hog+svm.zip_svm检测视频_特征提取_特征检测_视频特征提取

#include <iostream> #include <fstream> #include <opencv2/core/bufferpool.hpp> #include <opencv2/highgui/highgui.hpp> #include <opencv2/imgproc/imgproc.hpp> #include <opencv2/objdetect/objdetect.hpp> #include <opencv2/ml/ml.hpp> using namespace std; using namespace cv; using namespace cv::ml; int main() { //creat SVM classfier Ptr<SVM> svm = SVM::create(); //load train file svm = StatModel::load<SVM>("E:/vspro/ConsoleApplication2/ConsoleApplication2/SVM_HOG.xml"); if (!svm) { cout << "Load file failed..." << endl; } //HOG检测器，用来计算HOG描述子的 //检测窗口(128,64),块尺寸(16,16),块步长(8,8),cell尺寸(8,8),直方图bin个数9 HOGDescriptor hog(Size(64, 64), Size(16, 16), Size(8, 8), Size(8, 8), 9); //HOG描述子的维数，由图片大小、检测窗口大小、块大小、细胞单元中直方图bin个数决定 int DescriptorDim; //特征向量的维数，即HOG描述子的维数 DescriptorDim = svm->getVarCount(); //获取svecsmat，元素类型为float cv::Mat svecsmat = svm->getSupportVectors(); //特征向量维数 int svdim = svm->getVarCount(); int numofsv = svecsmat.rows; //alphamat和svindex必须初始化，否则getDecisionFunction()函数会报错 cv::Mat alphamat = cv::Mat::zeros(numofsv, svdim, CV_32F); cv::Mat svindex = cv::Mat::zeros(1, numofsv, CV_64F); cv::Mat Result; double rho = svm->getDecisionFunction(0, alphamat, svindex); //将alphamat元素的数据类型重新转成CV_32F alphamat.convertTo(alphamat, CV_32F); Result = -1 * alphamat * svecsmat; std::vector<float> vec; for (int i = 0; i < svdim; ++i) { vec.push_back(Result.at<float>(0, i)); } vec.push_back(rho); //saving HOGDetectorForOpenCV.txt std::ofstream fout("HOGDetectorForOpenCV.txt"); for (int i = 0; i < vec.size(); ++i) { fout << vec[i] << std::endl; } hog.setSVMDetector(vec); ///**************读入视频进行HOG检测******************/ cv::VideoCapture capture("E:/graduation project/1.mp4"); if (!capture.isOpened()) { std::cout << "Read video Failed !" << endl; } cv::Mat frame; int frame_num = capture.get(cv::CAP_PROP_FRAME_COUNT); std::cout << "total frame number is: " << frame_num << std::endl; int width = capture.get(cv::CAP_PROP_FRAME_WIDTH); int height = capture.get(cv::CAP_PROP_FRAME_HEIGHT); //cv::VideoWriter out; //用于保存检测结果 //out.open("test result.mp4", CV_FOURCC('D', 'I', 'V', 'X'), 25.0, cv::Size(width, height ), true); for (int i = 0; i < frame_num; ++i) { capture >> frame; cv::resize(frame, frame, cv::Size(width/2, height/2)); //目标矩形框数组 std::vector<cv::Rect> found, found_1, found_filtered; //对图片进行多尺度检测 hog.detectMultiScale(frame, found, 0, cv::Size(8, 8), cv::Size(16, 16), 1.2, 2); for (int i = 0; i < found.size(); i++) { if (found[i].x > 0 && found[i].y > 0 && (found[i].x + found[i].width) < frame.cols && (found[i].y + found[i].height) < frame.rows) found_1.push_back(found[i]); } //找出所有没有嵌套的矩形框r,并放入found_filtered中,如果有嵌套的话, //则取外面最大的那个矩形框放入found_filtered中 for (int i = 0; i < found_1.size(); i++) { cv::Rect r = found_1[i]; int j = 0; for (; j < found_1.size(); j++) if (j != i && (r & found_1[j]) == found_1[j]) break; if (j == found_1.size()) found_filtered.push_back(r); } //画矩形框，因为hog检测出的矩形框比实际目标框要稍微大些,所以这里需要做一些调整，可根据实际情况调整 for (int i = 0; i < found_filtered.size(); i++) { cv::Rect r = found_filtered[i]; //将检测矩形框缩小后绘制，根据实际情况调整 r.x += cvRound(r.width*0.1); r.width = cvRound(r.width*0.8); r.y += cvRound(r.height*0.1); r.height = cvRound(r.height*0.8); } for (int i = 0; i < found_filtered.size(); i++) { cv::Rect r = found_filtered[i]; cv::rectangle(frame, r.tl(), r.br(), cv::Scalar(0, 0, 255), 2); } cv::imshow("detect result", frame); //保存检测结果 //out << frame; if (cv::waitKey(30) == 'q') { break; } } capture.release(); //out.release(); return 1; }