003f, -2.30126386e-003f, -0.02286313f,
       0.11983398f, -0.04703261f, -0.08814441f, -0.07585249f, -0.10799607f,
       -0.03232087f, 0.01509786f, -0.04843464f, -0.03967846f, 0.09589416f,
       0.01352560f, -0.01458119f, 0.01050829f, -0.03038946f, 0.01608388f,
       1.11975556e-003f, -0.01250656f, 2.86211423e-003f, 0.04333691f,
       -0.14603497f, -0.01946543f, -0.02327525f, -0.01973944f, 0.07944400f,
       -0.02224544f, -0.06701808f, 0.03476532f, 0.11505594f, -0.02712801f,
       -0.01665113f, 0.06315716f, -0.08205860f, 0.07431999f, 0.04915778f,
       -0.04468752f, -0.01490402f, 0.07400476f, -0.11650901f, 0.05102430f,
       0.04559118f, -0.05916039f, 0.08840760f, -0.01587902f, -0.14890194f,
       0.07857784f, 0.04710254f, -0.05381983f, -0.07331945f, -0.03604643f,
       0.15611970f, 0.07649943f, -0.05959348f, -0.02776607f, 0.11098688f,
       0.03758875f, -0.04446875f, 0.04933187f, 0.01345535f, 0.06921103f,
       0.07364785f, 0.05518956f, 0.02899585f, 0.09375840f, 0.10518434f,
       -0.04420241f, 0.01915282f, -3.56386811e-003f, 0.14586878f, 0.10286101f,
       -0.04360626f, -0.12723237f, 0.09076386f, 0.11119842f, -0.06035013f,
       0.09674817f, 0.08938243f, 0.07065924f, 0.02603180f, 5.84815582e-003f,
       -0.05922065f, 0.12360309f, 3.59695964e-003f, 2.99844006e-003f,
       0.03697936f, 0.02043072f, 0.04168725f, 0.01025975f, -0.01359980f,
       -0.01600920f, 0.02581056f, 0.02329250f, 2.98100687e-003f, 0.01629762f,
       0.06652115f, 0.05855627f, 0.01237463f, -0.01297135f, 0.01761587f,
       0.05090865f, 0.06549342f, -0.04425945f, 2.43203156e-003f,
       3.07327788e-003f, 0.06678630f, -0.04303836f, 0.01082393f, -0.06476044f,
       0.04077786f, 0.12441979f, 0.08237778f, 0.07424165f, 0.04065890f,
       0.06905543f, 0.09556347f, 0.12724875f, -0.02132082f, 0.08514154f,
       -0.04175328f, -0.02666954f, 0.01897836f, 0.03317382f, 9.45465732e-003f,
       -0.01238974f, -0.04242500f, -0.01419479f, -0.03545213f, -0.02440874f,
       0.08684119f, 0.04212951f, 0.02462858f, -0.01104825f, -5.01706870e-003f,
       0.02968982f, 0.02597476f, -0.01568939f, 0.04514892f, 0.06974549f,
       0.08670278f, 0.06828108f, 0.10238872f, 0.05405957f, 0.06548470f,
       -0.03763957f, 0.01366090f, 0.07069602f, 0.05363748f, 0.04798120f,
       0.11706422f, 0.05466456f, -0.01869259f, 0.06344382f, 0.03106543f,
       0.08432506f, -0.02061096f, 0.03821088f, -6.92190882e-003f,
       6.40467042e-003f, -0.01271779f, 6.89014705e-005f, 0.04541415f,
       -0.01899539f, -0.05020239f, 0.03000903f, 0.01090422f, 4.52452758e-003f,
       0.02573632f, -0.02388454f, -0.04200457f, 1.72783900e-003f,
       -0.05978370f, -0.02720562f, 0.06573715f, 0.01154317f, 0.01265615f,
       0.07375994f, -9.19828378e-003f, -0.04914120f, 0.02124831f, 0.06455322f,
       0.04372910f, -0.03310043f, 0.03605788f, -6.78055827e-003f,
       9.36202332e-003f, 0.01747596f, -0.06406314f, -0.06812935f, 0.08080816f,
       -0.02778088f, 0.02735260f, 0.06393493f, 0.06652229f, 0.05676993f,
       0.08640018f, -7.59188086e-003f, -0.02012847f, -0.04741159f,
       -0.01657069f, -0.01624399f, 0.05547778f, -2.33309763e-003f,
       0.01120033f, 0.06141156f, -0.06285004f, -0.08732341f, -0.09313398f,
       -0.04267832f, 5.57443965e-003f, 0.04809862f, 0.01773641f,
       5.37361018e-003f, 0.14842421f, -0.06298012f, -0.02935147f, 0.11443478f,
       -0.05034208f, 5.65494271e-003f, 0.02076526f, -0.04577984f,
       -0.04735741f, 0.02961071f, -0.09307127f, -0.04417921f, -0.04990027f,
       -0.03940028f, 0.01306016f, 0.06267900f, 0.03758737f, 0.08460117f,
       0.13858789f, 0.04862388f, -0.06319809f, -0.05655516f, 0.01885816f,
       -0.03285607f, 0.03371567f, -0.07040928f, -0.04514049f, 0.01392166f,
