opencv/modules/cudaobjdetect/src/hog.cpp

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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
    {
        void set_up_constants(int nbins,
                              int block_stride_x, int block_stride_y,
                              int nblocks_win_x, int nblocks_win_y,
                              int ncells_block_x, int ncells_block_y,
                              const cudaStream_t& stream);

        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,
                           const cudaStream_t& stream);

        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,
                             const cudaStream_t& stream);

        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,
                                      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,
                                    const cudaStream_t& stream);
        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,
                                    const cudaStream_t& stream);

        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,
                                    const cudaStream_t& stream);
        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,
                                    const cudaStream_t& stream);

        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;
        void computeBlockHistograms(const GpuMat& img, GpuMat& block_hists, Stream& stream);
//        void computeGradient(const GpuMat& img, GpuMat& grad, GpuMat& qangle, Stream& stream);

        // 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
        // Pattern Recognition, volume 2, pages 886-893, June 2005
        // 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);
        computeBlockHistograms(img, block_hists, Stream::Null());

        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 );

        BufferPool   pool(stream);
        GpuMat       block_hists     = pool.getBuffer(1, getTotalHistSize(img.size()), CV_32FC1);
        Size         wins_per_img    = numPartsWithin(img.size(), win_size_, win_stride_);
        Size         blocks_per_win  = numPartsWithin(win_size_, block_size_, block_stride_);
        const size_t block_hist_size = getBlockHistogramSize();
        _descriptors.create(wins_per_img.area(), static_cast<int>(blocks_per_win.area() * block_hist_size), CV_32FC1);
        GpuMat       descriptors     = _descriptors.getGpuMat();

        computeBlockHistograms(img, block_hists, stream);

        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,
                                        descriptors,
                                        StreamAccessor::getStream(stream));
            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,
                                        descriptors,
                                        StreamAccessor::getStream(stream));
            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());
    }

    void HOG_Impl::computeBlockHistograms(const GpuMat& img, GpuMat& block_hists, Stream& stream)
    {
        BufferPool pool(stream);
        cv::Size blocks_per_win = numPartsWithin(win_size_, block_size_, block_stride_);
        float  angleScale = static_cast<float>(nbins_ / CV_PI);
        GpuMat grad       = pool.getBuffer(img.size(), CV_32FC2);
        GpuMat qangle     = pool.getBuffer(img.size(), CV_8UC2);

        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,
                              StreamAccessor::getStream(stream));

        switch (img.type())
        {
            case CV_8UC1:
                hog::compute_gradients_8UC1(nbins_,
                                            img.rows, img.cols, img,
                                            angleScale,
                                            grad, qangle,
                                            gamma_correction_,
                                            StreamAccessor::getStream(stream));
                break;
            case CV_8UC4:
                hog::compute_gradients_8UC4(nbins_,
                                            img.rows, img.cols, img,
                                            angleScale,
                                            grad, qangle,
                                            gamma_correction_,
                                            StreamAccessor::getStream(stream));
                break;
        }

        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,
                           cells_per_block_.width, cells_per_block_.height,
                           StreamAccessor::getStream(stream));

        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,
                             cells_per_block_.width, cells_per_block_.height,
                             StreamAccessor::getStream(stream));
    }
}

