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C++ OpenCV红绿灯检测Demo实现详解

作者：Frank学习路上

OpenCV(Open Source Computer Vision Library)是开源的计算机视觉和机器学习库,提供了C++、 C、 Python、 Java接口,并支持Windows、 Linux、 Android、 Mac OS平台,下面这篇文章主要给大家介绍了关于C++ OpenCV红绿灯检测Demo实现的相关资料,需要的朋友可以参考下

很久以来一直想实现红绿灯检测，今天它来了。

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：

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 <opencv2/imgproc/types_c.h>	//opencv3-4
#include <opencv2/imgproc/imgproc_c.h>	//出现很多未声明标识符的问题
#include <windows.h>
#include <iostream>

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 0
	if (!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 input
		if (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;
	else
		return false;
}

运行结果如下（b站视频）：

打包程序为exe

首先在VS的扩展和更新中安装Installer的扩展：

然后在解决方案下新建setup工程：

添加项目输出：

在主输出这里创建快捷方式，然后移动到User’s Desktop文件夹下：

然后添加工程所需文件，把工程所需的数据文件和依赖库都添加进来：

找依赖库的方式可以用这个命令，然后搜索并添加进来：

最后，点击生成，生成完成后，就可以安装了：

安装文件如下：

这样打包出来的安装程序在开发电脑上可以正常运行，但分发出去后其他电脑运行会闪退，我已经把所需的dll（opencv）都添加进来了，有大佬解释一下吗。

以上。

总结

到此这篇关于C++ OpenCV红绿灯检测Demo实现的文章就介绍到这了,更多相关C++ OpenCV红绿灯检测内容请搜索脚本之家以前的文章或继续浏览下面的相关文章希望大家以后多多支持脚本之家！

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