       0.08184422f, -0.07230316f, 0.02386871f, 0.02184591f, 0.02605764f,
       -0.01033954f, 9.29878280e-003f, 7.67351175e-003f, 0.15189242f,
       0.02069071f, -0.09738296f, -0.08894105f, -0.07768748f, 0.02332268f,
       -0.01778995f, -0.03258888f, -0.08180822f, -0.08492987f, 0.02290156f,
       -0.11368170f, -0.03554465f, -0.04533844f, -0.02861580f, 0.06782424f,
       0.01113123f, 0.02453644f, 0.12721945f, 0.08084814f, -0.03607795f,
       0.01109122f, 0.04803548f, -0.03489929f, 0.03399536f, -0.05682014f,
       8.59533902e-003f, -4.27904585e-003f, 0.03230887f, -0.01300198f,
       -0.01038137f, -0.07930113f, 8.33097473e-003f, 0.02296994f,
       -0.01306500f, -0.01881626f, 0.04413369f, 0.05729880f, -0.03761553f,
       0.01942326f, 1.64540811e-003f, -0.03811319f, 0.04190650f, -0.14978096f,
       -0.04514487f, 0.01209545f, -5.46460645e-003f, -0.01647195f,
       7.63064111e-003f, -0.07494587f, 0.08415288f, 0.10020141f, -0.01228561f,
       0.06553826f, 0.04554005f, 0.07890417f, 0.03041138f, 0.01752007f,
       0.09208256f, -3.74419295e-004f, 0.10549527f, 0.04686913f, 0.01894833f,
       -0.02651412f, -4.34682379e-003f, 5.44942822e-003f, 0.01444484f,
       0.05882156f, -0.03336544f, 0.04603891f, -0.10432546f, 0.01923928f,
       0.01842845f, -0.01712168f, -0.02222766f, 0.04693324f, -0.06202956f,
       -0.01422159f, 0.08732220f, -0.07706107f, 0.02661049f, -0.04300238f,
       -0.03092422f, -0.03552184f, -0.01886088f, -0.04979934f, 0.03906401f,
       0.04608644f, 0.04966111f, 0.04275464f, -0.04621769f, -0.02653212f,
       8.57011229e-003f, 0.03839684f, 0.05818764f, 0.03880796f,
       -2.76100676e-004f, 0.03076511f, -0.03266929f, -0.05374557f,
       0.04986527f, -9.45429131e-003f, 0.03582499f, -2.64564669e-003f,
       -1.07461517e-003f, 0.02962313f, -0.01483363f, 0.03060869f, 0.02448327f,
       0.01845641f, 0.03282966f, -0.03534438f, -0.01084059f, -0.01119136f,
       -1.85360224e-003f, -5.94652840e-004f, -0.04451817f, 2.98327743e-003f,
       0.06272484f, -0.02152076f, -3.05971340e-003f, -0.05070828f,
       0.01531762f, 0.01282815f, 0.05167150f, 9.46266949e-003f,
       -3.34558333e-003f, 0.11442288f, -0.03906701f, -2.67325155e-003f,
       0.03069184f, -0.01134165f, 0.02949462f, 0.02879886f, 0.03855566f,
       -0.03450781f, 0.09142872f, -0.02156654f, 0.06075062f, -0.06220816f,
       0.01944680f, 6.68372354e-003f, -0.06656796f, 8.70784000e-003f,
       0.03456013f, 0.02434320f, -0.13236357f, -0.04177035f, -0.02069627f,
       0.01068112f, 0.01505432f, -0.07517391f, -3.83571628e-003f,
       -0.06298508f, -0.02881260f, -0.13101046f, -0.07221562f,
       -5.79945277e-003f, -8.57300125e-003f, 0.03782469f, 0.02762164f,
       0.04942456f, -0.02936396f, 0.09597211f, 0.01921411f, 0.06101191f,
       -0.04787507f, -0.01379578f, -7.40224449e-003f, -0.02220136f,
       -0.01313756f, 7.77558051e-003f, 0.12296968f, 0.02939998f, 0.03594062f,
       -0.07788624f, -0.01133144f, 3.99316690e-004f, -0.06090347f,
       -0.01122066f, -4.68682544e-003f, 0.07633100f, -0.06748922f,
       -0.05640298f, -0.05265681f, -0.01139122f, -0.01624347f, -0.04715714f,
       -0.01099092f, 0.01048561f, 3.28499987e-003f, -0.05810167f,
       -0.07699911f, -0.03330683f, 0.04185145f, 0.03478536f, 0.02275165f,
       0.02304766f, 6.66040834e-003f, 0.10968148f, -5.93013782e-003f,
       -0.04858336f, -0.04203213f, -0.09316786f, -6.13074889e-003f,
       -0.02544625f, 0.01366201f, 9.18555818e-003f, -0.01846578f,
       -0.05622401f, -0.03989377f, -0.07810296f, 6.91275718e-003f,
       0.05957597f, -0.03901334f, 0.01572002f, -0.01193903f,
       -6.89400872e-003f, -0.03093356f, -0.04136098f, -0.01562869f,
