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C++?OpenCV實(shí)現(xiàn)之實(shí)現(xiàn)紅綠燈識(shí)別

更新時(shí)間：2022年08月02日 10:03:27 作者：猿力豬

本文以實(shí)現(xiàn)行車過程當(dāng)中的紅綠燈識(shí)別為目標(biāo)，核心的內(nèi)容包括：OpenCV輪廓識(shí)別原理以及OpenCV紅綠燈識(shí)別的實(shí)現(xiàn)具體步驟，感興趣的可以了解一下

前言

本文以實(shí)現(xiàn)行車過程當(dāng)中的紅綠燈識(shí)別為目標(biāo)，核心的內(nèi)容包括：OpenCV輪廓識(shí)別原理以及OpenCV紅綠燈識(shí)別的實(shí)現(xiàn)具體步驟

一、輪廓識(shí)別相關(guān)原理

什么是輪廓檢測(cè)

目前輪廓檢測(cè)方法有兩類，一類是利用傳統(tǒng)的邊緣檢測(cè)算子檢測(cè)目標(biāo)輪廓，另一類是從人類視覺系統(tǒng)中提取可以使用的數(shù)學(xué)模型完成目標(biāo)輪廓檢測(cè)

輪廓提取函數(shù)findContours

函數(shù)原型:

findContours( InputOutputArray image, OutputArrayOfArrays contours,
OutputArray hierarchy, int mode,
int method, Point offset=Point());

參數(shù)：

image：單通道圖像矩陣，可以是灰度圖，但更常用的是二值圖像，一般是經(jīng)過Canny、拉普拉斯等邊緣檢測(cè)算子處理過的二值圖像

contours: 定義為“vector<vector<Point>> contours”，是一個(gè)向量，并且是一個(gè)雙重向量，向量?jī)?nèi)每個(gè)元素保存了一組由連續(xù)的Point點(diǎn)構(gòu)成的點(diǎn)的集合的向量，每一組Point點(diǎn)集就是一個(gè)輪廓。有多少輪廓，向量contours就有多少元素

hierarchy: 也是一個(gè)向量，向量?jī)?nèi)每個(gè)元素保存了一個(gè)包含4個(gè)int整型的數(shù)組。向量?jī)?nèi)的元素和輪廓向量contours內(nèi)的元素是一一對(duì)應(yīng)的，向量的容量相同

int mode:

取值一：CV_CHAIN_APPROX_NONE 保存物體邊界上所有連續(xù)的輪廓點(diǎn)到 contours向量?jī)?nèi)

取值二：CV_CHAIN_APPROX_SIMPLE 僅保存輪廓的拐點(diǎn)信息，把所有輪廓拐點(diǎn)處的點(diǎn)保存入contours向量?jī)?nèi)，拐點(diǎn)與拐點(diǎn)之間直線段上的信息點(diǎn)不予保留

取值三和四：CV_CHAIN_APPROX_TC89_L1，CV_CHAIN_APPROX_TC89_KCOS使用teh-Chinl chain 近似算法

Point： Point偏移量，所有的輪廓信息相對(duì)于原始圖像對(duì)應(yīng)點(diǎn)的偏移量，相當(dāng)于在每一個(gè)檢測(cè)出的輪廓點(diǎn)上加上該偏移量，并且Point還可以是負(fù)值！

參數(shù)詳解引用出處：findContours函數(shù)參數(shù)詳解

二、案例實(shí)現(xiàn)

這是本案例所用到的素材，如下圖所示：

PS：視頻的效果比較好，如果方便的話可以自行外出拍攝取材

Step1：初始化配置

做好準(zhǔn)備工作，創(chuàng)建我們需要的內(nèi)容，設(shè)置亮度參數(shù)，以及我們導(dǎo)入的視頻路徑

    int redCount = 0;
    int greenCount = 0;
 
    Mat frame;
    Mat img;
    Mat imgYCrCb;
    Mat imgGreen;
    Mat imgRed;
 
    // 亮度參數(shù)
    double a = 0.3;
    double b = (1 - a) * 125;
 
    VideoCapture capture("C:/Users/86177/Desktop/image/123.mp4");//導(dǎo)入視頻的路徑
    if (!capture.isOpened())
    {
        cout << "Start device failed!\n" << endl;//啟動(dòng)設(shè)備失??！
        return -1;
    }

Step2：進(jìn)行幀處理

調(diào)整視頻亮度，分解YCrCb的三個(gè)成分，拆分紅色和綠色，方便對(duì)紅綠兩種顏色進(jìn)行特征提取

    // 幀處理
    while (1)
    {
        capture >> frame;
        //調(diào)整亮度
        frame.convertTo(img, img.type(), a, b);
 
        //轉(zhuǎn)換為YCrCb顏色空間
        cvtColor(img, imgYCrCb, CV_BGR2YCrCb);
 
        imgRed.create(imgYCrCb.rows, imgYCrCb.cols, CV_8UC1);
        imgGreen.create(imgYCrCb.rows, imgYCrCb.cols, CV_8UC1);
 
