opencv/modules/ocl/src/hog.cpp

/*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,
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//     derived from this software without specific prior written permission.
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// loss of use, data, or profits; or business interruption) however caused
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#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

static oclMat gauss_w_lut;
static bool hog_device_cpu;
/* pre-compute gaussian and interp_weight lookup tables if sigma is 4.0f */
static const float gaussian_interp_lut[] = 
{
    /* gaussian lut */
    0.01831564f, 0.02926831f, 0.04393693f, 0.06196101f, 0.08208500f, 0.10215643f, 
    0.11943297f, 0.13117145f, 0.13533528f, 0.13117145f, 0.11943297f, 0.10215643f, 
    0.08208500f, 0.06196101f, 0.04393693f, 0.02926831f, 0.02926831f, 0.04677062f, 
    0.07021102f, 0.09901341f, 0.13117145f, 0.16324551f, 0.19085334f, 0.20961139f, 
    0.21626517f, 0.20961139f, 0.19085334f, 0.16324551f, 0.13117145f, 0.09901341f, 
    0.07021102f, 0.04677062f, 0.04393693f, 0.07021102f, 0.10539922f, 0.14863673f, 
    0.19691168f, 0.24506053f, 0.28650481f, 0.31466395f, 0.32465246f, 0.31466395f, 
    0.28650481f, 0.24506053f, 0.19691168f, 0.14863673f, 0.10539922f, 0.07021102f, 
    0.06196101f, 0.09901341f, 0.14863673f, 0.20961139f, 0.27768996f, 0.34559074f, 
    0.40403652f, 0.44374731f, 0.45783335f, 0.44374731f, 0.40403652f, 0.34559074f, 
    0.27768996f, 0.20961139f, 0.14863673f, 0.09901341f, 0.08208500f, 0.13117145f, 
    0.19691168f, 0.27768996f, 0.36787945f, 0.45783335f, 0.53526145f, 0.58786964f, 
    0.60653067f, 0.58786964f, 0.53526145f, 0.45783335f, 0.36787945f, 0.27768996f, 
    0.19691168f, 0.13117145f, 0.10215643f, 0.16324551f, 0.24506053f, 0.34559074f, 
    0.45783335f, 0.56978285f, 0.66614360f, 0.73161560f, 0.75483960f, 0.73161560f, 
    0.66614360f, 0.56978285f, 0.45783335f, 0.34559074f, 0.24506053f, 0.16324551f, 
    0.11943297f, 0.19085334f, 0.28650481f, 0.40403652f, 0.53526145f, 0.66614360f, 
    0.77880079f, 0.85534531f, 0.88249689f, 0.85534531f, 0.77880079f, 0.66614360f, 
    0.53526145f, 0.40403652f, 0.28650481f, 0.19085334f, 0.13117145f, 0.20961139f, 
    0.31466395f, 0.44374731f, 0.58786964f, 0.73161560f, 0.85534531f, 0.93941307f, 
    0.96923321f, 0.93941307f, 0.85534531f, 0.73161560f, 0.58786964f, 0.44374731f, 
    0.31466395f, 0.20961139f, 0.13533528f, 0.21626517f, 0.32465246f, 0.45783335f, 
    0.60653067f, 0.75483960f, 0.88249689f, 0.96923321f, 1.00000000f, 0.96923321f, 
