struck

这篇文章是通过那篇伟大的benchmark上看到的，说这篇文章各种好，所以就去看了下，说实在的，都看了2次了，这篇文章还是木有看懂...于是看代码，但平时看的都是matlab的代码，而作者有提供C++的，因为要用到opencv，而opencv的配置，每次配置好了，但下次想跑还是不行，所以，真的不是太喜欢这，不过，opencv又确实是真的很好啊~~~

这个是跟踪时候的主函数，先贴出来

/*  * Struck: Structured Output Tracking with Kernels *  * Code to accompany the paper: *   Struck: Structured Output Tracking with Kernels *   Sam Hare, Amir Saffari, Philip H. S. Torr *   International Conference on Computer Vision (ICCV), 2011 *  * Copyright (C) 2011 Sam Hare, Oxford Brookes University, Oxford, UK *  * This file is part of Struck. *  * Struck is free software: you can redistribute it and/or modify * it under the terms of the GNU General Public License as published by * the Free Software Foundation, either version 3 of the License, or * (at your option) any later version. *  * Struck is distributed in the hope that it will be useful, * but WITHOUT ANY WARRANTY; without even the implied warranty of * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the * GNU General Public License for more details. *  * You should have received a copy of the GNU General Public License * along with Struck.  If not, see <http://www.gnu.org/licenses/>. *  */ #include "Tracker.h"#include "Config.h"#include <iostream>#include <fstream>#include <opencv/cv.h>#include <opencv/highgui.h>using namespace std;using namespace cv;static const int kLiveBoxWidth = 80;static const int kLiveBoxHeight = 80;void rectangle(Mat& rMat, const FloatRect& rRect, const Scalar& rColour){IntRect r(rRect);rectangle(rMat, Point(r.XMin(), r.YMin()), Point(r.XMax(), r.YMax()), rColour);}int main(int argc, char* argv[]){// read config filestring configPath = "config.txt";if (argc > 1){configPath = argv[1];}Config conf(configPath);cout << conf << endl;if (conf.features.size() == 0){cout << "error: no features specified in config" << endl;return EXIT_FAILURE;}ofstream outFile;if (conf.resultsPath != ""){outFile.open(conf.resultsPath.c_str(), ios::out);if (!outFile){cout << "error: could not open results file: " << conf.resultsPath << endl;return EXIT_FAILURE;}}// if no sequence specified then use the camerabool useCamera = (conf.sequenceName == "");VideoCapture cap;int startFrame = -1;int endFrame = -1;FloatRect initBB;string imgFormat;float scaleW = 1.f;float scaleH = 1.f;if (useCamera){if (!cap.open(0)){cout << "error: could not start camera capture" << endl;return EXIT_FAILURE;}startFrame = 0;endFrame = INT_MAX;Mat tmp;cap >> tmp;scaleW = (float)conf.frameWidth/tmp.cols;scaleH = (float)conf.frameHeight/tmp.rows;// conf.frameWidth = 320// conf.frameHeight = 240initBB = IntRect(conf.frameWidth/2-kLiveBoxWidth/2, conf.frameHeight/2-kLiveBoxHeight/2, kLiveBoxWidth, kLiveBoxHeight);cout << "press 'i' to initialise tracker" << endl;}else{// parse frames filestring framesFilePath = conf.sequenceBasePath+"/"+conf.sequenceName+"/"+conf.sequenceName+"_frames.txt";ifstream framesFile(framesFilePath.c_str(), ios::in);if (!framesFile){cout << "error: could not open sequence frames file: " << framesFilePath << endl;return EXIT_FAILURE;}string framesLine;getline(framesFile, framesLine);sscanf(framesLine.c_str(), "%d,%d", &startFrame, &endFrame);if (framesFile.fail() || startFrame == -1 || endFrame == -1){cout << "error: could not parse sequence frames file" << endl;return EXIT_FAILURE;}imgFormat = conf.sequenceBasePath+"/"+conf.sequenceName+"/imgs/img%05d.png";// read first frame to get sizechar imgPath[256];sprintf(imgPath, imgFormat.c_str(), startFrame);Mat tmp = cv::imread(imgPath, 0);scaleW = (float)conf.frameWidth/tmp.cols;scaleH = (float)conf.frameHeight/tmp.rows;// read init box from ground truth filestring gtFilePath = conf.sequenceBasePath+"/"+conf.sequenceName+"/"+conf.sequenceName+"_gt.txt";ifstream gtFile(gtFilePath.c_str(), ios::in);if (!gtFile){cout << "error: could not open sequence gt file: " << gtFilePath << endl;return EXIT_FAILURE;}string gtLine;getline(gtFile, gtLine);float xmin = -1.f;float ymin = -1.f;float width = -1.f;float height = -1.f;sscanf(gtLine.c_str(), "%f,%f,%f,%f", &xmin, &ymin, &width, &height);if (gtFile.fail() || xmin < 0.f || ymin < 0.f || width < 0.f || height < 0.f){cout << "error: could not parse sequence gt file" << endl;return EXIT_FAILURE;}initBB = FloatRect(xmin*scaleW, ymin*scaleH, width*scaleW, height*scaleH);}Tracker tracker(conf);if (!conf.quietMode){namedWindow("result");}Mat result(conf.frameHeight, conf.frameWidth, CV_8UC3);bool paused = false;bool doInitialise = false;srand(conf.seed);for (int frameInd = startFrame; frameInd <= endFrame; ++frameInd){Mat frame;if (useCamera){Mat frameOrig;cap >> frameOrig;resize(frameOrig, frame, Size(conf.frameWidth, conf.frameHeight));flip(frame, frame, 1);frame.copyTo(result);if (doInitialise){if (tracker.IsInitialised()){tracker.Reset();}else{tracker.Initialise(frame, initBB);}doInitialise = false;}else if (!tracker.IsInitialised()){rectangle(result, initBB, CV_RGB(255, 255, 255));}}else{char imgPath[256];sprintf(imgPath, imgFormat.c_str(), frameInd);Mat frameOrig = cv::imread(imgPath, 0);if (frameOrig.empty()){cout << "error: could not read frame: " << imgPath << endl;return EXIT_FAILURE;}resize(frameOrig, frame, Size(conf.frameWidth, conf.frameHeight));cvtColor(frame, result, CV_GRAY2RGB);if (frameInd == startFrame){tracker.Initialise(frame, initBB);}}if (tracker.IsInitialised()){tracker.Track(frame);if (!conf.quietMode && conf.debugMode){tracker.Debug();}rectangle(result, tracker.GetBB(), CV_RGB(0, 255, 0));if (outFile){const FloatRect& bb = tracker.GetBB();outFile << bb.XMin()/scaleW << "," << bb.YMin()/scaleH << "," << bb.Width()/scaleW << "," << bb.Height()/scaleH << endl;}}if (!conf.quietMode){imshow("result", result);int key = waitKey(paused ? 0 : 1);if (key != -1){if (key == 27 || key == 113) // esc q{break;}else if (key == 112) // p{paused = !paused;}else if (key == 105 && useCamera){doInitialise = true;}}if (conf.debugMode && frameInd == endFrame){cout << "\n\nend of sequence, press any key to exit" << endl;waitKey();}}}if (outFile.is_open()){outFile.close();}return EXIT_SUCCESS;}

