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/*M///////////////////////////////////////////////////////////////////////////////////////
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#include "precomp.hpp"

using namespace cv;
using namespace cv::cuda;

#if !defined (HAVE_CUDA) || defined (CUDA_DISABLER)

Ptr<cuda::HOG> cv::cuda::HOG::create(Size, Size, Size, Size, int) { throw_no_cuda(); return Ptr<cuda::HOG>(); }

#else

/****************************************************************************************\
      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 { namespace cuda { namespace device
{
    namespace hog
    {
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        void set_up_constants(int nbins, int block_stride_x, int block_stride_y,
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                              int nblocks_win_x, int nblocks_win_y,
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                              int ncells_block_x, int ncells_block_y);

        void compute_hists(int nbins, int block_stride_x, int block_stride_y,
                           int height, int width, const PtrStepSzf& grad,
                           const PtrStepSzb& qangle, float sigma, float* block_hists,
                           int cell_size_x, int cell_size_y, int ncells_block_x, int ncells_block_y);

        void normalize_hists(int nbins, int block_stride_x, int block_stride_y,
                             int height, int width, float* block_hists, float threshold, int cell_size_x, int cell_size_y, int ncells_block_x, int ncells_block_y);
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        void 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, float* block_hists, float* coefs, float free_coef,
                            float threshold, int cell_size_x, int ncells_block_x, unsigned char* labels);

        void compute_confidence_hists(int win_height, int win_width, int block_stride_y, int block_stride_x,
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                           int win_stride_y, int win_stride_x, int height, int width, float* block_hists,
                           float* coefs, float free_coef, float threshold, int cell_size_x, int ncells_block_x, float *confidences);

        void 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, float* block_hists, int cell_size_x, int ncells_block_x,
                                    cv::cuda::PtrStepSzf descriptors);
        void 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, float* block_hists, int cell_size_x, int ncells_block_x,
                                    cv::cuda::PtrStepSzf descriptors);

        void compute_gradients_8UC1(int nbins, int height, int width, const cv::cuda::PtrStepSzb& img,
                                    float angle_scale, cv::cuda::PtrStepSzf grad, cv::cuda::PtrStepSzb qangle, bool correct_gamma);
        void compute_gradients_8UC4(int nbins, int height, int width, const cv::cuda::PtrStepSzb& img,
                                    float angle_scale, cv::cuda::PtrStepSzf grad, cv::cuda::PtrStepSzb qangle, bool correct_gamma);
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        void resize_8UC1(const cv::cuda::PtrStepSzb& src, cv::cuda::PtrStepSzb dst);
        void resize_8UC4(const cv::cuda::PtrStepSzb& src, cv::cuda::PtrStepSzb dst);
    }
}}}

using namespace cv::cuda::device;

namespace
{
    class HOG_Impl : public cv::cuda::HOG
    {
    public:
        HOG_Impl(Size win_size,
                 Size block_size,
                 Size block_stride,
                 Size cell_size,
                 int nbins);

        virtual void setWinSigma(double win_sigma) { win_sigma_ = win_sigma; }
        virtual double getWinSigma() const;

        virtual void setL2HysThreshold(double threshold_L2hys) { threshold_L2hys_ = threshold_L2hys; }
        virtual double getL2HysThreshold() const { return threshold_L2hys_; }

        virtual void setGammaCorrection(bool gamma_correction) { gamma_correction_ = gamma_correction; }
        virtual bool getGammaCorrection() const { return gamma_correction_; }

        virtual void setNumLevels(int nlevels) { nlevels_ = nlevels; }
        virtual int getNumLevels() const { return nlevels_; }

        virtual void setHitThreshold(double hit_threshold) { hit_threshold_ = hit_threshold; }
        virtual double getHitThreshold() const { return hit_threshold_; }

        virtual void setWinStride(Size win_stride) { win_stride_ = win_stride; }
        virtual Size getWinStride() const { return win_stride_; }

