--- /dev/null
+/*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;
+
+#if !defined (HAVE_OPENCL)
+
+cv::ocl::HOGDescriptor::HOGDescriptor(Size, Size, Size, Size, int, double, double, bool, int) { throw_nogpu(); }
+size_t cv::ocl::HOGDescriptor::getDescriptorSize() const { throw_nogpu(); return 0; }
+size_t cv::ocl::HOGDescriptor::getBlockHistogramSize() const { throw_nogpu(); return 0; }
+double cv::ocl::HOGDescriptor::getWinSigma() const { throw_nogpu(); return 0; }
+bool cv::ocl::HOGDescriptor::checkDetectorSize() const { throw_nogpu(); return false; }
+void cv::ocl::HOGDescriptor::setSVMDetector(const vector<float>&) { throw_nogpu(); }
+void cv::ocl::HOGDescriptor::detect(const oclMat&, vector<Point>&, double, Size, Size) { throw_nogpu(); }
+void cv::ocl::HOGDescriptor::detectMultiScale(const oclMat&, vector<Rect>&, double, Size, Size, double, int) { throw_nogpu(); }
+void cv::ocl::HOGDescriptor::computeBlockHistograms(const oclMat&) { throw_nogpu(); }
+void cv::ocl::HOGDescriptor::getDescriptors(const oclMat&, Size, oclMat&, int) { throw_nogpu(); }
+std::vector<float> cv::ocl::HOGDescriptor::getDefaultPeopleDetector() { throw_nogpu(); return std::vector<float>(); }
+std::vector<float> cv::ocl::HOGDescriptor::getPeopleDetector48x96() { throw_nogpu(); return std::vector<float>(); }
+std::vector<float> cv::ocl::HOGDescriptor::getPeopleDetector64x128() { throw_nogpu(); return std::vector<float>(); }
+
+#else
+
+#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);
+ }
+}}}
+
+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);
+}
+
+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::computeGradient(const oclMat& img, oclMat& grad, oclMat& qangle)
+{
+ CV_Assert(img.type() == CV_8UC1 || img.type() == CV_8UC4);
+
+ grad.create(img.size(), CV_32FC2);
+
+ qangle.create(img.size(), CV_8UC2);
+
+ float angleScale = (float)(nbins / CV_PI);
+ switch (img.type())
+ {
+ case CV_8UC1:
+ hog::compute_gradients_8UC1(img.rows, img.cols, img, angleScale, grad, qangle, gamma_correction);
+ break;
+ case CV_8UC4:
+ hog::compute_gradients_8UC4(img.rows, img.cols, img, angleScale, grad, qangle, gamma_correction);
+ break;
+ }
+}
+
+
+void cv::ocl::HOGDescriptor::computeBlockHistograms(const oclMat& img)
+{
+ computeGradient(img, grad, qangle);
+
+ size_t block_hist_size = getBlockHistogramSize();
+ 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);
+
+ hog::compute_hists(nbins, block_stride.width, block_stride.height, img.rows, img.cols, grad, qangle, (float)getWinSigma(), block_hists);
+
+ hog::normalize_hists(nbins, block_stride.width, block_stride.height, img.rows, img.cols, 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);
+
+ 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(img.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, img.rows, img.cols, 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, img.rows, img.cols, 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;
+
+ computeBlockHistograms(img);
+
+ 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);
+
+ Size wins_per_img = numPartsWithin(img.size(), win_size, win_stride);
+ labels.create(1, wins_per_img.area(), CV_8U);
+
+ hog::classify_hists(win_size.height, win_size.width, block_stride.height, block_stride.width,
+ win_stride.height, win_stride.width, img.rows, img.cols, block_hists,
+ detector, (float)free_coef, (float)hit_threshold, labels);
+
+ labels.download(labels_host);
+ unsigned char* vec = labels_host.ptr();
+ for (int i = 0; i < wins_per_img.area(); i++)
+ {
+ int y = i / wins_per_img.width;
+ int x = i - wins_per_img.width * y;
+ if (vec[i])
+ hits.push_back(Point(x * win_stride.width, y * win_stride.height));
+ }
+}
+
+
+
+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);
+
+ 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);
+ image_scales.resize(levels);
+
+ std::vector<Rect> all_candidates;
+ vector<Point> locations;
+
+ for (size_t i = 0; i < level_scale.size(); i++)
+ {
+ scale = level_scale[i];
+ Size sz(cvRound(img.cols / scale), cvRound(img.rows / scale));
+ oclMat smaller_img;
+
+ if (sz == img.size())
+ smaller_img = img;
+ else
+ {
+ image_scales[i].create(sz, img.type());
+ resize(img, image_scales[i], image_scales[i].size(), 0, 0, INTER_LINEAR);
+ smaller_img = image_scales[i];
+ }
+
+ detect(smaller_img, 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,
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+ -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,
+ 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,
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+ -0.14603497f, -0.01946543f, -0.02327525f, -0.01973944f, 0.07944400f,
+ -0.02224544f, -0.06701808f, 0.03476532f, 0.11505594f, -0.02712801f,
+ -0.01665113f, 0.06315716f, -0.08205860f, 0.07431999f, 0.04915778f,
+ -0.04468752f, -0.01490402f, 0.07400476f, -0.11650901f, 0.05102430f,
+ 0.04559118f, -0.05916039f, 0.08840760f, -0.01587902f, -0.14890194f,
+ 0.07857784f, 0.04710254f, -0.05381983f, -0.07331945f, -0.03604643f,
+ 0.15611970f, 0.07649943f, -0.05959348f, -0.02776607f, 0.11098688f,
+ 0.03758875f, -0.04446875f, 0.04933187f, 0.01345535f, 0.06921103f,
+ 0.07364785f, 0.05518956f, 0.02899585f, 0.09375840f, 0.10518434f,
+ -0.04420241f, 0.01915282f, -3.56386811e-003f, 0.14586878f, 0.10286101f,
+ -0.04360626f, -0.12723237f, 0.09076386f, 0.11119842f, -0.06035013f,
+ 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;
+}
+
+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;
+
+ size_t globalThreads[3] = { img_block_width * 32, img_block_height * 2, 1 };
+ size_t localThreads[3] = { 32, 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;
+
+ args.push_back( make_pair( sizeof(cl_int), (void *)&width));
+ 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 *)&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(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();
+ 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(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();
+ 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(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);
+}
+
+static inline int divUp(int total, int grain)
+{
+ return (total + grain - 1) / grain;
+}
+
+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);
+}
+
+#endif
--- /dev/null
+/*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
+// Peng Xiao, pengxiao@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 implied warranties, including, but not limited to, the implied
+// 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*/
+
+#pragma OPENCL EXTENSION cl_amd_printf : enable
+#pragma OPENCL EXTENSION cl_khr_global_int32_base_atomics : enable
+#pragma OPENCL EXTENSION cl_khr_local_int32_base_atomics : enable
+
+// dynamically change the precision used for floating type
+
+#if defined (__ATI__) || defined (__NVIDIA__)
+#define F double
+#else
+#define F float
+#endif
+
+// Image read mode
+__constant sampler_t sampler = CLK_NORMALIZED_COORDS_FALSE | CLK_ADDRESS_CLAMP_TO_EDGE | CLK_FILTER_NEAREST;
+
+#define CV_PI_F 3.14159265f
+
+// print greyscale image to show image layout
+__kernel void printImage(image2d_t img)
+{
+ printf("(%d, %d) - %3d \n",
+ get_global_id(0),
+ get_global_id(1),
+ read_imageui(img, (int2)(get_global_id(0), get_global_id(1))).x
+ );
+}
+
+// Use integral image to calculate haar wavelets.
