QT使用Opencv库，网络摄像头做的视频监控系统带人脸检测功能

#include "widget.h" #include "ui_widget.h" #include <stdio.h> static CvHaarClassifierCascade* cascade = 0; static CvMemStorage* storage = 0; const char* cascade_name ="haarcascade_frontalface_alt.xml"; //人脸检测要用到的分类器 Widget::Widget(QWidget *parent) : QWidget(parent), ui(new Ui::Widget) { ui->setupUi(this); cam = NULL; timer = new QTimer(this); imag = new QImage(); // 初始化 /*信号和槽*/ connect(timer, SIGNAL(timeout()), this, SLOT(readFarme())); // 时间到，读取当前摄像头信息 connect(ui->openBtn, SIGNAL(clicked()), this, SLOT(openCamara())); connect(ui->takePhotoBtn, SIGNAL(clicked()), this, SLOT(takingPictures())); connect(ui->closeBtn, SIGNAL(clicked()), this, SLOT(closeCamara())); } Widget::~Widget() { delete ui; delete timer; delete imag; } /****************************** ********* 打开摄像头 *********** *******************************/ void Widget::openCamara() { cam = cvCreateCameraCapture(0);//打开摄像头，从摄像头中获取视频 timer->start(33); // 开始计时，超时则发出timeout()信号 } /********************************* ********* 读取摄像头信息 *********** **********************************/ void Widget::readFarme() { frame = cvQueryFrame(cam);// 从摄像头中抓取并返回每一帧 // 将抓取到的帧，转换为QImage格式。QImage::Format_RGB888不同的摄像头用不同的格式。 // QImage image((const uchar*)frame->imageData, frame->width, frame->height, QImage::Format_RGB888); QImage image = QImage((const uchar*)frame->imageData, frame->width, frame->height, QImage::Format_RGB888).rgbSwapped(); // image.save("01.jpg","JPG"); ui->label->setPixmap(QPixmap::fromImage(image)); // 将图片显示到label上 } /******************************* ***关闭摄像头，释放资源，必须释放*** ********************************/ void Widget::closeCamara() { timer->stop(); // 停止读取数据。 cvReleaseCapture(&cam);//释放内存； } /************************* ********* 拍照 *********** **************************/ void Widget::takingPictures() { frame = cvQueryFrame(cam);// 从摄像头中抓取并返回每一帧 // 将抓取到的帧，转换为QImage格式。QImage::Format_RGB888不同的摄像头用不同的格式。 // QImage image((const uchar*)frame->imageData, frame->width, frame->height, QImage::Format_RGB888); QImage qimage = QImage((const uchar*)frame->imageData, frame->width, frame->height, QImage::Format_RGB888).rgbSwapped(); ui->label_2->setPixmap(QPixmap::fromImage(qimage)); // 将图片显示到label上 bool b = qimage.save("01.jpg","JPG");//保存为图片文件 if(!b) return; } void Widget::detect_and_draw(IplImage *img) { static CvScalar colors[] = { {{0,0,255}}, {{0,128,255}}, {{0,255,255}}, {{0,255,0}}, {{255,128,0}}, {{255,255,0}}, {{255,0,0}}, {{255,0,255}} }; double scale = 1.3; IplImage* gray = cvCreateImage( cvSize(img->width,img->height), 8, 1 ); IplImage* small_img = cvCreateImage( cvSize( cvRound (img->width/scale), cvRound (img->height/scale)), 8, 1 ); cvCvtColor( img, gray, CV_BGR2GRAY ); cvResize( gray, small_img, CV_INTER_LINEAR ); cvEqualizeHist( small_img, small_img ); cvClearMemStorage( storage ); if( cascade ) { /*函数cvHaarDetectObjects检测图像中的目标，由OpenCV提供。*/ CvSeq* faces = cvHaarDetectObjects( small_img, cascade, storage, 1.1, 2, 0 , cvSize(30, 30) ); for( int i = 0; i < (faces ? faces->total : 0); i++ ) { CvRect* r = (CvRect*)cvGetSeqElem( faces, i ); CvPoint center; int radius; center.x = cvRound((r->x + r->width*0.5)*scale); center.y = cvRound((r->y + r->height*0.5)*scale); radius = cvRound((r->width + r->height)*0.25*scale); cvCircle( img, center, radius, colors[i%8], 3, 8, 0 ); } } cvShowImage( "result", img ); // cvResizeWindow( "result", 800, 600 ); cvReleaseImage( &gray ); cvReleaseImage( &small_img ); } void Widget::on_faceDetectBtn_clicked() { cascade = (CvHaarClassifierCascade*)cvLoad( cascade_name, 0, 0, 0 ); //加载人脸检测所用的分类器 if( !cascade ) { fprintf( stderr, "ERROR: Could not load classifier cascade\n" ); return ; } storage = cvCreateMemStorage(0); //动态存储结构，用来存储人脸在图像中的位置 cvNamedWindow( "result", 1 ); // const char* filename = "lena.jpg"; //待检测图像(包含绝对路径) const char* filename = "01.jpg";//待检测图像(包含绝对路径) IplImage* image = cvLoadImage( filename, 1 ); //加载图像 detect_and_draw( image ); //对加载的图像进行检测 cvWaitKey(0); cvReleaseImage( &image ); cvDestroyWindow("result"); }