gmm.rar_GMM背景建模_gmm 图像_混合高斯 背景

//gmm.cpp : 定义控制台应用程序的入口点。 #include "stdafx.h" #include "cv.h" #include "highgui.h" #include <opencv2/imgproc/imgproc.hpp> #include <opencv2/core/core.hpp> #include <opencv2/highgui/highgui.hpp> #include <stdio.h> #include <iostream> using namespace cv; using namespace std; //定义gmm模型用到的变量 #define GMM_MAX_COMPONT 6 #define GMM_LEARN_ALPHA 0.005 //该学习率越大的话，学习速度太快，效果不好 #define GMM_THRESHOD_SUMW 0.7 //如果取值太大了的话，则更多树的部分都被检测出来了 #define END_FRAME 200 bool pause=false; Mat w[GMM_MAX_COMPONT]; Mat u[GMM_MAX_COMPONT]; Mat sigma[GMM_MAX_COMPONT]; Mat fit_num,gmask,foreground; vector<Mat> output_m; Mat output_img; float temp_w,temp_sigma; unsigned char temp_u; int i=-1; //For connected components: int CVCONTOUR_APPROX_LEVEL = 2; // Approx.threshold - the bigger it is, the simpler is the boundary int CVCLOSE_ITR = 1; //Just some convienience macros #define CV_CVX_WHITE CV_RGB(0xff,0xff,0xff) #define CV_CVX_BLACK CV_RGB(0x00,0x00,0x00) //gmm整体初始化函数声明 void gmm_init(Mat img); //gmm第一帧初始化函数声明 void gmm_first_frame(Mat img); //gmm训练过程函数声明 void gmm_train(Mat img); //对输入图像每个像素gmm选择合适的个数函数声明 void gmm_fit_num(Mat img); //gmm测试函数的实现 void gmm_test(Mat img); //连通域去噪函数声明 void find_connected_components(Mat img); //void cvconnectedComponents(IplImage *mask, int poly1_hull0, float perimScale, int *num, CvRect *bbs, CvPoint *centers); int main(int argc, const char* argv[]) { Mat img,img_gray; char str_num[5]; // char *str_num;//why does this definition not work? String str="WavingTrees/b00";//string,the 's' can be a captial or lower-caseletters /****read the first image,and reset the array w,u,sigma****/ img=imread("WavingTrees/b00000.bmp"); if(img.empty()) return -1; output_img=Mat::zeros(img.size(),img.type()); cvtColor(img,img_gray,CV_BGR2GRAY);//covert the colorful image to the corresponding gray-level image /****initialization the three parameters ****/ gmm_init(img_gray); fit_num=Mat(img.size(),CV_8UC1,-1);//初始化为1 gmask=Mat(img.size(),CV_8UC1,-1); foreground=img.clone(); split(img,output_m); output_m[0]=Mat::zeros(img.size(),output_m[0].type()); output_m[1]=Mat::zeros(img.size(),output_m[0].type()); output_m[2]=Mat::zeros(img.size(),output_m[0].type()); namedWindow("src",WINDOW_AUTOSIZE); namedWindow("gmask",WINDOW_AUTOSIZE); //在定义视频输出对象时，文件名一定后面要加后缀，比如这里的.avi,否则是输出不了视频的！