+// N = 2
+// for simple haar paatern
+float icvCalcHaarPatternSum_2(image2d_t sumTex, __constant float src[2][5], int oldSize, int newSize, int y, int x)
+{
+
+ float ratio = (float)newSize / oldSize;
+
+ F d = 0;
+
+#pragma unroll
+ for (int k = 0; k < 2; ++k)
+ {
+ int dx1 = convert_int_rte(ratio * src[k][0]);
+ int dy1 = convert_int_rte(ratio * src[k][1]);
+ int dx2 = convert_int_rte(ratio * src[k][2]);
+ int dy2 = convert_int_rte(ratio * src[k][3]);
+
+ F t = 0;
+ t += read_imageui(sumTex, sampler, (int2)(x + dx1, y + dy1)).x;
+ t -= read_imageui(sumTex, sampler, (int2)(x + dx1, y + dy2)).x;
+ t -= read_imageui(sumTex, sampler, (int2)(x + dx2, y + dy1)).x;
+ t += read_imageui(sumTex, sampler, (int2)(x + dx2, y + dy2)).x;
+
+ d += t * src[k][4] / ((dx2 - dx1) * (dy2 - dy1));
+ }
+
+ return (float)d;
+}
+
+// N = 3
+float icvCalcHaarPatternSum_3(image2d_t sumTex, __constant float src[3][5], int oldSize, int newSize, int y, int x)
+{
+
+ float ratio = (float)newSize / oldSize;
+
+ F d = 0;
+
+#pragma unroll
+ for (int k = 0; k < 3; ++k)
+ {
+ int dx1 = convert_int_rte(ratio * src[k][0]);
+ int dy1 = convert_int_rte(ratio * src[k][1]);
+ int dx2 = convert_int_rte(ratio * src[k][2]);
+ int dy2 = convert_int_rte(ratio * src[k][3]);
+
+ F t = 0;
+ t += read_imageui(sumTex, sampler, (int2)(x + dx1, y + dy1)).x;
+ t -= read_imageui(sumTex, sampler, (int2)(x + dx1, y + dy2)).x;
+ t -= read_imageui(sumTex, sampler, (int2)(x + dx2, y + dy1)).x;
+ t += read_imageui(sumTex, sampler, (int2)(x + dx2, y + dy2)).x;
+
+ d += t * src[k][4] / ((dx2 - dx1) * (dy2 - dy1));
+ }
+
+ return (float)d;
+}
+
+// N = 4
+float icvCalcHaarPatternSum_4(image2d_t sumTex, __constant float src[4][5], int oldSize, int newSize, int y, int x)
+{
+
+ float ratio = (float)newSize / oldSize;
+
+ F d = 0;
+
+#pragma unroll
+ for (int k = 0; k < 4; ++k)
+ {
+ int dx1 = convert_int_rte(ratio * src[k][0]);
+ int dy1 = convert_int_rte(ratio * src[k][1]);
+ int dx2 = convert_int_rte(ratio * src[k][2]);
+ int dy2 = convert_int_rte(ratio * src[k][3]);
+
+ F t = 0;
+ t += read_imageui(sumTex, sampler, (int2)(x + dx1, y + dy1)).x;
+ t -= read_imageui(sumTex, sampler, (int2)(x + dx1, y + dy2)).x;
+ t -= read_imageui(sumTex, sampler, (int2)(x + dx2, y + dy1)).x;
+ t += read_imageui(sumTex, sampler, (int2)(x + dx2, y + dy2)).x;
+
+ d += t * src[k][4] / ((dx2 - dx1) * (dy2 - dy1));
+ }
+
+ return (float)d;
+}
+
+////////////////////////////////////////////////////////////////////////
+// Hessian
+
+__constant float c_DX [3][5] = { {0, 2, 3, 7, 1}, {3, 2, 6, 7, -2}, {6, 2, 9, 7, 1} };
+__constant float c_DY [3][5] = { {2, 0, 7, 3, 1}, {2, 3, 7, 6, -2}, {2, 6, 7, 9, 1} };
+__constant float c_DXY[4][5] = { {1, 1, 4, 4, 1}, {5, 1, 8, 4, -1}, {1, 5, 4, 8, -1}, {5, 5, 8, 8, 1} };
+
+__inline int calcSize(int octave, int layer)
+{
+ /* Wavelet size at first layer of first octave. */
+ const int HAAR_SIZE0 = 9;
+
+ /* Wavelet size increment between layers. This should be an even number,
+ such that the wavelet sizes in an octave are either all even or all odd.
+ This ensures that when looking for the neighbours of a sample, the layers
+ above and below are aligned correctly. */
+ const int HAAR_SIZE_INC = 6;
+
+ return (HAAR_SIZE0 + HAAR_SIZE_INC * layer) << octave;
+}
+
+
+//calculate targeted layer per-pixel determinant and trace with an integral image
+__kernel void icvCalcLayerDetAndTrace(
+ image2d_t sumTex, // input integral image
+ __global float * det, // output Determinant
+ __global float * trace, // output trace
+ int det_step, // the step of det in bytes
+ int trace_step, // the step of trace in bytes
+ int c_img_rows,
+ int c_img_cols,
+ int c_nOctaveLayers,
+ int c_octave,
+ int c_layer_rows
+ )
+{
+ det_step /= sizeof(*det);
+ trace_step /= sizeof(*trace);
+ // Determine the indices
+ const int gridDim_y = get_num_groups(1) / (c_nOctaveLayers + 2);
+ const int blockIdx_y = get_group_id(1) % gridDim_y;
+ const int blockIdx_z = get_group_id(1) / gridDim_y;
+
+ const int j = get_local_id(0) + get_group_id(0) * get_local_size(0);
+ const int i = get_local_id(1) + blockIdx_y * get_local_size(1);
+ const int layer = blockIdx_z;
+
+ const int size = calcSize(c_octave, layer);
+
+ const int samples_i = 1 + ((c_img_rows - size) >> c_octave);
+ const int samples_j = 1 + ((c_img_cols - size) >> c_octave);
+
+ // Ignore pixels where some of the kernel is outside the image
+ const int margin = (size >> 1) >> c_octave;
+
+ if (size <= c_img_rows && size <= c_img_cols && i < samples_i && j < samples_j)
+ {
+ const float dx = icvCalcHaarPatternSum_3(sumTex, c_DX , 9, size, i << c_octave, j << c_octave);
+ const float dy = icvCalcHaarPatternSum_3(sumTex, c_DY , 9, size, i << c_octave, j << c_octave);
+ const float dxy = icvCalcHaarPatternSum_4(sumTex, c_DXY, 9, size, i << c_octave, j << c_octave);
+
+ det [j + margin + det_step * (layer * c_layer_rows + i + margin)] = dx * dy - 0.81f * dxy * dxy;
+ trace[j + margin + trace_step * (layer * c_layer_rows + i + margin)] = dx + dy;
+ }
+}
+
+
+////////////////////////////////////////////////////////////////////////
+// NONMAX
+
+__constant float c_DM[5] = {0, 0, 9, 9, 1};
+
+bool within_check(image2d_t maskSumTex, int sum_i, int sum_j, int size)
+{
+ float ratio = (float)size / 9.0f;
+
+ float d = 0;
+
+ int dx1 = convert_int_rte(ratio * c_DM[0]);
+ int dy1 = convert_int_rte(ratio * c_DM[1]);
+ int dx2 = convert_int_rte(ratio * c_DM[2]);
+ int dy2 = convert_int_rte(ratio * c_DM[3]);
+
+ float t = 0;
+
+ t += read_imageui(maskSumTex, sampler, (int2)(sum_j + dx1, sum_i + dy1)).x;
+ t -= read_imageui(maskSumTex, sampler, (int2)(sum_j + dx1, sum_i + dy2)).x;
+ t -= read_imageui(maskSumTex, sampler, (int2)(sum_j + dx2, sum_i + dy1)).x;
+ t += read_imageui(maskSumTex, sampler, (int2)(sum_j + dx2, sum_i + dy2)).x;
+
+ d += t * c_DM[4] / ((dx2 - dx1) * (dy2 - dy1));
+
+ return (d >= 0.5f);
+}
+
+// Non-maximal suppression to further filtering the candidates from previous step
+__kernel
+ void icvFindMaximaInLayer_withmask(
+ __global const float * det,
+ __global const float * trace,
+ __global int4 * maxPosBuffer,
+ volatile __global unsigned int* maxCounter,
+ int counter_offset,
+ int det_step, // the step of det in bytes
+ int trace_step, // the step of trace in bytes
+ int c_img_rows,
+ int c_img_cols,
+ int c_nOctaveLayers,
+ int c_octave,
+ int c_layer_rows,
+ int c_layer_cols,
+ int c_max_candidates,
+ float c_hessianThreshold,
+ image2d_t maskSumTex
+ )
+{
+ volatile __local float N9[768]; // threads.x * threads.y * 3
+
+ det_step /= sizeof(*det);
+ trace_step /= sizeof(*trace);
+ maxCounter += counter_offset;
+
+ // Determine the indices
+ const int gridDim_y = get_num_groups(1) / c_nOctaveLayers;
+ const int blockIdx_y = get_group_id(1) % gridDim_y;
+ const int blockIdx_z = get_group_id(1) / gridDim_y;
+
+ const int layer = blockIdx_z + 1;
+
+ const int size = calcSize(c_octave, layer);
+
+ // Ignore pixels without a 3x3x3 neighbourhood in the layer above
+ const int margin = ((calcSize(c_octave, layer + 1) >> 1) >> c_octave) + 1;
+
+ const int j = get_local_id(0) + get_group_id(0) * (get_local_size(0) - 2) + margin - 1;
+ const int i = get_local_id(1) + blockIdx_y * (get_local_size(1) - 2) + margin - 1;
+
+ // Is this thread within the hessian buffer?
