/*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) 2000-2008, Intel Corporation, all rights reserved. // Copyright (C) 2009, Willow Garage Inc., all rights reserved. // Third party copyrights are property of their respective owners. // // 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 GpuMaterials 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" #if !defined (HAVE_CUDA) cv::gpu::HOGDescriptor::HOGDescriptor(Size, Size, Size, Size, bool, int, double, double, int) { throw_nogpu(); } size_t cv::gpu::HOGDescriptor::getDescriptorSize() const { throw_nogpu(); return 0; } size_t cv::gpu::HOGDescriptor::getBlockHistogramSize() const { throw_nogpu(); return 0; } double cv::gpu::HOGDescriptor::getWinSigma() const { throw_nogpu(); return 0; } bool cv::gpu::HOGDescriptor::checkDetectorSize() const { throw_nogpu(); return false; } void cv::gpu::HOGDescriptor::setSVMDetector(const vector&) { throw_nogpu(); } void cv::gpu::HOGDescriptor::computeGradient(const GpuMat&, GpuMat&, GpuMat&) { throw_nogpu(); } void cv::gpu::HOGDescriptor::detect(const GpuMat&, vector&, double, Size, Size) { throw_nogpu(); } void cv::gpu::HOGDescriptor::detectMultiScale(const GpuMat&, vector&, double, Size, Size, double, int) { throw_nogpu(); } int cv::gpu::HOGDescriptor::numPartsWithin(int, int, int) { throw_nogpu(); return 0; } cv::Size cv::gpu::HOGDescriptor::numPartsWithin(cv::Size, cv::Size, cv::Size) { throw_nogpu(); return cv::Size(); } std::vector cv::gpu::HOGDescriptor::getDefaultPeopleDetector() { throw_nogpu(); return std::vector(); } #else namespace cv { namespace gpu { namespace hog { 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::gpu::DevMem2Df& grad, const cv::gpu::DevMem2D& qangle, float sigma, float* block_hists); void normalize_hists(int nbins, int block_stride_x, int block_stride_y, int height, int width, float* block_hists, float threshold); void classify_hists(int win_height, int win_width, int block_stride_x, int block_stride_y, int height, int width, float* block_hists, float* coefs, float free_coef, float threshold, unsigned char* labels); void compute_gradients_8UC1(int nbins, int height, int width, const cv::gpu::DevMem2D& img, float angle_scale, cv::gpu::DevMem2Df grad, cv::gpu::DevMem2D qangle); void compute_gradients_8UC4(int nbins, int height, int width, const cv::gpu::DevMem2D& img, float angle_scale, cv::gpu::DevMem2Df grad, cv::gpu::DevMem2D qangle); void resize_8UC1(const cv::gpu::DevMem2D& src, cv::gpu::DevMem2D dst); void resize_8UC4(const cv::gpu::DevMem2D& src, cv::gpu::DevMem2D dst); }}} cv::gpu::HOGDescriptor::HOGDescriptor(Size win_size, Size block_size, Size block_stride, Size cell_size, bool gamma_correction, int nbins, double win_sigma, double threshold_L2hys, int nlevels) : win_size(win_size), block_size(block_size), block_stride(block_stride), cell_size(cell_size), gamma_correction(gamma_correction), nbins(nbins), win_sigma(win_sigma), threshold_L2hys(threshold_L2hys), 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)); CV_Assert(gamma_correction == true); 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::gpu::HOGDescriptor::getDescriptorSize() const { return numPartsWithin(win_size, block_size, block_stride).area() * getBlockHistogramSize(); } size_t cv::gpu::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::gpu::HOGDescriptor::getWinSigma() const { return win_sigma >= 0 ? win_sigma : (block_size.width + block_size.height) / 8.0; } bool cv::gpu::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::gpu::HOGDescriptor::setSVMDetector(const vector& detector) { std::vector 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::gpu::HOGDescriptor::computeGradient(const GpuMat& img, GpuMat& grad, GpuMat& 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(nbins, img.rows, img.cols, img, angleScale, grad, qangle); break; case CV_8UC4: hog::compute_gradients_8UC4(nbins, img.rows, img.cols, img, angleScale, grad, qangle); break; } } void cv::gpu::HOGDescriptor::detect(const GpuMat& img, vector& hits, double hit_threshold, Size win_stride, Size padding) { hits.clear(); if (detector.empty()) return; GpuMat grad, qangle; computeGradient(img, grad, qangle); if (win_stride == Size()) win_stride = block_stride; else CV_Assert(win_stride == block_stride); CV_Assert(padding == Size(0, 0)); 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); Size blocks_per_img = numPartsWithin(img.size(), block_size, block_stride); labels.create(1, wins_per_img.area(), CV_8U); block_hists.create(1, 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.ptr()); hog::normalize_hists(nbins, block_stride.width, block_stride.height, img.rows, img.cols, block_hists.ptr(), (float)threshold_L2hys); hog::classify_hists(win_size.height, win_size.width, block_stride.height, block_stride.width, img.rows, img.cols, block_hists.ptr(), detector.ptr(), (float)free_coef, (float)hit_threshold, labels.ptr()); 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::gpu::HOGDescriptor::detectMultiScale(const GpuMat& img, vector& 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 level_scale; double scale = 1.; int levels = 0; for (levels = 0; levels < nlevels; levels++) { level_scale.push_back(scale); if (cvRound(img.cols/scale) < win_size.width || cvRound(img.rows/scale) < win_size.height || scale0 <= 1) break; scale *= scale0; } levels = std::max(levels, 1); level_scale.resize(levels); std::vector all_candidates; vector locations; for (size_t i = 0; i < level_scale.size(); i++) { double scale = level_scale[i]; Size sz = Size_(img.size()) * (1.0/scale); GpuMat smaller_img; if (sz == img.size()) smaller_img = img; else { smaller_img.create(sz, img.type()); switch (img.type()) { case CV_8UC1: hog::resize_8UC1(img, smaller_img); break; case CV_8UC4: hog::resize_8UC4(img, smaller_img); break; } } detect(smaller_img, locations, hit_threshold, win_stride, padding); Size scaled_win_size = Size_(win_size) * scale; for (size_t j = 0; j < locations.size(); j++) all_candidates.push_back(Rect(Point2d(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::gpu::HOGDescriptor::numPartsWithin(int size, int part_size, int stride) { return (size - part_size + stride) / stride; } cv::Size cv::gpu::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 cv::gpu::HOGDescriptor::getDefaultPeopleDetector() { 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, 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-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(detector, detector + sizeof(detector)/sizeof(detector[0])); } #endif