【OpenCV】红绿灯检测C++Demo实现

很久以来一直想实现红绿灯检测，今天它来了。

文章目录

- 原理
- 代码实现
- 打包程序为exe

原理

OpenCV好强，能够提取红绿灯的轮廓，并根据颜色空间判断红绿，不依赖深度学习算法也能做到可用的效果/demo。

红绿灯检测的基本步骤如下：

轮廓检测、计数
red、green和light_out三种状态
提取颜色空间，红和绿
膨胀和腐蚀，去除噪点
判断3种状态

代码实现

基于网络上的代码做复现的时候，遇到了opencv不同版本所出现的标识符未声明问题，我这里是基于opencv4.5.4实现的，4.x的应该都可以运行。

创建trafficlight.h头文件，将一些引用和全局变量放进来：

#pragma once#include "opencv2/opencv.hpp"#include "opencv2/imgproc.hpp"#include //opencv3-4#include //出现很多未声明标识符的问题#include #include using namespace std;using namespace cv;// 函数声明int processImgR(Mat);int processImgG(Mat);bool isIntersected(Rect, Rect);void detect(Mat& frame);// 全局变量bool isFirstDetectedR = true;bool isFirstDetectedG = true;Rect* lastTrackBoxR;Rect* lastTrackBoxG;int lastTrackNumR;int lastTrackNumG;