并且这里只能是avi格式的，当参数为('P','I','M','1')时 VideoWriter output_src("src.avi",CV_FOURCC('P','I','M','1'),20,Size(160,120),1);//c++版本的opencv用Size函数即可，c版本的用cvSize()函数 //VideoWriter output_src("src.avi",CV_FOURCC('M','J','P','G'),5,Size(160,120),1);//c++版本的opencv用Size函数即可，c版本的用cvSize()函数 VideoWriter output_dst("dst.avi",CV_FOURCC('P','I','M','1'),20,Size(160,120),1);//这样输出的是3个通道的数据 while(1) { if(!pause) { /****read image from WavingTrees****/ i++; _itoa_s(i,str_num,10);//the latest name is _itoa_s or _itoa,not the itoa,although iota can be used,deprecated if(i<10) str+="00"; else if(i<100) str+="0"; else if(i>285)//we used the first 285 frames to learn the gmm model i=-1; str+=str_num; str+=".bmp"; img=imread(str); if(img.empty()) break; str="WavingTrees/b00";//after read,str must be reseted ; cvtColor(img,img_gray,CV_BGR2GRAY);//covert the colorful image to the corresponding gray-level image /****when it is the first frame,set the default parameter****/ if(1==i) { gmm_first_frame(img_gray); } //the train of gmm phase //if(1<i&&i<5&&i!=3)//由此可知当i大于等于3以后，就会一直出现错误，且错误在内部排序的部分 if(1<i<END_FRAME) { gmm_train(img_gray); }//end the train phase cout<<i<<endl; /****chose the fitting number of component in gmm****/ if(END_FRAME==i) { gmm_fit_num(img_gray); // cout<<fit_num<<endl;//其输出值有4个高斯的，但也有0个高斯的，why？照理说不可能的啊！ } /****start the test phase****/ if(i>=END_FRAME) { output_src<<img; gmm_test(img_gray); find_connected_components(img_gray); output_m[0]=gmask.clone(); output_m[1]=gmask.clone(); output_m[2]=gmask.clone(); merge(output_m,output_img); output_dst<<output_img; } if(285==i) { return 0; } imshow("src",img); imshow("gmask",gmask); } char c=(char)waitKey(1); if(c==27)//if press the ESC key,the exit the proggram break; if(c==' ') // pause=~pause;//if use '~',then the pause key cannot work,why? pause=!pause; } return 0; } //gmm初始化函数实现 void gmm_init(Mat img) { /****initialization the three parameters ****/ for(int j=0;j<GMM_MAX_COMPONT;j++) { w[j]=Mat(img.size(),CV_32FC1,0.0);//CV_32FC1本身体现了正负符号 u[j]=Mat(img.size(),CV_8UC1,-1);//为什么这里赋值为0时，后面的就一直出错？暂时还不知道原因，先赋值-1，其实内部存储的也是0 sigma[j]=Mat(img.size(),CV_32FC1,0.0);//float类型 } //为什么一下语句不能放在这个函数里面呢 // output_m[0]=Mat(img.size(),CV_8UC1,0); // output_m[1]=Mat(img.size(),CV_8UC1,0); // output_m[2]=Mat(img.size(),CV_8UC1,0); } //gmm第一帧初始化函数实现 void gmm_first_frame(Mat img) { for(int m=0;m<img.rows;m++) for(int n=0;n<img.cols;n++) { w[0].at<float>(m,n)=1.0; //if the pixvel is gray-clever,then we should use unsigned char,not the unsigned int u[0].at<unsigned char>(m,n)=img.at<unsigned char>(m,n);// 一定要注意其类型转换，否则会得不得预期的结果 sigma[0].at<float>(m,n)=15.0;//opencv 自带的gmm代码中用的是15.0 for(int k=1;k<GMM_MAX_COMPONT;k++) { /****when assigment this,we must be very carefully****/ w[k].at<float>(m,n)=0.0; u[k].at<unsigned char>(m,n)=-1; sigma[k].at<float>(m,n)=15.0;//防止后面排序时有分母为0的情况 } } } //gmm训练过程函数实现 void gmm_train(Mat img) { for(int m=0;m<img.rows;m++) for(int n=0;n<img.cols;n++) { int k=0; int nfit=0; for(;k<GMM_MAX_COMPONT;k++) { // if(w[k].at<float>(m,n)!=0)//只有在权值不为0的情况下才进行比较 // { int delam=abs(img.at<unsigned char>(m,n)-u[k].at<unsigned char>(m,n));//防止溢出 long dis=