root/modules/objdetect/src/hog.cpp

DEFINITIONS

1. numPartsWithin
2. numPartsWithin
3. getBlockHistogramSize
4. getDescriptorSize
5. getWinSigma
6. checkDetectorSize
7. setSVMDetector
8. read
9. write
10. load
11. save
12. copyTo
13. computeGradient
14. imgOffset
15. count4
16. init
17. getBlock
18. normalizeBlockHistogram
19. windowsInImage
20. getWindow
21. gcd
22. ocl_compute_gradients_8UC1
23. ocl_computeGradient
24. ocl_compute_hists
25. power_2up
26. ocl_normalize_hists
27. ocl_extract_descrs_by_rows
28. ocl_extract_descrs_by_cols
29. ocl_compute
30. compute
31. detect
32. detect
33. ocl_classify_hists
34. ocl_detect
35. ocl_detectMultiScale
36. detectMultiScale
37. detectMultiScale
38. isInstance
39. release
40. read
41. write
42. clone
43. getDefaultPeopleDetector
44. getDaimlerPeopleDetector
45. detectROI
46. detectMultiScaleROI
47. readALTModel
48. groupRectangles

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#include "precomp.hpp"
#include "cascadedetect.hpp"
#include "opencv2/core/core_c.h"
#include "opencl_kernels_objdetect.hpp"

#include <cstdio>
#include <iterator>
#include <limits>

/****************************************************************************************\
      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
{

#define NTHREADS 256

enum {DESCR_FORMAT_COL_BY_COL, DESCR_FORMAT_ROW_BY_ROW};

static int numPartsWithin(int size, int part_size, int stride)
{
    return (size - part_size + stride) / stride;
}

static Size numPartsWithin(cv::Size size, cv::Size part_size,
                                                cv::Size stride)
{
    return Size(numPartsWithin(size.width, part_size.width, stride.width),
        numPartsWithin(size.height, part_size.height, stride.height));
}

static size_t getBlockHistogramSize(Size block_size, Size cell_size, int nbins)
{
    Size cells_per_block = Size(block_size.width / cell_size.width,
        block_size.height / cell_size.height);
    return (size_t)(nbins * cells_per_block.area());
}

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());

    Mat detector_reordered(1, (int)svmDetector.size(), CV_32FC1);

    size_t block_hist_size = getBlockHistogramSize(blockSize, cellSize, nbins);
    cv::Size blocks_per_img = numPartsWithin(winSize, blockSize, blockStride);

    for (int i = 0; i < blocks_per_img.height; ++i)
        for (int j = 0; j < blocks_per_img.width; ++j)
        {
            const float *src = &svmDetector[0] + (j * blocks_per_img.height + i) * block_hist_size;
            float *dst = detector_reordered.ptr<float>() + (i * blocks_per_img.width + j) * block_hist_size;
            for (size_t k = 0; k < block_hist_size; ++k)
                dst[k] = src[k];
        }
    size_t descriptor_size = getDescriptorSize();
    free_coef = svmDetector.size() > descriptor_size ? svmDetector[descriptor_size] : 0;
    detector_reordered.copyTo(oclSvmDetector);
}

#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;
    if (obj["signedGradient"].empty())
        signedGradient = false;
    else
        obj["signedGradient"] >> signedGradient;

    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
       << "signedGradient" << signedGradient;
    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;
    c.signedGradient = signedGradient;
}

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* const lut = &_lut(0,0);
#if CV_SSE2
    const int indeces[] = { 0, 1, 2, 3 };
    __m128i idx = _mm_loadu_si128((const __m128i*)indeces);
    __m128i ifour = _mm_set1_epi32(4);

    float* const _data = &_lut(0, 0);
    if( gammaCorrection )
        for( i = 0; i < 256; i += 4 )
        {
            _mm_storeu_ps(_data + i, _mm_sqrt_ps(_mm_cvtepi32_ps(idx)));
            idx = _mm_add_epi32(idx, ifour);
        }
    else
        for( i = 0; i < 256; i += 4 )
        {
            _mm_storeu_ps(_data + i, _mm_cvtepi32_ps(idx));
            idx = _mm_add_epi32(idx, ifour);
        }
#else
    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;
#endif

    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* const 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);

    if (cn == 3)
    {
        int end = gradsize.width + 2;
        xmap -= 1, x = 0;
#if CV_SSE2
        __m128i ithree = _mm_set1_epi32(3);
        for ( ; x <= end - 4; x += 4)
            _mm_storeu_si128((__m128i*)(xmap + x), _mm_mullo_epi16(ithree,
                _mm_loadu_si128((const __m128i*)(xmap + x))));
#endif
        for ( ; x < end; ++x)
            xmap[x] *= 3;
        xmap += 1;
    }

    float angleScale = signedGradient ? (float)(nbins/(2.0*CV_PI)) : (float)(nbins/CV_PI);
    for( y = 0; y < gradsize.height; y++ )
    {
        const uchar* imgPtr  = img.ptr(ymap[y]);
        //In case subimage is used ptr() generates an assert for next and prev rows
        //(see http://code.opencv.org/issues/4149)
        const uchar* prevPtr = img.data + img.step*ymap[y-1];
        const uchar* nextPtr = img.data + img.step*ymap[y+1];

        float* gradPtr = grad.ptr<float>(y);
        uchar* qanglePtr = qangle.ptr(y);

        if( cn == 1 )
        {
            for( x = 0; x < width; x++ )
            {
                int x1 = xmap[x];
                dbuf[x] = (float)(lut[imgPtr[xmap[x+1]]] - lut[imgPtr[xmap[x-1]]]);
                dbuf[width + x] = (float)(lut[nextPtr[x1]] - lut[prevPtr[x1]]);
            }
        }
        else
        {
            x = 0;
#if CV_SSE2
            for( ; x <= width - 4; x += 4 )
            {
                int x0 = xmap[x], x1 = xmap[x+1], x2 = xmap[x+2], x3 = xmap[x+3];
                typedef const uchar* const T;
                T p02 = imgPtr + xmap[x+1], p00 = imgPtr + xmap[x-1];
                T p12 = imgPtr + xmap[x+2], p10 = imgPtr + xmap[x];
                T p22 = imgPtr + xmap[x+3], p20 = p02;
                T p32 = imgPtr + xmap[x+4], p30 = p12;

                __m128 _dx0 = _mm_sub_ps(_mm_set_ps(lut[p32[0]], lut[p22[0]], lut[p12[0]], lut[p02[0]]),
                                         _mm_set_ps(lut[p30[0]], lut[p20[0]], lut[p10[0]], lut[p00[0]]));
                __m128 _dx1 = _mm_sub_ps(_mm_set_ps(lut[p32[1]], lut[p22[1]], lut[p12[1]], lut[p02[1]]),
                                         _mm_set_ps(lut[p30[1]], lut[p20[1]], lut[p10[1]], lut[p00[1]]));
                __m128 _dx2 = _mm_sub_ps(_mm_set_ps(lut[p32[2]], lut[p22[2]], lut[p12[2]], lut[p02[2]]),
                                         _mm_set_ps(lut[p30[2]], lut[p20[2]], lut[p10[2]], lut[p00[2]]));

                __m128 _dy0 = _mm_sub_ps(_mm_set_ps(lut[nextPtr[x3]], lut[nextPtr[x2]], lut[nextPtr[x1]], lut[nextPtr[x0]]),
                                         _mm_set_ps(lut[prevPtr[x3]], lut[prevPtr[x2]], lut[prevPtr[x1]], lut[prevPtr[x0]]));
                __m128 _dy1 = _mm_sub_ps(_mm_set_ps(lut[nextPtr[x3+1]], lut[nextPtr[x2+1]], lut[nextPtr[x1+1]], lut[nextPtr[x0+1]]),
                                         _mm_set_ps(lut[prevPtr[x3+1]], lut[prevPtr[x2+1]], lut[prevPtr[x1+1]], lut[prevPtr[x0+1]]));
                __m128 _dy2 = _mm_sub_ps(_mm_set_ps(lut[nextPtr[x3+2]], lut[nextPtr[x2+2]], lut[nextPtr[x1+2]], lut[nextPtr[x0+2]]),
                                         _mm_set_ps(lut[prevPtr[x3+2]], lut[prevPtr[x2+2]], lut[prevPtr[x1+2]], lut[prevPtr[x0+2]]));