       -0.04604580f, 0.02865234f, -0.08678447f, -0.03232484f, -0.05364593f,
       -0.01445016f, -0.07003860f, -0.08669746f, -0.04520775f, 0.04274122f,
       0.03117515f, 0.08175703f, 0.01081109f, 0.06379741f, 0.06199206f,
       0.02865988f, 0.02360346f, 0.06725410f, -0.03248780f, -9.37702879e-003f,
       0.08265898f, -0.02245839f, 0.05125763f, -0.01862395f, 0.01973453f,
       -0.01994494f, -0.10770868f, 0.03180375f, 3.23935156e-003f,
       -0.02142080f, -0.04256190f, 0.04760900f, 0.04282863f, 0.05635953f,
       -0.01870849f, 0.05540622f, -0.03042666f, 0.01455277f, -0.06630179f,
       -0.05843807f, -0.03739681f, -0.09739155f, -0.03220233f, -0.05620182f,
       -0.10381401f, 0.07400211f, 4.20676917e-003f, 0.03258535f,
       2.14308966e-003f, 0.05121966f, -0.01274337f, 0.02384761f, 0.06335578f,
       -0.07905591f, 0.08375625f, -0.07898903f, -0.06508528f, -0.02498444f,
       0.06535810f, 0.03970535f, 0.04895468f, -0.01169566f, -0.03980601f,
       0.05682293f, 0.05925463f, -0.01165808f, -0.07936699f, -0.04208954f,
       0.01333987f, 0.09051196f, 0.10098671f, -0.03974256f, 0.01238771f,
       -0.07501741f, -0.03655440f, -0.04301528f, 0.09216860f,
       4.63579083e-004f, 0.02851115f, 0.02142735f, 1.28244064e-004f,
       0.02879687f, -0.08554889f, -0.04838862f, 0.08135369f, -0.05756533f,
       0.01413900f, 0.03451880f, -0.06619488f, -0.03053130f, 0.02961676f,
       -0.07384635f, 0.01135692f, 0.05283910f, -0.07778034f, -0.02107482f,
       -0.05511716f, -0.13473752f, 0.03030157f, 0.06722020f, -0.06218817f,
       -0.05826827f, 0.06254654f, 0.02895772f, -0.01664000f, -0.03620280f,
       -0.01612278f, -1.46097376e-003f, 0.14013411f, -8.96181818e-003f,
       -0.03250246f, 3.38630192e-003f, 2.64779478e-003f, 0.03359732f,
       -0.02411991f, -0.04229729f, 0.10666174f, -6.66579151f };
    return vector<float>(detector, detector + sizeof(detector)/sizeof(detector[0]));
}
//This function renurn 1981 SVM coeffs obtained from daimler's base.
//To use these coeffs the detection window size should be (48,96)
vector<float> HOGDescriptor::getDaimlerPeopleDetector()
{
    static const float detector[] = {
        0.294350f, -0.098796f, -0.129522f, 0.078753f,
        0.387527f, 0.261529f, 0.145939f, 0.061520f,
        0.328699f, 0.227148f, -0.066467f, -0.086723f,
        0.047559f, 0.106714f, 0.037897f, 0.111461f,
        -0.024406f, 0.304769f, 0.254676f, -0.069235f,
        0.082566f, 0.147260f, 0.326969f, 0.148888f,
        0.055270f, -0.087985f, 0.261720f, 0.143442f,
        0.026812f, 0.238212f, 0.194020f, 0.056341f,
        -0.025854f, -0.034444f, -0.156631f, 0.205174f,
        0.089008f, -0.139811f, -0.100147f, -0.037830f,
        -0.029230f, -0.055641f, 0.033248f, -0.016512f,
        0.155244f, 0.247315f, -0.124694f, -0.048414f,
        -0.062219f, 0.193683f, 0.004574f, 0.055089f,
        0.093565f, 0.167712f, 0.167581f, 0.018895f,
        0.215258f, 0.122609f, 0.090520f, -0.067219f,
        -0.049029f, -0.099615f, 0.241804f, -0.094893f,
        -0.176248f, 0.001727f, -0.134473f, 0.104442f,
        0.050942f, 0.081165f, 0.072156f, 0.121646f,
        0.002656f, -0.297974f, -0.133587f, -0.060121f,
        -0.092515f, -0.048974f, -0.084754f, -0.180111f,
        -0.038590f, 0.086283f, -0.134636f, -0.107249f,
        0.132890f, 0.141556f, 0.249425f, 0.130273f,
        -0.030031f, 0.073212f, -0.008155f, 0.019931f,
        0.071688f, 0.000300f, -0.019525f, -0.021725f,
        -0.040993f, -0.086841f, 0.070124f, 0.240033f,
        0.265350f, 0.043208f, 0.166754f, 0.091453f,
        0.060916f, -0.036972f, -0.091043f, 0.079873f,
        0.219781f, 0.158102f, -0.140618f, -0.043016f,
        0.124802f, 0.093668f, 0.103208f, 0.094872f,
