fixed HOG detector

[opencv.git] / opencv / src / cvaux / cvhog.cpp

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#include "_cvaux.h"
#include <iterator>

/****************************************************************************************\
      The code below is implementation of HOG (Histogram-of-Oriented Gradients)
      descriptor and object detection, introduced by Navneet Dalal and Bill Triggs.

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

namespace cv
{

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

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

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

void HOGDescriptor::setSVMDetector(const vector<float>& _svmDetector)
{
    svmDetector = _svmDetector;
    CV_Assert( checkDetectorSize() );
}

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

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

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

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

void HOGDescriptor::computeGradient(const Mat& img, Mat& grad, Mat& qangle,
                                    Size paddingTL, Size paddingBR) const
{
    CV_Assert( img.type() == CV_8U || img.type() == CV_8UC3 );
    
    Size gradsize(img.cols + paddingTL.width + paddingBR.width,
                  img.rows + paddingTL.height + paddingBR.height);
    grad.create(gradsize, CV_32FC2);  // <magnitude*(1-alpha), magnitude*alpha>
    qangle.create(gradsize, CV_8UC2); // [0..nbins-1] - quantized gradient orientation
    Size wholeSize;
    Point roiofs;
    img.locateROI(wholeSize, roiofs);

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

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

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

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

    const int borderType = (int)BORDER_REFLECT_101;

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

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

    int _nbins = nbins;
    float angleScale = (float)(_nbins/CV_PI);

    for( y = 0; y < gradsize.height; y++ )
    {
        const uchar* imgPtr = img.data + img.step*ymap[y];
        const uchar* prevPtr = img.data + img.step*ymap[y-1];
        const uchar* nextPtr = img.data + img.step*ymap[y+1];
        float* gradPtr = (float*)grad.ptr(y);
        uchar* qanglePtr = (uchar*)qangle.ptr(y);
        
        if( cn == 1 )
        {
            for( x = 0; x < width; x++ )
            {
                int x1 = xmap[x];
                dbuf[x] = (float)(lut[imgPtr[xmap[x+1]]] - lut[imgPtr[xmap[x-1]]]);
                dbuf[width + x] = (float)(lut[nextPtr[x1]] - lut[prevPtr[x1]]);
            }
        }
        else
        {
            for( x = 0; x < width; x++ )
            {
                int x1 = xmap[x]*3;
                const uchar* p2 = imgPtr + xmap[x+1]*3;
                const uchar* p0 = imgPtr + xmap[x-1]*3;
                float dx0, dy0, dx, dy, mag0, mag;

                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;
            }
        }
        
        cartToPolar( Dx, Dy, Mag, Angle, false );

        for( x = 0; x < width; x++ )
        {
            float mag = dbuf[x+width*2], angle = dbuf[x+width*3]*angleScale - 0.5f;
            int hidx = cvFloor(angle);
            angle -= hidx;
            if( hidx < 0 )
                hidx += _nbins;
            else if( hidx >= _nbins )
                hidx -= _nbins;
            assert( (unsigned)hidx < (unsigned)_nbins );

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


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

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

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

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

    const float* getBlock(Point pt, float* buf);
    virtual void normalizeBlockHistogram(float* histogram) const;
    
    vector<PixData> pixData;
    vector<BlockData> blockData;

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

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


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

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

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

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

    winSize = descriptor->winSize;
    Size blockSize = descriptor->blockSize;
    Size blockStride = descriptor->blockStride;
    Size cellSize = descriptor->cellSize;
    Size winSize = descriptor->winSize;
    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 i, cacheRows = blockCache.rows;
        ymaxCached.resize(cacheRows);
        for( i = 0; i < cacheRows; i++ )
            ymaxCached[i] = -1;
    }

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

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

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

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

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

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

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


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

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

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

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

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

    CV_Assert( blockHist != 0 );

    for( k = 0; k < blockHistogramSize; k++ )
        blockHist[k] = 0.f;
    
    const PixData* _pixData = &pixData[0];

