/*M///////////////////////////////////////////////////////////////////////////////////////
//
//  IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
//
//  By downloading, copying, installing or using the software you agree to this license.
//  If you do not agree to this license, do not download, install,
//  copy or use the software.
//
//
//                           License Agreement
//                For Open Source Computer Vision Library
//
// Copyright (C) 2010-2012, Multicoreware, Inc., all rights reserved.
// Copyright (C) 2010-2012, Advanced Micro Devices, Inc., all rights reserved.
// Third party copyrights are property of their respective owners.
//
// @Authors
//		Wenju He, wenju@multicorewareinc.com
//
// Redistribution and use in source and binary forms, with or without modification,
// are permitted provided that the following conditions are met:
//
//   * Redistribution's of source code must retain the above copyright notice,
//     this list of conditions and the following disclaimer.
//
//   * Redistribution's in binary form must reproduce the above copyright notice,
//     this list of conditions and the following disclaimer in the documentation
//     and/or other oclMaterials provided with the distribution.
//
//   * The name of the copyright holders may not be used to endorse or promote products
//     derived from this software without specific prior written permission.
//
// This software is provided by the copyright holders and contributors "as is" and
// any express or bpied warranties, including, but not limited to, the bpied
// warranties of merchantability and fitness for a particular purpose are disclaimed.
// In no event shall the Intel Corporation or contributors be liable for any direct,
// indirect, incidental, special, exemplary, or consequential damages
// (including, but not limited to, procurement of substitute goods or services;
// loss of use, data, or profits; or business interruption) however caused
// and on any theory of liability, whether in contract, strict liability,
// or tort (including negligence or otherwise) arising in any way out of
// the use of this software, even if advised of the possibility of such damage.
//
//M*/

#include "precomp.hpp"
using namespace cv;
using namespace cv::ocl;
using namespace std;


#define CELL_WIDTH 8
#define CELL_HEIGHT 8
#define CELLS_PER_BLOCK_X 2
#define CELLS_PER_BLOCK_Y 2
#define NTHREADS 256

namespace cv
{
    namespace ocl
    {
        ///////////////////////////OpenCL kernel strings///////////////////////////
        extern const char *objdetect_hog;
    }
}

namespace cv
{
    namespace ocl
    {
        namespace device
        {
            namespace hog
            {
                int cnbins;
                int cblock_stride_x;
                int cblock_stride_y;
                int cnblocks_win_x;
                int cnblocks_win_y;
                int cblock_hist_size;
                int cblock_hist_size_2up;
                int cdescr_size;
                int cdescr_width;

                void set_up_constants(int nbins, int block_stride_x, int block_stride_y,
                                      int nblocks_win_x, int nblocks_win_y);

                void compute_hists(int nbins, int block_stride_x, int blovck_stride_y,
                                   int height, int width, const cv::ocl::oclMat &grad,
                                   const cv::ocl::oclMat &qangle, float sigma, cv::ocl::oclMat &block_hists);

                void normalize_hists(int nbins, int block_stride_x, int block_stride_y,
                                     int height, int width, cv::ocl::oclMat &block_hists, float threshold);

                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, const cv::ocl::oclMat &block_hists, const cv::ocl::oclMat &coefs, float free_coef,
                                    float threshold, cv::ocl::oclMat &labels);

                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, const cv::ocl::oclMat &block_hists,
                                            cv::ocl::oclMat &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, const cv::ocl::oclMat &block_hists,
                                            cv::ocl::oclMat &descriptors);

                void compute_gradients_8UC1(int height, int width, const cv::ocl::oclMat &img,
                                            float angle_scale, cv::ocl::oclMat &grad, cv::ocl::oclMat &qangle, bool correct_gamma);
                void compute_gradients_8UC4(int height, int width, const cv::ocl::oclMat &img,
                                            float angle_scale, cv::ocl::oclMat &grad, cv::ocl::oclMat &qangle, bool correct_gamma);

                void resize( const oclMat &src, oclMat &dst, const Size sz);
            }
        }
    }
}

using namespace ::cv::ocl::device;

cv::ocl::HOGDescriptor::HOGDescriptor(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_)
    : win_size(win_size_),
      block_size(block_size_),
      block_stride(block_stride_),
      cell_size(cell_size_),
      nbins(nbins_),
      win_sigma(win_sigma_),
      threshold_L2hys(threshold_L2hys_),
      gamma_correction(gamma_correction_),
      nlevels(nlevels_)
{
    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);

    CV_Assert(block_stride == cell_size);

    CV_Assert(cell_size == Size(8, 8));

    Size cells_per_block = Size(block_size.width / cell_size.width, block_size.height / cell_size.height);
    CV_Assert(cells_per_block == Size(2, 2));

    cv::Size blocks_per_win = numPartsWithin(win_size, block_size, block_stride);
    hog::set_up_constants(nbins, block_stride.width, block_stride.height, blocks_per_win.width, blocks_per_win.height);

    effect_size = Size(0, 0);
}

size_t cv::ocl::HOGDescriptor::getDescriptorSize() const
{
    return numPartsWithin(win_size, block_size, block_stride).area() * getBlockHistogramSize();
}

size_t cv::ocl::HOGDescriptor::getBlockHistogramSize() const
{
    Size cells_per_block = Size(block_size.width / cell_size.width, block_size.height / cell_size.height);
    return (size_t)(nbins * cells_per_block.area());
}

double cv::ocl::HOGDescriptor::getWinSigma() const
{
    return win_sigma >= 0 ? win_sigma : (block_size.width + block_size.height) / 8.0;
}

bool cv::ocl::HOGDescriptor::checkDetectorSize() const
{
    size_t detector_size = detector.rows * detector.cols;
    size_t descriptor_size = getDescriptorSize();
    return detector_size == 0 || detector_size == descriptor_size || detector_size == descriptor_size + 1;
}

void cv::ocl::HOGDescriptor::setSVMDetector(const vector<float> &_detector)
{
    std::vector<float> detector_reordered(_detector.size());

    size_t block_hist_size = getBlockHistogramSize();
    cv::Size blocks_per_img = numPartsWithin(win_size, block_size, block_stride);

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

    this->detector.upload(Mat(detector_reordered).reshape(1, 1));

    size_t descriptor_size = getDescriptorSize();
    free_coef = _detector.size() > descriptor_size ? _detector[descriptor_size] : 0;

    CV_Assert(checkDetectorSize());
}

void cv::ocl::HOGDescriptor::init_buffer(const oclMat &img, Size win_stride)
{
    if (!image_scale.empty())
        return;

    if (effect_size == Size(0, 0))
        effect_size = img.size();

    grad.create(img.size(), CV_32FC2);
    qangle.create(img.size(), CV_8UC2);

    const size_t block_hist_size = getBlockHistogramSize();
    const Size blocks_per_img = numPartsWithin(img.size(), block_size, block_stride);
    block_hists.create(1, static_cast<int>(block_hist_size * blocks_per_img.area()), CV_32F);

    Size wins_per_img = numPartsWithin(img.size(), win_size, win_stride);
    labels.create(1, wins_per_img.area(), CV_8U);
}

void cv::ocl::HOGDescriptor::computeGradient(const oclMat &img, oclMat &grad, oclMat &qangle)
{
    CV_Assert(img.type() == CV_8UC1 || img.type() == CV_8UC4);

    float angleScale = (float)(nbins / CV_PI);
    switch (img.type())
    {
    case CV_8UC1:
        hog::compute_gradients_8UC1(effect_size.height, effect_size.width, img, angleScale, grad, qangle, gamma_correction);
        break;
    case CV_8UC4:
        hog::compute_gradients_8UC4(effect_size.height, effect_size.width, img, angleScale, grad, qangle, gamma_correction);
        break;
    }
}

void cv::ocl::HOGDescriptor::computeBlockHistograms(const oclMat &img)
{
    computeGradient(img, grad, qangle);

    hog::compute_hists(nbins, block_stride.width, block_stride.height, effect_size.height, effect_size.width,
                       grad, qangle, (float)getWinSigma(), block_hists);

    hog::normalize_hists(nbins, block_stride.width, block_stride.height, effect_size.height, effect_size.width,
                         block_hists, (float)threshold_L2hys);
}


void cv::ocl::HOGDescriptor::getDescriptors(const oclMat &img, Size win_stride, oclMat &descriptors, int descr_format)
{
    CV_Assert(win_stride.width % block_stride.width == 0 && win_stride.height % block_stride.height == 0);

    init_buffer(img, win_stride);

    computeBlockHistograms(img);

    const size_t block_hist_size = getBlockHistogramSize();
    Size blocks_per_win = numPartsWithin(win_size, block_size, block_stride);
    Size wins_per_img   = numPartsWithin(effect_size, win_size, win_stride);

    descriptors.create(wins_per_img.area(), static_cast<int>(blocks_per_win.area() * block_hist_size), CV_32F);

    switch (descr_format)
    {
    case DESCR_FORMAT_ROW_BY_ROW:
        hog::extract_descrs_by_rows(win_size.height, win_size.width, block_stride.height, block_stride.width,
                                    win_stride.height, win_stride.width, effect_size.height, effect_size.width, block_hists, descriptors);
        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, effect_size.height, effect_size.width, block_hists, descriptors);
        break;
    default:
        CV_Error(CV_StsBadArg, "Unknown descriptor format");
    }
}


void cv::ocl::HOGDescriptor::detect(const oclMat &img, vector<Point> &hits, double hit_threshold, Size win_stride, Size padding)
{
    CV_Assert(img.type() == CV_8UC1 || img.type() == CV_8UC4);
    CV_Assert(padding == Size(0, 0));