        0.080541f, 0.137711f, 0.160566f, -0.169231f,
        0.013983f, 0.309508f, -0.004217f, -0.057200f,
        -0.064489f, 0.014066f, 0.361009f, 0.251328f,
        -0.080983f, -0.044183f, 0.061436f, -0.037381f,
        -0.078786f, 0.030993f, 0.066314f, 0.037683f,
        0.152325f, -0.091683f, 0.070203f, 0.217856f,
        0.036435f, -0.076462f, 0.006254f, -0.094431f,
        0.154829f, -0.023038f, -0.196961f, -0.024594f,
        0.178465f, -0.050139f, -0.045932f, -0.000965f,
        0.109112f, 0.046165f, -0.159373f, -0.008713f,
        0.041307f, 0.097129f, -0.057211f, -0.064599f,
        0.077165f, 0.176167f, 0.138322f, 0.065753f,
        -0.104950f, 0.017933f, 0.136255f, -0.011598f,
        0.047007f, 0.080550f, 0.068619f, 0.084661f,
        -0.035493f, -0.091314f, -0.041411f, 0.060971f,
        -0.101912f, -0.079870f, -0.085977f, -0.022686f,
        0.079788f, -0.098064f, -0.054603f, 0.040383f,
        0.300794f, 0.128603f, 0.094844f, 0.047407f,
        0.101825f, 0.061832f, -0.162160f, -0.204553f,
        -0.035165f, 0.101450f, -0.016641f, -0.027140f,
        -0.134392f, -0.008743f, 0.102331f, 0.114853f,
        0.009644f, 0.062823f, 0.237339f, 0.167843f,
        0.053066f, -0.012592f, 0.043158f, 0.002305f,
        0.065001f, -0.038929f, -0.020356f, 0.152343f,
        0.043469f, -0.029967f, -0.042948f, 0.032481f,
        0.068488f, -0.110840f, -0.111083f, 0.111980f,
        -0.002072f, -0.005562f, 0.082926f, 0.006635f,
        -0.108153f, 0.024242f, -0.086464f, -0.189884f,
        -0.017492f, 0.191456f, -0.007683f, -0.128769f,
        -0.038017f, -0.132380f, 0.091926f, 0.079696f,
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        -0.022099f, 0.095776f, 0.409423f, 0.060635f,
        -0.065196f, 0.051828f, 0.027981f, -0.009609f,
        -0.137681f, -0.095011f, -0.019045f, 0.177278f,
        0.009759f, -0.092119f, -0.016958f, -0.133860f,
        -0.118421f, -0.032039f, -0.006214f, -0.084541f,
        0.063971f, -0.073642f, 0.165676f, 0.110443f,
        0.044131f, 0.046568f, 0.053292f, -0.055466f,
        0.015512f, 0.371947f, 0.232102f, -0.016923f,
        0.103979f, -0.091758f, 0.005907f, 0.209100f,
        0.157433f, 0.030518f, 0.250366f, 0.062322f,
        0.036720f, 0.094676f, 0.017306f, -0.010328f,
        -0.079012f, 0.016781f, -0.112435f, 0.061795f,
        0.042543f, -0.126799f, -0.009975f, -0.056760f,
        0.046424f, -0.194712f, -0.139399f, -0.037731f,
        0.157989f, -0.016261f, 0.123345f, 0.230563f,
        0.083300f, -0.016392f, 0.059567f, -0.016035f,
        -0.064767f, 0.231945f, 0.156629f, 0.034602f,
        0.145628f, 0.041315f, 0.034535f, 0.019967f,
        -0.089188f, -0.012091f, 0.307857f, 0.211405f,
        -0.025091f, -0.148249f, -0.129384f, 0.063536f,
        -0.068603f, -0.067941f, -0.035104f, 0.210832f,
        0.063810f, 0.062764f, -0.089889f, -0.030554f,
        0.014791f, -0.053362f, -0.037818f, -0.196640f,
        0.008388f, -0.082654f, 0.143056f, 0.064221f,
        0.069795f, 0.191040f, 0.097321f, -0.028679f,
        0.075794f, 0.313154f, 0.086240f, 0.207643f,
        0.017809f, 0.122867f, 0.224586f, 0.167403f,
        -0.023884f, 0.047434f, 0.344091f, 0.187745f,
        0.136177f, 0.141738f, 0.063799f, 0.045233f,
        -0.077342f, -0.003525f, -0.165041f, -0.025616f,
        -0.073745f, 0.164439f, 0.011200f, -0.145896f,
        -0.027954f, -0.061987f, -0.039874f, -0.142775f,
        0.151042f, -0.038238f, 0.053152f, 0.078615f,
        0.086061f, 0.100593f, 0.128046f, -0.071006f,
        -0.116558f, 0.208445f, 0.051086f, 0.076843f,
        0.023191f, -0.084781f, -0.011790f, 0.147807f,
        -0.048554f, -0.113932f, 0.283322f, 0.190934f,