        virtual void setScaleFactor(double scale0) { scale0_ = scale0; }
        virtual double getScaleFactor() const { return scale0_; }

        virtual void setGroupThreshold(int group_threshold) { group_threshold_ = group_threshold; }
        virtual int getGroupThreshold() const { return group_threshold_; }

        virtual void setDescriptorFormat(int descr_format) { descr_format_ = descr_format; }
        virtual int getDescriptorFormat() const { return descr_format_; }

        virtual size_t getDescriptorSize() const;

        virtual size_t getBlockHistogramSize() const;

        virtual void setSVMDetector(InputArray detector);

        virtual Mat getDefaultPeopleDetector() const;

        virtual void detect(InputArray img,
                            std::vector<Point>& found_locations,
                            std::vector<double>* confidences);

        virtual void detectMultiScale(InputArray img,
                                      std::vector<Rect>& found_locations,
                                      std::vector<double>* confidences);

        virtual void compute(InputArray img,
                             OutputArray descriptors,
                             Stream& stream);

    private:
        Size win_size_;
        Size block_size_;
        Size block_stride_;
        Size cell_size_;
        int nbins_;

        double win_sigma_;
        double threshold_L2hys_;
        bool gamma_correction_;
        int nlevels_;
        double hit_threshold_;
        Size win_stride_;
        double scale0_;
        int group_threshold_;
        int descr_format_;
        Size cells_per_block_;

    private:
        int getTotalHistSize(Size img_size) const;
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        void computeBlockHistograms(const GpuMat& img, GpuMat& block_hists);
        void computeGradient(const GpuMat& img, GpuMat& grad, GpuMat& qangle);
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        // Coefficients of the separating plane
        float free_coef_;
        GpuMat detector_;
    };

    HOG_Impl::HOG_Impl(Size win_size,
                       Size block_size,
                       Size block_stride,
                       Size cell_size,
                       int nbins) :
        win_size_(win_size),
        block_size_(block_size),
        block_stride_(block_stride),
        cell_size_(cell_size),
        nbins_(nbins),

        win_sigma_(-1.0),
        threshold_L2hys_(0.2),
        gamma_correction_(true),
        nlevels_(64),
        hit_threshold_(0.0),
        win_stride_(block_stride),
        scale0_(1.05),
        group_threshold_(2),
        descr_format_(DESCR_FORMAT_COL_BY_COL),
        cells_per_block_(block_size.width / cell_size.width, block_size.height / cell_size.height)
    {
        CV_Assert((win_size.width  - block_size.width ) % block_stride.width  == 0 &&
                  (win_size.height - block_size.height) % block_stride.height == 0);

        CV_Assert(block_size.width % cell_size.width == 0 &&
                  block_size.height % cell_size.height == 0);

        // Navneet Dalal and Bill Triggs. Histograms of oriented gradients for
        // human detection. In International Conference on Computer Vision and
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        // Pattern Recognition, volume 2, pages 886–893, June 2005
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        // http://lear.inrialpes.fr/people/triggs/pubs/Dalal-cvpr05.pdf (28.07.2015) [Figure 5]
        CV_Assert(block_stride == (block_size / 2));

        CV_Assert(cell_size.width == cell_size.height);
    }

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

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

    size_t HOG_Impl::getDescriptorSize() const
    {
        return numPartsWithin(win_size_, block_size_, block_stride_).area() * getBlockHistogramSize();
    }

    size_t HOG_Impl::getBlockHistogramSize() const
    {
        return nbins_ * cells_per_block_.area();
    }

    double HOG_Impl::getWinSigma() const
    {
        return win_sigma_ >= 0 ? win_sigma_ : (block_size_.width + block_size_.height) / 8.0;
    }

    void HOG_Impl::setSVMDetector(InputArray _detector)
    {
        const int descriptor_size = static_cast<int>(getDescriptorSize());

        const Mat detector = _detector.getMat();