                __m128 _mag0 = _mm_add_ps(_mm_mul_ps(_dx0, _dx0), _mm_mul_ps(_dy0, _dy0));
                __m128 _mag1 = _mm_add_ps(_mm_mul_ps(_dx1, _dx1), _mm_mul_ps(_dy1, _dy1));
                __m128 _mag2 = _mm_add_ps(_mm_mul_ps(_dx2, _dx2), _mm_mul_ps(_dy2, _dy2));

                __m128 mask = _mm_cmpgt_ps(_mag2, _mag1);
                _dx2 = _mm_or_ps(_mm_and_ps(_dx2, mask), _mm_andnot_ps(mask, _dx1));
                _dy2 = _mm_or_ps(_mm_and_ps(_dy2, mask), _mm_andnot_ps(mask, _dy1));

                mask = _mm_cmpgt_ps(_mm_max_ps(_mag2, _mag1), _mag0);
                _dx2 = _mm_or_ps(_mm_and_ps(_dx2, mask), _mm_andnot_ps(mask, _dx0));
                _dy2 = _mm_or_ps(_mm_and_ps(_dy2, mask), _mm_andnot_ps(mask, _dy0));

                _mm_storeu_ps(dbuf + x, _dx2);
                _mm_storeu_ps(dbuf + x + width, _dy2);
            }
#endif
            for( ; x < width; x++ )
            {
                int x1 = xmap[x];
                float dx0, dy0, dx, dy, mag0, mag;
                const uchar* p2 = imgPtr + xmap[x+1];
                const uchar* p0 = imgPtr + xmap[x-1];

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

                dbuf[x] = dx0;
                dbuf[x+width] = dy0;
            }
        }

        // computing angles and magnidutes
        cartToPolar( Dx, Dy, Mag, Angle, false );

        // filling the result matrix
        x = 0;
#if CV_SSE2
        __m128 fhalf = _mm_set1_ps(0.5f), fzero = _mm_setzero_ps();
        __m128 _angleScale = _mm_set1_ps(angleScale), fone = _mm_set1_ps(1.0f);
        __m128i ione = _mm_set1_epi32(1), _nbins = _mm_set1_epi32(nbins), izero = _mm_setzero_si128();

        for ( ; x <= width - 4; x += 4)
        {
            int x2 = x << 1;
            __m128 _mag = _mm_loadu_ps(dbuf + x + (width << 1));
            __m128 _angle = _mm_loadu_ps(dbuf + x + width * 3);
            _angle = _mm_sub_ps(_mm_mul_ps(_angleScale, _angle), fhalf);

            __m128 sign = _mm_and_ps(fone, _mm_cmplt_ps(_angle, fzero));
            __m128i _hidx = _mm_cvttps_epi32(_angle);
            _hidx = _mm_sub_epi32(_hidx, _mm_cvtps_epi32(sign));
            _angle = _mm_sub_ps(_angle, _mm_cvtepi32_ps(_hidx));

            __m128 ft0 = _mm_mul_ps(_mag, _mm_sub_ps(fone, _angle));
            __m128 ft1 = _mm_mul_ps(_mag, _angle);
            __m128 ft2 = _mm_unpacklo_ps(ft0, ft1);
            __m128 ft3 = _mm_unpackhi_ps(ft0, ft1);

            _mm_storeu_ps(gradPtr + x2, ft2);
            _mm_storeu_ps(gradPtr + x2 + 4, ft3);

            __m128i mask0 = _mm_sub_epi32(izero, _mm_srli_epi32(_hidx, 31));
            __m128i it0 = _mm_and_si128(mask0, _nbins);
            mask0 = _mm_cmplt_epi32(_hidx, _nbins);
            __m128i it1 = _mm_andnot_si128(mask0, _nbins);
            _hidx = _mm_add_epi32(_hidx, _mm_sub_epi32(it0, it1));

            it0 = _mm_packus_epi16(_mm_packs_epi32(_hidx, izero), izero);
            _hidx = _mm_add_epi32(ione, _hidx);
            _hidx = _mm_and_si128(_hidx, _mm_cmplt_epi32(_hidx, _nbins));
            it1 = _mm_packus_epi16(_mm_packs_epi32(_hidx, izero), izero);
            it0 = _mm_unpacklo_epi8(it0, it1);

            _mm_storel_epi64((__m128i*)(qanglePtr + x2), it0);
        }
#endif
        for( ; x < width; x++ )
        {
            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;

            if( hidx < 0 )
                hidx += nbins;
            else if( hidx >= nbins )
                hidx -= nbins;

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

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

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

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

    bool useCache;
    std::vector<int> ymaxCached;
    Size winSize;
    Size 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() :
    blockHistogramSize(), count1(), count2(), count4()
{
    useCache = false;
    descriptor = 0;
}

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

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

    {
        AutoBuffer<float> di(blockSize.height), dj(blockSize.width);
        float* _di = (float*)di, *_dj = (float*)dj;
        float bh = blockSize.height * 0.5f, bw = blockSize.width * 0.5f;

        i = 0;
    #if CV_SSE2
        const int a[] = { 0, 1, 2, 3 };
        __m128i idx = _mm_loadu_si128((__m128i*)a);
        __m128 _bw = _mm_set1_ps(bw), _bh = _mm_set1_ps(bh);
        __m128i ifour = _mm_set1_epi32(4);

        for (; i <= blockSize.height - 4; i += 4)
        {
            __m128 t = _mm_sub_ps(_mm_cvtepi32_ps(idx), _bh);
            t = _mm_mul_ps(t, t);
            idx = _mm_add_epi32(idx, ifour);
            _mm_storeu_ps(_di + i, t);
        }
    #endif
        for ( ; i < blockSize.height; ++i)
        {
            _di[i] = i - bh;
            _di[i] *= _di[i];
        }

        j = 0;
    #if CV_SSE2
        idx = _mm_loadu_si128((__m128i*)a);
        for (; j <= blockSize.width - 4; j += 4)
        {
            __m128 t = _mm_sub_ps(_mm_cvtepi32_ps(idx), _bw);
            t = _mm_mul_ps(t, t);
            idx = _mm_add_epi32(idx, ifour);
            _mm_storeu_ps(_dj + j, t);
        }
    #endif
        for ( ; j < blockSize.width; ++j)
        {
            _dj[j] = j - bw;
            _dj[j] *= _dj[j];
        }

        for(i = 0; i < blockSize.height; i++)
            for(j = 0; j < blockSize.width; j++)
                weights(i,j) = std::exp(-(_di[i] + _dj[j])*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 = grad.ptr<float>(pt.y) + pt.x*2;
    const uchar* qanglePtr = qangle.ptr(pt.y) + pt.x*2;

//    CV_Assert( blockHist != 0 );
    memset(blockHist, 0, sizeof(float) * blockHistogramSize);

    const PixData* _pixData = &pixData[0];

    for( k = 0; k < C1; k++ )
    {
        const PixData& pk = _pixData[k];
        const float* const 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;
    }

#if CV_SSE2
    float hist0[4], hist1[4];
    for( ; k < C2; k++ )
    {
        const PixData& pk = _pixData[k];
        const float* const a = gradPtr + pk.gradOfs;
        const uchar* const h = qanglePtr + pk.qangleOfs;
        int h0 = h[0], h1 = h[1];

        __m128 _a0 = _mm_set1_ps(a[0]), _a1 = _mm_set1_ps(a[1]);
        __m128 _w = _mm_mul_ps(_mm_set1_ps(pk.gradWeight), _mm_loadu_ps(pk.histWeights));
        __m128 _t0 = _mm_mul_ps(_a0, _w), _t1 = _mm_mul_ps(_a1, _w);

        _mm_storeu_ps(hist0, _t0);
        _mm_storeu_ps(hist1, _t1);

        float* hist = blockHist + pk.histOfs[0];
        float t0 = hist[h0] + hist0[0];
        float t1 = hist[h1] + hist1[0];
        hist[h0] = t0; hist[h1] = t1;

        hist = blockHist + pk.histOfs[1];
        t0 = hist[h0] + hist0[1];
        t1 = hist[h1] + hist1[1];
        hist[h0] = t0; hist[h1] = t1;
    }
#else
    for( ; k < C2; k++ )
    {
        const PixData& pk = _pixData[k];
        const float* const a = gradPtr + pk.gradOfs;
        float w, t0, t1, a0 = a[0], a1 = a[1];
        const uchar* const 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;
    }
#endif