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,
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            -0.097917f, -0.325788f, 0.141879f, 0.119738f, -0.230427f, -0.117419f,
            -0.114153f, 0.037903f, 0.116383f, 0.218773f, -0.101884f, 0.059466f,
            0.119255f, 0.010874f, -0.031449f, 0.045996f, 0.119931f, 0.273760f,
            0.311700f, 0.261794f, 0.194809f, 0.339829f, 0.239449f, 0.064140f,
            0.077597f, 0.098996f, 0.143534f, 0.184602f, 0.037507f, 0.225494f,
            0.096142f, -0.147370f, -0.207833f, -0.174742f, -0.086391f, -0.038942f,
            0.159577f, -0.088492f, -0.000989f, 0.108154f, -0.025890f, -0.072713f,
            0.025997f, -0.006803f, -0.086879f, -0.011290f, -0.269200f, -0.103450f,
            -0.124910f, -0.116340f, 0.141459f, 0.208800f, 0.042268f, 0.265034f,
            0.516474f, 0.217591f, -0.018843f, -0.313328f, -0.168363f, 0.047129f,
            0.090480f, -0.109852f, -0.018761f, 0.210669f, 0.281269f, -0.043591f,
            -0.034147f, -0.237772f, -0.134843f, -0.072481f, -0.103831f, 0.038355f,
            0.308619f, 0.148023f, -0.045867f, -0.123950f, -0.210860f, -0.064973f,
            -0.036308f, -0.046731f, -0.022099f, 0.095776f, 0.409423f, 0.060635f,
            -0.065196f, 0.051828f, 0.027981f, -0.009609f, -0.137681f, -0.095011f,
            -0.019045f, 0.177278f, 0.009759f, -0.092119f, -0.016958f, -0.133860f,
            -0.118421f, -0.032039f, -0.006214f, -0.084541f, 0.063971f, -0.073642f,
            0.165676f, 0.110443f, 0.044131f, 0.046568f, 0.053292f, -0.055466f,
            0.015512f, 0.371947f, 0.232102f, -0.016923f, 0.103979f, -0.091758f,
            0.005907f, 0.209100f, 0.157433f, 0.030518f, 0.250366f, 0.062322f,
            0.036720f, 0.094676f, 0.017306f, -0.010328f, -0.079012f, 0.016781f,
            -0.112435f, 0.061795f, 0.042543f, -0.126799f, -0.009975f, -0.056760f,
            0.046424f, -0.194712f, -0.139399f, -0.037731f, 0.157989f, -0.016261f,
            0.123345f, 0.230563f, 0.083300f, -0.016392f, 0.059567f, -0.016035f,
            -0.064767f, 0.231945f, 0.156629f, 0.034602f, 0.145628f, 0.041315f,
            0.034535f, 0.019967f, -0.089188f, -0.012091f, 0.307857f, 0.211405f,
            -0.025091f, -0.148249f, -0.129384f, 0.063536f, -0.068603f, -0.067941f,
            -0.035104f, 0.210832f, 0.063810f, 0.062764f, -0.089889f, -0.030554f,
            0.014791f, -0.053362f, -0.037818f, -0.196640f, 0.008388f, -0.082654f,
            0.143056f, 0.064221f, 0.069795f, 0.191040f, 0.097321f, -0.028679f,
            0.075794f, 0.313154f, 0.086240f, 0.207643f, 0.017809f, 0.122867f,
            0.224586f, 0.167403f, -0.023884f, 0.047434f, 0.344091f, 0.187745f,
            0.136177f, 0.141738f, 0.063799f, 0.045233f, -0.077342f, -0.003525f,
            -0.165041f, -0.025616f, -0.073745f, 0.164439f, 0.011200f, -0.145896f,
            -0.027954f, -0.061987f, -0.039874f, -0.142775f, 0.151042f, -0.038238f,
            0.053152f, 0.078615f, 0.086061f, 0.100593f, 0.128046f, -0.071006f,
            -0.116558f, 0.208445f, 0.051086f, 0.076843f, 0.023191f, -0.084781f,
            -0.011790f, 0.147807f, -0.048554f, -0.113932f, 0.283322f, 0.190934f,
            0.092789f, 0.033018f, -0.142428f, -0.142480f, -0.099023f, -0.041020f,
            -0.042760f, 0.203295f, -0.053475f, 0.042424f, 0.222839f, -0.019167f,
            -0.133176f, -0.276216f, -0.031998f, 0.117290f, 0.177827f, -0.059973f,
            -0.064744f, -0.117040f, -0.155482f, -0.099531f, 0.164121f, -0.026682f,
            -0.093810f, 0.238993f, -0.006506f, 0.007830f, 0.065819f, -0.203643f,
            -0.100925f, -0.053652f, -0.130770f, 0.026277f, 0.131796f, 0.032742f,
            0.127186f, 0.116694f, -0.161122f, -0.279773f, -0.252515f, -0.002638f,