        0.092789f, 0.033018f, -0.142428f, -0.142480f,
        -0.099023f, -0.041020f, -0.042760f, 0.203295f,
        -0.053475f, 0.042424f, 0.222839f, -0.019167f,
        -0.133176f, -0.276216f, -0.031998f, 0.117290f,
        0.177827f, -0.059973f, -0.064744f, -0.117040f,
        -0.155482f, -0.099531f, 0.164121f, -0.026682f,
        -0.093810f, 0.238993f, -0.006506f, 0.007830f,
        0.065819f, -0.203643f, -0.100925f, -0.053652f,
        -0.130770f, 0.026277f, 0.131796f, 0.032742f,
        0.127186f, 0.116694f, -0.161122f, -0.279773f,
        -0.252515f, -0.002638f, 0.042812f, 0.096776f,
        -0.123280f, 0.064858f, -0.010455f, -0.219760f,
        -0.239331f, -0.104363f, -0.058022f, -0.053584f,
        0.025611f, 0.005129f, -0.100418f, -0.045712f,
        -0.194418f, -0.126366f, -0.030530f, 0.051168f,
        0.215959f, 0.172402f, -0.054700f, -0.185995f,
        -0.278360f, -0.193693f, -0.040309f, 0.003735f,
        -0.007770f, 0.123556f, 0.190179f, -0.077315f,
        0.117403f, 0.212942f, 0.012160f, 0.000113f,
        0.027331f, 0.040202f, 0.033293f, 0.219438f,
        0.184174f, 0.259349f, 0.311206f, 0.082547f,
        -0.047875f, -0.078417f, 0.010746f, 0.082620f,
        0.311931f, 0.307605f, 0.003863f, 0.021405f,
        -0.026388f, -0.019572f, 0.020582f, -0.059353f,
        0.025199f, 0.261319f, 0.086316f, 0.143614f,
        0.107780f, 0.003900f, -0.188397f, -0.038563f,
        -0.106045f, -0.125154f, -0.010509f, 0.054021f,
        0.242130f, 0.279152f, 0.215546f, 0.346995f,
        0.440856f, 0.237452f, 0.234154f, 0.301646f,
        0.168929f, -0.208358f, -0.126848f, 0.010260f,
        0.121018f, -0.062975f, -0.052848f, 0.050341f,
        -0.061103f, -0.266482f, 0.107186f, 0.140221f,
        0.280065f, 0.287889f, 0.373198f, 0.151596f,
        0.013593f, 0.115616f, 0.014616f, -0.281710f,
        -0.237597f, -0.117305f, -0.000034f, -0.136739f,
        -0.196275f, -0.095225f, -0.125310f, -0.250514f,
        0.236804f, -0.071805f, -0.037421f, 0.048230f,
        0.321596f, 0.063632f, 0.024039f, -0.029133f,
        0.230983f, 0.160593f, -0.154355f, -0.013086f,
        -0.079929f, 0.094692f, 0.160391f, 0.180239f,
        0.053895f, 0.100759f, 0.288631f, 0.038191f,
        0.181692f, 0.229682f, 0.440166f, 0.063401f,
        0.006273f, 0.020865f, 0.338695f, 0.256244f,
        -0.043927f, 0.115617f, 0.003296f, 0.173965f,
        0.021318f, -0.040936f, -0.118932f, 0.182380f,
        0.235922f, -0.053233f, -0.015053f, -0.101057f,
        0.095341f, 0.051111f, 0.161831f, 0.032614f,
        0.159496f, 0.072375f, 0.025089f, 0.023748f,
        0.029151f, 0.161284f, -0.117717f, -0.036191f,
        -0.176822f, -0.162006f, 0.226542f, -0.078329f,
        0.043079f, -0.119172f, 0.054614f, -0.101365f,
        -0.064541f, -0.115304f, 0.135170f, 0.298872f,
        0.098060f, 0.089428f, -0.007497f, 0.110391f,
        -0.028824f, 0.020835f, -0.036804f, 0.125411f,
        0.192105f, -0.048931f, 0.003086f, -0.010681f,
        0.074698f, -0.016263f, 0.096063f, 0.060267f,
        -0.007277f, 0.139139f, -0.080635f, 0.036628f,
        0.086058f, 0.131979f, 0.085707f, 0.025301f,
        0.226094f, 0.194759f, 0.042193f, -0.157846f,
        -0.068402f, -0.141450f, -0.112659f, -0.076305f,
        -0.069085f, -0.114332f, -0.102005f, 0.132193f,
        -0.067042f, 0.106643f, 0.198964f, 0.171616f,
        0.167237f, -0.033730f, -0.026755f, 0.083621f,
        0.149459f, -0.002799f, -0.000318f, 0.011753f,
        0.065889f, -0.089375f, -0.049610f, 0.224579f,
        0.216548f, -0.034908f, -0.017851f, -0.088144f,
        0.007530f, 0.240268f, 0.073270f, 0.013263f,
        0.175323f, 0.012082f, 0.093993f, 0.015282f,
        0.105854f, 0.107990f, 0.077798f, -0.096166f,