    0.88249689f, 0.75483960f, 0.60653067f, 0.45783335f, 0.32465246f, 0.21626517f, 
    0.13117145f, 0.20961139f, 0.31466395f, 0.44374731f, 0.58786964f, 0.73161560f, 
    0.85534531f, 0.93941307f, 0.96923321f, 0.93941307f, 0.85534531f, 0.73161560f, 
    0.58786964f, 0.44374731f, 0.31466395f, 0.20961139f, 0.11943297f, 0.19085334f, 
    0.28650481f, 0.40403652f, 0.53526145f, 0.66614360f, 0.77880079f, 0.85534531f, 
    0.88249689f, 0.85534531f, 0.77880079f, 0.66614360f, 0.53526145f, 0.40403652f, 
    0.28650481f, 0.19085334f, 0.10215643f, 0.16324551f, 0.24506053f, 0.34559074f, 
    0.45783335f, 0.56978285f, 0.66614360f, 0.73161560f, 0.75483960f, 0.73161560f, 
    0.66614360f, 0.56978285f, 0.45783335f, 0.34559074f, 0.24506053f, 0.16324551f, 
    0.08208500f, 0.13117145f, 0.19691168f, 0.27768996f, 0.36787945f, 0.45783335f, 
    0.53526145f, 0.58786964f, 0.60653067f, 0.58786964f, 0.53526145f, 0.45783335f, 
    0.36787945f, 0.27768996f, 0.19691168f, 0.13117145f, 0.06196101f, 0.09901341f, 
    0.14863673f, 0.20961139f, 0.27768996f, 0.34559074f, 0.40403652f, 0.44374731f, 
    0.45783335f, 0.44374731f, 0.40403652f, 0.34559074f, 0.27768996f, 0.20961139f, 
    0.14863673f, 0.09901341f, 0.04393693f, 0.07021102f, 0.10539922f, 0.14863673f, 
    0.19691168f, 0.24506053f, 0.28650481f, 0.31466395f, 0.32465246f, 0.31466395f, 
    0.28650481f, 0.24506053f, 0.19691168f, 0.14863673f, 0.10539922f, 0.07021102f, 
    0.02926831f, 0.04677062f, 0.07021102f, 0.09901341f, 0.13117145f, 0.16324551f, 
    0.19085334f, 0.20961139f, 0.21626517f, 0.20961139f, 0.19085334f, 0.16324551f, 
    0.13117145f, 0.09901341f, 0.07021102f, 0.04677062f, 
    /* interp_weight lut */
    0.00390625f, 0.01171875f, 0.01953125f, 0.02734375f, 0.03515625f, 0.04296875f, 
    0.05078125f, 0.05859375f, 0.05859375f, 0.05078125f, 0.04296875f, 0.03515625f, 
    0.02734375f, 0.01953125f, 0.01171875f, 0.00390625f, 0.01171875f, 0.03515625f, 
    0.05859375f, 0.08203125f, 0.10546875f, 0.12890625f, 0.15234375f, 0.17578125f, 
    0.17578125f, 0.15234375f, 0.12890625f, 0.10546875f, 0.08203125f, 0.05859375f, 
    0.03515625f, 0.01171875f, 0.01953125f, 0.05859375f, 0.09765625f, 0.13671875f, 
    0.17578125f, 0.21484375f, 0.25390625f, 0.29296875f, 0.29296875f, 0.25390625f, 
    0.21484375f, 0.17578125f, 0.13671875f, 0.09765625f, 0.05859375f, 0.01953125f, 
    0.02734375f, 0.08203125f, 0.13671875f, 0.19140625f, 0.24609375f, 0.30078125f, 
    0.35546875f, 0.41015625f, 0.41015625f, 0.35546875f, 0.30078125f, 0.24609375f, 
    0.19140625f, 0.13671875f, 0.08203125f, 0.02734375f, 0.03515625f, 0.10546875f, 