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

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

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

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

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

    normalizeBlockHistogram(blockHist);

    return blockHist;
}


void HOGCache::normalizeBlockHistogram(float* _hist) const
{
    float* hist = &_hist[0];
    size_t i, sz = blockHistogramSize;
    
    float sum = 0;
    for( i = 0; i < sz; i++ )
        sum += hist[i]*hist[i];
    float scale = 1.f/(std::sqrt(sum)+sz*0.1f), thresh = (float)descriptor->L2HysThreshold;
    for( i = 0, sum = 0; i < sz; i++ )
    {
        hist[i] = std::min(hist[i]*scale, thresh);
        sum += hist[i]*hist[i];
    }
    scale = 1.f/(std::sqrt(sum)+1e-3f);
    for( i = 0; i < sz; i++ )
        hist[i] *= scale;
}
    
    
Size HOGCache::windowsInImage(Size imageSize, Size winStride) const
{
    return Size((imageSize.width - winSize.width)/winStride.width + 1,
                (imageSize.height - winSize.height)/winStride.height + 1);
}

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


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

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

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

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

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

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

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


void HOGDescriptor::detect(const Mat& img,
    vector<Point>& hits, double hitThreshold,
    Size winStride, Size padding, const vector<Point>& locations) const
{
    hits.clear();
    if( svmDetector.empty() )
        return;
    
    if( winStride == Size() )
        winStride = cellSize;
    Size cacheStride(gcd(winStride.width, blockStride.width),
                     gcd(winStride.height, blockStride.height));
    size_t nwindows = locations.size();
    padding.width = (int)alignSize(std::max(padding.width, 0), cacheStride.width);
    padding.height = (int)alignSize(std::max(padding.height, 0), cacheStride.height);
    Size paddedImgSize(img.cols + padding.width*2, img.rows + padding.height*2);
    
    HOGCache cache(this, img, padding, padding, nwindows == 0, cacheStride);

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

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

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

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

    for( size_t i = 0; i < nwindows; i++ )
    {
        Point pt0;
        if( !locations.empty() )
        {
            pt0 = locations[i];
            if( pt0.x < -padding.width || pt0.x > img.cols + padding.width - winSize.width ||
                pt0.y < -padding.height || pt0.y > img.rows + padding.height - winSize.height )
                continue;
        }
        else
        {
            pt0 = cache.getWindow(paddedImgSize, winStride, (int)i).tl() - Point(padding);
            CV_Assert(pt0.x % cacheStride.width == 0 && pt0.y % cacheStride.height == 0);
        }
        double s = rho;
        const float* svmVec = &svmDetector[0];
        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]);
            for( k = 0; k <= blockHistogramSize - 4; k += 4 )
                s += vec[k]*svmVec[k] + vec[k+1]*svmVec[k+1] +
                    vec[k+2]*svmVec[k+2] + vec[k+3]*svmVec[k+3];
            for( ; k < blockHistogramSize; k++ )
                s += vec[k]*svmVec[k];
        }
        if( s >= hitThreshold )
            hits.push_back(pt0);
    }
}

    
struct HOGInvoker
{
    HOGInvoker( const HOGDescriptor* _hog, const Mat& _img,
                double _hitThreshold, Size _winStride, Size _padding,
                const double* _levelScale, ConcurrentRectVector* _vec ) 
    {
        hog = _hog;
        img = _img;
        hitThreshold = _hitThreshold;
        winStride = _winStride;
        padding = _padding;
        levelScale = _levelScale;
        vec = _vec;
    }
    
    void operator()( const BlockedRange& range ) const
    {
        int i, i1 = range.begin(), i2 = range.end();
        double minScale = i1 > 0 ? levelScale[i1] : i2 > 1 ? levelScale[i1+1] : std::max(img.cols, img.rows);
        Size maxSz(cvCeil(img.cols/minScale), cvCeil(img.rows/minScale));
        Mat smallerImgBuf(maxSz, img.type());
        vector<Point> locations;
        