+ const int zoff = get_local_size(0) * get_local_size(1);
+ const int localLin = get_local_id(0) + get_local_id(1) * get_local_size(0) + zoff;
+ N9[localLin - zoff] =
+ det[det_step *
+ (c_layer_rows * (layer - 1) + min(max(i, 0), c_img_rows - 1)) // y
+ + min(max(j, 0), c_img_cols - 1)]; // x
+ N9[localLin ] =
+ det[det_step *
+ (c_layer_rows * (layer ) + min(max(i, 0), c_img_rows - 1)) // y
+ + min(max(j, 0), c_img_cols - 1)]; // x
+ N9[localLin + zoff] =
+ det[det_step *
+ (c_layer_rows * (layer + 1) + min(max(i, 0), c_img_rows - 1)) // y
+ + min(max(j, 0), c_img_cols - 1)]; // x
+
+ barrier(CLK_LOCAL_MEM_FENCE);
+
+ if (i < c_layer_rows - margin
+ && j < c_layer_cols - margin
+ && get_local_id(0) > 0
+ && get_local_id(0) < get_local_size(0) - 1
+ && get_local_id(1) > 0
+ && get_local_id(1) < get_local_size(1) - 1 // these are unnecessary conditions ported from CUDA
+ )
+ {
+ float val0 = N9[localLin];
+
+ if (val0 > c_hessianThreshold)
+ {
+ // Coordinates for the start of the wavelet in the sum image. There
+ // is some integer division involved, so don't try to simplify this
+ // (cancel out sampleStep) without checking the result is the same
+ const int sum_i = (i - ((size >> 1) >> c_octave)) << c_octave;
+ const int sum_j = (j - ((size >> 1) >> c_octave)) << c_octave;
+
+ if (within_check(maskSumTex, sum_i, sum_j, size))
+ {
+ // Check to see if we have a max (in its 26 neighbours)
+ const bool condmax = val0 > N9[localLin - 1 - get_local_size(0) - zoff]
+ && val0 > N9[localLin - get_local_size(0) - zoff]
+ && val0 > N9[localLin + 1 - get_local_size(0) - zoff]
+ && val0 > N9[localLin - 1 - zoff]
+ && val0 > N9[localLin - zoff]
+ && val0 > N9[localLin + 1 - zoff]
+ && val0 > N9[localLin - 1 + get_local_size(0) - zoff]
+ && val0 > N9[localLin + get_local_size(0) - zoff]
+ && val0 > N9[localLin + 1 + get_local_size(0) - zoff]
+
+ && val0 > N9[localLin - 1 - get_local_size(0)]
+ && val0 > N9[localLin - get_local_size(0)]
+ && val0 > N9[localLin + 1 - get_local_size(0)]
+ && val0 > N9[localLin - 1 ]
+ && val0 > N9[localLin + 1 ]
+ && val0 > N9[localLin - 1 + get_local_size(0)]
+ && val0 > N9[localLin + get_local_size(0)]
+ && val0 > N9[localLin + 1 + get_local_size(0)]
+
+ && val0 > N9[localLin - 1 - get_local_size(0) + zoff]
+ && val0 > N9[localLin - get_local_size(0) + zoff]
+ && val0 > N9[localLin + 1 - get_local_size(0) + zoff]
+ && val0 > N9[localLin - 1 + zoff]
+ && val0 > N9[localLin + zoff]
+ && val0 > N9[localLin + 1 + zoff]
+ && val0 > N9[localLin - 1 + get_local_size(0) + zoff]
+ && val0 > N9[localLin + get_local_size(0) + zoff]
+ && val0 > N9[localLin + 1 + get_local_size(0) + zoff]
+ ;
+
+ if(condmax)
+ {
+ unsigned int ind = atomic_inc(maxCounter);
+
+ if (ind < c_max_candidates)
+ {
+ const int laplacian = (int) copysign(1.0f, trace[trace_step* (layer * c_layer_rows + i) + j]);
+
+ maxPosBuffer[ind] = (int4)(j, i, layer, laplacian);
+ }
+ }
+ }
+ }
+ }
+}
+
+__kernel
+ void icvFindMaximaInLayer(
+ __global float * det,
+ __global float * trace,
+ __global int4 * maxPosBuffer,
+ volatile __global unsigned int* maxCounter,
+ int counter_offset,
+ int det_step, // the step of det in bytes
+ int trace_step, // the step of trace in bytes
+ int c_img_rows,
+ int c_img_cols,
+ int c_nOctaveLayers,
+ int c_octave,
+ int c_layer_rows,
+ int c_layer_cols,
+ int c_max_candidates,
+ float c_hessianThreshold
+ )
+{
+ volatile __local float N9[768]; // threads.x * threads.y * 3
+
+ det_step /= sizeof(float);
+ trace_step /= sizeof(float);
+ maxCounter += counter_offset;
+
+ // Determine the indices
+ const int gridDim_y = get_num_groups(1) / c_nOctaveLayers;
+ const int blockIdx_y = get_group_id(1) % gridDim_y;
+ const int blockIdx_z = get_group_id(1) / gridDim_y;
+
+ const int layer = blockIdx_z + 1;
+
+ const int size = calcSize(c_octave, layer);
+
+ // Ignore pixels without a 3x3x3 neighbourhood in the layer above
+ const int margin = ((calcSize(c_octave, layer + 1) >> 1) >> c_octave) + 1;
+
+ const int j = get_local_id(0) + get_group_id(0) * (get_local_size(0) - 2) + margin - 1;
+ const int i = get_local_id(1) + blockIdx_y * (get_local_size(1) - 2) + margin - 1;
+
+ // Is this thread within the hessian buffer?
+ const int zoff = get_local_size(0) * get_local_size(1);
+ const int localLin = get_local_id(0) + get_local_id(1) * get_local_size(0) + zoff;
+
+ int l_x = min(max(j, 0), c_img_cols - 1);
+ int l_y = c_layer_rows * layer + min(max(i, 0), c_img_rows - 1);
+
+ N9[localLin - zoff] =
+ det[det_step * (l_y - c_layer_rows) + l_x];
+ N9[localLin ] =
+ det[det_step * (l_y ) + l_x];
+ N9[localLin + zoff] =
+ det[det_step * (l_y + c_layer_rows) + l_x];
+ barrier(CLK_LOCAL_MEM_FENCE);
+
+ if (i < c_layer_rows - margin
+ && j < c_layer_cols - margin
+ && get_local_id(0) > 0
+ && get_local_id(0) < get_local_size(0) - 1
+ && get_local_id(1) > 0
+ && get_local_id(1) < get_local_size(1) - 1 // these are unnecessary conditions ported from CUDA
+ )
+ {
+ float val0 = N9[localLin];
+ if (val0 > c_hessianThreshold)
+ {
+ //printf(\"(%3d, %3d) N9[%3d]=%7.1f val0=%7.1f\\n\", l_x, l_y, localLin - zoff, N9[localLin], val0);
+ // Coordinates for the start of the wavelet in the sum image. There
+ // is some integer division involved, so don't try to simplify this
+ // (cancel out sampleStep) without checking the result is the same
+
+ // Check to see if we have a max (in its 26 neighbours)
+ const bool condmax = val0 > N9[localLin - 1 - get_local_size(0) - zoff]
+ && val0 > N9[localLin - get_local_size(0) - zoff]
+ && val0 > N9[localLin + 1 - get_local_size(0) - zoff]
+ && val0 > N9[localLin - 1 - zoff]
+ && val0 > N9[localLin - zoff]
+ && val0 > N9[localLin + 1 - zoff]
+ && val0 > N9[localLin - 1 + get_local_size(0) - zoff]
+ && val0 > N9[localLin + get_local_size(0) - zoff]
+ && val0 > N9[localLin + 1 + get_local_size(0) - zoff]
+
+ && val0 > N9[localLin - 1 - get_local_size(0)]
+ && val0 > N9[localLin - get_local_size(0)]
+ && val0 > N9[localLin + 1 - get_local_size(0)]
+ && val0 > N9[localLin - 1 ]