#if CV_SSE2
    for( ; k < C4; k++ )
    {
        const PixData& pk = _pixData[k];
        const float* const a = gradPtr + pk.gradOfs;
        const uchar* const h = qanglePtr + pk.qangleOfs;
        int h0 = h[0], h1 = h[1];

        __m128 _a0 = _mm_set1_ps(a[0]), _a1 = _mm_set1_ps(a[1]);
        __m128 _w = _mm_mul_ps(_mm_set1_ps(pk.gradWeight), _mm_loadu_ps(pk.histWeights));
        __m128 _t0 = _mm_mul_ps(_a0, _w), _t1 = _mm_mul_ps(_a1, _w);

        _mm_storeu_ps(hist0, _t0);
        _mm_storeu_ps(hist1, _t1);

        float* hist = blockHist + pk.histOfs[0];
        float t0 = hist[h0] + hist0[0];
        float t1 = hist[h1] + hist1[0];
        hist[h0] = t0; hist[h1] = t1;

        hist = blockHist + pk.histOfs[1];
        t0 = hist[h0] + hist0[1];
        t1 = hist[h1] + hist1[1];
        hist[h0] = t0; hist[h1] = t1;

        hist = blockHist + pk.histOfs[2];
        t0 = hist[h0] + hist0[2];
        t1 = hist[h1] + hist1[2];
        hist[h0] = t0; hist[h1] = t1;

        hist = blockHist + pk.histOfs[3];
        t0 = hist[h0] + hist0[3];
        t1 = hist[h1] + hist1[3];
        hist[h0] = t0; hist[h1] = t1;

//        __m128 _hist0 = _mm_set_ps((blockHist + pk.histOfs[3])[h0], (blockHist + pk.histOfs[2])[h0],
//            (blockHist + pk.histOfs[1])[h0], (blockHist + pk.histOfs[0])[h0]);
//        __m128 _hist1 = _mm_set_ps((blockHist + pk.histOfs[3])[h1], (blockHist + pk.histOfs[2])[h1],
//            (blockHist + pk.histOfs[1])[h1], (blockHist + pk.histOfs[0])[h1]);
//
//        _hist0 = _mm_add_ps(_t0, _hist0);
//        _hist1 = _mm_add_ps(_t1, _hist1);
//
//        _mm_storeu_ps(hist0, _hist0);
//        _mm_storeu_ps(hist1, _hist1);
//
//        (pk.histOfs[0] + blockHist)[h0] = hist0[0];
//        (pk.histOfs[1] + blockHist)[h0] = hist0[1];
//        (pk.histOfs[2] + blockHist)[h0] = hist0[2];
//        (pk.histOfs[3] + blockHist)[h0] = hist0[3];
//
//        (pk.histOfs[0] + blockHist)[h1] = hist1[0];
//        (pk.histOfs[1] + blockHist)[h1] = hist1[1];
//        (pk.histOfs[2] + blockHist)[h1] = hist1[2];
//        (pk.histOfs[3] + blockHist)[h1] = hist1[3];
    }
#else
    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;
    }
#endif

    normalizeBlockHistogram(blockHist);

    return blockHist;
}

void HOGCache::normalizeBlockHistogram(float* _hist) const
{
    float* hist = &_hist[0], sum = 0.0f, partSum[4];
    size_t i = 0, sz = blockHistogramSize;

#if CV_SSE2
    __m128 p0 = _mm_loadu_ps(hist);
    __m128 s = _mm_mul_ps(p0, p0);

    for (i = 4; i <= sz - 4; i += 4)
    {
        p0 = _mm_loadu_ps(hist + i);
        s = _mm_add_ps(s, _mm_mul_ps(p0, p0));
    }
    _mm_storeu_ps(partSum, s);
#else
    partSum[0] = 0.0f;
    partSum[1] = 0.0f;
    partSum[2] = 0.0f;
    partSum[3] = 0.0f;
    for ( ; i <= sz - 4; i += 4)
    {
        partSum[0] += hist[i] * hist[i];
        partSum[1] += hist[i+1] * hist[i+1];
        partSum[2] += hist[i+2] * hist[i+2];
        partSum[3] += hist[i+3] * hist[i+3];
    }
#endif
    float t0 = partSum[0] + partSum[1];
    float t1 = partSum[2] + partSum[3];
    sum = t0 + t1;
    for ( ; i < sz; ++i)
        sum += hist[i]*hist[i];

    float scale = 1.f/(std::sqrt(sum)+sz*0.1f), thresh = (float)descriptor->L2HysThreshold;
    i = 0, sum = 0.0f;

#if CV_SSE2
    __m128 _scale = _mm_set1_ps(scale);
    static __m128 _threshold = _mm_set1_ps(thresh);

    __m128 p = _mm_mul_ps(_scale, _mm_loadu_ps(hist));
    p = _mm_min_ps(p, _threshold);
    s = _mm_mul_ps(p, p);
    _mm_storeu_ps(hist, p);

    for(i = 4 ; i <= sz - 4; i += 4)
    {
        p = _mm_loadu_ps(hist + i);
        p = _mm_mul_ps(p, _scale);
        p = _mm_min_ps(p, _threshold);
        s = _mm_add_ps(s, _mm_mul_ps(p, p));
        _mm_storeu_ps(hist + i, p);
    }

    _mm_storeu_ps(partSum, s);
#else
    partSum[0] = 0.0f;
    partSum[1] = 0.0f;
    partSum[2] = 0.0f;
    partSum[3] = 0.0f;
    for( ; i <= sz - 4; i += 4)
    {
        hist[i] = std::min(hist[i]*scale, thresh);
        hist[i+1] = std::min(hist[i+1]*scale, thresh);
        hist[i+2] = std::min(hist[i+2]*scale, thresh);
        hist[i+3] = std::min(hist[i+3]*scale, thresh);
        partSum[0] += hist[i]*hist[i];
        partSum[1] += hist[i+1]*hist[i+1];
        partSum[2] += hist[i+2]*hist[i+2];
        partSum[3] += hist[i+3]*hist[i+3];
    }
#endif
    t0 = partSum[0] + partSum[1];
    t1 = partSum[2] + partSum[3];
    sum = t0 + t1;
    for( ; i < sz; ++i)
    {
        hist[i] = std::min(hist[i]*scale, thresh);
        sum += hist[i]*hist[i];
    }

    scale = 1.f/(std::sqrt(sum)+1e-3f), i = 0;
#if CV_SSE2
    __m128 _scale2 = _mm_set1_ps(scale);
    for ( ; i <= sz - 4; i += 4)
    {
        __m128 t = _mm_mul_ps(_scale2, _mm_loadu_ps(hist + i));
        _mm_storeu_ps(hist + i, t);
    }
#endif
    for ( ; i < sz; ++i)
        hist[i] *= scale;
}

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

Rect HOGCache::getWindow(const Size& imageSize, const 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 );
}

static inline int gcd(int a, int b)
{
    if( a < b )
        std::swap(a, b);
    while( b > 0 )
    {
        int r = a % b;
        a = b;
        b = r;
    }
    return a;
}

#ifdef HAVE_OPENCL

static bool ocl_compute_gradients_8UC1(int height, int width, InputArray _img, float angle_scale,
                                       UMat grad, UMat qangle, bool correct_gamma, int nbins)
{
    ocl::Kernel k("compute_gradients_8UC1_kernel", ocl::objdetect::objdetect_hog_oclsrc);
    if(k.empty())
        return false;

    UMat img = _img.getUMat();

    size_t localThreads[3] = { NTHREADS, 1, 1 };
    size_t globalThreads[3] = { width, height, 1 };
    char correctGamma = (correct_gamma) ? 1 : 0;
    int grad_quadstep = (int)grad.step >> 3;
    int qangle_elem_size = CV_ELEM_SIZE1(qangle.type());
    int qangle_step = (int)qangle.step / (2 * qangle_elem_size);

    int idx = 0;
    idx = k.set(idx, height);
    idx = k.set(idx, width);
    idx = k.set(idx, (int)img.step1());
    idx = k.set(idx, grad_quadstep);
    idx = k.set(idx, qangle_step);
    idx = k.set(idx, ocl::KernelArg::PtrReadOnly(img));
    idx = k.set(idx, ocl::KernelArg::PtrWriteOnly(grad));
    idx = k.set(idx, ocl::KernelArg::PtrWriteOnly(qangle));
    idx = k.set(idx, angle_scale);
    idx = k.set(idx, correctGamma);
    idx = k.set(idx, nbins);

    return k.run(2, globalThreads, localThreads, false);
}

static bool ocl_computeGradient(InputArray img, UMat grad, UMat qangle, int nbins, Size effect_size, bool gamma_correction, bool signedGradient)
{
    float angleScale = signedGradient ? (float)(nbins/(2.0*CV_PI)) : (float)(nbins/CV_PI);

    return ocl_compute_gradients_8UC1(effect_size.height, effect_size.width, img,
         angleScale, grad, qangle, gamma_correction, nbins);
}