        -0.079607f, 0.177820f, 0.142392f, 0.033337f,
        -0.078100f, -0.081616f, -0.046993f, 0.139459f,
        0.020272f, -0.123161f, 0.175269f, 0.105217f,
        0.057328f, 0.080909f, -0.012612f, -0.097081f,
        0.082060f, -0.096716f, -0.063921f, 0.201884f,
        0.128166f, -0.035051f, -0.032227f, -0.068139f,
        -0.115915f, 0.095080f, -0.086007f, -0.067543f,
        0.030776f, 0.032712f, 0.088937f, 0.054336f,
        -0.039329f, -0.114022f, 0.171672f, -0.112321f,
        -0.217646f, 0.065186f, 0.060223f, 0.192174f,
        0.055580f, -0.131107f, -0.144338f, 0.056730f,
        -0.034707f, -0.081616f, -0.135298f, -0.000614f,
        0.087189f, 0.014614f, 0.067709f, 0.107689f,
        0.225780f, 0.084361f, -0.008544f, 0.051649f,
        -0.048369f, -0.037739f, -0.060710f, 0.002654f,
        0.016935f, 0.085563f, -0.015961f, -0.019265f,
        0.111788f, 0.062376f, 0.202019f, 0.047713f,
        0.042261f, 0.069716f, 0.242913f, 0.021052f,
        -0.072812f, -0.155920f, -0.026436f, 0.035621f,
        -0.079300f, -0.028787f, -0.048329f, 0.084718f,
        -0.060565f, -0.083750f, -0.164075f, -0.040742f,
        -0.086219f, 0.015271f, -0.005204f, -0.016038f,
        0.045816f, -0.050433f, -0.077652f, 0.117109f,
        0.009611f, -0.009045f, -0.008634f, -0.055373f,
        -0.085968f, 0.028527f, -0.054736f, -0.168089f,
        0.175839f, 0.071205f, -0.023603f, 0.037907f,
        -0.004561f, -0.022634f, 0.123831f, 0.094469f,
        -0.072920f, -0.133642f, -0.014032f, -0.142754f,
        -0.026999f, -0.199409f, 0.013268f, 0.226989f,
        0.048650f, -0.170988f, -0.050141f, 0.007880f,
        0.061880f, 0.019078f, -0.043578f, -0.038139f,
        0.134814f, 0.054097f, -0.081670f, 0.176838f,
        0.047920f, -0.038176f, 0.050406f, -0.107181f,
        -0.036279f, 0.027060f, 0.081594f, -0.002820f,
        0.090507f, -0.033338f, -0.059571f, 0.013404f,
        -0.099860f, 0.073371f, 0.342805f, 0.098305f,
        -0.150910f, -0.020822f, -0.056960f, 0.046262f,
        -0.043413f, -0.149405f, -0.129105f, -0.010899f,
        -0.014229f, -0.179949f, -0.113044f, -0.049468f,
        -0.065513f, 0.090269f, -0.011919f, 0.087846f,
        0.095796f, 0.146127f, 0.101599f, 0.078066f,
        -0.084348f, -0.100002f, -0.020134f, -0.050169f,
        0.062122f, 0.014640f, 0.019143f, 0.036543f,
        0.180924f, -0.013976f, -0.066768f, -0.001090f,
        -0.070419f, -0.004839f, -0.001504f, 0.034483f,
        -0.044954f, -0.050336f, -0.088638f, -0.174782f,
        -0.116082f, -0.205507f, 0.015587f, -0.042839f,
        -0.096879f, -0.144097f, -0.050268f, -0.196796f,
        0.109639f, 0.271411f, 0.173732f, 0.108070f,
        0.156437f, 0.124255f, 0.097242f, 0.238693f,
        0.083941f, 0.109105f, 0.223940f, 0.267188f,
        0.027385f, 0.025819f, 0.125070f, 0.093738f,
        0.040353f, 0.038645f, -0.012730f, 0.144063f,
        0.052931f, -0.009138f, 0.084193f, 0.160272f,
        -0.041366f, 0.011951f, -0.121446f, -0.106713f,
        -0.047566f, 0.047984f, -0.255224f, -0.076116f,
        0.098685f, -0.150845f, -0.171513f, -0.156590f,
        0.058331f, 0.187493f, 0.413018f, 0.554265f,
        0.372242f, 0.237943f, 0.124571f, 0.110829f,
        0.010322f, -0.174477f, -0.067627f, -0.001979f,
        0.142913f, 0.040597f, 0.019907f, 0.025963f,
        -0.043585f, -0.120732f, 0.099937f, 0.091059f,
        0.247307f, 0.204226f, -0.042753f, -0.068580f,
        -0.119002f, 0.026722f, 0.034853f, -0.060934f,
        -0.025054f, -0.093026f, -0.035372f, -0.233209f,
        -0.049869f, -0.039151f, -0.022279f, -0.065380f,
        -9.063785f};
        return vector<float>(detector, detector + sizeof(detector)/sizeof(detector[0]));
}

