前景背景分离方法(二)高斯混合模型法GMM(Gaussian Mixture Model)

int main(){VideoCapture capture("D:/videos/shadow/use3.MPG");if( !capture.isOpened() ){cout<<"读取视频失败"<<endl;return -1;}//获取整个帧数long totalFrameNumber = capture.get(CV_CAP_PROP_FRAME_COUNT);cout<<"整个视频共"<<totalFrameNumber<<"帧"<<endl;//设置开始帧()long frameToStart = 200;capture.set( CV_CAP_PROP_POS_FRAMES,frameToStart);cout<<"从第"<<frameToStart<<"帧开始读"<<endl;//设置结束帧int frameToStop = 650;if(frameToStop < frameToStart){cout<<"结束帧小于开始帧，程序错误，即将退出！"<<endl;return -1;}else{cout<<"结束帧为：第"<<frameToStop<<"帧"<<endl;}double rate = capture.get(CV_CAP_PROP_FPS);int delay = 1000/rate;Mat frame;//前景图片Mat foreground;//使用默认参数调用混合高斯模型BackgroundSubtractorMOG mog;bool stop(false);//currentFrame是在循环体中控制读取到指定的帧后循环结束的变量long currentFrame = frameToStart;while( !stop ){if( !capture.read(frame) ){cout<<"从视频中读取图像失败或者读完整个视频"<<endl;return -2;}cvtColor(frame,frame,CV_RGB2GRAY);imshow("输入视频",frame);//参数为：输入图像、输出图像、学习速率mog(frame,foreground,0.01);imshow("前景",foreground);//按ESC键退出，按其他键会停止在当前帧int c = waitKey(delay);if ( (char)c == 27 || currentFrame >= frameToStop){stop = true;}if ( c >= 0){waitKey(0);}currentFrame++;}waitKey(0);}

//  基于混合高斯模型的运动目标检测//  Author： http://blog.csdn.net/icvpr  #include <iostream>#include <string>#include <opencv2/opencv.hpp>int main(int argc, char** argv){std::string videoFile = "../test.avi";cv::VideoCapture capture;capture.open(videoFile);if (!capture.isOpened()){std::cout<<"read video failure"<<std::endl;return -1;}cv::BackgroundSubtractorMOG2 mog;cv::Mat foreground;cv::Mat background;cv::Mat frame;long frameNo = 0;while (capture.read(frame)){++frameNo;std::cout<<frameNo<<std::endl;// 运动前景检测，并更新背景mog(frame, foreground, 0.001);       // 腐蚀cv::erode(foreground, foreground, cv::Mat());// 膨胀cv::dilate(foreground, foreground, cv::Mat());mog.getBackgroundImage(background);   // 返回当前背景图像cv::imshow("video", foreground);cv::imshow("background", background);if (cv::waitKey(25) > 0){break;}}return 0;}