然后创建main.cpp，将主函数和功能函数加进来：

//下一步：如何调整视频检测框，防止误检#include "trafficlight.h"/*1.轮廓检测、计数2.red、green和light_out三种状态3.提取颜色空间，红和绿4.膨胀和腐蚀，去除噪点5.判断3种状态*///主函数int main(){int redCount = 0;int greenCount = 0;Mat frame;Mat img;Mat imgYCrCb;Mat imgGreen;Mat imgRed;// 亮度参数double a = 0.3;double b = (1 - a) * 125;VideoCapture capture("traffic.mkv");//导入视频的路径/摄像头 0if (!capture.isOpened()){cout << "Start device failed!\n" << endl;//启动设备失败！return -1;}// 帧处理while (1){capture >> frame;//调整亮度frame.convertTo(img, img.type(), a, b);//转换为YCrCb颜色空间cvtColor(img, imgYCrCb, CV_BGR2YCrCb);imgRed.create(imgYCrCb.rows, imgYCrCb.cols, CV_8UC1);imgGreen.create(imgYCrCb.rows, imgYCrCb.cols, CV_8UC1);//分解YCrCb的三个成分vector<Mat> planes;split(imgYCrCb, planes);// 遍历以根据Cr分量拆分红色和绿色MatIterator_<uchar> it_Cr = planes[1].begin<uchar>(),it_Cr_end = planes[1].end<uchar>();MatIterator_<uchar> it_Red = imgRed.begin<uchar>();MatIterator_<uchar> it_Green = imgGreen.begin<uchar>();for (; it_Cr != it_Cr_end; ++it_Cr, ++it_Red, ++it_Green){// RED, 145<Cr<470 红色if (*it_Cr > 145 && *it_Cr < 470)*it_Red = 255;else*it_Red = 0;// GREEN 95<Cr<110 绿色if (*it_Cr > 95 && *it_Cr < 110)*it_Green = 255;else*it_Green = 0;}//膨胀和腐蚀dilate(imgRed, imgRed, Mat(15, 15, CV_8UC1), Point(-1, -1));erode(imgRed, imgRed, Mat(1, 1, CV_8UC1), Point(-1, -1));dilate(imgGreen, imgGreen, Mat(15, 15, CV_8UC1), Point(-1, -1));erode(imgGreen, imgGreen, Mat(1, 1, CV_8UC1), Point(-1, -1));redCount = processImgR(imgRed);greenCount = processImgG(imgGreen);cout << "red:" << redCount << ";  " << "green:" << greenCount << endl;//条件判断if (redCount == 0 && greenCount == 0){cv::putText(frame, "lights out", Point(40, 150), cv::FONT_HERSHEY_SIMPLEX, 2, cv::Scalar(255, 255, 255), 8, 8, 0);}else if (redCount > greenCount){cv::putText(frame, "red light", Point(40, 150), cv::FONT_HERSHEY_SIMPLEX, 2, cv::Scalar(0, 0, 255), 8, 8, 0);}else {cv::putText(frame, "green light", Point(40, 150), cv::FONT_HERSHEY_SIMPLEX, 2, cv::Scalar(0, 255, 0), 8, 8, 0);}imshow("video", frame);//imshow("Red", imgRed);//imshow("Green", imgGreen);// Handle with the keyboard inputif (waitKey(20) == 'q')break;}return 0;}//轮廓处理函数：红int processImgR(Mat src){Mat tmp;vector<vector<Point>> contours;vector<Vec4i> hierarchy;vector<Point> hull;CvPoint2D32f tempNode;CvMemStorage* storage = cvCreateMemStorage();CvSeq* pointSeq = cvCreateSeq(CV_32FC2, sizeof(CvSeq), sizeof(CvPoint2D32f), storage);Rect* trackBox;Rect* result;int resultNum = 0;int area = 0;src.copyTo(tmp);//提取轮廓findContours(tmp, contours, hierarchy, CV_RETR_CCOMP, CV_CHAIN_APPROX_SIMPLE);if (contours.size() > 0){trackBox = new Rect[contours.size()];result = new Rect[contours.size()];//确定要跟踪的区域for (int i = 0; i < contours.size(); i++){cvClearSeq(pointSeq);// 获取凸包的点集convexHull(Mat(contours[i]), hull, true);int hullcount = (int)hull.size();// 凸包的保存点for (int j = 0; j < hullcount - 1; j++){tempNode.x = hull[j].x;tempNode.y = hull[j].y;cvSeqPush(pointSeq, &tempNode);}trackBox[i] = cvBoundingRect(pointSeq);}if (isFirstDetectedR){lastTrackBoxR = new Rect[contours.size()];for (int i = 0; i < contours.size(); i++)lastTrackBoxR[i] = trackBox[i];lastTrackNumR = contours.size();isFirstDetectedR = false;}else{for (int i = 0; i < contours.size(); i++){for (int j = 0; j < lastTrackNumR; j++){if (isIntersected(trackBox[i], lastTrackBoxR[j])){result[resultNum] = trackBox[i];break;}}resultNum++;}delete[] lastTrackBoxR;lastTrackBoxR = new Rect[contours.size()];for (int i = 0; i < contours.size(); i++){lastTrackBoxR[i] = trackBox[i];}lastTrackNumR = contours.size();}delete[] trackBox;}else{isFirstDetectedR = true;result = NULL;}cvReleaseMemStorage(&storage);if (result != NULL){for (int i = 0; i < resultNum; i++){area += result[i].area();}}delete[] result;return area;}//轮廓处理函数：绿int processImgG(Mat src){Mat tmp;vector<vector<Point> > contours;vector<Vec4i> hierarchy;vector< Point > hull;CvPoint2D32f tempNode;CvMemStorage* storage = cvCreateMemStorage();CvSeq* pointSeq = cvCreateSeq(CV_32FC2, sizeof(CvSeq), sizeof(CvPoint2D32f), storage);Rect* trackBox;Rect* result;int resultNum = 0;int area = 0;src.copyTo(tmp);//提取轮廓findContours(tmp, contours, hierarchy, CV_RETR_CCOMP, CV_CHAIN_APPROX_SIMPLE);if (contours.size() > 0){trackBox = new Rect[contours.size()];result = new Rect[contours.size()];// 确定要跟踪的区域for (int i = 0; i < contours.size(); i++){cvClearSeq(pointSeq);// 获取凸包的点集convexHull(Mat(contours[i]), hull, true);int hullcount = (int)hull.size();// 保存凸包的点for (int j = 0; j < hullcount - 1; j++){tempNode.x = hull[j].x;tempNode.y = hull[j].y;cvSeqPush(pointSeq, &tempNode);}trackBox[i] = cvBoundingRect(pointSeq);}if (isFirstDetectedG){lastTrackBoxG = new Rect[contours.size()];for (int i = 0; i < contours.size(); i++)lastTrackBoxG[i] = trackBox[i];lastTrackNumG = contours.size();isFirstDetectedG = false;}else{for (int i = 0; i < contours.size(); i++){for (int j = 0; j < lastTrackNumG; j++){if (isIntersected(trackBox[i], lastTrackBoxG[j])){result[resultNum] = trackBox[i];break;}}resultNum++;}delete[] lastTrackBoxG;lastTrackBoxG = new Rect[contours.size()];for (int i = 0; i < contours.size(); i++){lastTrackBoxG[i] = trackBox[i];}lastTrackNumG = contours.size();}delete[] trackBox;}else{isFirstDetectedG = true;result = NULL;}cvReleaseMemStorage(&storage);if (result != NULL){for (int i = 0; i < resultNum; i++){area += result[i].area();}}delete[] result;return area;}//确定两个矩形区域是否相交bool isIntersected(Rect r1, Rect r2){int minX = max(r1.x, r2.x);int minY = max(r1.y, r2.y);int maxX = min(r1.x + r1.width, r2.x + r2.width);int maxY = min(r1.y + r1.height, r2.y + r2.height);//判断是否相交if (minX < maxX && minY < maxY)return true;elsereturn false;}