+ && val0 > N9[localLin + 1 ]
+ && val0 > N9[localLin - 1 + get_local_size(0)]
+ && val0 > N9[localLin + get_local_size(0)]
+ && val0 > N9[localLin + 1 + get_local_size(0)]
+
+ && val0 > N9[localLin - 1 - get_local_size(0) + zoff]
+ && val0 > N9[localLin - get_local_size(0) + zoff]
+ && val0 > N9[localLin + 1 - get_local_size(0) + zoff]
+ && val0 > N9[localLin - 1 + zoff]
+ && val0 > N9[localLin + zoff]
+ && val0 > N9[localLin + 1 + zoff]
+ && val0 > N9[localLin - 1 + get_local_size(0) + zoff]
+ && val0 > N9[localLin + get_local_size(0) + zoff]
+ && val0 > N9[localLin + 1 + get_local_size(0) + zoff]
+ ;
+
+ if(condmax)
+ {
+ unsigned int ind = atomic_inc(maxCounter);
+
+ if (ind < c_max_candidates)
+ {
+ const int laplacian = (int) copysign(1.0f, trace[trace_step* (layer * c_layer_rows + i) + j]);
+
+ maxPosBuffer[ind] = (int4)(j, i, layer, laplacian);
+ }
+ }
+ }
+ }
+}
+
+// solve 3x3 linear system Ax=b for floating point input
+inline bool solve3x3_float(volatile __local const float A[3][3], volatile __local const float b[3], volatile __local float x[3])
+{
+ float det = A[0][0] * (A[1][1] * A[2][2] - A[1][2] * A[2][1])
+ - A[0][1] * (A[1][0] * A[2][2] - A[1][2] * A[2][0])
+ + A[0][2] * (A[1][0] * A[2][1] - A[1][1] * A[2][0]);
+
+ if (det != 0)
+ {
+ F invdet = 1.0 / det;
+
+ x[0] = invdet *
+ (b[0] * (A[1][1] * A[2][2] - A[1][2] * A[2][1]) -
+ A[0][1] * (b[1] * A[2][2] - A[1][2] * b[2] ) +
+ A[0][2] * (b[1] * A[2][1] - A[1][1] * b[2] ));
+
+ x[1] = invdet *
+ (A[0][0] * (b[1] * A[2][2] - A[1][2] * b[2] ) -
+ b[0] * (A[1][0] * A[2][2] - A[1][2] * A[2][0]) +
+ A[0][2] * (A[1][0] * b[2] - b[1] * A[2][0]));
+
+ x[2] = invdet *
+ (A[0][0] * (A[1][1] * b[2] - b[1] * A[2][1]) -
+ A[0][1] * (A[1][0] * b[2] - b[1] * A[2][0]) +
+ b[0] * (A[1][0] * A[2][1] - A[1][1] * A[2][0]));
+
+ return true;
+ }
+ return false;
+}
+
+#define X_ROW 0
+#define Y_ROW 1
+#define LAPLACIAN_ROW 2
+#define OCTAVE_ROW 3
+#define SIZE_ROW 4
+#define ANGLE_ROW 5
+#define HESSIAN_ROW 6
+#define ROWS_COUNT 7
+
+////////////////////////////////////////////////////////////////////////
+// INTERPOLATION
+__kernel
+ void icvInterpolateKeypoint(
+ __global const float * det,
+ __global const int4 * maxPosBuffer,
+ __global float * keypoints,
+ volatile __global unsigned int * featureCounter,
+ int det_step,
+ int keypoints_step,
+ int c_img_rows,
+ int c_img_cols,
+ int c_octave,
+ int c_layer_rows,
+ int c_max_features
+ )
+{
+ det_step /= sizeof(*det);
+ keypoints_step /= sizeof(*keypoints);
+ __global float * featureX = keypoints + X_ROW * keypoints_step;
+ __global float * featureY = keypoints + Y_ROW * keypoints_step;
+ __global int * featureLaplacian = (__global int *)keypoints + LAPLACIAN_ROW * keypoints_step;
+ __global int * featureOctave = (__global int *)keypoints + OCTAVE_ROW * keypoints_step;
+ __global float * featureSize = keypoints + SIZE_ROW * keypoints_step;
+ __global float * featureHessian = keypoints + HESSIAN_ROW * keypoints_step;
+
+ const int4 maxPos = maxPosBuffer[get_group_id(0)];
+
+ const int j = maxPos.x - 1 + get_local_id(0);
+ const int i = maxPos.y - 1 + get_local_id(1);
+ const int layer = maxPos.z - 1 + get_local_id(2);
+
+ volatile __local float N9[3][3][3];
+
+ N9[get_local_id(2)][get_local_id(1)][get_local_id(0)] =
+ det[det_step * (c_layer_rows * layer + i) + j];
+ barrier(CLK_LOCAL_MEM_FENCE);
+
+ if (get_local_id(0) == 0 && get_local_id(1) == 0 && get_local_id(2) == 0)
+ {
+ volatile __local float dD[3];
+
+ //dx
+ dD[0] = -0.5f * (N9[1][1][2] - N9[1][1][0]);
+ //dy
+ dD[1] = -0.5f * (N9[1][2][1] - N9[1][0][1]);
+ //ds
+ dD[2] = -0.5f * (N9[2][1][1] - N9[0][1][1]);
+
+ volatile __local float H[3][3];
+
+ //dxx
+ H[0][0] = N9[1][1][0] - 2.0f * N9[1][1][1] + N9[1][1][2];
+ //dxy
+ H[0][1]= 0.25f * (N9[1][2][2] - N9[1][2][0] - N9[1][0][2] + N9[1][0][0]);
+ //dxs
+ H[0][2]= 0.25f * (N9[2][1][2] - N9[2][1][0] - N9[0][1][2] + N9[0][1][0]);
+ //dyx = dxy
+ H[1][0] = H[0][1];
+ //dyy
+ H[1][1] = N9[1][0][1] - 2.0f * N9[1][1][1] + N9[1][2][1];
+ //dys
+ H[1][2]= 0.25f * (N9[2][2][1] - N9[2][0][1] - N9[0][2][1] + N9[0][0][1]);
+ //dsx = dxs
+ H[2][0] = H[0][2];
+ //dsy = dys
+ H[2][1] = H[1][2];
+ //dss
+ H[2][2] = N9[0][1][1] - 2.0f * N9[1][1][1] + N9[2][1][1];
+
+ volatile __local float x[3];
+
+ if (solve3x3_float(H, dD, x))
+ {
+ if (fabs(x[0]) <= 1.f && fabs(x[1]) <= 1.f && fabs(x[2]) <= 1.f)
+ {
+ // if the step is within the interpolation region, perform it
+
+ const int size = calcSize(c_octave, maxPos.z);
+
+ const int sum_i = (maxPos.y - ((size >> 1) >> c_octave)) << c_octave;
+ const int sum_j = (maxPos.x - ((size >> 1) >> c_octave)) << c_octave;
+
+ const float center_i = sum_i + (float)(size - 1) / 2;
+ const float center_j = sum_j + (float)(size - 1) / 2;
+
+ const float px = center_j + x[0] * (1 << c_octave);
+ const float py = center_i + x[1] * (1 << c_octave);
+
+ const int ds = size - calcSize(c_octave, maxPos.z - 1);
+ const float psize = round(size + x[2] * ds);
+
+ /* The sampling intervals and wavelet sized for selecting an orientation
+ and building the keypoint descriptor are defined relative to 's' */
+ const float s = psize * 1.2f / 9.0f;
+
+ /* To find the dominant orientation, the gradients in x and y are
+ sampled in a circle of radius 6s using wavelets of size 4s.
+ We ensure the gradient wavelet size is even to ensure the
+ wavelet pattern is balanced and symmetric around its center */
+ const int grad_wav_size = 2 * convert_int_rte(2.0f * s);
+
+ // check when grad_wav_size is too big
+ if ((c_img_rows + 1) >= grad_wav_size && (c_img_cols + 1) >= grad_wav_size)
+ {
+ // Get a new feature index.
+ unsigned int ind = atomic_inc(featureCounter);
+
+ if (ind < c_max_features)
+ {
+ featureX[ind] = px;
+ featureY[ind] = py;
+ featureLaplacian[ind] = maxPos.w;
+ featureOctave[ind] = c_octave;
+ featureSize[ind] = psize;
+ featureHessian[ind] = N9[1][1][1];
+ }
+ } // grad_wav_size check
+ } // If the subpixel interpolation worked
+ }
+ } // If this is thread 0.