#define CELL_WIDTH 8
#define CELL_HEIGHT 8
#define CELLS_PER_BLOCK_X 2
#define CELLS_PER_BLOCK_Y 2

static bool ocl_compute_hists(int nbins, int block_stride_x, int block_stride_y, int height, int width,
                              UMat grad, UMat qangle, UMat gauss_w_lut, UMat block_hists, size_t block_hist_size)
{
    ocl::Kernel k("compute_hists_lut_kernel", ocl::objdetect::objdetect_hog_oclsrc);
    if(k.empty())
        return false;
    bool is_cpu = cv::ocl::Device::getDefault().type() == cv::ocl::Device::TYPE_CPU;
    cv::String opts;
    if(is_cpu)
       opts = "-D CPU ";
    else
        opts = cv::format("-D WAVE_SIZE=%d", k.preferedWorkGroupSizeMultiple());
    k.create("compute_hists_lut_kernel", ocl::objdetect::objdetect_hog_oclsrc, opts);
    if(k.empty())
        return false;

    int img_block_width = (width - CELLS_PER_BLOCK_X * CELL_WIDTH + block_stride_x)/block_stride_x;
    int img_block_height = (height - CELLS_PER_BLOCK_Y * CELL_HEIGHT + block_stride_y)/block_stride_y;
    int blocks_total = img_block_width * img_block_height;

    int qangle_elem_size = CV_ELEM_SIZE1(qangle.type());
    int grad_quadstep = (int)grad.step >> 2;
    int qangle_step = (int)qangle.step / qangle_elem_size;

    int blocks_in_group = 4;
    size_t localThreads[3] = { blocks_in_group * 24, 2, 1 };
    size_t globalThreads[3] = {((img_block_width * img_block_height + blocks_in_group - 1)/blocks_in_group) * localThreads[0], 2, 1 };

    int hists_size = (nbins * CELLS_PER_BLOCK_X * CELLS_PER_BLOCK_Y * 12) * sizeof(float);
    int final_hists_size = (nbins * CELLS_PER_BLOCK_X * CELLS_PER_BLOCK_Y) * sizeof(float);

    int smem = (hists_size + final_hists_size) * blocks_in_group;

    int idx = 0;
    idx = k.set(idx, block_stride_x);
    idx = k.set(idx, block_stride_y);
    idx = k.set(idx, nbins);
    idx = k.set(idx, (int)block_hist_size);
    idx = k.set(idx, img_block_width);
    idx = k.set(idx, blocks_in_group);
    idx = k.set(idx, blocks_total);
    idx = k.set(idx, grad_quadstep);
    idx = k.set(idx, qangle_step);
    idx = k.set(idx, ocl::KernelArg::PtrReadOnly(grad));
    idx = k.set(idx, ocl::KernelArg::PtrReadOnly(qangle));
    idx = k.set(idx, ocl::KernelArg::PtrReadOnly(gauss_w_lut));
    idx = k.set(idx, ocl::KernelArg::PtrWriteOnly(block_hists));
    idx = k.set(idx, (void*)NULL, (size_t)smem);

    return k.run(2, globalThreads, localThreads, false);
}

static int power_2up(unsigned int n)
{
    for(unsigned int i = 1; i<=1024; i<<=1)
        if(n < i)
            return i;
    return -1; // Input is too big
}

static bool ocl_normalize_hists(int nbins, int block_stride_x, int block_stride_y,
                                int height, int width, UMat block_hists, float threshold)
{
    int block_hist_size = nbins * CELLS_PER_BLOCK_X * CELLS_PER_BLOCK_Y;
    int img_block_width = (width - CELLS_PER_BLOCK_X * CELL_WIDTH + block_stride_x)
        / block_stride_x;
    int img_block_height = (height - CELLS_PER_BLOCK_Y * CELL_HEIGHT + block_stride_y)
        / block_stride_y;
    int nthreads;
    size_t globalThreads[3] = { 1, 1, 1  };
    size_t localThreads[3] = { 1, 1, 1  };

    int idx = 0;
    bool is_cpu = cv::ocl::Device::getDefault().type() == cv::ocl::Device::TYPE_CPU;
    cv::String opts;
    ocl::Kernel k;
    if ( nbins == 9 )
    {
        k.create("normalize_hists_36_kernel", ocl::objdetect::objdetect_hog_oclsrc, "");
        if(k.empty())
            return false;
        if(is_cpu)
           opts = "-D CPU ";
        else
            opts = cv::format("-D WAVE_SIZE=%d", k.preferedWorkGroupSizeMultiple());
        k.create("normalize_hists_36_kernel", ocl::objdetect::objdetect_hog_oclsrc, opts);
        if(k.empty())
            return false;

        int blocks_in_group = NTHREADS / block_hist_size;
        nthreads = blocks_in_group * block_hist_size;
        int num_groups = (img_block_width * img_block_height + blocks_in_group - 1)/blocks_in_group;
        globalThreads[0] = nthreads * num_groups;
        localThreads[0] = nthreads;
    }
    else
    {
        k.create("normalize_hists_kernel", ocl::objdetect::objdetect_hog_oclsrc, "");
        if(k.empty())
            return false;
        if(is_cpu)
           opts = "-D CPU ";
        else
            opts = cv::format("-D WAVE_SIZE=%d", k.preferedWorkGroupSizeMultiple());
        k.create("normalize_hists_kernel", ocl::objdetect::objdetect_hog_oclsrc, opts);
        if(k.empty())
            return false;

        nthreads = power_2up(block_hist_size);
        globalThreads[0] = img_block_width * nthreads;
        globalThreads[1] = img_block_height;
        localThreads[0] = nthreads;

        if ((nthreads < 32) || (nthreads > 512) )
            return false;

        idx = k.set(idx, nthreads);
        idx = k.set(idx, block_hist_size);
        idx = k.set(idx, img_block_width);
    }
    idx = k.set(idx, ocl::KernelArg::PtrReadWrite(block_hists));
    idx = k.set(idx, threshold);
    idx = k.set(idx, (void*)NULL,  nthreads * sizeof(float));

    return k.run(2, globalThreads, localThreads, false);
}

static bool ocl_extract_descrs_by_rows(int win_height, int win_width, int block_stride_y, int block_stride_x, int win_stride_y, int win_stride_x,
                                       int height, int width, UMat block_hists, UMat descriptors,
                                       int block_hist_size, int descr_size, int descr_width)
{
    ocl::Kernel k("extract_descrs_by_rows_kernel", ocl::objdetect::objdetect_hog_oclsrc);
    if(k.empty())
        return false;

    int win_block_stride_x = win_stride_x / block_stride_x;
    int win_block_stride_y = win_stride_y / block_stride_y;
    int img_win_width = (width - win_width + win_stride_x) / win_stride_x;
    int img_win_height = (height - win_height + win_stride_y) / win_stride_y;
    int img_block_width = (width - CELLS_PER_BLOCK_X * CELL_WIDTH + block_stride_x) /
        block_stride_x;

    int descriptors_quadstep = (int)descriptors.step >> 2;

    size_t globalThreads[3] = { img_win_width * NTHREADS, img_win_height, 1 };
    size_t localThreads[3] = { NTHREADS, 1, 1 };

    int idx = 0;
    idx = k.set(idx, block_hist_size);
    idx = k.set(idx, descriptors_quadstep);
    idx = k.set(idx, descr_size);
    idx = k.set(idx, descr_width);
    idx = k.set(idx, img_block_width);
    idx = k.set(idx, win_block_stride_x);
    idx = k.set(idx, win_block_stride_y);
    idx = k.set(idx, ocl::KernelArg::PtrReadOnly(block_hists));
    idx = k.set(idx, ocl::KernelArg::PtrWriteOnly(descriptors));