class HOGConfInvoker : public ParallelLoopBody
{
public:
       HOGConfInvoker( const HOGDescriptor* _hog, const Mat& _img,
                               double _hitThreshold, Size _padding,
                               std::vector<DetectionROI>* locs,
                               std::vector<Rect>* _vec, Mutex* _mtx )
       {
               hog = _hog;
               img = _img;
               hitThreshold = _hitThreshold;
               padding = _padding;
               locations = locs;
               vec = _vec;
               mtx = _mtx;
       }

       void operator()( const Range& range ) const
       {
               int i, i1 = range.start, i2 = range.end;

               Size maxSz(cvCeil(img.cols/(*locations)[0].scale), cvCeil(img.rows/(*locations)[0].scale));
               Mat smallerImgBuf(maxSz, img.type());
               vector<Point> dets;

               for( i = i1; i < i2; i++ )
               {
                       double scale = (*locations)[i].scale;

                       Size sz(cvRound(img.cols / scale), cvRound(img.rows / scale));
                       Mat smallerImg(sz, img.type(), smallerImgBuf.data);

                       if( sz == img.size() )
                               smallerImg = Mat(sz, img.type(), img.data, img.step);
                       else
                               resize(img, smallerImg, sz);

                       hog->detectROI(smallerImg, (*locations)[i].locations, dets, (*locations)[i].confidences, hitThreshold, Size(), padding);
                       Size scaledWinSize = Size(cvRound(hog->winSize.width*scale), cvRound(hog->winSize.height*scale));
                       mtx->lock();
                       for( size_t j = 0; j < dets.size(); j++ )
                       {
                               vec->push_back(Rect(cvRound(dets[j].x*scale),
                                                                       cvRound(dets[j].y*scale),
                                                                       scaledWinSize.width, scaledWinSize.height));
                       }
                       mtx->unlock();
               }
       }

       const HOGDescriptor* hog;
       Mat img;
       double hitThreshold;
       std::vector<DetectionROI>* locations;
       Size padding;
       std::vector<Rect>* vec;
       Mutex* mtx;
};

void HOGDescriptor::detectROI(const cv::Mat& img, const vector<cv::Point> &locations,
                                       CV_OUT std::vector<cv::Point>& foundLocations, CV_OUT std::vector<double>& confidences,
                                       double hitThreshold, cv::Size winStride,
                                       cv::Size padding) const
{
   foundLocations.clear();

   confidences.clear();

   if( svmDetector.empty() )
       return;

   if( locations.empty() )
       return;

   if( winStride == Size() )
       winStride = cellSize;

   Size cacheStride(gcd(winStride.width, blockStride.width),
                                    gcd(winStride.height, blockStride.height));

   size_t nwindows = locations.size();
   padding.width = (int)alignSize(std::max(padding.width, 0), cacheStride.width);
   padding.height = (int)alignSize(std::max(padding.height, 0), cacheStride.height);
   Size paddedImgSize(img.cols + padding.width*2, img.rows + padding.height*2);

   // HOGCache cache(this, img, padding, padding, nwindows == 0, cacheStride);
   HOGCache cache(this, img, padding, padding, true, cacheStride);
   if( !nwindows )
           nwindows = cache.windowsInImage(paddedImgSize, winStride).area();

   const HOGCache::BlockData* blockData = &cache.blockData[0];

   int nblocks = cache.nblocks.area();
   int blockHistogramSize = cache.blockHistogramSize;
   size_t dsize = getDescriptorSize();

   double rho = svmDetector.size() > dsize ? svmDetector[dsize] : 0;
   vector<float> blockHist(blockHistogramSize);

   for( size_t i = 0; i < nwindows; i++ )
   {
           Point pt0;
           pt0 = locations[i];
           if( pt0.x < -padding.width || pt0.x > img.cols + padding.width - winSize.width ||
                   pt0.y < -padding.height || pt0.y > img.rows + padding.height - winSize.height )
           {
               // out of image
               confidences.push_back(-10.0);
               continue;
           }