+}
+
+////////////////////////////////////////////////////////////////////////
+// Orientation
+
+#define ORI_SEARCH_INC 5
+#define ORI_WIN 60
+#define ORI_SAMPLES 113
+
+__constant float c_aptX[ORI_SAMPLES] = {-6, -5, -5, -5, -5, -5, -5, -5, -4, -4, -4, -4, -4, -4, -4, -4, -4, -3, -3, -3, -3, -3, -3, -3, -3, -3, -3, -3, -2, -2, -2, -2, -2, -2, -2, -2, -2, -2, -2, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 4, 4, 4, 4, 4, 4, 4, 4, 4, 5, 5, 5, 5, 5, 5, 5, 6};
+__constant float c_aptY[ORI_SAMPLES] = {0, -3, -2, -1, 0, 1, 2, 3, -4, -3, -2, -1, 0, 1, 2, 3, 4, -5, -4, -3, -2, -1, 0, 1, 2, 3, 4, 5, -5, -4, -3, -2, -1, 0, 1, 2, 3, 4, 5, -5, -4, -3, -2, -1, 0, 1, 2, 3, 4, 5, -6, -5, -4, -3, -2, -1, 0, 1, 2, 3, 4, 5, 6, -5, -4, -3, -2, -1, 0, 1, 2, 3, 4, 5, -5, -4, -3, -2, -1, 0, 1, 2, 3, 4, 5, -5, -4, -3, -2, -1, 0, 1, 2, 3, 4, 5, -4, -3, -2, -1, 0, 1, 2, 3, 4, -3, -2, -1, 0, 1, 2, 3, 0};
+__constant float c_aptW[ORI_SAMPLES] = {0.001455130288377404f, 0.001707611023448408f, 0.002547456417232752f, 0.003238451667129993f, 0.0035081731621176f,
+ 0.003238451667129993f, 0.002547456417232752f, 0.001707611023448408f, 0.002003900473937392f, 0.0035081731621176f, 0.005233579315245152f,
+ 0.00665318313986063f, 0.00720730796456337f, 0.00665318313986063f, 0.005233579315245152f, 0.0035081731621176f,
+ 0.002003900473937392f, 0.001707611023448408f, 0.0035081731621176f, 0.006141661666333675f, 0.009162282571196556f,
+ 0.01164754293859005f, 0.01261763460934162f, 0.01164754293859005f, 0.009162282571196556f, 0.006141661666333675f,
+ 0.0035081731621176f, 0.001707611023448408f, 0.002547456417232752f, 0.005233579315245152f, 0.009162282571196556f,
+ 0.01366852037608624f, 0.01737609319388866f, 0.0188232995569706f, 0.01737609319388866f, 0.01366852037608624f,
+ 0.009162282571196556f, 0.005233579315245152f, 0.002547456417232752f, 0.003238451667129993f, 0.00665318313986063f,
+ 0.01164754293859005f, 0.01737609319388866f, 0.02208934165537357f, 0.02392910048365593f, 0.02208934165537357f,
+ 0.01737609319388866f, 0.01164754293859005f, 0.00665318313986063f, 0.003238451667129993f, 0.001455130288377404f,
+ 0.0035081731621176f, 0.00720730796456337f, 0.01261763460934162f, 0.0188232995569706f, 0.02392910048365593f,
+ 0.02592208795249462f, 0.02392910048365593f, 0.0188232995569706f, 0.01261763460934162f, 0.00720730796456337f,
+ 0.0035081731621176f, 0.001455130288377404f, 0.003238451667129993f, 0.00665318313986063f, 0.01164754293859005f,
+ 0.01737609319388866f, 0.02208934165537357f, 0.02392910048365593f, 0.02208934165537357f, 0.01737609319388866f,
+ 0.01164754293859005f, 0.00665318313986063f, 0.003238451667129993f, 0.002547456417232752f, 0.005233579315245152f,
+ 0.009162282571196556f, 0.01366852037608624f, 0.01737609319388866f, 0.0188232995569706f, 0.01737609319388866f,
+ 0.01366852037608624f, 0.009162282571196556f, 0.005233579315245152f, 0.002547456417232752f, 0.001707611023448408f,
+ 0.0035081731621176f, 0.006141661666333675f, 0.009162282571196556f, 0.01164754293859005f, 0.01261763460934162f,
+ 0.01164754293859005f, 0.009162282571196556f, 0.006141661666333675f, 0.0035081731621176f, 0.001707611023448408f,
+ 0.002003900473937392f, 0.0035081731621176f, 0.005233579315245152f, 0.00665318313986063f, 0.00720730796456337f,
+ 0.00665318313986063f, 0.005233579315245152f, 0.0035081731621176f, 0.002003900473937392f, 0.001707611023448408f,
+ 0.002547456417232752f, 0.003238451667129993f, 0.0035081731621176f, 0.003238451667129993f, 0.002547456417232752f,
+ 0.001707611023448408f, 0.001455130288377404f};
+
+__constant float c_NX[2][5] = {{0, 0, 2, 4, -1}, {2, 0, 4, 4, 1}};
+__constant float c_NY[2][5] = {{0, 0, 4, 2, 1}, {0, 2, 4, 4, -1}};
+
+void reduce_32_sum(volatile __local float * data, float partial_reduction, int tid)
+{
+#define op(A, B) (A)+(B)
+ data[tid] = partial_reduction;
+ barrier(CLK_LOCAL_MEM_FENCE);
+
+ if (tid < 16)
+ {
+ data[tid] = partial_reduction = op(partial_reduction, data[tid + 16]);
+ data[tid] = partial_reduction = op(partial_reduction, data[tid + 8 ]);
+ data[tid] = partial_reduction = op(partial_reduction, data[tid + 4 ]);
+ data[tid] = partial_reduction = op(partial_reduction, data[tid + 2 ]);
+ data[tid] = partial_reduction = op(partial_reduction, data[tid + 1 ]);
+ }
+#undef op
+}
+
+__kernel
+ void icvCalcOrientation(
+ image2d_t sumTex,
+ __global float * keypoints,
+ int keypoints_step,
+ int c_img_rows,
+ int c_img_cols
+ )
+{
+ keypoints_step /= sizeof(*keypoints);
+ __global float* featureX = keypoints + X_ROW * keypoints_step;
+ __global float* featureY = keypoints + Y_ROW * keypoints_step;
+ __global float* featureSize = keypoints + SIZE_ROW * keypoints_step;
+ __global float* featureDir = keypoints + ANGLE_ROW * keypoints_step;
+
+ volatile __local float s_X[128];
+ volatile __local float s_Y[128];
+ volatile __local float s_angle[128];
+
+ volatile __local float s_sumx[32 * 4];
+ volatile __local float s_sumy[32 * 4];
+
+ /* The sampling intervals and wavelet sized for selecting an orientation
+ and building the keypoint descriptor are defined relative to 's' */
+ const float s = featureSize[get_group_id(0)] * 1.2f / 9.0f;
+
+ /* To find the dominant orientation, the gradients in x and y are
+ sampled in a circle of radius 6s using wavelets of size 4s.
+ We ensure the gradient wavelet size is even to ensure the
+ wavelet pattern is balanced and symmetric around its center */
+ const int grad_wav_size = 2 * convert_int_rte(2.0f * s);
+
+ // check when grad_wav_size is too big
+ if ((c_img_rows + 1) < grad_wav_size || (c_img_cols + 1) < grad_wav_size)
+ return;
+
+ // Calc X, Y, angle and store it to shared memory
+ const int tid = get_local_id(1) * get_local_size(0) + get_local_id(0);
+
+ float X = 0.0f, Y = 0.0f, angle = 0.0f;
+
+ if (tid < ORI_SAMPLES)
+ {
+ const float margin = (float)(grad_wav_size - 1) / 2.0f;
+ const int x = convert_int_rte(featureX[get_group_id(0)] + c_aptX[tid] * s - margin);
+ const int y = convert_int_rte(featureY[get_group_id(0)] + c_aptY[tid] * s - margin);
+
+ if (y >= 0 && y < (c_img_rows + 1) - grad_wav_size &&
+ x >= 0 && x < (c_img_cols + 1) - grad_wav_size)
+ {
+ X = c_aptW[tid] * icvCalcHaarPatternSum_2(sumTex, c_NX, 4, grad_wav_size, y, x);
+ Y = c_aptW[tid] * icvCalcHaarPatternSum_2(sumTex, c_NY, 4, grad_wav_size, y, x);
+
+ angle = atan2(Y, X);
+ if (angle < 0)
+ angle += 2.0f * CV_PI_F;
+ angle *= 180.0f / CV_PI_F;
+ }
+ }
+ s_X[tid] = X;
+ s_Y[tid] = Y;
+ s_angle[tid] = angle;
+ barrier(CLK_LOCAL_MEM_FENCE);
+
+ float bestx = 0, besty = 0, best_mod = 0;
+
+#pragma unroll
+ for (int i = 0; i < 18; ++i)
+ {
+ const int dir = (i * 4 + get_local_id(1)) * ORI_SEARCH_INC;
+
+ float sumx = 0.0f, sumy = 0.0f;
+ int d = abs(convert_int_rte(s_angle[get_local_id(0)]) - dir);
+ if (d < ORI_WIN / 2 || d > 360 - ORI_WIN / 2)
+ {
+ sumx = s_X[get_local_id(0)];
+ sumy = s_Y[get_local_id(0)];
+ }
+ d = abs(convert_int_rte(s_angle[get_local_id(0) + 32]) - dir);
+ if (d < ORI_WIN / 2 || d > 360 - ORI_WIN / 2)
+ {
+ sumx += s_X[get_local_id(0) + 32];
+ sumy += s_Y[get_local_id(0) + 32];
+ }
+ d = abs(convert_int_rte(s_angle[get_local_id(0) + 64]) - dir);
+ if (d < ORI_WIN / 2 || d > 360 - ORI_WIN / 2)
+ {
+ sumx += s_X[get_local_id(0) + 64];
+ sumy += s_Y[get_local_id(0) + 64];
+ }
+ d = abs(convert_int_rte(s_angle[get_local_id(0) + 96]) - dir);
+ if (d < ORI_WIN / 2 || d > 360 - ORI_WIN / 2)