    0.17578125f, 0.24609375f, 0.31640625f, 0.38671875f, 0.45703125f, 0.52734375f, 
    0.52734375f, 0.45703125f, 0.38671875f, 0.31640625f, 0.24609375f, 0.17578125f, 
    0.10546875f, 0.03515625f, 0.04296875f, 0.12890625f, 0.21484375f, 0.30078125f, 
    0.38671875f, 0.47265625f, 0.55859375f, 0.64453125f, 0.64453125f, 0.55859375f, 
    0.47265625f, 0.38671875f, 0.30078125f, 0.21484375f, 0.12890625f, 0.04296875f, 
    0.05078125f, 0.15234375f, 0.25390625f, 0.35546875f, 0.45703125f, 0.55859375f, 
    0.66015625f, 0.76171875f, 0.76171875f, 0.66015625f, 0.55859375f, 0.45703125f, 
    0.35546875f, 0.25390625f, 0.15234375f, 0.05078125f, 0.05859375f, 0.17578125f, 
    0.29296875f, 0.41015625f, 0.52734375f, 0.64453125f, 0.76171875f, 0.87890625f, 
    0.87890625f, 0.76171875f, 0.64453125f, 0.52734375f, 0.41015625f, 0.29296875f, 
    0.17578125f, 0.05859375f, 0.05859375f, 0.17578125f, 0.29296875f, 0.41015625f, 
    0.52734375f, 0.64453125f, 0.76171875f, 0.87890625f, 0.87890625f, 0.76171875f, 
    0.64453125f, 0.52734375f, 0.41015625f, 0.29296875f, 0.17578125f, 0.05859375f, 
    0.05078125f, 0.15234375f, 0.25390625f, 0.35546875f, 0.45703125f, 0.55859375f, 
    0.66015625f, 0.76171875f, 0.76171875f, 0.66015625f, 0.55859375f, 0.45703125f, 
    0.35546875f, 0.25390625f, 0.15234375f, 0.05078125f, 0.04296875f, 0.12890625f, 
    0.21484375f, 0.30078125f, 0.38671875f, 0.47265625f, 0.55859375f, 0.64453125f, 
    0.64453125f, 0.55859375f, 0.47265625f, 0.38671875f, 0.30078125f, 0.21484375f, 
    0.12890625f, 0.04296875f, 0.03515625f, 0.10546875f, 0.17578125f, 0.24609375f, 
    0.31640625f, 0.38671875f, 0.45703125f, 0.52734375f, 0.52734375f, 0.45703125f, 
    0.38671875f, 0.31640625f, 0.24609375f, 0.17578125f, 0.10546875f, 0.03515625f, 
    0.02734375f, 0.08203125f, 0.13671875f, 0.19140625f, 0.24609375f, 0.30078125f, 
    0.35546875f, 0.41015625f, 0.41015625f, 0.35546875f, 0.30078125f, 0.24609375f, 
    0.19140625f, 0.13671875f, 0.08203125f, 0.02734375f, 0.01953125f, 0.05859375f, 
    0.09765625f, 0.13671875f, 0.17578125f, 0.21484375f, 0.25390625f, 0.29296875f, 
    0.29296875f, 0.25390625f, 0.21484375f, 0.17578125f, 0.13671875f, 0.09765625f, 
    0.05859375f, 0.01953125f, 0.01171875f, 0.03515625f, 0.05859375f, 0.08203125f, 
    0.10546875f, 0.12890625f, 0.15234375f, 0.17578125f, 0.17578125f, 0.15234375f, 
    0.12890625f, 0.10546875f, 0.08203125f, 0.05859375f, 0.03515625f, 0.01171875f, 
    0.00390625f, 0.01171875f, 0.01953125f, 0.02734375f, 0.03515625f, 0.04296875f, 
    0.05078125f, 0.05859375f, 0.05859375f, 0.05078125f, 0.04296875f, 0.03515625f, 
    0.02734375f, 0.01953125f, 0.01171875f, 0.00390625f
};