        CV_Assert( detector.type() == CV_32FC1 );
        CV_Assert( detector.rows == 1 );
        CV_Assert( detector.cols == descriptor_size || detector.cols == descriptor_size + 1 );

        std::vector<float> detector_reordered(detector.ptr<float>(), detector.ptr<float>() + detector.cols);

        size_t block_hist_size = getBlockHistogramSize();
        Size blocks_per_win = numPartsWithin(win_size_, block_size_, block_stride_);

        for (int i = 0; i < blocks_per_win.height; ++i)
        {
            for (int j = 0; j < blocks_per_win.width; ++j)
            {
                const float* src = detector.ptr<float>() + (j * blocks_per_win.height + i) * block_hist_size;
                float* dst = &detector_reordered[0] + (i * blocks_per_win.width + j) * block_hist_size;
                for (size_t k = 0; k < block_hist_size; ++k)
                    dst[k] = src[k];
            }
        }

        detector_.upload(Mat(detector_reordered).reshape(1, 1));
        free_coef_ = detector.cols > descriptor_size ? detector.at<float>(0, descriptor_size) : 0;
    }

    static Mat getPeopleDetector64x128();
    static Mat getPeopleDetector48x96();

    Mat HOG_Impl::getDefaultPeopleDetector() const
    {
        CV_Assert( win_size_ == Size(64, 128) || win_size_ == Size(48, 96) );

        if (win_size_ == Size(64, 128))
            return getPeopleDetector64x128();
        else
            return getPeopleDetector48x96();
    }

    void HOG_Impl::detect(InputArray _img, std::vector<Point>& hits, std::vector<double>* confidences)
    {
        const GpuMat img = _img.getGpuMat();

        CV_Assert( img.type() == CV_8UC1 || img.type() == CV_8UC4 );
        CV_Assert( win_stride_.width % block_stride_.width == 0 && win_stride_.height % block_stride_.height == 0 );

        hits.clear();
        if (detector_.empty())
            return;

        BufferPool pool(Stream::Null());

        GpuMat block_hists = pool.getBuffer(1, getTotalHistSize(img.size()), CV_32FC1);
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        computeBlockHistograms(img, block_hists);
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        Size wins_per_img = numPartsWithin(img.size(), win_size_, win_stride_);

        if (confidences == NULL)
        {
            GpuMat labels = pool.getBuffer(1, wins_per_img.area(), CV_8UC1);

            hog::classify_hists(win_size_.height, win_size_.width,
                                block_stride_.height, block_stride_.width,
                                win_stride_.height, win_stride_.width,
                                img.rows, img.cols,
                                block_hists.ptr<float>(),
                                detector_.ptr<float>(),
                                (float)free_coef_,
                                (float)hit_threshold_,
                                cell_size_.width, cells_per_block_.width,
                                labels.ptr());

            Mat labels_host;
            labels.download(labels_host);
            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));
            }
        }
        else
        {
            GpuMat labels = pool.getBuffer(1, wins_per_img.area(), CV_32FC1);

            hog::compute_confidence_hists(win_size_.height, win_size_.width,
                                          block_stride_.height, block_stride_.width,
                                          win_stride_.height, win_stride_.width,
                                          img.rows, img.cols,
                                          block_hists.ptr<float>(),
                                          detector_.ptr<float>(),
                                          (float)free_coef_,
                                          (float)hit_threshold_,
                                          cell_size_.width, cells_per_block_.width,
                                          labels.ptr<float>());

            Mat labels_host;
            labels.download(labels_host);
            float* vec = labels_host.ptr<float>();

            confidences->clear();
            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] >= hit_threshold_)
                {
                    hits.push_back(Point(x * win_stride_.width, y * win_stride_.height));
                    confidences->push_back((double)vec[i]);
                }
            }
        }
    }

    void HOG_Impl::detectMultiScale(InputArray _img,
                                    std::vector<Rect>& found_locations,
                                    std::vector<double>* confidences)
    {
        const GpuMat img = _img.getGpuMat();