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

    if (win_stride == Size())
        win_stride = block_stride;
    else
        CV_Assert(win_stride.width % block_stride.width == 0 && win_stride.height % block_stride.height == 0);
    init_buffer(img, win_stride);

    computeBlockHistograms(img);

    hog::classify_hists(win_size.height, win_size.width, block_stride.height, block_stride.width,
                        win_stride.height, win_stride.width, effect_size.height, effect_size.width, block_hists,
                        detector, (float)free_coef, (float)hit_threshold, labels);

    labels.download(labels_host);
    unsigned char *vec = labels_host.ptr();
    Size wins_per_img = numPartsWithin(effect_size, win_size, win_stride);
    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));
    }
}



void cv::ocl::HOGDescriptor::detectMultiScale(const oclMat &img, vector<Rect> &found_locations, double hit_threshold,
        Size win_stride, Size padding, double scale0, int group_threshold)
{
    CV_Assert(img.type() == CV_8UC1 || img.type() == CV_8UC4);
    CV_Assert(scale0 > 1);

    vector<double> level_scale;
    double scale = 1.;
    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<Rect> all_candidates;
    vector<Point> locations;

    if (win_stride == Size())
        win_stride = block_stride;
    else
        CV_Assert(win_stride.width % block_stride.width == 0 && win_stride.height % block_stride.height == 0);
    init_buffer(img, win_stride);
    image_scale.create(img.size(), img.type());

    for (size_t i = 0; i < level_scale.size(); i++)
    {
        scale = level_scale[i];
        effect_size = Size(cvRound(img.cols / scale), cvRound(img.rows / scale));
        if (effect_size == img.size())
        {
            detect(img, locations, hit_threshold, win_stride, padding);
        }
        else
        {
            hog::resize( img, image_scale, effect_size);
            detect(image_scale, locations, hit_threshold, win_stride, padding);
        }
        Size scaled_win_size(cvRound(win_size.width * scale), cvRound(win_size.height * scale));
        for (size_t j = 0; j < locations.size(); j++)
            all_candidates.push_back(Rect(Point2d((CvPoint)locations[j]) * scale, scaled_win_size));
    }

    found_locations.assign(all_candidates.begin(), all_candidates.end());
    groupRectangles(found_locations, group_threshold, 0.2/*magic number copied from CPU version*/);
}

int cv::ocl::HOGDescriptor::numPartsWithin(int size, int part_size, int stride)
{
    return (size - part_size + stride) / stride;
}

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

std::vector<float> cv::ocl::HOGDescriptor::getDefaultPeopleDetector()
{
    return getPeopleDetector64x128();
}

