【OpenCV】红绿灯检测C++Demo实现

时间：2022-11-04 22:52:54

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

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：

  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

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）：

【OpenCV】红绿灯检测C++Demo实现

打包程序为exe

首先在VS的扩展和更新中安装Installer的扩展：

【OpenCV】红绿灯检测C++Demo实现

然后在解决方案下新建setup工程：

【OpenCV】红绿灯检测C++Demo实现

添加项目输出：

【OpenCV】红绿灯检测C++Demo实现

在主输出这里创建快捷方式，然后移动到User’s Desktop文件夹下：

【OpenCV】红绿灯检测C++Demo实现

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：

【OpenCV】红绿灯检测C++Demo实现

找依赖库的方式可以用这个命令，然后搜索并添加进来：

【OpenCV】红绿灯检测C++Demo实现

最后，点击生成，生成完成后，就可以安装了：

【OpenCV】红绿灯检测C++Demo实现

安装文件如下：

【OpenCV】红绿灯检测C++Demo实现

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。

标签：

相关文章