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 cdescr_size;
                int cdescr_width;
                int cdescr_height;

                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, float sigma, const cv::ocl::oclMat &grad,
                                   const cv::ocl::oclMat &qangle, 
                                   const cv::ocl::oclMat &gauss_w_lut, 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);
            }
        }
    }
}

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

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

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(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);

	if (queryDeviceInfo<IS_CPU_DEVICE, bool>())
        hog_device_cpu = true;
    else
        hog_device_cpu = false;
}

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()) + 256, CV_32F);

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

    vector<float> v_lut = vector<float>(gaussian_interp_lut, gaussian_interp_lut + 
        sizeof(gaussian_interp_lut) / sizeof(gaussian_interp_lut[0]));
    Mat m_lut(v_lut);
    gauss_w_lut.upload(m_lut.reshape(1,1));
}

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, this->grad, this->qangle);

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

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,
        0.359030f, 0.260717f, 0.170335f, 0.013683f, -0.142596f, -0.026138f,
        -0.011878f, -0.150519f, 0.047159f, -0.107062f, -0.147347f, -0.187689f,
        -0.186027f, -0.208048f, 0.058468f, -0.073026f, -0.236556f, -0.079788f,
        -0.146216f, -0.058563f, -0.101361f, -0.071294f, -0.071093f, 0.116919f,
        0.234304f, 0.306781f, 0.321866f, 0.240000f, 0.073261f, -0.012173f,
        0.026479f, 0.050173f, 0.166127f, 0.228955f, 0.061905f, 0.156460f,
        0.205990f, 0.120672f, 0.037350f, 0.167884f, 0.290099f, 0.420900f,
        -0.012601f, 0.189839f, 0.306378f, 0.118383f, -0.095598f, -0.072360f,
        -0.132496f, -0.224259f, -0.126021f, 0.022714f, 0.284039f, 0.051369f,
        -0.000927f, -0.058735f, -0.083354f, -0.141254f, -0.187578f, -0.202669f,
        0.048902f, 0.246597f, 0.441863f, 0.342519f, 0.066979f, 0.215286f,
        0.188191f, -0.072240f, -0.208142f, -0.030196f, 0.178141f, 0.136985f,
        -0.043374f, -0.181098f, 0.091815f, 0.116177f, -0.126690f, -0.386625f,
        0.368165f, 0.269149f, -0.088042f, -0.028823f, 0.092961f, 0.024099f,
        0.046112f, 0.176756f, 0.135849f, 0.124955f, 0.195467f, -0.037218f,
        0.167217f, 0.188938f, 0.053528f, -0.066561f, 0.133721f, -0.070565f,
        0.115898f, 0.152435f, -0.116993f, -0.110592f, -0.179005f, 0.026668f,
        0.080530f, 0.075084f, -0.070401f, 0.012497f, 0.021849f, -0.139764f,
        -0.022020f, -0.096301f, -0.064954f, -0.127446f, -0.013806f, -0.108315f,
        0.156285f, 0.149867f, -0.011382f, 0.064532f, 0.029168f, 0.027393f,
        0.069716f, 0.153735f, 0.038459f, 0.230714f, 0.253840f, 0.059522f,
        -0.045053f, 0.014083f, 0.071103f, 0.068747f, 0.095887f, 0.005832f,
        0.144887f, 0.026357f, -0.067359f, -0.044151f, -0.123283f, -0.019911f,
        0.005318f, 0.109208f, -0.003201f, -0.021734f, 0.142025f, -0.066907f,
        -0.120070f, -0.188639f, 0.012472f, -0.048704f, -0.012366f, -0.184828f,
        0.168591f, 0.267166f, 0.058208f, -0.044101f, 0.033500f, 0.178558f,
        0.104550f, 0.122418f, 0.080177f, 0.173246f, 0.298537f, 0.064173f,
        0.053397f, 0.174341f, 0.230984f, 0.117025f, 0.166242f, 0.227781f,