std::vector<float> cv::ocl::HOGDescriptor::getPeopleDetector48x96()
{
    static const float detector[] =
    {
        0.294350f, -0.098796f, -0.129522f, 0.078753f, 0.387527f, 0.261529f,
        0.145939f, 0.061520f, 0.328699f, 0.227148f, -0.066467f, -0.086723f,
        0.047559f, 0.106714f, 0.037897f, 0.111461f, -0.024406f, 0.304769f,
        0.254676f, -0.069235f, 0.082566f, 0.147260f, 0.326969f, 0.148888f,
        0.055270f, -0.087985f, 0.261720f, 0.143442f, 0.026812f, 0.238212f,
        0.194020f, 0.056341f, -0.025854f, -0.034444f, -0.156631f, 0.205174f,
        0.089008f, -0.139811f, -0.100147f, -0.037830f, -0.029230f, -0.055641f,
        0.033248f, -0.016512f, 0.155244f, 0.247315f, -0.124694f, -0.048414f,
        -0.062219f, 0.193683f, 0.004574f, 0.055089f, 0.093565f, 0.167712f,
        0.167581f, 0.018895f, 0.215258f, 0.122609f, 0.090520f, -0.067219f,
        -0.049029f, -0.099615f, 0.241804f, -0.094893f, -0.176248f, 0.001727f,
        -0.134473f, 0.104442f, 0.050942f, 0.081165f, 0.072156f, 0.121646f,
        0.002656f, -0.297974f, -0.133587f, -0.060121f, -0.092515f, -0.048974f,
        -0.084754f, -0.180111f, -0.038590f, 0.086283f, -0.134636f, -0.107249f,
        0.132890f, 0.141556f, 0.249425f, 0.130273f, -0.030031f, 0.073212f,
        -0.008155f, 0.019931f, 0.071688f, 0.000300f, -0.019525f, -0.021725f,
        -0.040993f, -0.086841f, 0.070124f, 0.240033f, 0.265350f, 0.043208f,
        0.166754f, 0.091453f, 0.060916f, -0.036972f, -0.091043f, 0.079873f,
        0.219781f, 0.158102f, -0.140618f, -0.043016f, 0.124802f, 0.093668f,
        0.103208f, 0.094872f, 0.080541f, 0.137711f, 0.160566f, -0.169231f,
        0.013983f, 0.309508f, -0.004217f, -0.057200f, -0.064489f, 0.014066f,
        0.361009f, 0.251328f, -0.080983f, -0.044183f, 0.061436f, -0.037381f,
        -0.078786f, 0.030993f, 0.066314f, 0.037683f, 0.152325f, -0.091683f,
        0.070203f, 0.217856f, 0.036435f, -0.076462f, 0.006254f, -0.094431f,
        0.154829f, -0.023038f, -0.196961f, -0.024594f, 0.178465f, -0.050139f,
        -0.045932f, -0.000965f, 0.109112f, 0.046165f, -0.159373f, -0.008713f,
        0.041307f, 0.097129f, -0.057211f, -0.064599f, 0.077165f, 0.176167f,
        0.138322f, 0.065753f, -0.104950f, 0.017933f, 0.136255f, -0.011598f,
        0.047007f, 0.080550f, 0.068619f, 0.084661f, -0.035493f, -0.091314f,
        -0.041411f, 0.060971f, -0.101912f, -0.079870f, -0.085977f, -0.022686f,
        0.079788f, -0.098064f, -0.054603f, 0.040383f, 0.300794f, 0.128603f,
        0.094844f, 0.047407f, 0.101825f, 0.061832f, -0.162160f, -0.204553f,
        -0.035165f, 0.101450f, -0.016641f, -0.027140f, -0.134392f, -0.008743f,
        0.102331f, 0.114853f, 0.009644f, 0.062823f, 0.237339f, 0.167843f,
        0.053066f, -0.012592f, 0.043158f, 0.002305f, 0.065001f, -0.038929f,
        -0.020356f, 0.152343f, 0.043469f, -0.029967f, -0.042948f, 0.032481f,
        0.068488f, -0.110840f, -0.111083f, 0.111980f, -0.002072f, -0.005562f,
        0.082926f, 0.006635f, -0.108153f, 0.024242f, -0.086464f, -0.189884f,
        -0.017492f, 0.191456f, -0.007683f, -0.128769f, -0.038017f, -0.132380f,
        0.091926f, 0.079696f, -0.106728f, -0.007656f, 0.172744f, 0.011576f,
        0.009883f, 0.083258f, -0.026516f, 0.145534f, 0.153924f, -0.130290f,
        -0.108945f, 0.124490f, -0.003186f, -0.100485f, 0.015024f, -0.060512f,
        0.026288f, -0.086713f, -0.169012f, 0.076517f, 0.215778f, 0.043701f,
        -0.131642f, -0.012585f, -0.045181f, -0.118183f, -0.241544f, -0.167293f,
        -0.020107f, -0.019917f, -0.101827f, -0.107096f, -0.010503f, 0.044938f,
        0.189680f, 0.217119f, -0.046086f, 0.044508f, 0.199716f, -0.036004f,
        -0.148927f, 0.013355f, -0.078279f, 0.030451f, 0.056301f, -0.024609f,
        0.083224f, 0.099533f, -0.039432f, -0.138880f, 0.005482f, -0.024120f,
        -0.140468f, -0.066381f, -0.017057f, 0.009260f, -0.058004f, -0.028486f,
        -0.061610f, 0.007483f, -0.158309f, -0.150687f, -0.044595f, -0.105121f,
        -0.045763f, -0.006618f, -0.024419f, -0.117713f, -0.119366f, -0.175941f,
        -0.071542f, 0.119027f, 0.111362f, 0.043080f, 0.034889f, 0.093003f,
        0.007842f, 0.057368f, -0.108834f, -0.079968f, 0.230959f, 0.020205f,
        0.011470f, 0.098877f, 0.101310f, -0.030215f, -0.018018f, -0.059552f,
        -0.106157f, 0.021866f, -0.036471f, 0.080051f, 0.041165f, -0.082101f,
        0.117726f, 0.030961f, -0.054763f, -0.084102f, -0.185778f, -0.061305f,
        -0.038089f, -0.110728f, -0.264010f, 0.076675f, -0.077111f, -0.137644f,
        0.036232f, 0.277995f, 0.019116f, 0.107738f, 0.144003f, 0.080304f,
        0.215036f, 0.228897f, 0.072713f, 0.077773f, 0.120168f, 0.075324f,
        0.062730f, 0.122478f, -0.049008f, 0.164912f, 0.162450f, 0.041246f,
        0.009891f, -0.097827f, -0.038700f, -0.023027f, -0.120020f, 0.203364f,
        0.248474f, 0.149810f, -0.036276f, -0.082814f, -0.090343f, -0.027143f,
        -0.075689f, -0.320310f, -0.000500f, -0.143334f, -0.065077f, -0.186936f,
        0.129372f, 0.116431f, 0.181699f, 0.170436f, 0.418854f, 0.460045f,
        0.333719f, 0.230515f, 0.047822f, -0.044954f, -0.068086f, 0.140179f,
        -0.044821f, 0.085550f, 0.092483f, -0.107296f, -0.130670f, -0.206629f,
        0.114601f, -0.317869f, -0.076663f, 0.038680f, 0.212753f, -0.016059f,
        -0.126526f, -0.163602f, 0.210154f, 0.099887f, -0.126366f, 0.118453f,
        0.019309f, -0.021611f, -0.096499f, -0.111809f, -0.200489f, 0.142854f,
        0.228840f, -0.353346f, -0.179151f, 0.116834f, 0.252389f, -0.031728f,
        -0.188135f, -0.158998f, 0.386523f, 0.122315f, 0.209944f, 0.394023f,
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        0.033293f, 0.219438f, 0.184174f, 0.259349f, 0.311206f, 0.082547f,
        -0.047875f, -0.078417f, 0.010746f, 0.082620f, 0.311931f, 0.307605f,
        0.003863f, 0.021405f, -0.026388f, -0.019572f, 0.020582f, -0.059353f,