        for( i = i1; i < i2; i++ )
        {
            double scale = levelScale[i];
            Size sz(cvRound(img.cols/scale), cvRound(img.rows/scale));
            Mat smallerImg(sz, img.type(), smallerImgBuf.data);
            if( sz == img.size() )
                smallerImg = Mat(sz, img.type(), img.data, img.step);
            else
                resize(img, smallerImg, sz);
            hog->detect(smallerImg, locations, hitThreshold, winStride, padding);
            Size scaledWinSize = Size(cvRound(hog->winSize.width*scale), cvRound(hog->winSize.height*scale));
            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));
        }
    }
    
    const HOGDescriptor* hog;
    Mat img;
    double hitThreshold;
    Size winStride;
    Size padding;
    const double* levelScale;
    ConcurrentRectVector* vec;
};


void HOGDescriptor::detectMultiScale(
    const Mat& img, vector<Rect>& foundLocations,
    double hitThreshold, Size winStride, Size padding,
    double scale0, int groupThreshold) const
{
    double scale = 1.;
    const int maxLevels = 64;
    int levels = 0;

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

    ConcurrentRectVector allCandidates;
    
    parallel_for(BlockedRange(0, (int)levelScale.size()),
                 HOGInvoker(this, img, hitThreshold, winStride, padding, &levelScale[0], &allCandidates));
    
    foundLocations.resize(allCandidates.size());
    std::copy(allCandidates.begin(), allCandidates.end(), foundLocations.begin());