           double s = rho;
           const float* svmVec = &svmDetector[0];
           int j, k;

           for( j = 0; j < nblocks; j++, svmVec += blockHistogramSize )
           {
                   const HOGCache::BlockData& bj = blockData[j];
                   Point pt = pt0 + bj.imgOffset;
                   // need to devide this into 4 parts!
                   const float* vec = cache.getBlock(pt, &blockHist[0]);
                   for( k = 0; k <= blockHistogramSize - 4; k += 4 )
                           s += vec[k]*svmVec[k] + vec[k+1]*svmVec[k+1] +
                                   vec[k+2]*svmVec[k+2] + vec[k+3]*svmVec[k+3];
                   for( ; k < blockHistogramSize; k++ )
                           s += vec[k]*svmVec[k];
           }
           // cv::waitKey();
           confidences.push_back(s);

           if( s >= hitThreshold )
                   foundLocations.push_back(pt0);
   }
 }

void HOGDescriptor::detectMultiScaleROI(const cv::Mat& img,
                                                           CV_OUT std::vector<cv::Rect>& foundLocations,
                                                           std::vector<DetectionROI>& locations,
                                                           double hitThreshold,
                                                           int groupThreshold) const
{
   std::vector<Rect> allCandidates;
   Mutex mtx;

   parallel_for_(Range(0, (int)locations.size()),
                        HOGConfInvoker(this, img, hitThreshold, Size(8, 8), &locations, &allCandidates, &mtx));

   foundLocations.resize(allCandidates.size());
   std::copy(allCandidates.begin(), allCandidates.end(), foundLocations.begin());
   cv::groupRectangles(foundLocations, groupThreshold, 0.2);
}

void HOGDescriptor::readALTModel(std::string modelfile)
{
   // read model from SVMlight format..
   FILE *modelfl;
   if ((modelfl = fopen(modelfile.c_str(), "rb")) == NULL)
   {
       std::string eerr("file not exist");
       std::string efile(__FILE__);
       std::string efunc(__FUNCTION__);
       throw Exception(CV_StsError, eerr, efile, efunc, __LINE__);
   }
   char version_buffer[10];
   if (!fread (&version_buffer,sizeof(char),10,modelfl))
   {
       std::string eerr("version?");
       std::string efile(__FILE__);
       std::string efunc(__FUNCTION__);
       throw Exception(CV_StsError, eerr, efile, efunc, __LINE__);
   }
   if(strcmp(version_buffer,"V6.01")) {
       std::string eerr("version doesnot match");
       std::string efile(__FILE__);
       std::string efunc(__FUNCTION__);
       throw Exception(CV_StsError, eerr, efile, efunc, __LINE__);
   }
   /* read version number */
   int version = 0;
   if (!fread (&version,sizeof(int),1,modelfl))
   { throw Exception(); }
   if (version < 200)
   {
       std::string eerr("version doesnot match");
       std::string efile(__FILE__);
       std::string efunc(__FUNCTION__);
       throw Exception();
   }
   int kernel_type;
   size_t nread;
   nread=fread(&(kernel_type),sizeof(int),1,modelfl);

   {// ignore these
       int poly_degree;
       nread=fread(&(poly_degree),sizeof(int),1,modelfl);

       double rbf_gamma;
       nread=fread(&(rbf_gamma),sizeof(double), 1, modelfl);
       double coef_lin;
       nread=fread(&(coef_lin),sizeof(double),1,modelfl);
       double coef_const;
       nread=fread(&(coef_const),sizeof(double),1,modelfl);
       int l;
       nread=fread(&l,sizeof(int),1,modelfl);
       char* custom = new char[l];
       nread=fread(custom,sizeof(char),l,modelfl);
       delete[] custom;
   }
   int totwords;
   nread=fread(&(totwords),sizeof(int),1,modelfl);
   {// ignore these
       int totdoc;
       nread=fread(&(totdoc),sizeof(int),1,modelfl);
       int sv_num;
       nread=fread(&(sv_num), sizeof(int),1,modelfl);
   }

   double linearbias;
   nread=fread(&linearbias, sizeof(double), 1, modelfl);

   std::vector<float> detector;
   detector.clear();
   if(kernel_type == 0) { /* linear kernel */
       /* save linear wts also */
       double *linearwt = new double[totwords+1];
       int length = totwords;
       nread = fread(linearwt, sizeof(double), totwords + 1, modelfl);
       if(nread != static_cast<size_t>(length) + 1)
           throw Exception();

       for(int i = 0; i < length; i++)
           detector.push_back((float)linearwt[i]);

       detector.push_back((float)-linearbias);
       setSVMDetector(detector);
       delete linearwt;
   } else {
       throw Exception();
   }
   fclose(modelfl);
}

}