        0.120623f, 0.176952f, -0.011393f, -0.086483f, -0.008270f, 0.051700f,
        -0.153369f, -0.058837f, -0.057639f, -0.060115f, 0.026349f, -0.160745f,
        -0.037894f, -0.048575f, 0.041052f, -0.022112f, 0.060365f, 0.051906f,
        0.162657f, 0.138519f, -0.050185f, -0.005938f, 0.071301f, 0.127686f,
        0.062342f, 0.144400f, 0.072600f, 0.198436f, 0.246219f, -0.078185f,
        -0.036169f, 0.075934f, 0.047328f, -0.013601f, 0.087205f, 0.019900f,
        0.022606f, -0.015365f, -0.092506f, 0.075275f, -0.116375f, 0.050500f,
        0.045118f, 0.166567f, 0.072073f, 0.060371f, 0.131747f, -0.169863f,
        -0.039352f, -0.047486f, -0.039797f, -0.204312f, 0.021710f, 0.129443f,
        -0.021173f, 0.173416f, -0.070794f, -0.063986f, 0.069689f, -0.064099f,
        -0.123201f, -0.017372f, -0.206870f, 0.065863f, 0.113226f, 0.024707f,
        -0.071341f, -0.066964f, -0.098278f, -0.062927f, 0.075840f, 0.014716f,
        0.019378f, 0.132699f, -0.074191f, -0.089557f, -0.078446f, -0.197488f,
        -0.173665f, 0.052583f, 0.044361f, 0.113549f, 0.098492f, 0.077379f,
        -0.011146f, -0.192593f, -0.164435f, 0.045568f, 0.205699f, 0.049187f,
        -0.082281f, 0.134874f, 0.185499f, 0.034968f, -0.119561f, -0.112372f,
        -0.115091f, -0.054042f, -0.183816f, -0.078100f, 0.190695f, 0.091617f,
        0.004257f, -0.041135f, -0.061453f, -0.141592f, -0.194809f, -0.120638f,
        0.020168f, 0.109672f, 0.067398f, -0.015238f, -0.239145f, -0.264671f,
        -0.185176f, 0.050472f, 0.020793f, 0.035678f, 0.022839f, -0.052055f,
        -0.127968f, -0.113049f, -0.228416f, -0.258281f, -0.053437f, 0.076424f,
        0.061450f, 0.237478f, 0.003618f, -0.055865f, -0.108087f, -0.028937f,
        0.045585f, 0.052829f, -0.001471f, 0.022826f, 0.059565f, -0.104430f,
        -0.077266f, -0.211882f, -0.212078f, 0.028074f, 0.075846f, 0.016265f,
        0.161879f, 0.134477f, 0.008935f, -0.048041f, 0.074692f, 0.004928f,
        -0.025156f, 0.192874f, 0.074410f, 0.308732f, 0.267400f, 0.094208f,
        -0.005251f, 0.042041f, -0.032148f, 0.015588f, 0.252869f, 0.175302f,
        0.022892f, 0.081673f, 0.063208f, 0.162626f, 0.194426f, 0.233890f,
        0.262292f, 0.186930f, 0.084079f, -0.286388f, -0.213034f, -0.048867f,
        -0.207669f, -0.170050f, 0.011673f, -0.092958f, -0.192786f, -0.273536f,
        0.230904f, 0.266732f, 0.320519f, 0.297155f, 0.548169f, 0.304922f,
        0.132687f, 0.247333f, 0.212488f, -0.271472f, -0.142105f, -0.002627f,
        -0.119215f, 0.128383f, 0.100079f, -0.057490f, -0.121902f, -0.228892f,
        0.202292f, -0.399795f, -0.371326f, -0.095836f, -0.063626f, -0.161375f,
        -0.311180f, -0.294797f, 0.242122f, 0.011788f, 0.095573f, 0.322523f,
        0.511840f, 0.322880f, 0.313259f, 0.173331f, 0.002542f, -0.029802f,
        0.324766f, -0.326170f, -0.340547f, -0.138288f, -0.002963f, -0.114060f,
        -0.377312f, -0.442570f, 0.212446f, -0.007759f, -0.011576f, 0.169711f,
        0.308689f, 0.317348f, 0.539390f, 0.332845f, 0.057331f, -0.068180f,
        0.101994f, 0.266995f, 0.209570f, 0.355730f, 0.091635f, 0.170238f,
        0.125215f, 0.274154f, 0.070223f, 0.025515f, 0.049946f, -0.000550f,
        0.043715f, -0.141843f, 0.020844f, 0.129871f, 0.256588f, 0.105015f,
        0.148339f, 0.170682f, 0.028792f, 0.074037f, 0.160042f, 0.405137f,
        0.246187f, 0.352160f, 0.168951f, 0.222263f, 0.264439f, 0.065945f,
        0.021963f, -0.075084f, 0.093105f, 0.027318f, 0.098864f, 0.057566f,
        -0.080282f, 0.185032f, 0.314419f, 0.333727f, 0.125798f, 0.294919f,