    return k.run(2, globalThreads, localThreads, false);
}

static bool ocl_extract_descrs_by_cols(int win_height, int win_width, int block_stride_y, int block_stride_x, int win_stride_y, int win_stride_x,
                                       int height, int width, UMat block_hists, UMat descriptors,
                                       int block_hist_size, int descr_size, int nblocks_win_x, int nblocks_win_y)
{
    ocl::Kernel k("extract_descrs_by_cols_kernel", ocl::objdetect::objdetect_hog_oclsrc);
    if(k.empty())
        return false;

    int win_block_stride_x = win_stride_x / block_stride_x;
    int win_block_stride_y = win_stride_y / block_stride_y;
    int img_win_width = (width - win_width + win_stride_x) / win_stride_x;
    int img_win_height = (height - win_height + win_stride_y) / win_stride_y;
    int img_block_width = (width - CELLS_PER_BLOCK_X * CELL_WIDTH + block_stride_x) /
        block_stride_x;

    int descriptors_quadstep = (int)descriptors.step >> 2;

    size_t globalThreads[3] = { img_win_width * NTHREADS, img_win_height, 1 };
    size_t localThreads[3] = { NTHREADS, 1, 1 };

    int idx = 0;
    idx = k.set(idx, block_hist_size);
    idx = k.set(idx, descriptors_quadstep);
    idx = k.set(idx, descr_size);
    idx = k.set(idx, nblocks_win_x);
    idx = k.set(idx, nblocks_win_y);
    idx = k.set(idx, img_block_width);
    idx = k.set(idx, win_block_stride_x);
    idx = k.set(idx, win_block_stride_y);
    idx = k.set(idx, ocl::KernelArg::PtrReadOnly(block_hists));
    idx = k.set(idx, ocl::KernelArg::PtrWriteOnly(descriptors));

    return k.run(2, globalThreads, localThreads, false);
}

static bool ocl_compute(InputArray _img, Size win_stride, std::vector<float>& _descriptors, int descr_format, Size blockSize,
                        Size cellSize, int nbins, Size blockStride, Size winSize, float sigma, bool gammaCorrection, double L2HysThreshold, bool signedGradient)
{
    Size imgSize = _img.size();
    Size effect_size = imgSize;

    UMat grad(imgSize, CV_32FC2);
    int qangle_type = ocl::Device::getDefault().isIntel() ? CV_32SC2 : CV_8UC2;
    UMat qangle(imgSize, qangle_type);

    const size_t block_hist_size = getBlockHistogramSize(blockSize, cellSize, nbins);
    const Size blocks_per_img = numPartsWithin(imgSize, blockSize, blockStride);
    UMat block_hists(1, static_cast<int>(block_hist_size * blocks_per_img.area()) + 256, CV_32F);

    Size wins_per_img = numPartsWithin(imgSize, winSize, win_stride);
    UMat labels(1, wins_per_img.area(), CV_8U);

    float scale = 1.f / (2.f * sigma * sigma);
    Mat gaussian_lut(1, 512, CV_32FC1);
    int idx = 0;
    for(int i=-8; i<8; i++)
        for(int j=-8; j<8; j++)
            gaussian_lut.at<float>(idx++) = std::exp(-(j * j + i * i) * scale);
    for(int i=-8; i<8; i++)
        for(int j=-8; j<8; j++)
            gaussian_lut.at<float>(idx++) = (8.f - fabs(j + 0.5f)) * (8.f - fabs(i + 0.5f)) / 64.f;

    if(!ocl_computeGradient(_img, grad, qangle, nbins, effect_size, gammaCorrection, signedGradient))
        return false;

    UMat gauss_w_lut;
    gaussian_lut.copyTo(gauss_w_lut);
    if(!ocl_compute_hists(nbins, blockStride.width, blockStride.height, effect_size.height,
        effect_size.width, grad, qangle, gauss_w_lut, block_hists, block_hist_size))
        return false;

    if(!ocl_normalize_hists(nbins, blockStride.width, blockStride.height, effect_size.height,
        effect_size.width, block_hists, (float)L2HysThreshold))
        return false;

    Size blocks_per_win = numPartsWithin(winSize, blockSize, blockStride);
    wins_per_img = numPartsWithin(effect_size, winSize, win_stride);

    int descr_size = blocks_per_win.area()*(int)block_hist_size;
    int descr_width = (int)block_hist_size*blocks_per_win.width;

    UMat descriptors(wins_per_img.area(), static_cast<int>(blocks_per_win.area() * block_hist_size), CV_32F);
    switch (descr_format)
    {
    case DESCR_FORMAT_ROW_BY_ROW:
        if(!ocl_extract_descrs_by_rows(winSize.height, winSize.width,
            blockStride.height, blockStride.width, win_stride.height, win_stride.width, effect_size.height,
            effect_size.width, block_hists, descriptors, (int)block_hist_size, descr_size, descr_width))
            return false;
        break;
    case DESCR_FORMAT_COL_BY_COL:
        if(!ocl_extract_descrs_by_cols(winSize.height, winSize.width,
            blockStride.height, blockStride.width, win_stride.height, win_stride.width, effect_size.height, effect_size.width,
            block_hists, descriptors, (int)block_hist_size, descr_size, blocks_per_win.width, blocks_per_win.height))
            return false;
        break;
    default:
        return false;
    }
    descriptors.reshape(1, (int)descriptors.total()).getMat(ACCESS_READ).copyTo(_descriptors);
    return true;
}
#endif //HAVE_OPENCL

void HOGDescriptor::compute(InputArray _img, std::vector<float>& descriptors,
    Size winStride, Size padding, const std::vector<Point>& locations) const
{
    if( winStride == Size() )
        winStride = cellSize;
    Size cacheStride(gcd(winStride.width, blockStride.width),
                     gcd(winStride.height, blockStride.height));

    Size imgSize = _img.size();

    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(imgSize.width + padding.width*2, imgSize.height + padding.height*2);

    CV_OCL_RUN(_img.dims() <= 2 && _img.type() == CV_8UC1 && _img.isUMat(),
        ocl_compute(_img, winStride, descriptors, DESCR_FORMAT_COL_BY_COL, blockSize,
        cellSize, nbins, blockStride, winSize, (float)getWinSigma(), gammaCorrection, L2HysThreshold, signedGradient))

    Mat img = _img.getMat();
    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 each window
    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 )
                memcpy(dst, src, blockHistogramSize * sizeof(float));
        }
    }
}

void HOGDescriptor::detect(const Mat& img,
    std::vector<Point>& hits, std::vector<double>& weights, double hitThreshold,
    Size winStride, Size padding, const std::vector<Point>& locations) const
{
    hits.clear();
    weights.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;
    std::vector<float> blockHist(blockHistogramSize);

#if CV_SSE2
    float partSum[4];
#endif

    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];

        int j, k;
        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]);
#if CV_SSE2
            __m128 _vec = _mm_loadu_ps(vec);
            __m128 _svmVec = _mm_loadu_ps(svmVec);
            __m128 sum = _mm_mul_ps(_svmVec, _vec);

            for( k = 4; k <= blockHistogramSize - 4; k += 4 )
            {
                _vec = _mm_loadu_ps(vec + k);
                _svmVec = _mm_loadu_ps(svmVec + k);

                sum = _mm_add_ps(sum, _mm_mul_ps(_vec, _svmVec));
            }

            _mm_storeu_ps(partSum, sum);
            double t0 = partSum[0] + partSum[1];
            double t1 = partSum[2] + partSum[3];
            s += t0 + t1;
#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];
#endif
            for( ; k < blockHistogramSize; k++ )
                s += vec[k]*svmVec[k];
        }
        if( s >= hitThreshold )
        {
            hits.push_back(pt0);
            weights.push_back(s);
        }
    }
}

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

private:
    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;
};