        0.025199f, 0.261319f, 0.086316f, 0.143614f, 0.107780f, 0.003900f,
        -0.188397f, -0.038563f, -0.106045f, -0.125154f, -0.010509f, 0.054021f,
        0.242130f, 0.279152f, 0.215546f, 0.346995f, 0.440856f, 0.237452f,
        0.234154f, 0.301646f, 0.168929f, -0.208358f, -0.126848f, 0.010260f,
        0.121018f, -0.062975f, -0.052848f, 0.050341f, -0.061103f, -0.266482f,
        0.107186f, 0.140221f, 0.280065f, 0.287889f, 0.373198f, 0.151596f,
        0.013593f, 0.115616f, 0.014616f, -0.281710f, -0.237597f, -0.117305f,
        -0.000034f, -0.136739f, -0.196275f, -0.095225f, -0.125310f, -0.250514f,
        0.236804f, -0.071805f, -0.037421f, 0.048230f, 0.321596f, 0.063632f,
        0.024039f, -0.029133f, 0.230983f, 0.160593f, -0.154355f, -0.013086f,
        -0.079929f, 0.094692f, 0.160391f, 0.180239f, 0.053895f, 0.100759f,
        0.288631f, 0.038191f, 0.181692f, 0.229682f, 0.440166f, 0.063401f,
        0.006273f, 0.020865f, 0.338695f, 0.256244f, -0.043927f, 0.115617f,
        0.003296f, 0.173965f, 0.021318f, -0.040936f, -0.118932f, 0.182380f,
        0.235922f, -0.053233f, -0.015053f, -0.101057f, 0.095341f, 0.051111f,
        0.161831f, 0.032614f, 0.159496f, 0.072375f, 0.025089f, 0.023748f,
        0.029151f, 0.161284f, -0.117717f, -0.036191f, -0.176822f, -0.162006f,
        0.226542f, -0.078329f, 0.043079f, -0.119172f, 0.054614f, -0.101365f,
        -0.064541f, -0.115304f, 0.135170f, 0.298872f, 0.098060f, 0.089428f,
        -0.007497f, 0.110391f, -0.028824f, 0.020835f, -0.036804f, 0.125411f,
        0.192105f, -0.048931f, 0.003086f, -0.010681f, 0.074698f, -0.016263f,
        0.096063f, 0.060267f, -0.007277f, 0.139139f, -0.080635f, 0.036628f,
        0.086058f, 0.131979f, 0.085707f, 0.025301f, 0.226094f, 0.194759f,
        0.042193f, -0.157846f, -0.068402f, -0.141450f, -0.112659f, -0.076305f,
        -0.069085f, -0.114332f, -0.102005f, 0.132193f, -0.067042f, 0.106643f,
        0.198964f, 0.171616f, 0.167237f, -0.033730f, -0.026755f, 0.083621f,
        0.149459f, -0.002799f, -0.000318f, 0.011753f, 0.065889f, -0.089375f,
        -0.049610f, 0.224579f, 0.216548f, -0.034908f, -0.017851f, -0.088144f,
        0.007530f, 0.240268f, 0.073270f, 0.013263f, 0.175323f, 0.012082f,
        0.093993f, 0.015282f, 0.105854f, 0.107990f, 0.077798f, -0.096166f,
        -0.079607f, 0.177820f, 0.142392f, 0.033337f, -0.078100f, -0.081616f,
        -0.046993f, 0.139459f, 0.020272f, -0.123161f, 0.175269f, 0.105217f,
        0.057328f, 0.080909f, -0.012612f, -0.097081f, 0.082060f, -0.096716f,
        -0.063921f, 0.201884f, 0.128166f, -0.035051f, -0.032227f, -0.068139f,
        -0.115915f, 0.095080f, -0.086007f, -0.067543f, 0.030776f, 0.032712f,
        0.088937f, 0.054336f, -0.039329f, -0.114022f, 0.171672f, -0.112321f,
        -0.217646f, 0.065186f, 0.060223f, 0.192174f, 0.055580f, -0.131107f,
        -0.144338f, 0.056730f, -0.034707f, -0.081616f, -0.135298f, -0.000614f,
        0.087189f, 0.014614f, 0.067709f, 0.107689f, 0.225780f, 0.084361f,
        -0.008544f, 0.051649f, -0.048369f, -0.037739f, -0.060710f, 0.002654f,
        0.016935f, 0.085563f, -0.015961f, -0.019265f, 0.111788f, 0.062376f,
        0.202019f, 0.047713f, 0.042261f, 0.069716f, 0.242913f, 0.021052f,
        -0.072812f, -0.155920f, -0.026436f, 0.035621f, -0.079300f, -0.028787f,
        -0.048329f, 0.084718f, -0.060565f, -0.083750f, -0.164075f, -0.040742f,
        -0.086219f, 0.015271f, -0.005204f, -0.016038f, 0.045816f, -0.050433f,
        -0.077652f, 0.117109f, 0.009611f, -0.009045f, -0.008634f, -0.055373f,
        -0.085968f, 0.028527f, -0.054736f, -0.168089f, 0.175839f, 0.071205f,
        -0.023603f, 0.037907f, -0.004561f, -0.022634f, 0.123831f, 0.094469f,
        -0.072920f, -0.133642f, -0.014032f, -0.142754f, -0.026999f, -0.199409f,
        0.013268f, 0.226989f, 0.048650f, -0.170988f, -0.050141f, 0.007880f,
        0.061880f, 0.019078f, -0.043578f, -0.038139f, 0.134814f, 0.054097f,
        -0.081670f, 0.176838f, 0.047920f, -0.038176f, 0.050406f, -0.107181f,
        -0.036279f, 0.027060f, 0.081594f, -0.002820f, 0.090507f, -0.033338f,
        -0.059571f, 0.013404f, -0.099860f, 0.073371f, 0.342805f, 0.098305f,
        -0.150910f, -0.020822f, -0.056960f, 0.046262f, -0.043413f, -0.149405f,
        -0.129105f, -0.010899f, -0.014229f, -0.179949f, -0.113044f, -0.049468f,
        -0.065513f, 0.090269f, -0.011919f, 0.087846f, 0.095796f, 0.146127f,
        0.101599f, 0.078066f, -0.084348f, -0.100002f, -0.020134f, -0.050169f,
        0.062122f, 0.014640f, 0.019143f, 0.036543f, 0.180924f, -0.013976f,
        -0.066768f, -0.001090f, -0.070419f, -0.004839f, -0.001504f, 0.034483f,
        -0.044954f, -0.050336f, -0.088638f, -0.174782f, -0.116082f, -0.205507f,
        0.015587f, -0.042839f, -0.096879f, -0.144097f, -0.050268f, -0.196796f,
        0.109639f, 0.271411f, 0.173732f, 0.108070f, 0.156437f, 0.124255f,
        0.097242f, 0.238693f, 0.083941f, 0.109105f, 0.223940f, 0.267188f,
        0.027385f, 0.025819f, 0.125070f, 0.093738f, 0.040353f, 0.038645f,
        -0.012730f, 0.144063f, 0.052931f, -0.009138f, 0.084193f, 0.160272f,
        -0.041366f, 0.011951f, -0.121446f, -0.106713f, -0.047566f, 0.047984f,
        -0.255224f, -0.076116f, 0.098685f, -0.150845f, -0.171513f, -0.156590f,
        0.058331f, 0.187493f, 0.413018f, 0.554265f, 0.372242f, 0.237943f,
        0.124571f, 0.110829f, 0.010322f, -0.174477f, -0.067627f, -0.001979f,
        0.142913f, 0.040597f, 0.019907f, 0.025963f, -0.043585f, -0.120732f,
        0.099937f, 0.091059f, 0.247307f, 0.204226f, -0.042753f, -0.068580f,
        -0.119002f, 0.026722f, 0.034853f, -0.060934f, -0.025054f, -0.093026f,
        -0.035372f, -0.233209f, -0.049869f, -0.039151f, -0.022279f, -0.065380f,
        -9.063785f
    };
    return vector<float>(detector, detector + sizeof(detector) / sizeof(detector[0]));
}