    groupRectangles(foundLocations, groupThreshold, 0.2);
}

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,
       0.12937020f, -0.04986651f, 0.10182415f, 0.05028650f, 0.12515625f,
       0.09175041f, 0.06404983f, 0.01523394f, 0.09460562f, 0.06106631f,
       -0.14266998f, -0.02926703f, 0.02762171f, 0.02164151f,
       -9.58488265e-004f, -0.04231362f, -0.09866509f, 0.04322244f,
       0.05872034f, -0.04838847f, 0.06319253f, 0.02443798f, -0.03606876f,
       9.38737206e-003f, 0.04289991f, -0.01027411f, 0.08156885f, 0.08751175f,
       -0.13191354f, 8.16054735e-003f, -0.01452161f, 0.02952677f, 0.03615945f,
       -2.09128903e-003f, 0.02246693f, 0.09623287f, 0.09412123f, -0.02924758f,
       -0.07815186f, -0.02203079f, -2.02566991e-003f, 0.01094733f,
       -0.01442332f, 0.02838561f, 0.11882371f, 7.28798332e-003f, -0.10345965f,
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       -0.08758243f, -7.67420698e-003f, 0.08862378f, 0.06098121f, 0.06565887f,
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       0.07375994f, -9.19828378e-003f, -0.04914120f, 0.02124831f, 0.06455322f,
       0.04372910f, -0.03310043f, 0.03605788f, -6.78055827e-003f,
       9.36202332e-003f, 0.01747596f, -0.06406314f, -0.06812935f, 0.08080816f,
       -0.02778088f, 0.02735260f, 0.06393493f, 0.06652229f, 0.05676993f,
       0.08640018f, -7.59188086e-003f, -0.02012847f, -0.04741159f,
       -0.01657069f, -0.01624399f, 0.05547778f, -2.33309763e-003f,
       0.01120033f, 0.06141156f, -0.06285004f, -0.08732341f, -0.09313398f,
       -0.04267832f, 5.57443965e-003f, 0.04809862f, 0.01773641f,
       5.37361018e-003f, 0.14842421f, -0.06298012f, -0.02935147f, 0.11443478f,
       -0.05034208f, 5.65494271e-003f, 0.02076526f, -0.04577984f,
       -0.04735741f, 0.02961071f, -0.09307127f, -0.04417921f, -0.04990027f,
       -0.03940028f, 0.01306016f, 0.06267900f, 0.03758737f, 0.08460117f,
       0.13858789f, 0.04862388f, -0.06319809f, -0.05655516f, 0.01885816f,
       -0.03285607f, 0.03371567f, -0.07040928f, -0.04514049f, 0.01392166f,
       0.08184422f, -0.07230316f, 0.02386871f, 0.02184591f, 0.02605764f,
       -0.01033954f, 9.29878280e-003f, 7.67351175e-003f, 0.15189242f,
       0.02069071f, -0.09738296f, -0.08894105f, -0.07768748f, 0.02332268f,
       -0.01778995f, -0.03258888f, -0.08180822f, -0.08492987f, 0.02290156f,
       -0.11368170f, -0.03554465f, -0.04533844f, -0.02861580f, 0.06782424f,
       0.01113123f, 0.02453644f, 0.12721945f, 0.08084814f, -0.03607795f,
       0.01109122f, 0.04803548f, -0.03489929f, 0.03399536f, -0.05682014f,
       8.59533902e-003f, -4.27904585e-003f, 0.03230887f, -0.01300198f,
       -0.01038137f, -0.07930113f, 8.33097473e-003f, 0.02296994f,
       -0.01306500f, -0.01881626f, 0.04413369f, 0.05729880f, -0.03761553f,
       0.01942326f, 1.64540811e-003f, -0.03811319f, 0.04190650f, -0.14978096f,
       -0.04514487f, 0.01209545f, -5.46460645e-003f, -0.01647195f,
       7.63064111e-003f, -0.07494587f, 0.08415288f, 0.10020141f, -0.01228561f,
       0.06553826f, 0.04554005f, 0.07890417f, 0.03041138f, 0.01752007f,
       0.09208256f, -3.74419295e-004f, 0.10549527f, 0.04686913f, 0.01894833f,
       -0.02651412f, -4.34682379e-003f, 5.44942822e-003f, 0.01444484f,
       0.05882156f, -0.03336544f, 0.04603891f, -0.10432546f, 0.01923928f,
       0.01842845f, -0.01712168f, -0.02222766f, 0.04693324f, -0.06202956f,
       -0.01422159f, 0.08732220f, -0.07706107f, 0.02661049f, -0.04300238f,
       -0.03092422f, -0.03552184f, -0.01886088f, -0.04979934f, 0.03906401f,
       0.04608644f, 0.04966111f, 0.04275464f, -0.04621769f, -0.02653212f,
       8.57011229e-003f, 0.03839684f, 0.05818764f, 0.03880796f,
       -2.76100676e-004f, 0.03076511f, -0.03266929f, -0.05374557f,
       0.04986527f, -9.45429131e-003f, 0.03582499f, -2.64564669e-003f,