#ifdef HAVE_OPENCL

static bool ocl_classify_hists(int win_height, int win_width, int block_stride_y, int block_stride_x,
                               int win_stride_y, int win_stride_x, int height, int width,
                               const UMat& block_hists, UMat detector,
                               float free_coef, float threshold, UMat& labels, Size descr_size, int block_hist_size)
{
    int nthreads;
    bool is_cpu = cv::ocl::Device::getDefault().type() == cv::ocl::Device::TYPE_CPU;
    cv::String opts;

    ocl::Kernel k;
    int idx = 0;
    switch (descr_size.width)
    {
    case 180:
        nthreads = 180;
        k.create("classify_hists_180_kernel", ocl::objdetect::objdetect_hog_oclsrc, "");
        if(k.empty())
            return false;
        if(is_cpu)
           opts = "-D CPU ";
        else
            opts = cv::format("-D WAVE_SIZE=%d", k.preferedWorkGroupSizeMultiple());
        k.create("classify_hists_180_kernel", ocl::objdetect::objdetect_hog_oclsrc, opts);
        if(k.empty())
            return false;
        idx = k.set(idx, descr_size.width);
        idx = k.set(idx, descr_size.height);
        break;

    case 252:
        nthreads = 256;
        k.create("classify_hists_252_kernel", ocl::objdetect::objdetect_hog_oclsrc, "");
        if(k.empty())
            return false;
        if(is_cpu)
           opts = "-D CPU ";
        else
            opts = cv::format("-D WAVE_SIZE=%d", k.preferedWorkGroupSizeMultiple());
        k.create("classify_hists_252_kernel", ocl::objdetect::objdetect_hog_oclsrc, opts);
        if(k.empty())
            return false;
        idx = k.set(idx, descr_size.width);
        idx = k.set(idx, descr_size.height);
        break;

    default:
        nthreads = 256;
        k.create("classify_hists_kernel", ocl::objdetect::objdetect_hog_oclsrc, "");
        if(k.empty())
            return false;
        if(is_cpu)
           opts = "-D CPU ";
        else
            opts = cv::format("-D WAVE_SIZE=%d", k.preferedWorkGroupSizeMultiple());
        k.create("classify_hists_kernel", ocl::objdetect::objdetect_hog_oclsrc, opts);
        if(k.empty())
            return false;
        idx = k.set(idx, descr_size.area());
        idx = k.set(idx, descr_size.height);
    }

    int win_block_stride_x = win_stride_x / block_stride_x;
    int win_block_stride_y = win_stride_y / block_stride_y;
    int img_win_width = (width - win_width + win_stride_x) / win_stride_x;
    int img_win_height = (height - win_height + win_stride_y) / win_stride_y;
    int img_block_width = (width - CELLS_PER_BLOCK_X * CELL_WIDTH + block_stride_x) /
        block_stride_x;

    size_t globalThreads[3] = { img_win_width * nthreads, img_win_height, 1 };
    size_t localThreads[3] = { nthreads, 1, 1 };

    idx = k.set(idx, block_hist_size);
    idx = k.set(idx, img_win_width);
    idx = k.set(idx, img_block_width);
    idx = k.set(idx, win_block_stride_x);
    idx = k.set(idx, win_block_stride_y);
    idx = k.set(idx, ocl::KernelArg::PtrReadOnly(block_hists));
    idx = k.set(idx, ocl::KernelArg::PtrReadOnly(detector));
    idx = k.set(idx, free_coef);
    idx = k.set(idx, threshold);
    idx = k.set(idx, ocl::KernelArg::PtrWriteOnly(labels));

    return k.run(2, globalThreads, localThreads, false);
}

static bool ocl_detect(InputArray img, std::vector<Point> &hits, double hit_threshold, Size win_stride,
                       const UMat& oclSvmDetector, Size blockSize, Size cellSize, int nbins, Size blockStride, Size winSize,
                       bool gammaCorrection, double L2HysThreshold, float sigma, float free_coef, bool signedGradient)
{
    hits.clear();
    if (oclSvmDetector.empty())
        return false;

    Size imgSize = img.size();
    Size effect_size = imgSize;
    UMat grad(imgSize, CV_32FC2);
    int qangle_type = ocl::Device::getDefault().isIntel() ? CV_32SC2 : CV_8UC2;
    UMat qangle(imgSize, qangle_type);

    const size_t block_hist_size = getBlockHistogramSize(blockSize, cellSize, nbins);
    const Size blocks_per_img = numPartsWithin(imgSize, blockSize, blockStride);
    UMat block_hists(1, static_cast<int>(block_hist_size * blocks_per_img.area()) + 256, CV_32F);

    Size wins_per_img = numPartsWithin(imgSize, winSize, win_stride);
    UMat labels(1, wins_per_img.area(), CV_8U);

    float scale = 1.f / (2.f * sigma * sigma);
    Mat gaussian_lut(1, 512, CV_32FC1);
    int idx = 0;
    for(int i=-8; i<8; i++)
        for(int j=-8; j<8; j++)
            gaussian_lut.at<float>(idx++) = std::exp(-(j * j + i * i) * scale);
    for(int i=-8; i<8; i++)
        for(int j=-8; j<8; j++)
            gaussian_lut.at<float>(idx++) = (8.f - fabs(j + 0.5f)) * (8.f - fabs(i + 0.5f)) / 64.f;

    if(!ocl_computeGradient(img, grad, qangle, nbins, effect_size, gammaCorrection, signedGradient))
        return false;

    UMat gauss_w_lut;
    gaussian_lut.copyTo(gauss_w_lut);
    if(!ocl_compute_hists(nbins, blockStride.width, blockStride.height, effect_size.height,
        effect_size.width, grad, qangle, gauss_w_lut, block_hists, block_hist_size))
        return false;

    if(!ocl_normalize_hists(nbins, blockStride.width, blockStride.height, effect_size.height,
        effect_size.width, block_hists, (float)L2HysThreshold))
        return false;

    Size blocks_per_win = numPartsWithin(winSize, blockSize, blockStride);

    Size descr_size((int)block_hist_size*blocks_per_win.width, blocks_per_win.height);

    if(!ocl_classify_hists(winSize.height, winSize.width, blockStride.height,
        blockStride.width, win_stride.height, win_stride.width,
        effect_size.height, effect_size.width, block_hists, oclSvmDetector,
        free_coef, (float)hit_threshold, labels, descr_size, (int)block_hist_size))
        return false;

    Mat labels_host = labels.getMat(ACCESS_READ);
    unsigned char *vec = labels_host.ptr();
    for (int i = 0; i < wins_per_img.area(); i++)
    {
        int y = i / wins_per_img.width;
        int x = i - wins_per_img.width * y;
        if (vec[i])
        {
            hits.push_back(Point(x * win_stride.width, y * win_stride.height));
        }
    }
    return true;
}

static bool ocl_detectMultiScale(InputArray _img, std::vector<Rect> &found_locations, std::vector<double>& level_scale,
                                              double hit_threshold, Size win_stride, double group_threshold,
                                              const UMat& oclSvmDetector, Size blockSize, Size cellSize,
                                              int nbins, Size blockStride, Size winSize, bool gammaCorrection,
                                              double L2HysThreshold, float sigma, float free_coef, bool signedGradient)
{
    std::vector<Rect> all_candidates;
    std::vector<Point> locations;
    UMat image_scale;
    Size imgSize = _img.size();
    image_scale.create(imgSize, _img.type());

    for (size_t i = 0; i<level_scale.size() ; i++)
    {
        double scale = level_scale[i];
        Size effect_size = Size(cvRound(imgSize.width / scale), cvRound(imgSize.height / scale));
        if (effect_size == imgSize)
        {
            if(!ocl_detect(_img, locations, hit_threshold, win_stride, oclSvmDetector, blockSize, cellSize, nbins,
                blockStride, winSize, gammaCorrection, L2HysThreshold, sigma, free_coef, signedGradient))
                return false;
        }
        else
        {
            resize(_img, image_scale, effect_size);
            if(!ocl_detect(image_scale, locations, hit_threshold, win_stride, oclSvmDetector, blockSize, cellSize, nbins,
                blockStride, winSize, gammaCorrection, L2HysThreshold, sigma, free_coef, signedGradient))
                return false;
        }
        Size scaled_win_size(cvRound(winSize.width * scale),
            cvRound(winSize.height * scale));
        for (size_t j = 0; j < locations.size(); j++)
            all_candidates.push_back(Rect(Point2d(locations[j]) * scale, scaled_win_size));
    }
    found_locations.assign(all_candidates.begin(), all_candidates.end());
    groupRectangles(found_locations, (int)group_threshold, 0.2);
    clipObjects(imgSize, found_locations, 0, 0);

    return true;
}
#endif //HAVE_OPENCL

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

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

    if(winStride == Size())
        winStride = blockStride;

