OpenCV实现智能视频监控
时间:2022-03-20 09:49:07|栏目:C代码|点击: 次
本文实例为大家分享了OpenCV实现智能视频监控的具体代码,供大家参考,具体内容如下
之前在做毕设的时候网上找个完整的实现代码挺麻烦的,自己做完分享一下
因为代码较为简单,没有将代码分开写在不同文件,有需要自己整合下哈
使用环境Visual Studio 2010 和 OpenCV 2.4.9
#include <opencv2/opencv.hpp> #include <opencv2/highgui/highgui.hpp> #include <ctime> using namespace std; using namespace cv; int videoplay(); void on_Trackbar(int ,void*); char* str_gettime(); int bSums(Mat src); char g_str[17]; int g_nNum = 0;//图片名称 int g_nDelay = 0; int g_npic = 0; Mat g_filpdstMat; int g_pointnum = 1000;//设置像素点阈值生成图片 int g_pixel = 0;//像素点 int main() { VideoCapture capture(0); //视频输出VideoWriter CvVideoWriter* outavi = NULL; //VideoWriter outavi; //outavi.open("sre.avi",-1, 5.0, Size(640, 480), true); outavi = cvCreateVideoWriter("录像.avi", -1, 5.0, cvSize(640, 480), 1); namedWindow("摄像头",WINDOW_AUTOSIZE); namedWindow("移动轨迹",WINDOW_AUTOSIZE); IplImage *pcpframe = NULL; Mat tempframe, currentframe, preframe, cpframe; Mat frame,jpg; int framenum = 0; //读取一帧处理 while (1) { if(!capture.isOpened()) { cout << "读取失败" << endl; return -1; } capture >> frame;//读取摄像头把每一帧传给frame frame.copyTo(cpframe);//把frame赋给cpframe,不影响frame tempframe = frame;//把frame赋给tempframe,影响frame flip(tempframe,g_filpdstMat,1);//水平翻转图像 pcpframe = &IplImage(cpframe);//为了释放窗口,把Mat转化为IplImage使用 //cpframe=cvarrToMat(pcpframe); //ipl转化矩阵 pBinary = &IplImage(Img) //7帧截取一次录入视频,频繁截取运转不过来 if(framenum % 7 == 0) { //录像写入 cvWriteFrame(outavi, pcpframe); } //判断帧数,若为第一帧,把该帧作为对比帧 //若大于等于第二帧,则进行帧差法处理 framenum++; if (framenum == 1) { cvtColor(g_filpdstMat, preframe, CV_BGR2GRAY); } if (framenum >= 2) { cvtColor(g_filpdstMat, currentframe, CV_BGR2GRAY); //灰度图 absdiff(currentframe,preframe,currentframe);//帧差法 threshold(currentframe, currentframe, 30, 255.0, CV_THRESH_BINARY); //二值化 erode(currentframe, currentframe,Mat());//腐蚀 dilate(currentframe, currentframe,Mat());//膨胀 g_pixel = bSums(currentframe);//调用函数bSums,计算白色像素点,赋值给g_pixel //小延迟后输出当前像素点数值,防止数据刷太快看不清 g_nDelay++; if(g_nDelay > 5) { cout<< "当前白色像素点:" <<g_pixel << endl; cout << "按ESC退出" << endl; g_nDelay = 0; } //创建像素点滑轨 createTrackbar("像素点:","移动轨迹",&g_pointnum, 20000,on_Trackbar); on_Trackbar(0, 0);//调用回调函数 //显示图像 imshow("摄像头", g_filpdstMat); imshow("移动轨迹", currentframe); } //把当前帧保存作为下一次处理的前一帧 cvtColor(g_filpdstMat, preframe, CV_BGR2GRAY); //判断退出,并销毁录像窗口,否则下一步录像无法打开 if((char)waitKey(10) == 27){cvReleaseVideoWriter(&outavi);break;} }//end while while(1) { //显示提示窗口 jpg = imread("模式选择.jpg", 1); imshow("模式选择",jpg); //设置key选择操作 char key; key = waitKey(0); if(key == 'p' || key == 'P')//播放视频 videoplay(); if(key == 'q' || key == 'Q')//退出 break; } return 0; } //打开录像 int videoplay() { VideoCapture video("录像.avi"); if(!video.isOpened()) { fprintf(stderr,"打开失败\n"); return false; } while(1) { Mat frame; video>>frame; if(frame.empty()) { break; } cvNamedWindow("视频", CV_WINDOW_AUTOSIZE); imshow("视频",frame); waitKey(30); } cvDestroyWindow("视频"); return 0; } //滑轨设定阈值判定是否保存当前摄像头图片 void on_Trackbar(int ,void*) { //保存来人图片 if(g_pixel > g_pointnum) { g_npic++; if(g_npic > 5)//为了避免风吹草动,小延迟之后才保存图片 { //保存图片 cout << endl << endl; cout << "场地异常,警报响应,准备拍照...\a" << endl; imwrite(str_gettime(),g_filpdstMat); cout << "当前白色像素点:" <<g_pixel << endl; cout << "按ESC退出" << endl; cout << endl; g_npic = 0; } } } //获取当前日期 char* str_gettime() { char tmpbuf[10]; //从tz设置时区环境变量 _tzset();//时间函数 //显示当前日期 _strdate(tmpbuf); g_str[0] = tmpbuf[6]; g_str[1] = tmpbuf[7]; g_str[2] = tmpbuf[0]; g_str[3] = tmpbuf[1]; g_str[4] = tmpbuf[3]; g_str[5] = tmpbuf[4]; _strtime(tmpbuf); //时分秒 g_str[6] = tmpbuf[0]; g_str[7] = tmpbuf[1]; g_str[8] = tmpbuf[3]; g_str[9] = tmpbuf[4]; g_str[10] = tmpbuf[6]; g_str[11] = tmpbuf[7]; //规定图片jpg格式 g_str[12] = '.'; g_str[13] = 'j'; g_str[14] = 'p'; g_str[15] = 'g'; g_str[16] = '\0'; //显示获取图像时间 printf("生成图片:%s\n", g_str); return g_str; } int bSums(Mat src) { int counter = 0; //迭代器访问像素点 Mat_<uchar>::iterator it = src.begin<uchar>(); Mat_<uchar>::iterator itend = src.end<uchar>(); for (; it!=itend; ++it) { if((*it)>0) counter+=1;//二值化后,像素点是0或者255 } return counter; }