        0.386002f, 0.217619f, -0.183517f, -0.278622f, -0.002342f, -0.027821f,
        -0.134266f, -0.331843f, -0.008296f, 0.124564f, 0.053712f, -0.369016f,
        -0.095036f, 0.209381f, 0.423760f, 0.371760f, 0.106397f, 0.369408f,
        0.485608f, 0.231201f, -0.138685f, -0.349208f, -0.070083f, 0.028991f,
        -0.081630f, -0.395992f, -0.146791f, -0.027354f, 0.063396f, -0.272484f,
        0.058299f, 0.338207f, 0.110767f, -0.052642f, -0.233848f, -0.027448f,
        0.030328f, 0.155572f, -0.093826f, 0.019331f, 0.120638f, 0.006292f,
        -0.106083f, -0.236290f, -0.140933f, -0.088067f, -0.025138f, -0.208395f,
        -0.025502f, 0.144192f, -0.048353f, -0.106144f, -0.305121f, -0.114147f,
        0.090963f, 0.327727f, 0.035606f, -0.093779f, 0.002651f, -0.171081f,
        -0.188131f, -0.216571f, -0.209101f, -0.054402f, 0.157147f, -0.057127f,
        0.066584f, 0.008988f, 0.041191f, 0.034456f, -0.078255f, 0.052099f,
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        0.026521f, 0.171945f, 0.087318f, 0.034495f, -0.034089f, 0.154410f,
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        -0.054920f, 0.144955f, 0.036517f, -0.085412f, -0.229070f, -0.143710f,
        -0.049486f, 0.156634f, -0.008673f, -0.064778f, 0.082344f, 0.145673f,
        0.002912f, -0.210121f, -0.116564f, 0.078425f, 0.220908f, -0.067594f,
        0.048610f, 0.084912f, -0.066202f, -0.112515f, -0.217767f, -0.082640f,
        -0.017414f, 0.230265f, -0.070735f, 0.066073f, 0.215256f, 0.071157f,
        -0.087220f, -0.202235f, -0.011918f, 0.099562f, 0.174716f, -0.063845f,
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        -0.021917f, 0.068014f, -0.086893f, -0.251746f, -0.267171f, 0.037383f,
        0.003966f, 0.033571f, -0.151506f, 0.025437f, -0.020626f, -0.308454f,
        -0.343143f, -0.092263f, -0.026261f, -0.028345f, 0.036036f, 0.035169f,
        0.129470f, 0.122205f, 0.015661f, -0.070612f, -0.094333f, -0.066055f,
        -0.041083f, 0.159146f, 0.073184f, 0.110044f, 0.174471f, 0.078069f,
        -0.014881f, 0.008116f, 0.013209f, 0.075857f, 0.195605f, 0.062714f,
        0.067955f, 0.056544f, -0.153908f, -0.141749f, -0.072550f, 0.033523f,
        -0.024665f, 0.134487f, 0.079076f, 0.133562f, 0.227130f, 0.018054f,
        0.004928f, 0.169162f, 0.065152f, 0.072160f, 0.131631f, 0.096303f,
        0.054288f, 0.106256f, 0.114632f, 0.119038f, 0.515200f, 0.247429f,
        0.199134f, 0.211957f, 0.127558f, -0.294684f, -0.194890f, -0.049988f,
        -0.112247f, -0.008122f, -0.006176f, 0.037035f, -0.110881f, -0.249989f,
        0.152434f, 0.234621f, 0.153340f, 0.349283f, 0.683049f, 0.157174f,
        0.124844f, 0.099136f, 0.064407f, -0.248400f, -0.155323f, -0.026498f,
        -0.023450f, 0.049051f, -0.114187f, 0.007195f, -0.176825f, -0.376926f,
        0.366159f, -0.179938f, -0.148508f, 0.006043f, 0.170048f, 0.097866f,
        -0.102658f, -0.260430f, 0.248868f, 0.037019f, -0.118111f, 0.078176f,
        0.194171f, 0.211328f, 0.368612f, 0.361213f, 0.130013f, 0.094650f,
        0.227396f, -0.178058f, -0.114782f, -0.008093f, 0.231080f, -0.011843f,
        -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 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,
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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 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 descr_width = nblocks_win_x * block_hist_size;
    cdescr_width = descr_width;
    cdescr_height = nblocks_win_y;