        CV_Assert( img.type() == CV_8UC1 || img.type() == CV_8UC4 );
        CV_Assert( confidences == NULL || group_threshold_ == 0 );

        std::vector<double> level_scale;
        double scale = 1.0;
        int levels = 0;
        for (levels = 0; levels < nlevels_; levels++)
        {
            level_scale.push_back(scale);

            if (cvRound(img.cols / scale) < win_size_.width ||
                cvRound(img.rows / scale) < win_size_.height ||
                scale0_ <= 1)
            {
                break;
            }

            scale *= scale0_;
        }
        levels = std::max(levels, 1);
        level_scale.resize(levels);

        std::vector<Point> level_hits;
        std::vector<double> level_confidences;

        BufferPool pool(Stream::Null());

        found_locations.clear();
        for (size_t i = 0; i < level_scale.size(); i++)
        {
            scale = level_scale[i];

            Size sz(cvRound(img.cols / scale), cvRound(img.rows / scale));

            GpuMat smaller_img;
            if (sz == img.size())
            {
                smaller_img = img;
            }
            else
            {
                smaller_img = pool.getBuffer(sz, img.type());
                switch (img.type())
                {
                    case CV_8UC1: hog::resize_8UC1(img, smaller_img); break;
                    case CV_8UC4: hog::resize_8UC4(img, smaller_img); break;
                }
            }

            detect(smaller_img, level_hits,
                   confidences ? &level_confidences : NULL);

            Size scaled_win_size(cvRound(win_size_.width * scale),
                                 cvRound(win_size_.height * scale));

            for (size_t j = 0; j < level_hits.size(); j++)
            {
                found_locations.push_back(Rect(Point2d(level_hits[j]) * scale, scaled_win_size));
                if (confidences)
                    confidences->push_back(level_confidences[j]);
            }
        }

        if (group_threshold_ > 0)
        {
            groupRectangles(found_locations, group_threshold_, 0.2/*magic number copied from CPU version*/);
        }
    }

    void HOG_Impl::compute(InputArray _img,
                           OutputArray _descriptors,
                           Stream& stream)
    {
        const GpuMat img = _img.getGpuMat();

        CV_Assert( img.type() == CV_8UC1 || img.type() == CV_8UC4 );
        CV_Assert( win_stride_.width % block_stride_.width == 0 && win_stride_.height % block_stride_.height == 0 );
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        CV_Assert( !stream );

        BufferPool pool(stream);

        GpuMat block_hists = pool.getBuffer(1, getTotalHistSize(img.size()), CV_32FC1);
        computeBlockHistograms(img, block_hists);
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        const size_t block_hist_size = getBlockHistogramSize();
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        Size blocks_per_win = numPartsWithin(win_size_, block_size_, block_stride_);
        Size wins_per_img   = numPartsWithin(img.size(), win_size_, win_stride_);
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        _descriptors.create(wins_per_img.area(), static_cast<int>(blocks_per_win.area() * block_hist_size), CV_32FC1);
        GpuMat descriptors = _descriptors.getGpuMat();
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        switch (descr_format_)
        {
        case DESCR_FORMAT_ROW_BY_ROW:
            hog::extract_descrs_by_rows(win_size_.height, win_size_.width,
                                        block_stride_.height, block_stride_.width,
                                        win_stride_.height, win_stride_.width,
                                        img.rows, img.cols,
                                        block_hists.ptr<float>(),
                                        cell_size_.width, cells_per_block_.width,
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                                        descriptors);
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            break;
        case DESCR_FORMAT_COL_BY_COL:
            hog::extract_descrs_by_cols(win_size_.height, win_size_.width,
                                        block_stride_.height, block_stride_.width,
                                        win_stride_.height, win_stride_.width,
                                        img.rows, img.cols,
                                        block_hists.ptr<float>(),
                                        cell_size_.width, cells_per_block_.width,
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                                        descriptors);
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            break;
        default:
            CV_Error(cv::Error::StsBadArg, "Unknown descriptor format");
        }
    }

    int HOG_Impl::getTotalHistSize(Size img_size) const
    {
        size_t block_hist_size = getBlockHistogramSize();
        Size blocks_per_img = numPartsWithin(img_size, block_size_, block_stride_);
        return static_cast<int>(block_hist_size * blocks_per_img.area());
    }

