Qt+opencv+摄像头+人脸检测

#include "cameramanage.h" #include "ui_cameramanage.h" #include <QPixmap> #include <QFileDialog> #include <QPicture> #include <opencv2/objdetect.hpp> #include <iostream> //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 const char* cascade_name_class ="haarcascade_eye_tree_eyeglasses.xml"; CameraManage::CameraManage(QWidget *parent) : QMainWindow(parent), ui(new Ui::CameraManage) { ui->setupUi(this); cam = NULL; timer = new QTimer(this); timer_1 = new QTimer(this); // image = 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(timer, SIGNAL(timeout()), this, SLOT(readFarme())); // 时间到，读取当前摄像头信息 // connect(timer_1, SIGNAL(timeout()), this, SLOT(on_videoDete_clicked())); connect(ui->open, SIGNAL(clicked()), this, SLOT(on_open_clicked())); connect(ui->close, SIGNAL(clicked()), this, SLOT(on_close_clicked())); connect(ui->takePhono, SIGNAL(clicked()), this, SLOT(on_takePhono_clicked())); connect(ui->videoDete, SIGNAL(clicked()), this, SLOT(on_videoDete_clicked())); } CameraManage::~CameraManage() { delete ui; delete timer; // delete image; } QImage CameraManage::Mat2QImage(Mat& cvImg) { QImage qImg; if(cvImg.channels()==3) //3 channels color image { cv::cvtColor(cvImg,cvImg,CV_BGR2RGB); qImg =QImage((const unsigned char*)(cvImg.data), cvImg.cols, cvImg.rows, cvImg.cols*cvImg.channels(), QImage::Format_RGB888); } else if(cvImg.channels()==1) //grayscale image { qImg =QImage((const unsigned char*)(cvImg.data), cvImg.cols,cvImg.rows, cvImg.cols*cvImg.channels(), QImage::Format_Indexed8); } else { qImg =QImage((const unsigned char*)(cvImg.data), cvImg.cols,cvImg.rows, cvImg.cols*cvImg.channels(), QImage::Format_RGB888); } return qImg; } QImage CameraManage::ScaleImage2Label(QImage qImage, QLabel* qLabel) { QImage qScaledImage; QSize qImageSize = qImage.size(); QSize qLabelSize = qLabel->size(); double dWidthRatio = 1.0*qImageSize.width() / qLabelSize.width(); double dHeightRatio = 1.0*qImageSize.height() / qLabelSize.height(); if (dWidthRatio>dHeightRatio) { qScaledImage = qImage.scaledToWidth(qLabelSize.width()); } else { qScaledImage = qImage.scaledToHeight(qLabelSize.height()); } return qScaledImage; } void CameraManage::on_open_clicked() { cap.open(0); timer->start(33); // 开始计时，超时则发出timeout()信号 } void CameraManage::readFarme() { cap >> frame; // imshow("nihao",frame); image = Mat2QImage(frame); // image = QImage(image.scaled(240, 120)); // change for the usb camera QImage scaleImage = ScaleImage2Label( image, ui->lable ); // 显示到label上 ui->lable->setPixmap(QPixmap::fromImage(scaleImage)); ui->lable->setAlignment(Qt::AlignCenter); ui->lable->show(); } void CameraManage::on_close_clicked() { timer->stop(); // 停止读取数据。 cap.release(); //释放内存 frame.release(); ui->lable->setText("video closed"); } void CameraManage::on_takePhono_clicked() { bool b = image.save("01.jpg","JPG");//保存为图片文件 if(!b) return; } void CameraManage::on_faceDete_clicked() { Mat image_dete; const char* filename = "01.jpg";//待检测图像(包含绝对路径) image_dete = imread(filename, CV_LOAD_IMAGE_COLOR); //加载图像 // detectAndDraw( image_dete); } /************************* ********* method two *********** **************************/ void CameraManage::detectAndDraw( Mat& img, CascadeClassifier& cascade, CascadeClassifier& nestedCascade, double scale, bool tryflip ) { double t = 0; vector<Rect> faces, faces2; const static Scalar colors[] = { Scalar(255,0,0), Scalar(255,128,0), Scalar(255,255,0), Scalar(0,255,0), Scalar(0,128,255), Scalar(0,255,255), Scalar(0,0,255), Scalar(255,0,255) }; Mat gray, smallImg; cvtColor( img, gray, COLOR_BGR2GRAY ); double fx = 1 / scale; cv::resize( gray, smallImg, Size(), fx, fx, INTER_LINEAR ); equalizeHist( smallImg, smallImg ); t = (double)getTickCount(); cascade.detectMultiScale( smallImg, faces, 1.1, 2, 0 //|CASCADE_FIND_BIGGEST_OBJECT //|CASCADE_DO_ROUGH_SEARCH |CASCADE_SCALE_IMAGE, Size(30, 30) ); if( tryflip ) { flip(smallImg, smallImg, 1); cascade.detectMultiScale( smallImg, faces2, 1.1, 2, 0 //|CASCADE_FIND_BIGGEST_OBJECT //|CASCADE_DO_ROUGH_SEARCH |CASCADE_SCALE_IMAGE, Size(30, 30) ); for( vector<Rect>::const_iterator r = faces2.begin(); r != faces2.end(); ++r ) { faces.push_back(Rect(smallImg.cols - r->x - r->width, r->y, r->width, r->height)); } } t = (double)getTickCount() - t; printf( "detection time = %g ms\n", t*1000/getTickFrequency()); for ( size_t i = 0; i < faces.size(); i++ ) { Rect r = faces[i]; Mat smallImgROI; vector<Rect> nestedObjects; Point center; Scalar color = colors[i%8]; int radius; double aspect_ratio = (double)r.width/r.height; if( 0.75 < aspect_ratio && aspect_ratio < 1.3 ) { 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); circle( img, center, radius, color, 3, 8, 0 ); } else rectangle( img, cvPoint(cvRound(r.x*scale), cvRound(r.y*scale)), cvPoint(cvRound((r.x + r.width-1)*scale), cvRound((r.y + r.height-1)*scale)), color, 3, 8, 0); if( nestedCascade.empty() ) continue; smallImgROI = smallImg( r ); nestedCascade.detectMultiScale( smallImgROI, nestedObjects, 1.1, 2, 0 //|CASCADE_FIND_BIGGEST_OBJECT //|CASCADE_DO_ROUGH_SEARCH //|CASCADE_DO_CANNY_PRUNING |CASCADE_SCALE_IMAGE, Size(30, 30) ); for ( size_t j = 0; j < nestedObjects.size(); j++ ) { Rect nr = nestedObjects[j]; center.x = cvRound((r.x + nr.x + nr.width*0.5)*scale); center.y = cvRound((r.y + nr.y + nr.height*0.5)*scale); radius = cvRound((nr.width + nr.height)*0.25*scale); circle( img, center, radius, color, 3, 8, 0 ); } } // imshow( "result", img ); QImage image_de = Mat2QImage( img ); QImage scaleImage = ScaleImage2Label( image_de, ui->lable ); ui->lable->setPixmap(QPixmap::fromImage(scaleImage)); ui->lable->setAlignment(Qt::AlignCenter); ui->lable->show(); } void CameraManage::on_videoDete_clicked() { // timer_1->start(33); CascadeClassifier cascade, nestedCascade; if( !cascade.load(cascade_name) || !nestedCascade.load(cascade_name_class) ) { fprintf( stderr, "ERROR: Could not load classifier cascade\n" ); return ; } capDete.open(0); bool tryflip = 1; double scale = 1; if( capDete.isOpened() ) { for(;;) { capDete >> fram_dete;