+ {
+ sumx += s_X[get_local_id(0) + 96];
+ sumy += s_Y[get_local_id(0) + 96];
+ }
+
+ reduce_32_sum(s_sumx + get_local_id(1) * 32, sumx, get_local_id(0));
+ reduce_32_sum(s_sumy + get_local_id(1) * 32, sumy, get_local_id(0));
+
+ const float temp_mod = sumx * sumx + sumy * sumy;
+ if (temp_mod > best_mod)
+ {
+ best_mod = temp_mod;
+ bestx = sumx;
+ besty = sumy;
+ }
+
+ barrier(CLK_LOCAL_MEM_FENCE);
+ }
+
+ if (get_local_id(0) == 0)
+ {
+ s_X[get_local_id(1)] = bestx;
+ s_Y[get_local_id(1)] = besty;
+ s_angle[get_local_id(1)] = best_mod;
+ }
+ barrier(CLK_LOCAL_MEM_FENCE);
+
+ if (get_local_id(1) == 0 && get_local_id(0) == 0)
+ {
+ int bestIdx = 0;
+
+ if (s_angle[1] > s_angle[bestIdx])
+ bestIdx = 1;
+ if (s_angle[2] > s_angle[bestIdx])
+ bestIdx = 2;
+ if (s_angle[3] > s_angle[bestIdx])
+ bestIdx = 3;
+
+ float kp_dir = atan2(s_Y[bestIdx], s_X[bestIdx]);
+ if (kp_dir < 0)
+ kp_dir += 2.0f * CV_PI_F;
+ kp_dir *= 180.0f / CV_PI_F;
+
+ featureDir[get_group_id(0)] = kp_dir;
+ }
+}
+
+#undef ORI_SEARCH_INC
+#undef ORI_WIN
+#undef ORI_SAMPLES
+
+////////////////////////////////////////////////////////////////////////
+// Descriptors
+
+#define PATCH_SZ 20
+
+__constant float c_DW[PATCH_SZ * PATCH_SZ] =
+{
+ 3.695352233989979e-006f, 8.444558261544444e-006f, 1.760426494001877e-005f, 3.34794785885606e-005f, 5.808438800158911e-005f, 9.193058212986216e-005f, 0.0001327334757661447f, 0.0001748319627949968f, 0.0002100782439811155f, 0.0002302826324012131f, 0.0002302826324012131f, 0.0002100782439811155f, 0.0001748319627949968f, 0.0001327334757661447f, 9.193058212986216e-005f, 5.808438800158911e-005f, 3.34794785885606e-005f, 1.760426494001877e-005f, 8.444558261544444e-006f, 3.695352233989979e-006f,
+ 8.444558261544444e-006f, 1.929736572492402e-005f, 4.022897701361217e-005f, 7.650675252079964e-005f, 0.0001327334903180599f, 0.0002100782585330308f, 0.0003033203829545528f, 0.0003995231236331165f, 0.0004800673632416874f, 0.0005262381164357066f, 0.0005262381164357066f, 0.0004800673632416874f, 0.0003995231236331165f, 0.0003033203829545528f, 0.0002100782585330308f, 0.0001327334903180599f, 7.650675252079964e-005f, 4.022897701361217e-005f, 1.929736572492402e-005f, 8.444558261544444e-006f,
+ 1.760426494001877e-005f, 4.022897701361217e-005f, 8.386484114453197e-005f, 0.0001594926579855382f, 0.0002767078403849155f, 0.0004379475140012801f, 0.0006323281559161842f, 0.0008328808471560478f, 0.001000790391117334f, 0.001097041997127235f, 0.001097041997127235f, 0.001000790391117334f, 0.0008328808471560478f, 0.0006323281559161842f, 0.0004379475140012801f, 0.0002767078403849155f, 0.0001594926579855382f, 8.386484114453197e-005f, 4.022897701361217e-005f, 1.760426494001877e-005f,
+ 3.34794785885606e-005f, 7.650675252079964e-005f, 0.0001594926579855382f, 0.0003033203247468919f, 0.0005262380582280457f, 0.0008328807889483869f, 0.001202550483867526f, 0.001583957928232849f, 0.001903285388834775f, 0.002086334861814976f, 0.002086334861814976f, 0.001903285388834775f, 0.001583957928232849f, 0.001202550483867526f, 0.0008328807889483869f, 0.0005262380582280457f, 0.0003033203247468919f, 0.0001594926579855382f, 7.650675252079964e-005f, 3.34794785885606e-005f,
+ 5.808438800158911e-005f, 0.0001327334903180599f, 0.0002767078403849155f, 0.0005262380582280457f, 0.0009129836107604206f, 0.001444985857233405f, 0.002086335094645619f, 0.002748048631474376f, 0.00330205773934722f, 0.003619635012000799f, 0.003619635012000799f, 0.00330205773934722f, 0.002748048631474376f, 0.002086335094645619f, 0.001444985857233405f, 0.0009129836107604206f, 0.0005262380582280457f, 0.0002767078403849155f, 0.0001327334903180599f, 5.808438800158911e-005f,
+ 9.193058212986216e-005f, 0.0002100782585330308f, 0.0004379475140012801f, 0.0008328807889483869f, 0.001444985857233405f, 0.002286989474669099f, 0.00330205773934722f, 0.004349356517195702f, 0.00522619066759944f, 0.005728822201490402f, 0.005728822201490402f, 0.00522619066759944f, 0.004349356517195702f, 0.00330205773934722f, 0.002286989474669099f, 0.001444985857233405f, 0.0008328807889483869f, 0.0004379475140012801f, 0.0002100782585330308f, 9.193058212986216e-005f,
+ 0.0001327334757661447f, 0.0003033203829545528f, 0.0006323281559161842f, 0.001202550483867526f, 0.002086335094645619f, 0.00330205773934722f, 0.004767658654600382f, 0.006279794964939356f, 0.007545807864516974f, 0.008271530270576477f, 0.008271530270576477f, 0.007545807864516974f, 0.006279794964939356f, 0.004767658654600382f, 0.00330205773934722f, 0.002086335094645619f, 0.001202550483867526f, 0.0006323281559161842f, 0.0003033203829545528f, 0.0001327334757661447f,
+ 0.0001748319627949968f, 0.0003995231236331165f, 0.0008328808471560478f, 0.001583957928232849f, 0.002748048631474376f, 0.004349356517195702f, 0.006279794964939356f, 0.008271529339253902f, 0.009939077310264111f, 0.01089497376233339f, 0.01089497376233339f, 0.009939077310264111f, 0.008271529339253902f, 0.006279794964939356f, 0.004349356517195702f, 0.002748048631474376f, 0.001583957928232849f, 0.0008328808471560478f, 0.0003995231236331165f, 0.0001748319627949968f,
+ 0.0002100782439811155f, 0.0004800673632416874f, 0.001000790391117334f, 0.001903285388834775f, 0.00330205773934722f, 0.00522619066759944f, 0.007545807864516974f, 0.009939077310264111f, 0.01194280479103327f, 0.01309141051024199f, 0.01309141051024199f, 0.01194280479103327f, 0.009939077310264111f, 0.007545807864516974f, 0.00522619066759944f, 0.00330205773934722f, 0.001903285388834775f, 0.001000790391117334f, 0.0004800673632416874f, 0.0002100782439811155f,
+ 0.0002302826324012131f, 0.0005262381164357066f, 0.001097041997127235f, 0.002086334861814976f, 0.003619635012000799f, 0.005728822201490402f, 0.008271530270576477f, 0.01089497376233339f, 0.01309141051024199f, 0.01435048412531614f, 0.01435048412531614f, 0.01309141051024199f, 0.01089497376233339f, 0.008271530270576477f, 0.005728822201490402f, 0.003619635012000799f, 0.002086334861814976f, 0.001097041997127235f, 0.0005262381164357066f, 0.0002302826324012131f,
+ 0.0002302826324012131f, 0.0005262381164357066f, 0.001097041997127235f, 0.002086334861814976f, 0.003619635012000799f, 0.005728822201490402f, 0.008271530270576477f, 0.01089497376233339f, 0.01309141051024199f, 0.01435048412531614f, 0.01435048412531614f, 0.01309141051024199f, 0.01089497376233339f, 0.008271530270576477f, 0.005728822201490402f, 0.003619635012000799f, 0.002086334861814976f, 0.001097041997127235f, 0.0005262381164357066f, 0.0002302826324012131f,
+ 0.0002100782439811155f, 0.0004800673632416874f, 0.001000790391117334f, 0.001903285388834775f, 0.00330205773934722f, 0.00522619066759944f, 0.007545807864516974f, 0.009939077310264111f, 0.01194280479103327f, 0.01309141051024199f, 0.01309141051024199f, 0.01194280479103327f, 0.009939077310264111f, 0.007545807864516974f, 0.00522619066759944f, 0.00330205773934722f, 0.001903285388834775f, 0.001000790391117334f, 0.0004800673632416874f, 0.0002100782439811155f,
+ 0.0001748319627949968f, 0.0003995231236331165f, 0.0008328808471560478f, 0.001583957928232849f, 0.002748048631474376f, 0.004349356517195702f, 0.006279794964939356f, 0.008271529339253902f, 0.009939077310264111f, 0.01089497376233339f, 0.01089497376233339f, 0.009939077310264111f, 0.008271529339253902f, 0.006279794964939356f, 0.004349356517195702f, 0.002748048631474376f, 0.001583957928232849f, 0.0008328808471560478f, 0.0003995231236331165f, 0.0001748319627949968f,
