基于opencv的车辆检测

// 定义控制台应用程序的入口点。 // #include "opencv2/objdetect/objdetect.hpp" #include "opencv2/highgui/highgui.hpp" #include "opencv2/imgproc/imgproc.hpp" #include "opencv2/ml/ml.hpp" #include <iostream> #include <stdio.h> using namespace std; using namespace cv; void detectAndDraw(Mat& img, CascadeClassifier& cascade, double scale) { double t = 0; vector<Rect> car; const static Scalar colors[] = { CV_RGB(0, 0, 255),}; Mat gray, smallImg(cvRound(img.rows / scale), cvRound(img.cols / scale), CV_8UC1);//将图片缩小，加快检测速度 cvtColor(img, gray, CV_BGR2GRAY);//因为用的是类haar特征，所以都是基于灰度图像的，这里要转换成灰度图像 resize(gray, smallImg, smallImg.size(), 0, 0, INTER_LINEAR);//将尺寸缩小到1/scale,用线性插值 equalizeHist(smallImg, smallImg);//直方图均衡 t = (double)cvGetTickCount();//用来计算算法执行时间 cascade.detectMultiScale(smallImg,car, 1.1, 2, 0 //|CV_HAAR_FIND_BIGGEST_OBJECT //|CV_HAAR_DO_ROUGH_SEARCH | CV_HAAR_SCALE_IMAGE , Size(30, 30)); t = (double)cvGetTickCount() - t;//相减为算法执行的时间 printf("detection time = %g ms\n", t / ((double)cvGetTickFrequency()*1000.)); for (vector<Rect>::const_iterator r =car.begin(); r != car.end(); r++) { Mat smallImgROI; vector<Rect> nestedObjects; Point center; Scalar color = colors[0]; int radius; center.x = cvRound((r->x + r->width*0.5)*scale);//还原成原来的大小 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); smallImgROI = smallImg(*r); } cv::imshow("result", img); } int main(int argc, const char** argv) { VideoCapture capture("4.flv"); //【2】循环显示每一帧 while (1) { Mat frame;//定义一个Mat变量，用于存储每一帧的图像 capture >> frame; //读取当前帧 imshow("读取视频", frame); //显示当前帧 waitKey(1); //延时30ms CascadeClassifier cascade;//创建级联分类器对象 double scale = 3.0; if (!cascade.load("model.xml"))//从指定的文件目录中加载级联分类器 { cerr << "ERROR: Could not load classifier cascade" << endl; return 0; } if (!frame.empty())//读取图片数据不能为空 { detectAndDraw(frame, cascade, scale); //waitKey(0); } }return 0; }