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    void HOG_Impl::computeBlockHistograms(const GpuMat& img, GpuMat& block_hists)
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    {
        cv::Size blocks_per_win = numPartsWithin(win_size_, block_size_, block_stride_);
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        hog::set_up_constants(nbins_, block_stride_.width, block_stride_.height, blocks_per_win.width, blocks_per_win.height, cells_per_block_.width, cells_per_block_.height);
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        BufferPool pool(Stream::Null());
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        GpuMat grad = pool.getBuffer(img.size(), CV_32FC2);
        GpuMat qangle = pool.getBuffer(img.size(), CV_8UC2);
        computeGradient(img, grad, qangle);

        block_hists.create(1, getTotalHistSize(img.size()), CV_32FC1);
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        hog::compute_hists(nbins_,
                           block_stride_.width, block_stride_.height,
                           img.rows, img.cols,
                           grad, qangle,
                           (float)getWinSigma(),
                           block_hists.ptr<float>(),
                           cell_size_.width, cell_size_.height,
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                           cells_per_block_.width, cells_per_block_.height);
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        hog::normalize_hists(nbins_,
                             block_stride_.width, block_stride_.height,
                             img.rows, img.cols,
                             block_hists.ptr<float>(),
                             (float)threshold_L2hys_,
                             cell_size_.width, cell_size_.height,
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                             cells_per_block_.width, cells_per_block_.height);
    }

    void HOG_Impl::computeGradient(const GpuMat& img, GpuMat& grad, GpuMat& qangle)
    {
        grad.create(img.size(), CV_32FC2);
        qangle.create(img.size(), CV_8UC2);

        float angleScale = (float)(nbins_ / CV_PI);
        switch (img.type())
        {
            case CV_8UC1:
                hog::compute_gradients_8UC1(nbins_, img.rows, img.cols, img, angleScale, grad, qangle, gamma_correction_);
                break;
            case CV_8UC4:
                hog::compute_gradients_8UC4(nbins_, img.rows, img.cols, img, angleScale, grad, qangle, gamma_correction_);
                break;
        }
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    }
}

Ptr<cuda::HOG> cv::cuda::HOG::create(Size win_size,
                                     Size block_size,
                                     Size block_stride,
                                     Size cell_size,
                                     int nbins)
{
    return makePtr<HOG_Impl>(win_size, block_size, block_stride, cell_size, nbins);
}