+ 0.0001327334757661447f, 0.0003033203829545528f, 0.0006323281559161842f, 0.001202550483867526f, 0.002086335094645619f, 0.00330205773934722f, 0.004767658654600382f, 0.006279794964939356f, 0.007545807864516974f, 0.008271530270576477f, 0.008271530270576477f, 0.007545807864516974f, 0.006279794964939356f, 0.004767658654600382f, 0.00330205773934722f, 0.002086335094645619f, 0.001202550483867526f, 0.0006323281559161842f, 0.0003033203829545528f, 0.0001327334757661447f,
+ 9.193058212986216e-005f, 0.0002100782585330308f, 0.0004379475140012801f, 0.0008328807889483869f, 0.001444985857233405f, 0.002286989474669099f, 0.00330205773934722f, 0.004349356517195702f, 0.00522619066759944f, 0.005728822201490402f, 0.005728822201490402f, 0.00522619066759944f, 0.004349356517195702f, 0.00330205773934722f, 0.002286989474669099f, 0.001444985857233405f, 0.0008328807889483869f, 0.0004379475140012801f, 0.0002100782585330308f, 9.193058212986216e-005f,
+ 5.808438800158911e-005f, 0.0001327334903180599f, 0.0002767078403849155f, 0.0005262380582280457f, 0.0009129836107604206f, 0.001444985857233405f, 0.002086335094645619f, 0.002748048631474376f, 0.00330205773934722f, 0.003619635012000799f, 0.003619635012000799f, 0.00330205773934722f, 0.002748048631474376f, 0.002086335094645619f, 0.001444985857233405f, 0.0009129836107604206f, 0.0005262380582280457f, 0.0002767078403849155f, 0.0001327334903180599f, 5.808438800158911e-005f,
+ 3.34794785885606e-005f, 7.650675252079964e-005f, 0.0001594926579855382f, 0.0003033203247468919f, 0.0005262380582280457f, 0.0008328807889483869f, 0.001202550483867526f, 0.001583957928232849f, 0.001903285388834775f, 0.002086334861814976f, 0.002086334861814976f, 0.001903285388834775f, 0.001583957928232849f, 0.001202550483867526f, 0.0008328807889483869f, 0.0005262380582280457f, 0.0003033203247468919f, 0.0001594926579855382f, 7.650675252079964e-005f, 3.34794785885606e-005f,
+ 1.760426494001877e-005f, 4.022897701361217e-005f, 8.386484114453197e-005f, 0.0001594926579855382f, 0.0002767078403849155f, 0.0004379475140012801f, 0.0006323281559161842f, 0.0008328808471560478f, 0.001000790391117334f, 0.001097041997127235f, 0.001097041997127235f, 0.001000790391117334f, 0.0008328808471560478f, 0.0006323281559161842f, 0.0004379475140012801f, 0.0002767078403849155f, 0.0001594926579855382f, 8.386484114453197e-005f, 4.022897701361217e-005f, 1.760426494001877e-005f,
+ 8.444558261544444e-006f, 1.929736572492402e-005f, 4.022897701361217e-005f, 7.650675252079964e-005f, 0.0001327334903180599f, 0.0002100782585330308f, 0.0003033203829545528f, 0.0003995231236331165f, 0.0004800673632416874f, 0.0005262381164357066f, 0.0005262381164357066f, 0.0004800673632416874f, 0.0003995231236331165f, 0.0003033203829545528f, 0.0002100782585330308f, 0.0001327334903180599f, 7.650675252079964e-005f, 4.022897701361217e-005f, 1.929736572492402e-005f, 8.444558261544444e-006f,
+ 3.695352233989979e-006f, 8.444558261544444e-006f, 1.760426494001877e-005f, 3.34794785885606e-005f, 5.808438800158911e-005f, 9.193058212986216e-005f, 0.0001327334757661447f, 0.0001748319627949968f, 0.0002100782439811155f, 0.0002302826324012131f, 0.0002302826324012131f, 0.0002100782439811155f, 0.0001748319627949968f, 0.0001327334757661447f, 9.193058212986216e-005f, 5.808438800158911e-005f, 3.34794785885606e-005f, 1.760426494001877e-005f, 8.444558261544444e-006f, 3.695352233989979e-006f
+};
+
+// utility for linear filter
+inline uchar readerGet(
+ image2d_t src,
+ const float centerX, const float centerY, const float win_offset, const float cos_dir, const float sin_dir,
+ int i, int j
+ )
+{
+ float pixel_x = centerX + (win_offset + j) * cos_dir + (win_offset + i) * sin_dir;
+ float pixel_y = centerY - (win_offset + j) * sin_dir + (win_offset + i) * cos_dir;
+ return (uchar)read_imageui(src, sampler, (float2)(pixel_x, pixel_y)).x;
+}
+
+inline float linearFilter(
+ image2d_t src,
+ const float centerX, const float centerY, const float win_offset, const float cos_dir, const float sin_dir,
+ float y, float x
+ )
+{
+ x -= 0.5f;
+ y -= 0.5f;
+
+ float out = 0.0f;
+
+ const int x1 = convert_int_rtn(x);
+ const int y1 = convert_int_rtn(y);
+ const int x2 = x1 + 1;
+ const int y2 = y1 + 1;
+
+ uchar src_reg = readerGet(src, centerX, centerY, win_offset, cos_dir, sin_dir, y1, x1);
+ out = out + src_reg * ((x2 - x) * (y2 - y));
+
+ src_reg = readerGet(src, centerX, centerY, win_offset, cos_dir, sin_dir, y1, x2);
+ out = out + src_reg * ((x - x1) * (y2 - y));
+
+ src_reg = readerGet(src, centerX, centerY, win_offset, cos_dir, sin_dir, y2, x1);
+ out = out + src_reg * ((x2 - x) * (y - y1));
+
+ src_reg = readerGet(src, centerX, centerY, win_offset, cos_dir, sin_dir, y2, x2);
+ out = out + src_reg * ((x - x1) * (y - y1));
+
+ return out;
+}
+
+void calc_dx_dy(
+ image2d_t imgTex,
+ volatile __local float s_dx_bin[25],
+ volatile __local float s_dy_bin[25],
+ volatile __local float s_PATCH[6][6],
+ __global const float* featureX,
+ __global const float* featureY,
+ __global const float* featureSize,
+ __global const float* featureDir
+ )
+{
+ const float centerX = featureX[get_group_id(0)];
+ const float centerY = featureY[get_group_id(0)];
+ const float size = featureSize[get_group_id(0)];
+ const float descriptor_dir = featureDir[get_group_id(0)] * (float)(CV_PI_F / 180.0f);
+
+ /* The sampling intervals and wavelet sized for selecting an orientation
+ and building the keypoint descriptor are defined relative to 's' */
+ const float s = size * 1.2f / 9.0f;
+
+ /* Extract a window of pixels around the keypoint of size 20s */
+ const int win_size = (int)((PATCH_SZ + 1) * s);
+
+ float sin_dir;
+ float cos_dir;
+ sin_dir = sincos(descriptor_dir, &cos_dir);
+
+ /* Nearest neighbour version (faster) */
+ const float win_offset = -(float)(win_size - 1) / 2;
+
+ // Compute sampling points
+ // since grids are 2D, need to compute xBlock and yBlock indices
+ const int xBlock = (get_group_id(1) & 3); // get_group_id(1) % 4
+ const int yBlock = (get_group_id(1) >> 2); // floor(get_group_id(1)/4)
+ const int xIndex = xBlock * 5 + get_local_id(0);
+ const int yIndex = yBlock * 5 + get_local_id(1);
+
+ const float icoo = ((float)yIndex / (PATCH_SZ + 1)) * win_size;
+ const float jcoo = ((float)xIndex / (PATCH_SZ + 1)) * win_size;
+
+ s_PATCH[get_local_id(1)][get_local_id(0)] = linearFilter(imgTex, centerX, centerY, win_offset, cos_dir, sin_dir, icoo, jcoo);
+
+ barrier(CLK_LOCAL_MEM_FENCE);
+
+ if (get_local_id(0) < 5 && get_local_id(1) < 5)
+ {
+ const int tid = get_local_id(1) * 5 + get_local_id(0);
+
+ const float dw = c_DW[yIndex * PATCH_SZ + xIndex];
+
+ const float vx = (
+ s_PATCH[get_local_id(1) ][get_local_id(0) + 1] -
+ s_PATCH[get_local_id(1) ][get_local_id(0) ] +
+ s_PATCH[get_local_id(1) + 1][get_local_id(0) + 1] -
+ s_PATCH[get_local_id(1) + 1][get_local_id(0) ])
+ * dw;
+ const float vy = (
+ s_PATCH[get_local_id(1) + 1][get_local_id(0) ] -
+ s_PATCH[get_local_id(1) ][get_local_id(0) ] +
+ s_PATCH[get_local_id(1) + 1][get_local_id(0) + 1] -
+ s_PATCH[get_local_id(1) ][get_local_id(0) + 1])
+ * dw;
+ s_dx_bin[tid] = vx;
+ s_dy_bin[tid] = vy;
+ }
+}
+void reduce_sum25(
+ volatile __local float* sdata1,
+ volatile __local float* sdata2,
+ volatile __local float* sdata3,
+ volatile __local float* sdata4,
+ int tid
+ )
+{
+ // first step is to reduce from 25 to 16
