CascadeClassifier cascade;//級(jí)聯(lián)分類器(模型)
    cascade.load("D:/Qt_Opencv_Project/cars.xml");//讀取級(jí)聯(lián)分類器

    Mat frame;
    VideoCapture cap("D:/VideoTraining/carMove.mp4");
    while (cap.read(frame))
    {
        imshow("video",frame);//將讀到的幀顯示出來(lái)
        datectCarDaw(frame,cascade,2);//將讀到的幀傳入函數(shù)用作識(shí)別
        waitKey(25);//延時(shí)25ms,避免播放過(guò)快
    }

    Mat gray;
    //轉(zhuǎn)換為灰度圖,色彩通道縮小一半
    cvtColor(frame,gray,CV_RGB2GRAY);

    //再將灰度圖縮小一半
    Mat smalling(cvRound(frame.rows/scale),cvRound(frame.cols/scale),CV_8UC1);
    resize(gray,smalling,smalling.size(),0,0,INTER_LINEAR);

    //直方圖均衡化:將縮小一半的灰度圖進(jìn)行均值化   使其更加黑白分明
    equalizeHist(smalling,smalling);

    //調(diào)用級(jí)聯(lián)分類器進(jìn)行模型匹配并框出內(nèi)容
    vector<Rect>cars;
    //此函數(shù)參數(shù)說(shuō)明: 待檢測(cè)的圖片幀  被檢測(cè)物體的矩形向量容器  每次搜索減小的圖像比例  
    //檢測(cè)目標(biāo)周圍相鄰矩形的最小個(gè)數(shù)(此處設(shè)為2個(gè)) 類型 目標(biāo)區(qū)域的大小尺寸
    cascade.detectMultiScale(smalling,cars,1.05,5,0|CV_HAAR_SCALE_IMAGE,Size(25,25));
    
    vector<Rect>::const_iterator iter;
    //繪制方塊,標(biāo)記目標(biāo),注意,標(biāo)記要畫在原幀上,要講方框的大小和幀坐標(biāo)擴(kuò)大,因?yàn)槭歉鶕?jù)灰度圖識(shí)別的,灰度圖被縮小了
    for(iter=cars.begin();iter!=cars.end();iter++)
    {
        rectangle(frame,
                  cvPoint(cvRound(iter->x*scale),cvRound(iter->y*scale)),
                  cvPoint(cvRound((iter->x+iter->width)*scale),cvRound((iter->y+iter->height)*scale)),
                  Scalar(0,255,0),2,8
                    );
    }
    imshow("frame",frame);

#include <iostream>
#include <opencv2/opencv.hpp>
using namespace cv;
using namespace std;
 
void datectCarDaw(Mat &frame,CascadeClassifier cascade,double scale)
{
    Mat gray;
    //轉(zhuǎn)換為灰度圖,色彩通道縮小一半
    cvtColor(frame,gray,CV_RGB2GRAY);
    //imshow("huidu",gray);
    //再將灰度圖縮小一半
    Mat smalling(cvRound(frame.rows/scale),cvRound(frame.cols/scale),CV_8UC1);
    resize(gray,smalling,smalling.size(),0,0,INTER_LINEAR);
 
    //直方圖均衡化:將縮小一半的灰度圖進(jìn)行均值化   使其更加黑白分明
    equalizeHist(smalling,smalling);
    //imshow("smalling",smalling);
    //調(diào)用級(jí)聯(lián)分類器進(jìn)行模型匹配并框出內(nèi)容
    vector<Rect>cars;
    //此函數(shù)參數(shù)說(shuō)明: 待檢測(cè)的圖片幀  被檢測(cè)物體的矩形向量容器  每次搜索減小的圖像比例  檢測(cè)目標(biāo)周圍相鄰矩形的最小個(gè)數(shù)(此處設(shè)為2個(gè)) 類型 目標(biāo)區(qū)域的大小尺寸
    cascade.detectMultiScale(smalling,cars,1.05,5,0|CV_HAAR_SCALE_IMAGE,Size(25,25));
 
    vector<Rect>::const_iterator iter;
    //繪制方塊,標(biāo)記目標(biāo),注意,標(biāo)記要畫在原幀上,要講方框的大小和幀坐標(biāo)擴(kuò)大,因?yàn)槭歉鶕?jù)灰度圖識(shí)別的,灰度圖被縮小了
    for(iter=cars.begin();iter!=cars.end();iter++)
    {
        rectangle(frame,
                  cvPoint(cvRound(iter->x*scale),cvRound(iter->y*scale)),
                  cvPoint(cvRound((iter->x+iter->width)*scale),cvRound((iter->y+iter->height)*scale)),
                  Scalar(0,255,0),2,8
                    );
 
    }
    imshow("frame",frame);
}
 
int main(int argc, char *argv[])
{
    CascadeClassifier cascade;//級(jí)聯(lián)分類器(模型)
    cascade.load("D:/Qt_Opencv_Project/cars.xml");//讀取級(jí)聯(lián)分類器
    //cascade.load("D:/Qt_Opencv_Project/face.xml");
    Mat frame;
    VideoCapture cap("D:/VideoTraining/carMove.mp4");
    //VideoCapture cap(0);
    while (cap.read(frame))
    {
        imshow("video",frame);//將讀到的幀顯示出來(lái)
        datectCarDaw(frame,cascade,2);//將讀到的幀傳入函數(shù)用作識(shí)別
        waitKey(25);//延時(shí)25ms,避免播放過(guò)快
    }
    return 0;
}