namespace
{
    static Mat getPeopleDetector48x96()
    {
        static 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, 0.091815f, 0.116177f, -0.126690f, -0.386625f,
            0.368165f, 0.269149f, -0.088042f, -0.028823f, 0.092961f, 0.024099f,
            0.046112f, 0.176756f, 0.135849f, 0.124955f, 0.195467f, -0.037218f,
            0.167217f, 0.188938f, 0.053528f, -0.066561f, 0.133721f, -0.070565f,
            0.115898f, 0.152435f, -0.116993f, -0.110592f, -0.179005f, 0.026668f,
            0.080530f, 0.075084f, -0.070401f, 0.012497f, 0.021849f, -0.139764f,
            -0.022020f, -0.096301f, -0.064954f, -0.127446f, -0.013806f, -0.108315f,
            0.156285f, 0.149867f, -0.011382f, 0.064532f, 0.029168f, 0.027393f,
            0.069716f, 0.153735f, 0.038459f, 0.230714f, 0.253840f, 0.059522f,
            -0.045053f, 0.014083f, 0.071103f, 0.068747f, 0.095887f, 0.005832f,
            0.144887f, 0.026357f, -0.067359f, -0.044151f, -0.123283f, -0.019911f,
            0.005318f, 0.109208f, -0.003201f, -0.021734f, 0.142025f, -0.066907f,
            -0.120070f, -0.188639f, 0.012472f, -0.048704f, -0.012366f, -0.184828f,
            0.168591f, 0.267166f, 0.058208f, -0.044101f, 0.033500f, 0.178558f,
            0.104550f, 0.122418f, 0.080177f, 0.173246f, 0.298537f, 0.064173f,
            0.053397f, 0.174341f, 0.230984f, 0.117025f, 0.166242f, 0.227781f,
            0.120623f, 0.176952f, -0.011393f, -0.086483f, -0.008270f, 0.051700f,
            -0.153369f, -0.058837f, -0.057639f, -0.060115f, 0.026349f, -0.160745f,
            -0.037894f, -0.048575f, 0.041052f, -0.022112f, 0.060365f, 0.051906f,
            0.162657f, 0.138519f, -0.050185f, -0.005938f, 0.071301f, 0.127686f,
            0.062342f, 0.144400f, 0.072600f, 0.198436f, 0.246219f, -0.078185f,
            -0.036169f, 0.075934f, 0.047328f, -0.013601f, 0.087205f, 0.019900f,
            0.022606f, -0.015365f, -0.092506f, 0.075275f, -0.116375f, 0.050500f,
            0.045118f, 0.166567f, 0.072073f, 0.060371f, 0.131747f, -0.169863f,
            -0.039352f, -0.047486f, -0.039797f, -0.204312f, 0.021710f, 0.129443f,
            -0.021173f, 0.173416f, -0.070794f, -0.063986f, 0.069689f, -0.064099f,
            -0.123201f, -0.017372f, -0.206870f, 0.065863f, 0.113226f, 0.024707f,
            -0.071341f, -0.066964f, -0.098278f, -0.062927f, 0.075840f, 0.014716f,
            0.019378f, 0.132699f, -0.074191f, -0.089557f, -0.078446f, -0.197488f,
            -0.173665f, 0.052583f, 0.044361f, 0.113549f, 0.098492f, 0.077379f,
            -0.011146f, -0.192593f, -0.164435f, 0.045568f, 0.205699f, 0.049187f,
            -0.082281f, 0.134874f, 0.185499f, 0.034968f, -0.119561f, -0.112372f,
            -0.115091f, -0.054042f, -0.183816f, -0.078100f, 0.190695f, 0.091617f,
            0.004257f, -0.041135f, -0.061453f, -0.141592f, -0.194809f, -0.120638f,
            0.020168f, 0.109672f, 0.067398f, -0.015238f, -0.239145f, -0.264671f,
            -0.185176f, 0.050472f, 0.020793f, 0.035678f, 0.022839f, -0.052055f,
            -0.127968f, -0.113049f, -0.228416f, -0.258281f, -0.053437f, 0.076424f,
            0.061450f, 0.237478f, 0.003618f, -0.055865f, -0.108087f, -0.028937f,
            0.045585f, 0.052829f, -0.001471f, 0.022826f, 0.059565f, -0.104430f,
            -0.077266f, -0.211882f, -0.212078f, 0.028074f, 0.075846f, 0.016265f,
            0.161879f, 0.134477f, 0.008935f, -0.048041f, 0.074692f, 0.004928f,
            -0.025156f, 0.192874f, 0.074410f, 0.308732f, 0.267400f, 0.094208f,
            -0.005251f, 0.042041f, -0.032148f, 0.015588f, 0.252869f, 0.175302f,
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            -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 Mat(1, static_cast<int>(sizeof(detector)/sizeof(detector[0])), CV_32FC1, detector);
    }