std::vector<float> cv::ocl::HOGDescriptor::getPeopleDetector64x128()
{
    static const float detector[] =
    {
        0.05359386f, -0.14721455f, -0.05532170f, 0.05077307f,
        0.11547081f, -0.04268804f, 0.04635834f, -0.05468199f, 0.08232084f,
        0.10424068f, -0.02294518f, 0.01108519f, 0.01378693f, 0.11193510f,
        0.01268418f, 0.08528346f, -0.06309239f, 0.13054633f, 0.08100729f,
        -0.05209739f, -0.04315529f, 0.09341384f, 0.11035026f, -0.07596218f,
        -0.05517511f, -0.04465296f, 0.02947334f, 0.04555536f,
        -3.55954492e-003f, 0.07818956f, 0.07730991f, 0.07890715f, 0.06222893f,
        0.09001380f, -0.03574381f, 0.03414327f, 0.05677258f, -0.04773581f,
        0.03746637f, -0.03521175f, 0.06955440f, -0.03849038f, 0.01052293f,
        0.01736112f, 0.10867710f, 0.08748853f, 3.29739624e-003f, 0.10907028f,
        0.07913758f, 0.10393070f, 0.02091867f, 0.11594022f, 0.13182420f,
        0.09879354f, 0.05362710f, -0.06745391f, -7.01260753e-003f,
        5.24702156e-003f, 0.03236255f, 0.01407916f, 0.02207983f, 0.02537322f,
        0.04547948f, 0.07200756f, 0.03129894f, -0.06274468f, 0.02107014f,
        0.06035208f, 0.08636236f, 4.53164103e-003f, 0.02193363f, 0.02309801f,
        0.05568166f, -0.02645093f, 0.04448695f, 0.02837519f, 0.08975694f,
        0.04461516f, 0.08975355f, 0.07514391f, 0.02306982f, 0.10410084f,
        0.06368385f, 0.05943464f, 4.58420580e-003f, 0.05220337f, 0.06675851f,
        0.08358569f, 0.06712101f, 0.06559004f, -0.03930482f, -9.15936660e-003f,
        -0.05897915f, 0.02816453f, 0.05032348f, 0.06780671f, 0.03377650f,
        -6.09417039e-004f, -0.01795146f, -0.03083684f, -0.01302475f,
        -0.02972313f, 7.88706727e-003f, -0.03525961f, -2.50397739e-003f,
        0.05245084f, 0.11791293f, -0.02167498f, 0.05299332f, 0.06640524f,
        0.05190265f, -8.27316567e-003f, 0.03033127f, 0.05842173f,
        -4.01050318e-003f, -6.25105947e-003f, 0.05862958f, -0.02465461f,
        0.05546781f, -0.08228195f, -0.07234028f, 0.04640540f, -0.01308254f,
        -0.02506191f, 0.03100746f, -0.04665651f, -0.04591486f, 0.02949927f,
        0.06035462f, 0.02244646f, -0.01698639f, 0.01040041f, 0.01131170f,
        0.05419579f, -0.02130277f, -0.04321722f, -0.03665198f, 0.01126490f,
        -0.02606488f, -0.02228328f, -0.02255680f, -0.03427236f,
        -7.75165204e-003f, -0.06195229f, 8.21638294e-003f, 0.09535975f,
        -0.03709979f, -0.06942501f, 0.14579427f, -0.05448192f, -0.02055904f,
        0.05747357f, 0.02781788f, -0.07077577f, -0.05178314f, -0.10429011f,
        -0.11235505f, 0.07529039f, -0.07559302f, -0.08786739f, 0.02983843f,
        0.02667585f, 0.01382199f, -0.01797496f, -0.03141199f, -0.02098101f,
        0.09029204f, 0.04955018f, 0.13718739f, 0.11379953f, 1.80019124e-003f,
        -0.04577610f, -1.11108483e-003f, -0.09470536f, -0.11596080f,
        0.04489342f, 0.01784211f, 3.06850672e-003f, 0.10781866f,
        3.36498418e-003f, -0.10842580f, -0.07436839f, -0.10535070f,
        -0.01866805f, 0.16057891f, -5.07316366e-003f, -0.04295658f,
        -5.90488780e-003f, 8.82003549e-003f, -0.01492646f, -0.05029279f,
        -0.12875880f, 8.78831954e-004f, -0.01297184f, -0.07592774f,
        -0.02668831f, -6.93787413e-004f, 0.02406698f, -0.01773298f,
        -0.03855745f, -0.05877856f, 0.03259695f, 0.12826584f, 0.06292590f,
        -4.10733931e-003f, 0.10996531f, 0.01332991f, 0.02088735f, 0.04037504f,
        -0.05210760f, 0.07760046f, 0.06399347f, -0.05751930f, -0.10053057f,
        0.07505023f, -0.02139782f, 0.01796176f, 2.34400877e-003f, -0.04208319f,
        0.07355055f, 0.05093350f, -0.02996780f, -0.02219072f, 0.03355330f,
        0.04418742f, -0.05580705f, -0.05037573f, -0.04548179f, 0.01379514f,
        0.02150671f, -0.02194211f, -0.13682702f, 0.05464972f, 0.01608082f,
        0.05309116f, 0.04701022f, 1.33690401e-003f, 0.07575664f, 0.09625306f,
        8.92647635e-003f, -0.02819123f, 0.10866830f, -0.03439325f,
        -0.07092371f, -0.06004780f, -0.02712298f, -7.07467366e-003f,
        -0.01637020f, 0.01336790f, -0.10313606f, 0.04906582f, -0.05732445f,
        -0.02731079f, 0.01042235f, -0.08340668f, 0.03686501f, 0.06108340f,
        0.01322748f, -0.07809529f, 0.03774724f, -0.03413248f, -0.06096525f,
        -0.04212124f, -0.07982176f, -1.25973229e-003f, -0.03045501f,
        -0.01236493f, -0.06312395f, 0.04789570f, -0.04602066f, 0.08576570f,
        0.02521080f, 0.02988098f, 0.10314583f, 0.07060035f, 0.04520544f,
        -0.04426654f, 0.13146530f, 0.08386490f, 0.02164590f, -2.12280243e-003f,
        -0.03686353f, -0.02074944f, -0.03829959f, -0.01530596f, 0.02689708f,
        0.11867401f, -0.06043470f, -0.02785023f, -0.04775074f, 0.04878745f,
        0.06350956f, 0.03494788f, 0.01467400f, 1.17890188e-003f, 0.04379614f,
        2.03681854e-003f, -0.03958609f, -0.01072688f, 6.43705716e-003f,
        0.02996500f, -0.03418507f, -0.01960307f, -0.01219154f,
        -4.37000440e-003f, -0.02549453f, 0.02646318f, -0.01632513f,
        6.46516960e-003f, -0.01929734f, 4.78711911e-003f, 0.04962371f,
        0.03809111f, 0.07265724f, 0.05758125f, -0.03741554f, 0.01648608f,
        -8.45285598e-003f, 0.03996826f, -0.08185477f, 0.02638875f,
        -0.04026615f, -0.02744674f, -0.04071517f, 1.05096330e-003f,
        -0.04741232f, -0.06733172f, 8.70434940e-003f, -0.02192543f,
        1.35350740e-003f, -0.03056974f, -0.02975521f, -0.02887780f,
        -0.01210713f, -0.04828526f, -0.09066251f, -0.09969629f, -0.03665164f,
        -8.88111943e-004f, -0.06826669f, -0.01866150f, -0.03627640f,
        -0.01408288f, 0.01874239f, -0.02075835f, 0.09145175f, -0.03547291f,
        0.05396780f, 0.04198981f, 0.01301925f, -0.03384354f, -0.12201976f,
        0.06830920f, -0.03715654f, 9.55848210e-003f, 5.05685573e-003f,
        0.05659294f, 3.90764466e-003f, 0.02808490f, -0.05518097f, -0.03711621f,
        -0.02835565f, -0.04420464f, -0.01031947f, 0.01883466f,
        -8.49525444e-003f, -0.09419250f, -0.01269387f, -0.02133371f,
        -0.10190815f, -0.07844430f, 2.43644323e-003f, -4.09610150e-003f,
        0.01202551f, -0.06452291f, -0.10593818f, -0.02464746f, -0.02199699f,
        -0.07401930f, 0.07285886f, 8.87513801e-004f, 9.97662079e-003f,
        8.46779719e-003f, 0.03730333f, -0.02905126f, 0.03573337f, -0.04393689f,
        -0.12014472f, 0.03176554f, -2.76015815e-003f, 0.10824566f, 0.05090732f,
        -3.30179278e-003f, -0.05123822f, 5.04784798e-003f, -0.05664124f,
        -5.99415926e-003f, -0.05341901f, -0.01221393f, 0.01291318f,
        9.91760660e-003f, -7.56987557e-003f, -0.06193124f, -2.24549137e-003f,
        0.01987562f, -0.02018840f, -0.06975540f, -0.06601523f, -0.03349112f,
        -0.08910118f, -0.03371435f, -0.07406893f, -0.02248047f, -0.06159951f,
        2.77751544e-003f, -0.05723337f, -0.04792468f, 0.07518548f,
        2.77279224e-003f, 0.04211938f, 0.03100502f, 0.05278448f, 0.03954679f,
        -0.03006846f, -0.03851741f, -0.02792403f, -0.02875333f, 0.01531280f,
        0.02186953f, -0.01989829f, 2.50679464e-003f, -0.10258728f,
        -0.04785743f, -0.02887216f, 3.85063468e-003f, 0.01112236f,
        8.29218887e-003f, -0.04822981f, -0.04503597f, -0.03713100f,
        -0.06988008f, -0.11002295f, -2.69209221e-003f, 1.85383670e-003f,
        -0.05921049f, -0.06105053f, -0.08458050f, -0.04527602f,
        8.90329306e-004f, -0.05875023f, -2.68602883e-003f, -0.01591195f,
        0.03631859f, 0.05493166f, 0.07300330f, 5.53333294e-003f, 0.06400407f,
        0.01847740f, -5.76280477e-003f, -0.03210877f, 4.25160583e-003f,
        0.01166520f, -1.44864211e-003f, 0.02253744f, -0.03367080f, 0.06983195f,
        -4.22323542e-003f, -8.89401045e-003f, -0.07943393f, 0.05199728f,
        0.06065201f, 0.04133492f, 1.44032843e-003f, -0.09585235f, -0.03964731f,
        0.04232114f, 0.01750465f, -0.04487902f, -7.59733608e-003f, 0.02011171f,
        0.04673622f, 0.09011173f, -0.07869188f, -0.04682482f, -0.05080139f,
        -3.99383716e-003f, -0.05346331f, 0.01085723f, -0.03599333f,
        -0.07097908f, 0.03551549f, 0.02680387f, 0.03471529f, 0.01790393f,
        0.05471273f, 9.62048303e-003f, -0.03180215f, 0.05864431f, 0.02330614f,
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        0.09674817f, 0.08938243f, 0.07065924f, 0.02603180f, 5.84815582e-003f,