最重要的在于tracker里面，因此，贴到下面

/*  * Struck: Structured Output Tracking with Kernels *  * Code to accompany the paper: *   Struck: Structured Output Tracking with Kernels *   Sam Hare, Amir Saffari, Philip H. S. Torr *   International Conference on Computer Vision (ICCV), 2011 *  * Copyright (C) 2011 Sam Hare, Oxford Brookes University, Oxford, UK *  * This file is part of Struck. *  * Struck is free software: you can redistribute it and/or modify * it under the terms of the GNU General Public License as published by * the Free Software Foundation, either version 3 of the License, or * (at your option) any later version. *  * Struck is distributed in the hope that it will be useful, * but WITHOUT ANY WARRANTY; without even the implied warranty of * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the * GNU General Public License for more details. *  * You should have received a copy of the GNU General Public License * along with Struck.  If not, see <http://www.gnu.org/licenses/>. *  */#include "Tracker.h"#include "Config.h"#include "ImageRep.h"#include "Sampler.h"#include "Sample.h"#include "GraphUtils/GraphUtils.h"#include "HaarFeatures.h"#include "RawFeatures.h"#include "HistogramFeatures.h"#include "MultiFeatures.h"#include "Kernels.h"#include "LaRank.h"#include <opencv/cv.h>#include <opencv/highgui.h>#include <Eigen/Core>#include <vector>#include <algorithm>using namespace cv;using namespace std;using namespace Eigen;Tracker::Tracker(const Config& conf) :m_config(conf),m_initialised(false),m_pLearner(0),m_debugImage(2*conf.searchRadius+1, 2*conf.searchRadius+1, CV_32FC1),m_needsIntegralImage(false){Reset();}Tracker::~Tracker(){delete m_pLearner;for (int i = 0; i < (int)m_features.size(); ++i){delete m_features[i];delete m_kernels[i];}}void Tracker::Reset(){m_initialised = false;m_debugImage.setTo(0);if (m_pLearner) delete m_pLearner;for (int i = 0; i < (int)m_features.size(); ++i){delete m_features[i];delete m_kernels[i];}m_features.clear();m_kernels.clear();m_needsIntegralImage = false;m_needsIntegralHist = false;int numFeatures = m_config.features.size();vector<int> featureCounts;for (int i = 0; i < numFeatures; ++i){switch (m_config.features[i].feature){case Config::kFeatureTypeHaar:m_features.push_back(new HaarFeatures(m_config));m_needsIntegralImage = true;break;case Config::kFeatureTypeRaw:m_features.push_back(new RawFeatures(m_config));break;case Config::kFeatureTypeHistogram:m_features.push_back(new HistogramFeatures(m_config));m_needsIntegralHist = true;break;}featureCounts.push_back(m_features.back()->GetCount());switch (m_config.features[i].kernel){case Config::kKernelTypeLinear:m_kernels.push_back(new LinearKernel());break;case Config::kKernelTypeGaussian:m_kernels.push_back(new GaussianKernel(m_config.features[i].params[0]));break;case Config::kKernelTypeIntersection:m_kernels.push_back(new IntersectionKernel());break;case Config::kKernelTypeChi2:m_kernels.push_back(new Chi2Kernel());break;}}if (numFeatures > 1){MultiFeatures* f = new MultiFeatures(m_features);m_features.push_back(f);MultiKernel* k = new MultiKernel(m_kernels, featureCounts);m_kernels.push_back(k);}m_pLearner = new LaRank(m_config, *m_features.back(), *m_kernels.back());}void Tracker::Initialise(const cv::Mat& frame, FloatRect bb){m_bb = IntRect(bb);ImageRep image(frame, m_needsIntegralImage, m_needsIntegralHist);for (int i = 0; i < 1; ++i){UpdateLearner(image);}m_initialised = true;}void Tracker::Track(const cv::Mat& frame){assert(m_initialised);ImageRep image(frame, m_needsIntegralImage, m_needsIntegralHist);vector<FloatRect> rects = Sampler::PixelSamples(m_bb, m_config.searchRadius);vector<FloatRect> keptRects;keptRects.reserve(rects.size());for (int i = 0; i < (int)rects.size(); ++i){if (!rects[i].IsInside(image.GetRect())) continue;keptRects.push_back(rects[i]);}MultiSample sample(image, keptRects);vector<double> scores;m_pLearner->Eval(sample, scores);double bestScore = -DBL_MAX;int bestInd = -1;for (int i = 0; i < (int)keptRects.size(); ++i){if (scores[i] > bestScore){bestScore = scores[i];bestInd = i;}}UpdateDebugImage(keptRects, m_bb, scores);if (bestInd != -1){m_bb = keptRects[bestInd];UpdateLearner(image);#if VERBOSEcout << "track score: " << bestScore << endl;#endif}}void Tracker::UpdateDebugImage(const vector<FloatRect>& samples, const FloatRect& centre, const vector<double>& scores){double mn = VectorXd::Map(&scores[0], scores.size()).minCoeff();double mx = VectorXd::Map(&scores[0], scores.size()).maxCoeff();m_debugImage.setTo(0);for (int i = 0; i < (int)samples.size(); ++i){int x = (int)(samples[i].XMin() - centre.XMin());int y = (int)(samples[i].YMin() - centre.YMin());m_debugImage.at<float>(m_config.searchRadius+y, m_config.searchRadius+x) = (float)((scores[i]-mn)/(mx-mn));}}void Tracker::Debug(){imshow("tracker", m_debugImage);m_pLearner->Debug();}void Tracker::UpdateLearner(const ImageRep& image){// note these return the centre sample at index 0vector<FloatRect> rects = Sampler::RadialSamples(m_bb, 2*m_config.searchRadius, 5, 16);//vector<FloatRect> rects = Sampler::PixelSamples(m_bb, 2*m_config.searchRadius, true);vector<FloatRect> keptRects;keptRects.push_back(rects[0]); // the true samplefor (int i = 1; i < (int)rects.size(); ++i){if (!rects[i].IsInside(image.GetRect())) continue;keptRects.push_back(rects[i]);}#if VERBOSEcout << keptRects.size() << " samples" << endl;#endifMultiSample sample(image, keptRects);m_pLearner->Update(sample, 0);}