/*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) 2008-2012, 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 materials 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*/ #include #include #if defined VISUALIZE_GENERATION # define show(a, b) \ do { \ cv::imshow(a,b); \ cv::waitkey(0); \ } while(0) #else # define show(a, b) #endif #include #include #include // ============ Octave ============ // sft::Octave::Octave(int np, int nn, int ls, int shr) : logScale(ls), npositives(np), nnegatives(nn), shrinkage(shr) { int maxSample = npositives + nnegatives; responses.create(maxSample, 1, CV_32FC1); } sft::Octave::~Octave(){} bool sft::Octave::train( const cv::Mat& trainData, const cv::Mat& _responses, const cv::Mat& varIdx, const cv::Mat& sampleIdx, const cv::Mat& varType, const cv::Mat& missingDataMask) { bool update = false; return cv::Boost::train(trainData, CV_COL_SAMPLE, _responses, varIdx, sampleIdx, varType, missingDataMask, params, update); } namespace { using namespace sft; class Preprocessor { public: Preprocessor(int shr) : shrinkage(shr) {} void apply(const Mat& frame, Mat integrals) { CV_Assert(frame.type() == CV_8UC3); int h = frame.rows; int w = frame.cols; cv::Mat channels, gray; channels.create(h * BINS, w, CV_8UC1); channels.setTo(0); cvtColor(frame, gray, CV_BGR2GRAY); cv::Mat df_dx, df_dy, mag, angle; cv::Sobel(gray, df_dx, CV_32F, 1, 0); cv::Sobel(gray, df_dy, CV_32F, 0, 1); cv::cartToPolar(df_dx, df_dy, mag, angle, true); mag *= (1.f / (8 * sqrt(2.f))); cv::Mat nmag; mag.convertTo(nmag, CV_8UC1); angle *= 6 / 360.f; for (int y = 0; y < h; ++y) { uchar* magnitude = nmag.ptr(y); float* ang = angle.ptr(y); for (int x = 0; x < w; ++x) { channels.ptr(y + (h * (int)ang[x]))[x] = magnitude[x]; } } cv::Mat luv, shrunk; cv::cvtColor(frame, luv, CV_BGR2Luv); std::vector splited; for (int i = 0; i < 3; ++i) splited.push_back(channels(cv::Rect(0, h * (7 + i), w, h))); split(luv, splited); cv::resize(channels, shrunk, cv::Size(), 1.0 / shrinkage, 1.0 / shrinkage, CV_INTER_AREA); cv::integral(shrunk, integrals, cv::noArray(), CV_32S); } int shrinkage; enum {BINS = 10}; }; } // ToDo: parallelize it void sft::Octave::processPositives(const Dataset& dataset, const FeaturePool& pool) { Preprocessor prepocessor(shrinkage); int cols = (64 * pow(2, logScale) + 1) * (128 * pow(2, logScale) + 1); integrals.create(pool.size(), cols, CV_32SC1); int total = 0; // float* responce = responce.ptr(0); for (svector::const_iterator it = dataset.pos.begin(); it != dataset.pos.end(); ++it) { const string& curr = *it; dprintf("Process candidate positive image %s\n", curr.c_str()); cv::Mat channels = integrals.col(total).reshape(0, (128 * pow(2, logScale) + 1)); cv::Mat sample = cv::imread(curr); prepocessor.apply(sample, channels); responses.ptr(total)[0] = 1.f; ++total; if (total >= npositives) break; } dprintf("Processing positives finished:\n\trequested %d positives, collected %d samples.\n", npositives, total); npositives = total; nnegatives *= total / (float)npositives; } bool sft::Octave::train(const Dataset& dataset, const FeaturePool& pool) { // 1. fill integrals and classes return false; } // ========= FeaturePool ========= // sft::FeaturePool::FeaturePool(cv::Size m, int n) : model(m), nfeatures(n) { CV_Assert(m != cv::Size() && n > 0); fill(nfeatures); } sft::FeaturePool::~FeaturePool(){} void sft::FeaturePool::fill(int desired) { int mw = model.width; int mh = model.height; int maxPoolSize = (mw -1) * mw / 2 * (mh - 1) * mh / 2 * N_CHANNELS; nfeatures = std::min(desired, maxPoolSize); pool.reserve(nfeatures); sft::Random::engine eng(seed); sft::Random::engine eng_ch(seed); sft::Random::uniform chRand(0, N_CHANNELS - 1); sft::Random::uniform xRand(0, model.width - 2); sft::Random::uniform yRand(0, model.height - 2); sft::Random::uniform wRand(1, model.width - 1); sft::Random::uniform hRand(1, model.height - 1); while (pool.size() < size_t(nfeatures)) { int x = xRand(eng); int y = yRand(eng); int w = 1 + wRand(eng, model.width - x - 1); int h = 1 + hRand(eng, model.height - y - 1); CV_Assert(w > 0); CV_Assert(h > 0); CV_Assert(w + x < model.width); CV_Assert(h + y < model.height); int ch = chRand(eng_ch); sft::ICF f(x, y, w, h, ch); if (std::find(pool.begin(), pool.end(),f) == pool.end()) pool.push_back(f); } } // ============ Dataset ============ // namespace { using namespace sft; string itoa(long i) { char s[65]; sprintf(s, "%ld", i); return std::string(s); } void glob(const string& path, svector& ret) { glob_t glob_result; glob(path.c_str(), GLOB_TILDE, 0, &glob_result); ret.clear(); ret.reserve(glob_result.gl_pathc); for(uint i = 0; i < glob_result.gl_pathc; ++i) { ret.push_back(std::string(glob_result.gl_pathv[i])); dprintf("%s\n", ret[i].c_str()); } globfree(&glob_result); } } // in the default case data folders should be alligned as following: // 1. positives: /octave_/pos/*.png // 2. negatives: /octave_/neg/*.png Dataset::Dataset(const string& path, const int oct) { dprintf("%s\n", "get dataset file names..."); dprintf("%s\n", "Positives globbing..."); glob(path + "/pos/octave_" + itoa(oct) + "/*.png", pos); dprintf("%s\n", "Negatives globbing..."); glob(path + "/neg/octave_" + itoa(oct) + "/*.png", neg); // Check: files not empty CV_Assert(pos.size() != size_t(0)); CV_Assert(neg.size() != size_t(0)); }