        -0.05922065f, 0.12360309f, 3.59695964e-003f, 2.99844006e-003f,
        0.03697936f, 0.02043072f, 0.04168725f, 0.01025975f, -0.01359980f,
        -0.01600920f, 0.02581056f, 0.02329250f, 2.98100687e-003f, 0.01629762f,
        0.06652115f, 0.05855627f, 0.01237463f, -0.01297135f, 0.01761587f,
        0.05090865f, 0.06549342f, -0.04425945f, 2.43203156e-003f,
        3.07327788e-003f, 0.06678630f, -0.04303836f, 0.01082393f, -0.06476044f,
        0.04077786f, 0.12441979f, 0.08237778f, 0.07424165f, 0.04065890f,
        0.06905543f, 0.09556347f, 0.12724875f, -0.02132082f, 0.08514154f,
        -0.04175328f, -0.02666954f, 0.01897836f, 0.03317382f, 9.45465732e-003f,
        -0.01238974f, -0.04242500f, -0.01419479f, -0.03545213f, -0.02440874f,
        0.08684119f, 0.04212951f, 0.02462858f, -0.01104825f, -5.01706870e-003f,
        0.02968982f, 0.02597476f, -0.01568939f, 0.04514892f, 0.06974549f,
        0.08670278f, 0.06828108f, 0.10238872f, 0.05405957f, 0.06548470f,
        -0.03763957f, 0.01366090f, 0.07069602f, 0.05363748f, 0.04798120f,
        0.11706422f, 0.05466456f, -0.01869259f, 0.06344382f, 0.03106543f,
        0.08432506f, -0.02061096f, 0.03821088f, -6.92190882e-003f,
        6.40467042e-003f, -0.01271779f, 6.89014705e-005f, 0.04541415f,
        -0.01899539f, -0.05020239f, 0.03000903f, 0.01090422f, 4.52452758e-003f,
        0.02573632f, -0.02388454f, -0.04200457f, 1.72783900e-003f,
        -0.05978370f, -0.02720562f, 0.06573715f, 0.01154317f, 0.01265615f,
        0.07375994f, -9.19828378e-003f, -0.04914120f, 0.02124831f, 0.06455322f,
        0.04372910f, -0.03310043f, 0.03605788f, -6.78055827e-003f,
        9.36202332e-003f, 0.01747596f, -0.06406314f, -0.06812935f, 0.08080816f,
        -0.02778088f, 0.02735260f, 0.06393493f, 0.06652229f, 0.05676993f,
        0.08640018f, -7.59188086e-003f, -0.02012847f, -0.04741159f,
        -0.01657069f, -0.01624399f, 0.05547778f, -2.33309763e-003f,
        0.01120033f, 0.06141156f, -0.06285004f, -0.08732341f, -0.09313398f,
        -0.04267832f, 5.57443965e-003f, 0.04809862f, 0.01773641f,
        5.37361018e-003f, 0.14842421f, -0.06298012f, -0.02935147f, 0.11443478f,
        -0.05034208f, 5.65494271e-003f, 0.02076526f, -0.04577984f,
        -0.04735741f, 0.02961071f, -0.09307127f, -0.04417921f, -0.04990027f,
        -0.03940028f, 0.01306016f, 0.06267900f, 0.03758737f, 0.08460117f,
        0.13858789f, 0.04862388f, -0.06319809f, -0.05655516f, 0.01885816f,
        -0.03285607f, 0.03371567f, -0.07040928f, -0.04514049f, 0.01392166f,
        0.08184422f, -0.07230316f, 0.02386871f, 0.02184591f, 0.02605764f,
        -0.01033954f, 9.29878280e-003f, 7.67351175e-003f, 0.15189242f,
        0.02069071f, -0.09738296f, -0.08894105f, -0.07768748f, 0.02332268f,
        -0.01778995f, -0.03258888f, -0.08180822f, -0.08492987f, 0.02290156f,
        -0.11368170f, -0.03554465f, -0.04533844f, -0.02861580f, 0.06782424f,
        0.01113123f, 0.02453644f, 0.12721945f, 0.08084814f, -0.03607795f,
        0.01109122f, 0.04803548f, -0.03489929f, 0.03399536f, -0.05682014f,
        8.59533902e-003f, -4.27904585e-003f, 0.03230887f, -0.01300198f,
        -0.01038137f, -0.07930113f, 8.33097473e-003f, 0.02296994f,
        -0.01306500f, -0.01881626f, 0.04413369f, 0.05729880f, -0.03761553f,
        0.01942326f, 1.64540811e-003f, -0.03811319f, 0.04190650f, -0.14978096f,
        -0.04514487f, 0.01209545f, -5.46460645e-003f, -0.01647195f,
        7.63064111e-003f, -0.07494587f, 0.08415288f, 0.10020141f, -0.01228561f,
        0.06553826f, 0.04554005f, 0.07890417f, 0.03041138f, 0.01752007f,
        0.09208256f, -3.74419295e-004f, 0.10549527f, 0.04686913f, 0.01894833f,
        -0.02651412f, -4.34682379e-003f, 5.44942822e-003f, 0.01444484f,
        0.05882156f, -0.03336544f, 0.04603891f, -0.10432546f, 0.01923928f,
        0.01842845f, -0.01712168f, -0.02222766f, 0.04693324f, -0.06202956f,
        -0.01422159f, 0.08732220f, -0.07706107f, 0.02661049f, -0.04300238f,
        -0.03092422f, -0.03552184f, -0.01886088f, -0.04979934f, 0.03906401f,
        0.04608644f, 0.04966111f, 0.04275464f, -0.04621769f, -0.02653212f,
        8.57011229e-003f, 0.03839684f, 0.05818764f, 0.03880796f,
        -2.76100676e-004f, 0.03076511f, -0.03266929f, -0.05374557f,
        0.04986527f, -9.45429131e-003f, 0.03582499f, -2.64564669e-003f,
        -1.07461517e-003f, 0.02962313f, -0.01483363f, 0.03060869f, 0.02448327f,
        0.01845641f, 0.03282966f, -0.03534438f, -0.01084059f, -0.01119136f,
        -1.85360224e-003f, -5.94652840e-004f, -0.04451817f, 2.98327743e-003f,
        0.06272484f, -0.02152076f, -3.05971340e-003f, -0.05070828f,
        0.01531762f, 0.01282815f, 0.05167150f, 9.46266949e-003f,
        -3.34558333e-003f, 0.11442288f, -0.03906701f, -2.67325155e-003f,
        0.03069184f, -0.01134165f, 0.02949462f, 0.02879886f, 0.03855566f,
        -0.03450781f, 0.09142872f, -0.02156654f, 0.06075062f, -0.06220816f,
        0.01944680f, 6.68372354e-003f, -0.06656796f, 8.70784000e-003f,
        0.03456013f, 0.02434320f, -0.13236357f, -0.04177035f, -0.02069627f,
        0.01068112f, 0.01505432f, -0.07517391f, -3.83571628e-003f,
        -0.06298508f, -0.02881260f, -0.13101046f, -0.07221562f,
        -5.79945277e-003f, -8.57300125e-003f, 0.03782469f, 0.02762164f,
        0.04942456f, -0.02936396f, 0.09597211f, 0.01921411f, 0.06101191f,
        -0.04787507f, -0.01379578f, -7.40224449e-003f, -0.02220136f,
        -0.01313756f, 7.77558051e-003f, 0.12296968f, 0.02939998f, 0.03594062f,
        -0.07788624f, -0.01133144f, 3.99316690e-004f, -0.06090347f,
        -0.01122066f, -4.68682544e-003f, 0.07633100f, -0.06748922f,
        -0.05640298f, -0.05265681f, -0.01139122f, -0.01624347f, -0.04715714f,
        -0.01099092f, 0.01048561f, 3.28499987e-003f, -0.05810167f,
        -0.07699911f, -0.03330683f, 0.04185145f, 0.03478536f, 0.02275165f,
        0.02304766f, 6.66040834e-003f, 0.10968148f, -5.93013782e-003f,
        -0.04858336f, -0.04203213f, -0.09316786f, -6.13074889e-003f,
        -0.02544625f, 0.01366201f, 9.18555818e-003f, -0.01846578f,
        -0.05622401f, -0.03989377f, -0.07810296f, 6.91275718e-003f,
        0.05957597f, -0.03901334f, 0.01572002f, -0.01193903f,
        -6.89400872e-003f, -0.03093356f, -0.04136098f, -0.01562869f,
        -0.04604580f, 0.02865234f, -0.08678447f, -0.03232484f, -0.05364593f,
        -0.01445016f, -0.07003860f, -0.08669746f, -0.04520775f, 0.04274122f,
        0.03117515f, 0.08175703f, 0.01081109f, 0.06379741f, 0.06199206f,
        0.02865988f, 0.02360346f, 0.06725410f, -0.03248780f, -9.37702879e-003f,
        0.08265898f, -0.02245839f, 0.05125763f, -0.01862395f, 0.01973453f,
        -0.01994494f, -0.10770868f, 0.03180375f, 3.23935156e-003f,
        -0.02142080f, -0.04256190f, 0.04760900f, 0.04282863f, 0.05635953f,
        -0.01870849f, 0.05540622f, -0.03042666f, 0.01455277f, -0.06630179f,
        -0.05843807f, -0.03739681f, -0.09739155f, -0.03220233f, -0.05620182f,
        -0.10381401f, 0.07400211f, 4.20676917e-003f, 0.03258535f,
        2.14308966e-003f, 0.05121966f, -0.01274337f, 0.02384761f, 0.06335578f,
        -0.07905591f, 0.08375625f, -0.07898903f, -0.06508528f, -0.02498444f,
        0.06535810f, 0.03970535f, 0.04895468f, -0.01169566f, -0.03980601f,
        0.05682293f, 0.05925463f, -0.01165808f, -0.07936699f, -0.04208954f,
        0.01333987f, 0.09051196f, 0.10098671f, -0.03974256f, 0.01238771f,
        -0.07501741f, -0.03655440f, -0.04301528f, 0.09216860f,
        4.63579083e-004f, 0.02851115f, 0.02142735f, 1.28244064e-004f,
        0.02879687f, -0.08554889f, -0.04838862f, 0.08135369f, -0.05756533f,
        0.01413900f, 0.03451880f, -0.06619488f, -0.03053130f, 0.02961676f,
        -0.07384635f, 0.01135692f, 0.05283910f, -0.07778034f, -0.02107482f,
        -0.05511716f, -0.13473752f, 0.03030157f, 0.06722020f, -0.06218817f,
        -0.05826827f, 0.06254654f, 0.02895772f, -0.01664000f, -0.03620280f,
        -0.01612278f, -1.46097376e-003f, 0.14013411f, -8.96181818e-003f,
        -0.03250246f, 3.38630192e-003f, 2.64779478e-003f, 0.03359732f,
        -0.02411991f, -0.04229729f, 0.10666174f, -6.66579151f
    };
    return vector<float>(detector, detector + sizeof(detector) / sizeof(detector[0]));
}