+ if (tid < 9) // use 9 threads
+ {
+ sdata1[tid] += sdata1[tid + 16];
+ sdata2[tid] += sdata2[tid + 16];
+ sdata3[tid] += sdata3[tid + 16];
+ sdata4[tid] += sdata4[tid + 16];
+ }
+
+ // sum (reduce) from 16 to 1 (unrolled - aligned to a half-warp)
+ if (tid < 8)
+ {
+ sdata1[tid] += sdata1[tid + 8];
+ sdata1[tid] += sdata1[tid + 4];
+ sdata1[tid] += sdata1[tid + 2];
+ sdata1[tid] += sdata1[tid + 1];
+
+ sdata2[tid] += sdata2[tid + 8];
+ sdata2[tid] += sdata2[tid + 4];
+ sdata2[tid] += sdata2[tid + 2];
+ sdata2[tid] += sdata2[tid + 1];
+
+ sdata3[tid] += sdata3[tid + 8];
+ sdata3[tid] += sdata3[tid + 4];
+ sdata3[tid] += sdata3[tid + 2];
+ sdata3[tid] += sdata3[tid + 1];
+
+ sdata4[tid] += sdata4[tid + 8];
+ sdata4[tid] += sdata4[tid + 4];
+ sdata4[tid] += sdata4[tid + 2];
+ sdata4[tid] += sdata4[tid + 1];
+ }
+}
+
+__kernel
+ void compute_descriptors64(
+ image2d_t imgTex,
+ volatile __global float * descriptors,
+ __global const float * keypoints,
+ int descriptors_step,
+ int keypoints_step
+ )
+{
+ descriptors_step /= sizeof(float);
+ keypoints_step /= sizeof(float);
+
+ __global const float * featureX = keypoints + X_ROW * keypoints_step;
+ __global const float * featureY = keypoints + Y_ROW * keypoints_step;
+ __global const float * featureSize = keypoints + SIZE_ROW * keypoints_step;
+ __global const float * featureDir = keypoints + ANGLE_ROW * keypoints_step;
+
+ // 2 floats (dx,dy) for each thread (5x5 sample points in each sub-region)
+ volatile __local float sdx[25];
+ volatile __local float sdy[25];
+ volatile __local float sdxabs[25];
+ volatile __local float sdyabs[25];
+ volatile __local float s_PATCH[6][6];
+
+ calc_dx_dy(imgTex, sdx, sdy, s_PATCH, featureX, featureY, featureSize, featureDir);
+ barrier(CLK_LOCAL_MEM_FENCE);
+
+ const int tid = get_local_id(1) * get_local_size(0) + get_local_id(0);
+
+ if (tid < 25)
+ {
+ sdxabs[tid] = fabs(sdx[tid]); // |dx| array
+ sdyabs[tid] = fabs(sdy[tid]); // |dy| array
+ barrier(CLK_LOCAL_MEM_FENCE);
+
+ reduce_sum25(sdx, sdy, sdxabs, sdyabs, tid);
+ barrier(CLK_LOCAL_MEM_FENCE);
+
+ volatile __global float* descriptors_block = descriptors + descriptors_step * get_group_id(0) + (get_group_id(1) << 2);
+
+ // write dx, dy, |dx|, |dy|
+ if (tid == 0)
+ {
+ descriptors_block[0] = sdx[0];
+ descriptors_block[1] = sdy[0];
+ descriptors_block[2] = sdxabs[0];
+ descriptors_block[3] = sdyabs[0];
+ }
+ }
+}
+__kernel
+ void compute_descriptors128(
+ image2d_t imgTex,
+ __global volatile float * descriptors,
+ __global float * keypoints,
+ int descriptors_step,
+ int keypoints_step
+ )
+{
+ descriptors_step /= sizeof(*descriptors);
+ keypoints_step /= sizeof(*keypoints);
+
+ __global float * featureX = keypoints + X_ROW * keypoints_step;
+ __global float * featureY = keypoints + Y_ROW * keypoints_step;
+ __global float* featureSize = keypoints + SIZE_ROW * keypoints_step;
+ __global float* featureDir = keypoints + ANGLE_ROW * keypoints_step;
+
+ // 2 floats (dx,dy) for each thread (5x5 sample points in each sub-region)
+ volatile __local float sdx[25];
+ volatile __local float sdy[25];
+
+ // sum (reduce) 5x5 area response
+ volatile __local float sd1[25];
+ volatile __local float sd2[25];
+ volatile __local float sdabs1[25];
+ volatile __local float sdabs2[25];
+ volatile __local float s_PATCH[6][6];
+
+ calc_dx_dy(imgTex, sdx, sdy, s_PATCH, featureX, featureY, featureSize, featureDir);
+ barrier(CLK_LOCAL_MEM_FENCE);
+
+ const int tid = get_local_id(1) * get_local_size(0) + get_local_id(0);
+
+ if (tid < 25)
+ {
+ if (sdy[tid] >= 0)
+ {
+ sd1[tid] = sdx[tid];
+ sdabs1[tid] = fabs(sdx[tid]);
+ sd2[tid] = 0;
+ sdabs2[tid] = 0;
+ }
+ else
+ {
+ sd1[tid] = 0;
+ sdabs1[tid] = 0;
+ sd2[tid] = sdx[tid];
+ sdabs2[tid] = fabs(sdx[tid]);
+ }
+ barrier(CLK_LOCAL_MEM_FENCE);
+
+ reduce_sum25(sd1, sd2, sdabs1, sdabs2, tid);
+ barrier(CLK_LOCAL_MEM_FENCE);
+
+ volatile __global float* descriptors_block = descriptors + descriptors_step * get_group_id(0) + (get_group_id(1) << 3);
+
+ // write dx (dy >= 0), |dx| (dy >= 0), dx (dy < 0), |dx| (dy < 0)
+ if (tid == 0)
+ {
+ descriptors_block[0] = sd1[0];
+ descriptors_block[1] = sdabs1[0];
+ descriptors_block[2] = sd2[0];
+ descriptors_block[3] = sdabs2[0];
+ }
+
+ if (sdx[tid] >= 0)
+ {
+ sd1[tid] = sdy[tid];
+ sdabs1[tid] = fabs(sdy[tid]);
+ sd2[tid] = 0;
+ sdabs2[tid] = 0;
+ }
+ else
+ {
+ sd1[tid] = 0;
+ sdabs1[tid] = 0;
+ sd2[tid] = sdy[tid];
+ sdabs2[tid] = fabs(sdy[tid]);
+ }
+ barrier(CLK_LOCAL_MEM_FENCE);
+
+ reduce_sum25(sd1, sd2, sdabs1, sdabs2, tid);
+ barrier(CLK_LOCAL_MEM_FENCE);
+
+ // write dy (dx >= 0), |dy| (dx >= 0), dy (dx < 0), |dy| (dx < 0)
+ if (tid == 0)
+ {
+ descriptors_block[4] = sd1[0];
+ descriptors_block[5] = sdabs1[0];
+ descriptors_block[6] = sd2[0];
+ descriptors_block[7] = sdabs2[0];
+ }
+ }
+}
+
+__kernel
+ void normalize_descriptors128(__global float * descriptors, int descriptors_step)
+{
+ descriptors_step /= sizeof(*descriptors);
+ // no need for thread ID
+ __global float* descriptor_base = descriptors + descriptors_step * get_group_id(0);
+
+ // read in the unnormalized descriptor values (squared)
+ volatile __local float sqDesc[128];
+ const float lookup = descriptor_base[get_local_id(0)];
+ sqDesc[get_local_id(0)] = lookup * lookup;
+ barrier(CLK_LOCAL_MEM_FENCE);
+
+ if (get_local_id(0) < 64)
+ sqDesc[get_local_id(0)] += sqDesc[get_local_id(0) + 64];
+ barrier(CLK_LOCAL_MEM_FENCE);
+
+ // reduction to get total
+ if (get_local_id(0) < 32)
+ {
+ volatile __local float* smem = sqDesc;
+
+ smem[get_local_id(0)] += smem[get_local_id(0) + 32];
+ smem[get_local_id(0)] += smem[get_local_id(0) + 16];
+ smem[get_local_id(0)] += smem[get_local_id(0) + 8];
+ smem[get_local_id(0)] += smem[get_local_id(0) + 4];
+ smem[get_local_id(0)] += smem[get_local_id(0) + 2];
+ smem[get_local_id(0)] += smem[get_local_id(0) + 1];
+ }
+
+ // compute length (square root)
+ volatile __local float len;
+ if (get_local_id(0) == 0)
+ {
+ len = sqrt(sqDesc[0]);
+ }
+ barrier(CLK_LOCAL_MEM_FENCE);
+
+ // normalize and store in output
+ descriptor_base[get_local_id(0)] = lookup / len;
+}
+__kernel
+ void normalize_descriptors64(__global float * descriptors, int descriptors_step)
+{
+ descriptors_step /= sizeof(*descriptors);
+ // no need for thread ID
+ __global float* descriptor_base = descriptors + descriptors_step * get_group_id(0);
+
+ // read in the unnormalized descriptor values (squared)
+ volatile __local float sqDesc[64];
+ const float lookup = descriptor_base[get_local_id(0)];
+ sqDesc[get_local_id(0)] = lookup * lookup;
+ barrier(CLK_LOCAL_MEM_FENCE);
+
+ // reduction to get total
+ if (get_local_id(0) < 32)
+ {
+ volatile __local float* smem = sqDesc;
+
+ smem[get_local_id(0)] += smem[get_local_id(0) + 32];
+ smem[get_local_id(0)] += smem[get_local_id(0) + 16];
+ smem[get_local_id(0)] += smem[get_local_id(0) + 8];
+ smem[get_local_id(0)] += smem[get_local_id(0) + 4];
+ smem[get_local_id(0)] += smem[get_local_id(0) + 2];
+ smem[get_local_id(0)] += smem[get_local_id(0) + 1];
+ }
+
+ // compute length (square root)
+ volatile __local float len;
+ if (get_local_id(0) == 0)
+ {
+ len = sqrt(sqDesc[0]);
+ }
+ barrier(CLK_LOCAL_MEM_FENCE);
+
+ // normalize and store in output
+ descriptor_base[get_local_id(0)] = lookup / len;
+}
projects / profile / ivi / opencv.git / commitdiff