    Mat getPeopleDetector64x128()
    {
        static 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,
           0.07561217f, -0.02049661f, 4.44177445e-003f, 0.01609347f, -0.04893158f,
           -0.08758243f, -7.67420698e-003f, 0.08862378f, 0.06098121f, 0.06565887f,
           7.32981879e-003f, 0.03558407f, -0.03874352f, -0.02490055f,
           -0.06771075f, 0.09939223f, -0.01066077f, 0.01382995f, -0.07289080f,
           7.47184316e-003f, 0.10621431f, -0.02878659f, 0.02383525f, -0.03274646f,
           0.02137008f, 0.03837290f, 0.02450992f, -0.04296818f, -0.02895143f,
           0.05327370f, 0.01499020f, 0.04998732f, 0.12938657f, 0.09391870f,
           0.04292390f, -0.03359194f, -0.06809492f, 0.01125796f, 0.17290455f,
           -0.03430733f, -0.06255233f, -0.01813114f, 0.11726857f, -0.06127599f,
           -0.08677909f, -0.03429872f, 0.04684938f, 0.08161420f, 0.03538774f,
           0.01833884f, 0.11321855f, 0.03261845f, -0.04826299f, 0.01752407f,
           -0.01796414f, -0.10464549f, -3.30041884e-003f, 2.29343961e-004f,
           0.01457292f, -0.02132982f, -0.02602923f, -9.87351313e-003f,
           0.04273872f, -0.02103316f, -0.07994065f, 0.02614958f, -0.02111666f,
           -0.06964913f, -0.13453490f, -0.06861878f, -6.09341264e-003f,
           0.08251446f, 0.15612499f, 2.46531400e-003f, 8.88424646e-003f,
           -0.04152999f, 0.02054853f, 0.05277953f, -0.03087788f, 0.02817579f,
           0.13939077f, 0.07641046f, -0.03627627f, -0.03015098f, -0.04041540f,
           -0.01360690f, -0.06227205f, -0.02738223f, 0.13577610f, 0.15235767f,
           -0.05392922f, -0.11175954f, 0.02157129f, 0.01146481f, -0.05264937f,
           -0.06595174f, -0.02749175f, 0.11812254f, 0.17404149f, -0.06137035f,
           -0.11003478f, -0.01351621f, -0.01745916f, -0.08577441f, -0.04469909f,
           -0.06106115f, 0.10559758f, 0.20806813f, -0.09174948f, 7.09621934e-004f,
           0.03579374f, 0.07215115f, 0.02221742f, 0.01827742f, -7.90785067e-003f,
           0.01489554f, 0.14519960f, -0.06425831f, 0.02990399f, -1.80181325e-003f,
           -0.01401528f, -0.04171134f, -3.70530109e-003f, -0.09090481f,
           0.09520713f, 0.08845516f, -0.02651753f, -0.03016730f, 0.02562448f,
           0.03563816f, -0.03817881f, 0.01433385f, 0.02256983f, 0.02872120f,
           0.01001934f, -0.06332260f, 0.04338406f, 0.07001807f, -0.04705722f,
           -0.07318907f, 0.02630457f, 0.03106382f, 0.06648342f, 0.10913180f,
           -0.01630815f, 0.02910308f, 0.02895109f, 0.08040254f, 0.06969310f,
           0.06797734f, 6.08639978e-003f, 4.16588830e-003f, 0.08926726f,
           -0.03123648f, 0.02700146f, 0.01168734f, -0.01631594f, 4.61015804e-003f,
           8.51359498e-003f, -0.03544224f, 0.03571994f, 4.29766066e-003f,
           -0.01970077f, -8.79793242e-003f, 0.09607988f, 0.01544222f,
           -0.03923707f, 0.07308586f, 0.06061262f, 1.31683104e-004f,
           -7.98222050e-003f, 0.02399261f, -0.06084389f, -0.02743429f,
           -0.05475523f, -0.04131311f, 0.03559756f, 0.03055342f, 0.02981433f,
           0.14860515f, 0.01766787f, 0.02945257f, 0.04898238f, 0.01026922f,
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           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 Mat(1, static_cast<int>(sizeof(detector)/sizeof(detector[0])), CV_32FC1, detector);
    }
}

#endif