            0.042812f, 0.096776f, -0.123280f, 0.064858f, -0.010455f, -0.219760f,
            -0.239331f, -0.104363f, -0.058022f, -0.053584f, 0.025611f, 0.005129f,
            -0.100418f, -0.045712f, -0.194418f, -0.126366f, -0.030530f, 0.051168f,
            0.215959f, 0.172402f, -0.054700f, -0.185995f, -0.278360f, -0.193693f,
            -0.040309f, 0.003735f, -0.007770f, 0.123556f, 0.190179f, -0.077315f,
            0.117403f, 0.212942f, 0.012160f, 0.000113f, 0.027331f, 0.040202f,
            0.033293f, 0.219438f, 0.184174f, 0.259349f, 0.311206f, 0.082547f,
            -0.047875f, -0.078417f, 0.010746f, 0.082620f, 0.311931f, 0.307605f,
            0.003863f, 0.021405f, -0.026388f, -0.019572f, 0.020582f, -0.059353f,
            0.025199f, 0.261319f, 0.086316f, 0.143614f, 0.107780f, 0.003900f,
            -0.188397f, -0.038563f, -0.106045f, -0.125154f, -0.010509f, 0.054021f,
            0.242130f, 0.279152f, 0.215546f, 0.346995f, 0.440856f, 0.237452f,
            0.234154f, 0.301646f, 0.168929f, -0.208358f, -0.126848f, 0.010260f,
            0.121018f, -0.062975f, -0.052848f, 0.050341f, -0.061103f, -0.266482f,
            0.107186f, 0.140221f, 0.280065f, 0.287889f, 0.373198f, 0.151596f,
            0.013593f, 0.115616f, 0.014616f, -0.281710f, -0.237597f, -0.117305f,
            -0.000034f, -0.136739f, -0.196275f, -0.095225f, -0.125310f, -0.250514f,
            0.236804f, -0.071805f, -0.037421f, 0.048230f, 0.321596f, 0.063632f,
            0.024039f, -0.029133f, 0.230983f, 0.160593f, -0.154355f, -0.013086f,
            -0.079929f, 0.094692f, 0.160391f, 0.180239f, 0.053895f, 0.100759f,
            0.288631f, 0.038191f, 0.181692f, 0.229682f, 0.440166f, 0.063401f,
            0.006273f, 0.020865f, 0.338695f, 0.256244f, -0.043927f, 0.115617f,
            0.003296f, 0.173965f, 0.021318f, -0.040936f, -0.118932f, 0.182380f,
            0.235922f, -0.053233f, -0.015053f, -0.101057f, 0.095341f, 0.051111f,
            0.161831f, 0.032614f, 0.159496f, 0.072375f, 0.025089f, 0.023748f,
            0.029151f, 0.161284f, -0.117717f, -0.036191f, -0.176822f, -0.162006f,
            0.226542f, -0.078329f, 0.043079f, -0.119172f, 0.054614f, -0.101365f,
            -0.064541f, -0.115304f, 0.135170f, 0.298872f, 0.098060f, 0.089428f,
            -0.007497f, 0.110391f, -0.028824f, 0.020835f, -0.036804f, 0.125411f,
            0.192105f, -0.048931f, 0.003086f, -0.010681f, 0.074698f, -0.016263f,
            0.096063f, 0.060267f, -0.007277f, 0.139139f, -0.080635f, 0.036628f,
            0.086058f, 0.131979f, 0.085707f, 0.025301f, 0.226094f, 0.194759f,
            0.042193f, -0.157846f, -0.068402f, -0.141450f, -0.112659f, -0.076305f,
            -0.069085f, -0.114332f, -0.102005f, 0.132193f, -0.067042f, 0.106643f,
            0.198964f, 0.171616f, 0.167237f, -0.033730f, -0.026755f, 0.083621f,
            0.149459f, -0.002799f, -0.000318f, 0.011753f, 0.065889f, -0.089375f,
            -0.049610f, 0.224579f, 0.216548f, -0.034908f, -0.017851f, -0.088144f,
            0.007530f, 0.240268f, 0.073270f, 0.013263f, 0.175323f, 0.012082f,
            0.093993f, 0.015282f, 0.105854f, 0.107990f, 0.077798f, -0.096166f,
            -0.079607f, 0.177820f, 0.142392f, 0.033337f, -0.078100f, -0.081616f,
            -0.046993f, 0.139459f, 0.020272f, -0.123161f, 0.175269f, 0.105217f,
            0.057328f, 0.080909f, -0.012612f, -0.097081f, 0.082060f, -0.096716f,
            -0.063921f, 0.201884f, 0.128166f, -0.035051f, -0.032227f, -0.068139f,
            -0.115915f, 0.095080f, -0.086007f, -0.067543f, 0.030776f, 0.032712f,
            0.088937f, 0.054336f, -0.039329f, -0.114022f, 0.171672f, -0.112321f,
            -0.217646f, 0.065186f, 0.060223f, 0.192174f, 0.055580f, -0.131107f,
            -0.144338f, 0.056730f, -0.034707f, -0.081616f, -0.135298f, -0.000614f,
