/*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, int, double, double, bool, 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::detect(const GpuMat&, vector&, double, Size, Size) { throw_nogpu(); } void cv::gpu::HOGDescriptor::detectMultiScale(const GpuMat&, vector&, double, Size, Size, double, int) { throw_nogpu(); } void cv::gpu::HOGDescriptor::computeBlockHistograms(const GpuMat&) { throw_nogpu(); } void cv::gpu::HOGDescriptor::getDescriptors(const GpuMat&, Size, GpuMat&, int) { throw_nogpu(); } std::vector cv::gpu::HOGDescriptor::getDefaultPeopleDetector() { throw_nogpu(); return std::vector(); } std::vector cv::gpu::HOGDescriptor::getPeopleDetector48x96() { throw_nogpu(); return std::vector(); } std::vector cv::gpu::HOGDescriptor::getPeopleDetector64x128() { 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_y, int block_stride_x, int win_stride_y, int win_stride_x, int height, int width, float* block_hists, float* coefs, float free_coef, float threshold, unsigned char* 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, float* block_hists, cv::gpu::DevMem2Df 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, float* block_hists, cv::gpu::DevMem2Df descriptors); 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, bool correct_gamma); 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, bool correct_gamma); 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, 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::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, gamma_correction); break; case CV_8UC4: hog::compute_gradients_8UC4(nbins, img.rows, img.cols, img, angleScale, grad, qangle, gamma_correction); break; } } void cv::gpu::HOGDescriptor::computeBlockHistograms(const GpuMat& 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, 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); } void cv::gpu::HOGDescriptor::getDescriptors(const GpuMat& img, Size win_stride, GpuMat& descriptors, int descr_format) { CV_Assert(win_stride.width % block_stride.width == 0 && win_stride.height % block_stride.height == 0); computeBlockHistograms(img); const int 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(), 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.ptr(), 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.ptr(), descriptors); break; default: CV_Error(CV_StsBadArg, "Unknown descriptor format"); } } void cv::gpu::HOGDescriptor::detect(const GpuMat& img, vector& hits, double hit_threshold, Size win_stride, Size padding) { 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.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(cvRound(img.cols / scale), cvRound(img.rows / 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(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::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() { return getPeopleDetector64x128(); } std::vector cv::gpu::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, 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cv::gpu::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, 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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