       -1.07461517e-003f, 0.02962313f, -0.01483363f, 0.03060869f, 0.02448327f,
       0.01845641f, 0.03282966f, -0.03534438f, -0.01084059f, -0.01119136f,
       -1.85360224e-003f, -5.94652840e-004f, -0.04451817f, 2.98327743e-003f,
       0.06272484f, -0.02152076f, -3.05971340e-003f, -0.05070828f,
       0.01531762f, 0.01282815f, 0.05167150f, 9.46266949e-003f,
       -3.34558333e-003f, 0.11442288f, -0.03906701f, -2.67325155e-003f,
       0.03069184f, -0.01134165f, 0.02949462f, 0.02879886f, 0.03855566f,
       -0.03450781f, 0.09142872f, -0.02156654f, 0.06075062f, -0.06220816f,
       0.01944680f, 6.68372354e-003f, -0.06656796f, 8.70784000e-003f,
       0.03456013f, 0.02434320f, -0.13236357f, -0.04177035f, -0.02069627f,
       0.01068112f, 0.01505432f, -0.07517391f, -3.83571628e-003f,
       -0.06298508f, -0.02881260f, -0.13101046f, -0.07221562f,
       -5.79945277e-003f, -8.57300125e-003f, 0.03782469f, 0.02762164f,
       0.04942456f, -0.02936396f, 0.09597211f, 0.01921411f, 0.06101191f,
       -0.04787507f, -0.01379578f, -7.40224449e-003f, -0.02220136f,
       -0.01313756f, 7.77558051e-003f, 0.12296968f, 0.02939998f, 0.03594062f,
       -0.07788624f, -0.01133144f, 3.99316690e-004f, -0.06090347f,
       -0.01122066f, -4.68682544e-003f, 0.07633100f, -0.06748922f,
       -0.05640298f, -0.05265681f, -0.01139122f, -0.01624347f, -0.04715714f,
       -0.01099092f, 0.01048561f, 3.28499987e-003f, -0.05810167f,
       -0.07699911f, -0.03330683f, 0.04185145f, 0.03478536f, 0.02275165f,
       0.02304766f, 6.66040834e-003f, 0.10968148f, -5.93013782e-003f,
       -0.04858336f, -0.04203213f, -0.09316786f, -6.13074889e-003f,
       -0.02544625f, 0.01366201f, 9.18555818e-003f, -0.01846578f,
       -0.05622401f, -0.03989377f, -0.07810296f, 6.91275718e-003f,
       0.05957597f, -0.03901334f, 0.01572002f, -0.01193903f,
       -6.89400872e-003f, -0.03093356f, -0.04136098f, -0.01562869f,
       -0.04604580f, 0.02865234f, -0.08678447f, -0.03232484f, -0.05364593f,
       -0.01445016f, -0.07003860f, -0.08669746f, -0.04520775f, 0.04274122f,
       0.03117515f, 0.08175703f, 0.01081109f, 0.06379741f, 0.06199206f,
       0.02865988f, 0.02360346f, 0.06725410f, -0.03248780f, -9.37702879e-003f,
       0.08265898f, -0.02245839f, 0.05125763f, -0.01862395f, 0.01973453f,
       -0.01994494f, -0.10770868f, 0.03180375f, 3.23935156e-003f,
       -0.02142080f, -0.04256190f, 0.04760900f, 0.04282863f, 0.05635953f,
       -0.01870849f, 0.05540622f, -0.03042666f, 0.01455277f, -0.06630179f,
       -0.05843807f, -0.03739681f, -0.09739155f, -0.03220233f, -0.05620182f,
       -0.10381401f, 0.07400211f, 4.20676917e-003f, 0.03258535f,
       2.14308966e-003f, 0.05121966f, -0.01274337f, 0.02384761f, 0.06335578f,
       -0.07905591f, 0.08375625f, -0.07898903f, -0.06508528f, -0.02498444f,
       0.06535810f, 0.03970535f, 0.04895468f, -0.01169566f, -0.03980601f,
       0.05682293f, 0.05925463f, -0.01165808f, -0.07936699f, -0.04208954f,
       0.01333987f, 0.09051196f, 0.10098671f, -0.03974256f, 0.01238771f,
       -0.07501741f, -0.03655440f, -0.04301528f, 0.09216860f,
       4.63579083e-004f, 0.02851115f, 0.02142735f, 1.28244064e-004f,
       0.02879687f, -0.08554889f, -0.04838862f, 0.08135369f, -0.05756533f,
       0.01413900f, 0.03451880f, -0.06619488f, -0.03053130f, 0.02961676f,
       -0.07384635f, 0.01135692f, 0.05283910f, -0.07778034f, -0.02107482f,
       -0.05511716f, -0.13473752f, 0.03030157f, 0.06722020f, -0.06218817f,
       -0.05826827f, 0.06254654f, 0.02895772f, -0.01664000f, -0.03620280f,
       -0.01612278f, -1.46097376e-003f, 0.14013411f, -8.96181818e-003f,
       -0.03250246f, 3.38630192e-003f, 2.64779478e-003f, 0.03359732f,
       -0.02411991f, -0.04229729f, 0.10666174f, -6.66579151f };
    return vector<float>(detector, detector + sizeof(detector)/sizeof(detector[0]));
}

}