/* Returns the nearest upper power of two, works only for
the typical GPU thread count (pert block) values */
static int power_2up(unsigned int n)
{
    if (n < 1) return 1;
    else if (n < 2) return 2;
    else if (n < 4) return 4;
    else if (n < 8) return 8;
    else if (n < 16) return 16;
    else if (n < 32) return 32;
    else if (n < 64) return 64;
    else if (n < 128) return 128;
    else if (n < 256) return 256;
    else if (n < 512) return 512;
    else if (n < 1024) return 1024;
    return -1; // Input is too big
}

void cv::ocl::device::hog::set_up_constants(int nbins, int block_stride_x, int block_stride_y,
        int nblocks_win_x, int nblocks_win_y)
{
    cnbins = nbins;
    cblock_stride_x = block_stride_x;
    cblock_stride_y = block_stride_y;
    cnblocks_win_x = nblocks_win_x;
    cnblocks_win_y = nblocks_win_y;

    int block_hist_size = nbins * CELLS_PER_BLOCK_X * CELLS_PER_BLOCK_Y;
    cblock_hist_size = block_hist_size;

    int block_hist_size_2up = power_2up(block_hist_size);
    cblock_hist_size_2up = block_hist_size_2up;

    int descr_width = nblocks_win_x * block_hist_size;
    cdescr_width = descr_width;

    int descr_size = descr_width * nblocks_win_y;
    cdescr_size = descr_size;
}

static inline int divUp(int total, int grain)
{
    return (total + grain - 1) / grain;
}

static void openCLExecuteKernel_hog(Context *clCxt , const char **source, string kernelName, 
                                    size_t globalThreads[3], size_t localThreads[3], 
                                    vector< pair<size_t, const void *> > &args)
{
    size_t wave_size = 0;
    queryDeviceInfo(WAVEFRONT_SIZE, &wave_size);
    if (wave_size <= 16)
    {
        char build_options[64];
        sprintf(build_options, (wave_size == 16) ? "-D WAVE_SIZE_16" : "-D WAVE_SIZE_1");
        openCLExecuteKernel(clCxt, source, kernelName, globalThreads, localThreads, args, -1, -1, build_options);
    }
    else
        openCLExecuteKernel(clCxt, source, kernelName, globalThreads, localThreads, args, -1, -1);
}

void cv::ocl::device::hog::compute_hists(int nbins, int block_stride_x, int block_stride_y,
        int height, int width, const cv::ocl::oclMat &grad,
        const cv::ocl::oclMat &qangle, float sigma, cv::ocl::oclMat &block_hists)
{
    Context *clCxt = Context::getContext();
    string kernelName = "compute_hists_kernel";
    vector< pair<size_t, const void *> > args;

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

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

    int grad_quadstep = grad.step >> 2;
    int qangle_step = qangle.step;

    // Precompute gaussian spatial window parameter
    float scale = 1.f / (2.f * sigma * sigma);

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

    args.push_back( make_pair( sizeof(cl_int), (void *)&cblock_stride_x));
    args.push_back( make_pair( sizeof(cl_int), (void *)&cblock_stride_y));
    args.push_back( make_pair( sizeof(cl_int), (void *)&cnbins));
    args.push_back( make_pair( sizeof(cl_int), (void *)&cblock_hist_size));
    args.push_back( make_pair( sizeof(cl_int), (void *)&img_block_width));
    args.push_back( make_pair( sizeof(cl_int), (void *)&blocks_in_group));
    args.push_back( make_pair( sizeof(cl_int), (void *)&blocks_total));
    args.push_back( make_pair( sizeof(cl_int), (void *)&grad_quadstep));
    args.push_back( make_pair( sizeof(cl_int), (void *)&qangle_step));
    args.push_back( make_pair( sizeof(cl_mem), (void *)&grad.data));
    args.push_back( make_pair( sizeof(cl_mem), (void *)&qangle.data));
    args.push_back( make_pair( sizeof(cl_float), (void *)&scale));
    args.push_back( make_pair( sizeof(cl_mem), (void *)&block_hists.data));
    args.push_back( make_pair( smem, (void *)NULL));

    openCLExecuteKernel_hog(clCxt, &objdetect_hog, kernelName, globalThreads, localThreads, args);
}

void cv::ocl::device::hog::normalize_hists(int nbins, int block_stride_x, int block_stride_y,
        int height, int width, cv::ocl::oclMat &block_hists, float threshold)
{
    Context *clCxt = Context::getContext();
    string kernelName = "normalize_hists_kernel";
    vector< pair<size_t, const void *> > args;

    int block_hist_size = nbins * CELLS_PER_BLOCK_X * CELLS_PER_BLOCK_Y;
    int nthreads = power_2up(block_hist_size);

    int img_block_width = (width - CELLS_PER_BLOCK_X * CELL_WIDTH + block_stride_x) / block_stride_x;
    int img_block_height = (height - CELLS_PER_BLOCK_Y * CELL_HEIGHT + block_stride_y) / block_stride_y;
    size_t globalThreads[3] = { img_block_width * nthreads, img_block_height, 1 };
    size_t localThreads[3] = { nthreads, 1, 1  };

    if ((nthreads < 32) || (nthreads > 512) )
        cv::ocl::error("normalize_hists: histogram's size is too small or too big", __FILE__, __LINE__, "normalize_hists");

    args.push_back( make_pair( sizeof(cl_int), (void *)&nthreads));
    args.push_back( make_pair( sizeof(cl_int), (void *)&block_hist_size));
    args.push_back( make_pair( sizeof(cl_int), (void *)&img_block_width));
    args.push_back( make_pair( sizeof(cl_mem), (void *)&block_hists.data));
    args.push_back( make_pair( sizeof(cl_float), (void *)&threshold));
    args.push_back( make_pair( nthreads * sizeof(float), (void *)NULL));

    openCLExecuteKernel_hog(clCxt, &objdetect_hog, kernelName, globalThreads, localThreads, args);
}

void cv::ocl::device::hog::classify_hists(int win_height, int win_width, int block_stride_y,
        int block_stride_x, int win_stride_y, int win_stride_x, int height,
        int width, const cv::ocl::oclMat &block_hists, const cv::ocl::oclMat &coefs, float free_coef,
        float threshold, cv::ocl::oclMat &labels)
{
    Context *clCxt = Context::getContext();
    string kernelName = "classify_hists_kernel";
    vector< pair<size_t, const void *> > args;

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

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

    args.push_back( make_pair( sizeof(cl_int), (void *)&cblock_hist_size));
    args.push_back( make_pair( sizeof(cl_int), (void *)&cdescr_size));
    args.push_back( make_pair( sizeof(cl_int), (void *)&cdescr_width));
    args.push_back( make_pair( sizeof(cl_int), (void *)&img_win_width));
    args.push_back( make_pair( sizeof(cl_int), (void *)&img_block_width));
    args.push_back( make_pair( sizeof(cl_int), (void *)&win_block_stride_x));
    args.push_back( make_pair( sizeof(cl_int), (void *)&win_block_stride_y));
    args.push_back( make_pair( sizeof(cl_mem), (void *)&block_hists.data));
    args.push_back( make_pair( sizeof(cl_mem), (void *)&coefs.data));
    args.push_back( make_pair( sizeof(cl_float), (void *)&free_coef));
    args.push_back( make_pair( sizeof(cl_float), (void *)&threshold));
    args.push_back( make_pair( sizeof(cl_mem), (void *)&labels.data));

    openCLExecuteKernel_hog(clCxt, &objdetect_hog, kernelName, globalThreads, localThreads, args);
}

void cv::ocl::device::hog::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,
        const cv::ocl::oclMat &block_hists, cv::ocl::oclMat &descriptors)
{
    Context *clCxt = Context::getContext();
    string kernelName = "extract_descrs_by_rows_kernel";
    vector< pair<size_t, const void *> > args;

    int win_block_stride_x = win_stride_x / block_stride_x;
    int win_block_stride_y = win_stride_y / block_stride_y;
    int img_win_width = (width - win_width + win_stride_x) / win_stride_x;
    int img_win_height = (height - win_height + win_stride_y) / win_stride_y;
    int img_block_width = (width - CELLS_PER_BLOCK_X * CELL_WIDTH + block_stride_x) / block_stride_x;
    int descriptors_quadstep = descriptors.step >> 2;