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

void cv::ocl::device::hog::compute_hists(int nbins, 
                                         int block_stride_x, int block_stride_y,
                                         int height, int width, float sigma, 
                                         const cv::ocl::oclMat &grad, 
                                         const cv::ocl::oclMat &qangle, 
                                         const cv::ocl::oclMat &gauss_w_lut, 
                                         cv::ocl::oclMat &block_hists)
{
    Context *clCxt = Context::getContext();
    vector< pair<size_t, const void *> > args;
    string kernelName = (sigma == 4.0f) ? "compute_hists_lut_kernel" : 
        "compute_hists_kernel";

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

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

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

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

    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));
    if (kernelName.compare("compute_hists_lut_kernel") == 0)
        args.push_back( make_pair( sizeof(cl_mem), (void *)&gauss_w_lut.data));
    else
        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(clCxt, &objdetect_hog, kernelName, globalThreads, 
        localThreads, args, -1, -1);
}

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();
    vector< pair<size_t, const void *> > args;
    string kernelName;

    int block_hist_size = nbins * CELLS_PER_BLOCK_X * CELLS_PER_BLOCK_Y;
    int img_block_width = (width - CELLS_PER_BLOCK_X * CELL_WIDTH + block_stride_x) 
        / block_stride_x;
    int img_block_height = (height - CELLS_PER_BLOCK_Y * CELL_HEIGHT + block_stride_y) 
        / block_stride_y;
    int nthreads;
    size_t globalThreads[3] = { 1, 1, 1  };
    size_t localThreads[3] = { 1, 1, 1  };
    
    if ( nbins == 9 )
    {
        /* optimized for the case of 9 bins */
        kernelName = "normalize_hists_36_kernel";
        int blocks_in_group = NTHREADS / block_hist_size;
        nthreads = blocks_in_group * block_hist_size;
        int num_groups = divUp( img_block_width * img_block_height, blocks_in_group);
        globalThreads[0] = nthreads * num_groups;
        localThreads[0] = nthreads;
    }
    else
    {
        kernelName = "normalize_hists_kernel";
        nthreads = power_2up(block_hist_size);
        globalThreads[0] = img_block_width * nthreads;
        globalThreads[1] = img_block_height;
        localThreads[0] = nthreads;

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

    if(hog_device_cpu)
        openCLExecuteKernel(clCxt, &objdetect_hog, kernelName, globalThreads, 
                             localThreads, args, -1, -1, "-D CPU");
    else
        openCLExecuteKernel(clCxt, &objdetect_hog, kernelName, globalThreads, 
                             localThreads, args, -1, -1);
}

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();
    vector< pair<size_t, const void *> > args;

    int nthreads;
    string kernelName;
    switch (cdescr_width)
    {
    case 180:
        nthreads = 180;
        kernelName = "classify_hists_180_kernel";
        args.push_back( make_pair( sizeof(cl_int), (void *)&cdescr_width));
        args.push_back( make_pair( sizeof(cl_int), (void *)&cdescr_height));
        break;
    case 252:
        nthreads = 256;
        kernelName = "classify_hists_252_kernel";
        args.push_back( make_pair( sizeof(cl_int), (void *)&cdescr_width));
        args.push_back( make_pair( sizeof(cl_int), (void *)&cdescr_height));
        break;
    default:
        nthreads = 256;
        kernelName = "classify_hists_kernel";
        args.push_back( make_pair( sizeof(cl_int), (void *)&cdescr_size));
        args.push_back( make_pair( sizeof(cl_int), (void *)&cdescr_width));
    }

    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 *)&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));

    if(hog_device_cpu)
        openCLExecuteKernel(clCxt, &objdetect_hog, kernelName, globalThreads, 
                             localThreads, args, -1, -1, "-D CPU");
    else
        openCLExecuteKernel(clCxt, &objdetect_hog, kernelName, globalThreads, 
                             localThreads, args, -1, -1);
}

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);
}