    CV_OCL_RUN(_img.dims() <= 2 && _img.type() == CV_8UC1 && scale0 > 1 && winStride.width % blockStride.width == 0 &&
        winStride.height % blockStride.height == 0 && padding == Size(0,0) && _img.isUMat(),
        ocl_detectMultiScale(_img, foundLocations, levelScale, hitThreshold, winStride, finalThreshold, oclSvmDetector,
        blockSize, cellSize, nbins, blockStride, winSize, gammaCorrection, L2HysThreshold, (float)getWinSigma(), free_coef, signedGradient));

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

    Mutex mtx;
    Mat img = _img.getMat();
    Range range(0, (int)levelScale.size());
    HOGInvoker invoker(this, img, hitThreshold, winStride, padding, &levelScale[0], &allCandidates, &mtx, &tempWeights, &tempScales);
    parallel_for_(range, invoker);

    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, foundWeights, (int)finalThreshold, 0.2);
    clipObjects(imgSize, foundLocations, 0, &foundWeights);
}

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

template<typename _ClsName> struct RTTIImpl
{
public:
    static int isInstance(const void* ptr)
    {
        static _ClsName dummy;
        static void* dummyp = &dummy;
        union
        {
            const void* p;
            const void** pp;
        } a, b;
        a.p = dummyp;
        b.p = ptr;
        return *a.pp == *b.pp;
    }
    static void release(void** dbptr)
    {
        if(dbptr && *dbptr)
        {
            delete (_ClsName*)*dbptr;
            *dbptr = 0;
        }
    }
    static void* read(CvFileStorage* fs, CvFileNode* n)
    {
        FileNode fn(fs, n);
        _ClsName* obj = new _ClsName;
        if(obj->read(fn))
            return obj;
        delete obj;
        return 0;
    }

    static void write(CvFileStorage* _fs, const char* name, const void* ptr, CvAttrList)
    {
        if(ptr && _fs)
        {
            FileStorage fs(_fs, false);
            ((const _ClsName*)ptr)->write(fs, String(name));
        }
    }

    static void* clone(const void* ptr)
    {
        if(!ptr)
            return 0;
        return new _ClsName(*(const _ClsName*)ptr);
    }
};

typedef RTTIImpl<HOGDescriptor> HOGRTTI;

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

std::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,
        -8.88111943e-004f, -0.06826669f, -0.01866150f, -0.03627640f,
        -0.01408288f, 0.01874239f, -0.02075835f, 0.09145175f, -0.03547291f,
        0.05396780f, 0.04198981f, 0.01301925f, -0.03384354f, -0.12201976f,
        0.06830920f, -0.03715654f, 9.55848210e-003f, 5.05685573e-003f,
        0.05659294f, 3.90764466e-003f, 0.02808490f, -0.05518097f, -0.03711621f,
        -0.02835565f, -0.04420464f, -0.01031947f, 0.01883466f,
        -8.49525444e-003f, -0.09419250f, -0.01269387f, -0.02133371f,
        -0.10190815f, -0.07844430f, 2.43644323e-003f, -4.09610150e-003f,
        0.01202551f, -0.06452291f, -0.10593818f, -0.02464746f, -0.02199699f,
        -0.07401930f, 0.07285886f, 8.87513801e-004f, 9.97662079e-003f,
        8.46779719e-003f, 0.03730333f, -0.02905126f, 0.03573337f, -0.04393689f,
        -0.12014472f, 0.03176554f, -2.76015815e-003f, 0.10824566f, 0.05090732f,
        -3.30179278e-003f, -0.05123822f, 5.04784798e-003f, -0.05664124f,
        -5.99415926e-003f, -0.05341901f, -0.01221393f, 0.01291318f,
        9.91760660e-003f, -7.56987557e-003f, -0.06193124f, -2.24549137e-003f,
        0.01987562f, -0.02018840f, -0.06975540f, -0.06601523f, -0.03349112f,
        -0.08910118f, -0.03371435f, -0.07406893f, -0.02248047f, -0.06159951f,
        2.77751544e-003f, -0.05723337f, -0.04792468f, 0.07518548f,
        2.77279224e-003f, 0.04211938f, 0.03100502f, 0.05278448f, 0.03954679f,
        -0.03006846f, -0.03851741f, -0.02792403f, -0.02875333f, 0.01531280f,
        0.02186953f, -0.01989829f, 2.50679464e-003f, -0.10258728f,
        -0.04785743f, -0.02887216f, 3.85063468e-003f, 0.01112236f,
        8.29218887e-003f, -0.04822981f, -0.04503597f, -0.03713100f,
        -0.06988008f, -0.11002295f, -2.69209221e-003f, 1.85383670e-003f,
        -0.05921049f, -0.06105053f, -0.08458050f, -0.04527602f,
        8.90329306e-004f, -0.05875023f, -2.68602883e-003f, -0.01591195f,
        0.03631859f, 0.05493166f, 0.07300330f, 5.53333294e-003f, 0.06400407f,
        0.01847740f, -5.76280477e-003f, -0.03210877f, 4.25160583e-003f,
        0.01166520f, -1.44864211e-003f, 0.02253744f, -0.03367080f, 0.06983195f,
        -4.22323542e-003f, -8.89401045e-003f, -0.07943393f, 0.05199728f,
        0.06065201f, 0.04133492f, 1.44032843e-003f, -0.09585235f, -0.03964731f,
        0.04232114f, 0.01750465f, -0.04487902f, -7.59733608e-003f, 0.02011171f,
        0.04673622f, 0.09011173f, -0.07869188f, -0.04682482f, -0.05080139f,
        -3.99383716e-003f, -0.05346331f, 0.01085723f, -0.03599333f,
        -0.07097908f, 0.03551549f, 0.02680387f, 0.03471529f, 0.01790393f,
        0.05471273f, 9.62048303e-003f, -0.03180215f, 0.05864431f, 0.02330614f,
        0.01633144f, -0.05616681f, -0.10245429f, -0.08302189f, 0.07291322f,
        -0.01972590f, -0.02619633f, -0.02485327f, -0.04627592f,
        1.48853404e-003f, 0.05514185f, -0.01270860f, -0.01948900f, 0.06373586f,
        0.05002292f, -0.03009798f, 8.76216311e-003f, -0.02474238f,
        -0.05504891f, 1.74034527e-003f, -0.03333667f, 0.01524987f, 0.11663762f,
        -1.32344989e-003f, -0.06608453f, 0.05687166f, -6.89525274e-004f,
        -0.04402352f, 0.09450210f, -0.04222684f, -0.05360983f, 0.01779531f,
        0.02561388f, -0.11075410f, -8.77790991e-003f, -0.01099504f,
        -0.10380266f, 0.03103457f, -0.02105741f, -0.07371717f, 0.05146710f,
        0.10581432f, -0.08617968f, -0.02892107f, 0.01092199f, 0.14551543f,
        -2.24320893e-003f, -0.05818033f, -0.07390742f, 0.05701261f,
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        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 std::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)
std::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,
        -0.106728f, -0.007656f, 0.172744f, 0.011576f,
        0.009883f, 0.083258f, -0.026516f, 0.145534f,
        0.153924f, -0.130290f, -0.108945f, 0.124490f,
        -0.003186f, -0.100485f, 0.015024f, -0.060512f,
        0.026288f, -0.086713f, -0.169012f, 0.076517f,
        0.215778f, 0.043701f, -0.131642f, -0.012585f,
        -0.045181f, -0.118183f, -0.241544f, -0.167293f,
        -0.020107f, -0.019917f, -0.101827f, -0.107096f,
        -0.010503f, 0.044938f, 0.189680f, 0.217119f,
        -0.046086f, 0.044508f, 0.199716f, -0.036004f,
        -0.148927f, 0.013355f, -0.078279f, 0.030451f,
        0.056301f, -0.024609f, 0.083224f, 0.099533f,
        -0.039432f, -0.138880f, 0.005482f, -0.024120f,
        -0.140468f, -0.066381f, -0.017057f, 0.009260f,
        -0.058004f, -0.028486f, -0.061610f, 0.007483f,
        -0.158309f, -0.150687f, -0.044595f, -0.105121f,
        -0.045763f, -0.006618f, -0.024419f, -0.117713f,
        -0.119366f, -0.175941f, -0.071542f, 0.119027f,
        0.111362f, 0.043080f, 0.034889f, 0.093003f,
        0.007842f, 0.057368f, -0.108834f, -0.079968f,
        0.230959f, 0.020205f, 0.011470f, 0.098877f,
        0.101310f, -0.030215f, -0.018018f, -0.059552f,
        -0.106157f, 0.021866f, -0.036471f, 0.080051f,
        0.041165f, -0.082101f, 0.117726f, 0.030961f,
        -0.054763f, -0.084102f, -0.185778f, -0.061305f,
        -0.038089f, -0.110728f, -0.264010f, 0.076675f,
        -0.077111f, -0.137644f, 0.036232f, 0.277995f,
        0.019116f, 0.107738f, 0.144003f, 0.080304f,
        0.215036f, 0.228897f, 0.072713f, 0.077773f,
        0.120168f, 0.075324f, 0.062730f, 0.122478f,
        -0.049008f, 0.164912f, 0.162450f, 0.041246f,
        0.009891f, -0.097827f, -0.038700f, -0.023027f,
        -0.120020f, 0.203364f, 0.248474f, 0.149810f,
        -0.036276f, -0.082814f, -0.090343f, -0.027143f,
        -0.075689f, -0.320310f, -0.000500f, -0.143334f,
        -0.065077f, -0.186936f, 0.129372f, 0.116431f,
        0.181699f, 0.170436f, 0.418854f, 0.460045f,
        0.333719f, 0.230515f, 0.047822f, -0.044954f,
        -0.068086f, 0.140179f, -0.044821f, 0.085550f,
        0.092483f, -0.107296f, -0.130670f, -0.206629f,
        0.114601f, -0.317869f, -0.076663f, 0.038680f,
        0.212753f, -0.016059f, -0.126526f, -0.163602f,
        0.210154f, 0.099887f, -0.126366f, 0.118453f,
        0.019309f, -0.021611f, -0.096499f, -0.111809f,
        -0.200489f, 0.142854f, 0.228840f, -0.353346f,
        -0.179151f, 0.116834f, 0.252389f, -0.031728f,
        -0.188135f, -0.158998f, 0.386523f, 0.122315f,
        0.209944f, 0.394023f, 0.359030f, 0.260717f,
        0.170335f, 0.013683f, -0.142596f, -0.026138f,
        -0.011878f, -0.150519f, 0.047159f, -0.107062f,
        -0.147347f, -0.187689f, -0.186027f, -0.208048f,
        0.058468f, -0.073026f, -0.236556f, -0.079788f,
        -0.146216f, -0.058563f, -0.101361f, -0.071294f,
        -0.071093f, 0.116919f, 0.234304f, 0.306781f,
        0.321866f, 0.240000f, 0.073261f, -0.012173f,
        0.026479f, 0.050173f, 0.166127f, 0.228955f,
        0.061905f, 0.156460f, 0.205990f, 0.120672f,
        0.037350f, 0.167884f, 0.290099f, 0.420900f,
        -0.012601f, 0.189839f, 0.306378f, 0.118383f,
        -0.095598f, -0.072360f, -0.132496f, -0.224259f,
        -0.126021f, 0.022714f, 0.284039f, 0.051369f,
        -0.000927f, -0.058735f, -0.083354f, -0.141254f,
        -0.187578f, -0.202669f, 0.048902f, 0.246597f,
        0.441863f, 0.342519f, 0.066979f, 0.215286f,
        0.188191f, -0.072240f, -0.208142f, -0.030196f,
        0.178141f, 0.136985f, -0.043374f, -0.181098f,
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        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 std::vector<float>(detector, detector + sizeof(detector)/sizeof(detector[0]));
}