            0.087189f, 0.014614f, 0.067709f, 0.107689f, 0.225780f, 0.084361f,
            -0.008544f, 0.051649f, -0.048369f, -0.037739f, -0.060710f, 0.002654f,
            0.016935f, 0.085563f, -0.015961f, -0.019265f, 0.111788f, 0.062376f,
            0.202019f, 0.047713f, 0.042261f, 0.069716f, 0.242913f, 0.021052f,
            -0.072812f, -0.155920f, -0.026436f, 0.035621f, -0.079300f, -0.028787f,
            -0.048329f, 0.084718f, -0.060565f, -0.083750f, -0.164075f, -0.040742f,
            -0.086219f, 0.015271f, -0.005204f, -0.016038f, 0.045816f, -0.050433f,
            -0.077652f, 0.117109f, 0.009611f, -0.009045f, -0.008634f, -0.055373f,
            -0.085968f, 0.028527f, -0.054736f, -0.168089f, 0.175839f, 0.071205f,
            -0.023603f, 0.037907f, -0.004561f, -0.022634f, 0.123831f, 0.094469f,
            -0.072920f, -0.133642f, -0.014032f, -0.142754f, -0.026999f, -0.199409f,
            0.013268f, 0.226989f, 0.048650f, -0.170988f, -0.050141f, 0.007880f,
            0.061880f, 0.019078f, -0.043578f, -0.038139f, 0.134814f, 0.054097f,
            -0.081670f, 0.176838f, 0.047920f, -0.038176f, 0.050406f, -0.107181f,
            -0.036279f, 0.027060f, 0.081594f, -0.002820f, 0.090507f, -0.033338f,
            -0.059571f, 0.013404f, -0.099860f, 0.073371f, 0.342805f, 0.098305f,
            -0.150910f, -0.020822f, -0.056960f, 0.046262f, -0.043413f, -0.149405f,
            -0.129105f, -0.010899f, -0.014229f, -0.179949f, -0.113044f, -0.049468f,
            -0.065513f, 0.090269f, -0.011919f, 0.087846f, 0.095796f, 0.146127f,
            0.101599f, 0.078066f, -0.084348f, -0.100002f, -0.020134f, -0.050169f,
            0.062122f, 0.014640f, 0.019143f, 0.036543f, 0.180924f, -0.013976f,
            -0.066768f, -0.001090f, -0.070419f, -0.004839f, -0.001504f, 0.034483f,
            -0.044954f, -0.050336f, -0.088638f, -0.174782f, -0.116082f, -0.205507f,
            0.015587f, -0.042839f, -0.096879f, -0.144097f, -0.050268f, -0.196796f,
            0.109639f, 0.271411f, 0.173732f, 0.108070f, 0.156437f, 0.124255f,
            0.097242f, 0.238693f, 0.083941f, 0.109105f, 0.223940f, 0.267188f,
            0.027385f, 0.025819f, 0.125070f, 0.093738f, 0.040353f, 0.038645f,
            -0.012730f, 0.144063f, 0.052931f, -0.009138f, 0.084193f, 0.160272f,
            -0.041366f, 0.011951f, -0.121446f, -0.106713f, -0.047566f, 0.047984f,
            -0.255224f, -0.076116f, 0.098685f, -0.150845f, -0.171513f, -0.156590f,
            0.058331f, 0.187493f, 0.413018f, 0.554265f, 0.372242f, 0.237943f,
            0.124571f, 0.110829f, 0.010322f, -0.174477f, -0.067627f, -0.001979f,
            0.142913f, 0.040597f, 0.019907f, 0.025963f, -0.043585f, -0.120732f,
            0.099937f, 0.091059f, 0.247307f, 0.204226f, -0.042753f, -0.068580f,
            -0.119002f, 0.026722f, 0.034853f, -0.060934f, -0.025054f, -0.093026f,
            -0.035372f, -0.233209f, -0.049869f, -0.039151f, -0.022279f, -0.065380f,
            -9.063785f };

        return 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,
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           -0.02651412f, -4.34682379e-003f, 5.44942822e-003f, 0.01444484f,
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           -3.34558333e-003f, 0.11442288f, -0.03906701f, -2.67325155e-003f,
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           -0.06298508f, -0.02881260f, -0.13101046f, -0.07221562f,
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           -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,
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           -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,
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           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