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

    args.push_back( make_pair( sizeof(cl_int), (void *)&cblock_hist_size));
    args.push_back( make_pair( sizeof(cl_int), (void *)&descriptors_quadstep));
    args.push_back( make_pair( sizeof(cl_int), (void *)&cdescr_size));
    args.push_back( make_pair( sizeof(cl_int), (void *)&cdescr_width));
    args.push_back( make_pair( sizeof(cl_int), (void *)&img_block_width));
    args.push_back( make_pair( sizeof(cl_int), (void *)&win_block_stride_x));
    args.push_back( make_pair( sizeof(cl_int), (void *)&win_block_stride_y));
    args.push_back( make_pair( sizeof(cl_mem), (void *)&block_hists.data));
    args.push_back( make_pair( sizeof(cl_mem), (void *)&descriptors.data));

    openCLExecuteKernel(clCxt, &objdetect_hog, kernelName, globalThreads, localThreads, args, -1, -1);
}

void cv::ocl::device::hog::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,
        const cv::ocl::oclMat &block_hists, cv::ocl::oclMat &descriptors)
{
    Context *clCxt = Context::getContext();
    string kernelName = "extract_descrs_by_cols_kernel";
    vector< pair<size_t, const void *> > args;

    int win_block_stride_x = win_stride_x / block_stride_x;
    int win_block_stride_y = win_stride_y / block_stride_y;
    int img_win_width = (width - win_width + win_stride_x) / win_stride_x;
    int img_win_height = (height - win_height + win_stride_y) / win_stride_y;
    int img_block_width = (width - CELLS_PER_BLOCK_X * CELL_WIDTH + block_stride_x) / block_stride_x;
    int descriptors_quadstep = descriptors.step >> 2;

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

    args.push_back( make_pair( sizeof(cl_int), (void *)&cblock_hist_size));
    args.push_back( make_pair( sizeof(cl_int), (void *)&descriptors_quadstep));
    args.push_back( make_pair( sizeof(cl_int), (void *)&cdescr_size));
    args.push_back( make_pair( sizeof(cl_int), (void *)&cnblocks_win_x));
    args.push_back( make_pair( sizeof(cl_int), (void *)&cnblocks_win_y));
    args.push_back( make_pair( sizeof(cl_int), (void *)&img_block_width));
    args.push_back( make_pair( sizeof(cl_int), (void *)&win_block_stride_x));
    args.push_back( make_pair( sizeof(cl_int), (void *)&win_block_stride_y));
    args.push_back( make_pair( sizeof(cl_mem), (void *)&block_hists.data));
    args.push_back( make_pair( sizeof(cl_mem), (void *)&descriptors.data));

    openCLExecuteKernel(clCxt, &objdetect_hog, kernelName, globalThreads, localThreads, args, -1, -1);
}

void cv::ocl::device::hog::compute_gradients_8UC1(int height, int width, const cv::ocl::oclMat &img,
        float angle_scale, cv::ocl::oclMat &grad, cv::ocl::oclMat &qangle, bool correct_gamma)
{
    Context *clCxt = Context::getContext();
    string kernelName = "compute_gradients_8UC1_kernel";
    vector< pair<size_t, const void *> > args;

    size_t localThreads[3] = { NTHREADS, 1, 1 };
    size_t globalThreads[3] = { width, height, 1 };
    char correctGamma = (correct_gamma) ? 1 : 0;
    int img_step = img.step;
    int grad_quadstep = grad.step >> 3;
    int qangle_step = qangle.step >> 1;

    args.push_back( make_pair( sizeof(cl_int), (void *)&height));
    args.push_back( make_pair( sizeof(cl_int), (void *)&width));
    args.push_back( make_pair( sizeof(cl_int), (void *)&img_step));
    args.push_back( make_pair( sizeof(cl_int), (void *)&grad_quadstep));
    args.push_back( make_pair( sizeof(cl_int), (void *)&qangle_step));
    args.push_back( make_pair( sizeof(cl_mem), (void *)&img.data));
    args.push_back( make_pair( sizeof(cl_mem), (void *)&grad.data));
    args.push_back( make_pair( sizeof(cl_mem), (void *)&qangle.data));
    args.push_back( make_pair( sizeof(cl_float), (void *)&angle_scale));
    args.push_back( make_pair( sizeof(cl_char), (void *)&correctGamma));
    args.push_back( make_pair( sizeof(cl_int), (void *)&cnbins));

    openCLExecuteKernel(clCxt, &objdetect_hog, kernelName, globalThreads, localThreads, args, -1, -1);
}

void cv::ocl::device::hog::compute_gradients_8UC4(int height, int width, const cv::ocl::oclMat &img,
        float angle_scale, cv::ocl::oclMat &grad, cv::ocl::oclMat &qangle, bool correct_gamma)
{
    Context *clCxt = Context::getContext();
    string kernelName = "compute_gradients_8UC4_kernel";
    vector< pair<size_t, const void *> > args;

    size_t localThreads[3] = { NTHREADS, 1, 1 };
    size_t globalThreads[3] = { width, height, 1 };

    char correctGamma = (correct_gamma) ? 1 : 0;
    int img_step = img.step >> 2;
    int grad_quadstep = grad.step >> 3;
    int qangle_step = qangle.step >> 1;

    args.push_back( make_pair( sizeof(cl_int), (void *)&height));
    args.push_back( make_pair( sizeof(cl_int), (void *)&width));
    args.push_back( make_pair( sizeof(cl_int), (void *)&img_step));
    args.push_back( make_pair( sizeof(cl_int), (void *)&grad_quadstep));
    args.push_back( make_pair( sizeof(cl_int), (void *)&qangle_step));
    args.push_back( make_pair( sizeof(cl_mem), (void *)&img.data));
    args.push_back( make_pair( sizeof(cl_mem), (void *)&grad.data));
    args.push_back( make_pair( sizeof(cl_mem), (void *)&qangle.data));
    args.push_back( make_pair( sizeof(cl_float), (void *)&angle_scale));
    args.push_back( make_pair( sizeof(cl_char), (void *)&correctGamma));
    args.push_back( make_pair( sizeof(cl_int), (void *)&cnbins));

    openCLExecuteKernel(clCxt, &objdetect_hog, kernelName, globalThreads, localThreads, args, -1, -1);
}

void cv::ocl::device::hog::resize( const oclMat &src, oclMat &dst, const Size sz)
{
    CV_Assert( (src.channels() == dst.channels()) );
    Context *clCxt = Context::getContext();

    string kernelName = (src.type() == CV_8UC1) ? "resize_8UC1_kernel" : "resize_8UC4_kernel";
    size_t blkSizeX = 16, blkSizeY = 16;
    size_t glbSizeX = sz.width % blkSizeX == 0 ? sz.width : (sz.width / blkSizeX + 1) * blkSizeX;
    size_t glbSizeY = sz.height % blkSizeY == 0 ? sz.height : (sz.height / blkSizeY + 1) * blkSizeY;
    size_t globalThreads[3] = {glbSizeX, glbSizeY, 1};
    size_t localThreads[3] = {blkSizeX, blkSizeY, 1};

    float ifx = (float)src.cols / sz.width;
    float ify = (float)src.rows / sz.height;
    int src_step = static_cast<int>(src.step);
    int dst_step = static_cast<int>(dst.step);

    vector< pair<size_t, const void *> > args;
    args.push_back( make_pair(sizeof(cl_mem), (void *)&dst.data));
    args.push_back( make_pair(sizeof(cl_mem), (void *)&src.data));
    args.push_back( make_pair(sizeof(cl_int), (void *)&dst.offset));
    args.push_back( make_pair(sizeof(cl_int), (void *)&src.offset));
    args.push_back( make_pair(sizeof(cl_int), (void *)&dst_step));
    args.push_back( make_pair(sizeof(cl_int), (void *)&src_step));
    args.push_back( make_pair(sizeof(cl_int), (void *)&src.cols));
    args.push_back( make_pair(sizeof(cl_int), (void *)&src.rows));
    args.push_back( make_pair(sizeof(cl_int), (void *)&sz.width));
    args.push_back( make_pair(sizeof(cl_int), (void *)&sz.height));
    args.push_back( make_pair(sizeof(cl_float), (void *)&ifx));
    args.push_back( make_pair(sizeof(cl_float), (void *)&ify));

    openCLExecuteKernel(clCxt, &objdetect_hog, kernelName, globalThreads, localThreads, args, -1, -1);
}