class HOGConfInvoker :
    public ParallelLoopBody
{
public:
    HOGConfInvoker( const HOGDescriptor* _hog, const Mat& _img,
        double _hitThreshold, const 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());
        std::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.ptr());

            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 std::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() || 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;
    std::vector<float> blockHist(blockHistogramSize);

#if CV_SSE2
    float partSum[4];
#endif

    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]);
#if CV_SSE2
            __m128 _vec = _mm_loadu_ps(vec);
            __m128 _svmVec = _mm_loadu_ps(svmVec);
            __m128 sum = _mm_mul_ps(_svmVec, _vec);

            for( k = 4; k <= blockHistogramSize - 4; k += 4 )
            {
                _vec = _mm_loadu_ps(vec + k);
                _svmVec = _mm_loadu_ps(svmVec + k);

                sum = _mm_add_ps(sum, _mm_mul_ps(_vec, _svmVec));
            }

            _mm_storeu_ps(partSum, sum);
            double t0 = partSum[0] + partSum[1];
            double t1 = partSum[2] + partSum[3];
            s += t0 + t1;
#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];
#endif
            for( ; k < blockHistogramSize; k++ )
                s += vec[k]*svmVec[k];
        }
        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(String modelfile)
{
    // read model from SVMlight format..
    FILE *modelfl;
    if ((modelfl = fopen(modelfile.c_str(), "rb")) == NULL)
    {
        String eerr("file not exist");
        String efile(__FILE__);
        String efunc(__FUNCTION__);
        throw Exception(Error::StsError, eerr, efile, efunc, __LINE__);
    }
    char version_buffer[10];
    if (!fread (&version_buffer,sizeof(char),10,modelfl))
    {
        String eerr("version?");
        String efile(__FILE__);
        String efunc(__FUNCTION__);
        throw Exception(Error::StsError, eerr, efile, efunc, __LINE__);
    }
    if(strcmp(version_buffer,"V6.01")) {
        String eerr("version doesnot match");
        String efile(__FILE__);
        String efunc(__FUNCTION__);
        throw Exception(Error::StsError, eerr, efile, efunc, __LINE__);
    }
    /* read version number */
    int version = 0;
    if (!fread (&version,sizeof(int),1,modelfl))
    { throw Exception(); }
    if (version < 200)
    {
        String eerr("version doesnot match");
        String efile(__FILE__);
        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) {
            delete [] linearwt;
            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);
}

void HOGDescriptor::groupRectangles(std::vector<cv::Rect>& rectList, std::vector<double>& weights, int groupThreshold, double eps) const
{
    if( groupThreshold <= 0 || rectList.empty() )
    {
        return;
    }

    CV_Assert(rectList.size() == weights.size());

    std::vector<int> labels;
    int nclasses = partition(rectList, labels, SimilarRects(eps));

    std::vector<cv::Rect_<double> > rrects(nclasses);
    std::vector<int> numInClass(nclasses, 0);
    std::vector<double> foundWeights(nclasses, -std::numeric_limits<double>::max());
    int i, j, nlabels = (int)labels.size();

    for( i = 0; i < nlabels; i++ )
    {
        int cls = labels[i];
        rrects[cls].x += rectList[i].x;
        rrects[cls].y += rectList[i].y;
        rrects[cls].width += rectList[i].width;
        rrects[cls].height += rectList[i].height;
        foundWeights[cls] = max(foundWeights[cls], weights[i]);
        numInClass[cls]++;
    }

    for( i = 0; i < nclasses; i++ )
    {
        // find the average of all ROI in the cluster
        cv::Rect_<double> r = rrects[i];
        double s = 1.0/numInClass[i];
        rrects[i] = cv::Rect_<double>(cv::saturate_cast<double>(r.x*s),
            cv::saturate_cast<double>(r.y*s),
            cv::saturate_cast<double>(r.width*s),
            cv::saturate_cast<double>(r.height*s));
    }

    rectList.clear();
    weights.clear();

    for( i = 0; i < nclasses; i++ )
    {
        cv::Rect r1 = rrects[i];
        int n1 = numInClass[i];
        double w1 = foundWeights[i];
        if( n1 <= groupThreshold )
            continue;
        // filter out small rectangles inside large rectangles
        for( j = 0; j < nclasses; j++ )
        {
            int n2 = numInClass[j];

            if( j == i || n2 <= groupThreshold )
                continue;

            cv::Rect r2 = rrects[j];

            int dx = cv::saturate_cast<int>( r2.width * eps );
            int dy = cv::saturate_cast<int>( r2.height * eps );

            if( r1.x >= r2.x - dx &&
                r1.y >= r2.y - dy &&
                r1.x + r1.width <= r2.x + r2.width + dx &&
                r1.y + r1.height <= r2.y + r2.height + dy &&
                (n2 > std::max(3, n1) || n1 < 3) )
                break;
        }

        if( j == nclasses )
        {
            rectList.push_back(r1);
            weights.push_back(w1);
        }
    }
}
}