        //分解YCrCb的三個(gè)成分
        vector<Mat> planes;
        split(imgYCrCb, planes);
        // 遍歷以根據(jù)Cr分量拆分紅色和綠色
        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 紅色
            if (*it_Cr > 145 && *it_Cr < 470)
                *it_Red = 255;
            else
                *it_Red = 0;
 
            // GREEN 95<Cr<110 綠色
            if (*it_Cr > 95 && *it_Cr < 110)
                *it_Green = 255;
            else
                *it_Green = 0;
        }

Step3：膨脹腐蝕處理

去除其他噪點(diǎn)，提高我們需要的紅綠燈的特征

        //膨脹和腐蝕
        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;

Step4：紅綠燈提示判斷

加入紅綠燈識(shí)別結(jié)果顯示，方便我們查看效果

        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);
        }

Step5：輪廓提取

分別對(duì)紅燈和綠燈進(jìn)行輪廓特征提取，提高辨識(shí)度

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);
 
    //提取輪廓
    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()];
 
        //確定要跟蹤的區(qū)域
        for (int i = 0; i < contours.size(); i++)
        {
            cvClearSeq(pointSeq);
            // 獲取凸包的點(diǎn)集
            convexHull(Mat(contours[i]), hull, true);
            int hullcount = (int)hull.size();
            // 凸包的保存點(diǎn)
            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;
}
 
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);
    //提取輪廓
    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()];
 
        // 確定要跟蹤的區(qū)域
        for (int i = 0; i < contours.size(); i++)
        {
            cvClearSeq(pointSeq);
            // 獲取凸包的點(diǎn)集
            convexHull(Mat(contours[i]), hull, true);
            int hullcount = (int)hull.size();
            // 保存凸包的點(diǎn)
            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;
}

Step6：判斷是否相交

//確定兩個(gè)矩形區(qū)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);
 
    if (minX < maxX && minY < maxY)
        return true;
    else
        return false;
}

案例效果

完整代碼

#include "opencv2/opencv.hpp"
#include "opencv2/imgproc.hpp"
#include <windows.h>
#include <iostream>
 
using namespace std;
using namespace cv;
 
// Function headers
int processImgR(Mat);
int processImgG(Mat);
bool isIntersected(Rect, Rect);
 
// Global variables
bool isFirstDetectedR = true;
bool isFirstDetectedG = true;
Rect* lastTrackBoxR;
Rect* lastTrackBoxG;
int lastTrackNumR;
int lastTrackNumG;
 
//主函數(shù)
int main()
{
    int redCount = 0;
    int greenCount = 0;
 
    Mat frame;
    Mat img;
    Mat imgYCrCb;
    Mat imgGreen;
    Mat imgRed;
 
    // 亮度參數(shù)
    double a = 0.3;
    double b = (1 - a) * 125;
 
    VideoCapture capture("C:/Users/86177/Desktop/image/123.mp4");//導(dǎo)入視頻的路徑
    if (!capture.isOpened())
    {
        cout << "Start device failed!\n" << endl;//啟動(dòng)設(shè)備失??！
        return -1;
    }
 
    // 幀處理
    while (1)
    {
        capture >> frame;
        //調(diào)整亮度
        frame.convertTo(img, img.type(), a, b);
 
        //轉(zhuǎn)換為YCrCb顏色空間
        cvtColor(img, imgYCrCb, CV_BGR2YCrCb);
 
        imgRed.create(imgYCrCb.rows, imgYCrCb.cols, CV_8UC1);
        imgGreen.create(imgYCrCb.rows, imgYCrCb.cols, CV_8UC1);
 
        //分解YCrCb的三個(gè)成分
        vector<Mat> planes;
        split(imgYCrCb, planes);
        // 遍歷以根據(jù)Cr分量拆分紅色和綠色
        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 紅色
            if (*it_Cr > 145 && *it_Cr < 470)
                *it_Red = 255;
            else
                *it_Red = 0;
 
            // GREEN 95<Cr<110 綠色
            if (*it_Cr > 95 && *it_Cr < 110)
                *it_Green = 255;
            else
                *it_Green = 0;
        }
 
        //膨脹和腐蝕
        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;
 
        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 (cvWaitKey(20) == 'q')
            break;
    }
 
    return 0;
}
 
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);
 
    //提取輪廓
    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()];
 
        //確定要跟蹤的區(qū)域
        for (int i = 0; i < contours.size(); i++)
        {
            cvClearSeq(pointSeq);
            // 獲取凸包的點(diǎn)集
            convexHull(Mat(contours[i]), hull, true);
            int hullcount = (int)hull.size();
            // 凸包的保存點(diǎn)
            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;
}
 
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);
    //提取輪廓
    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()];
 
        // 確定要跟蹤的區(qū)域
        for (int i = 0; i < contours.size(); i++)
        {
            cvClearSeq(pointSeq);
            // 獲取凸包的點(diǎn)集
            convexHull(Mat(contours[i]), hull, true);
            int hullcount = (int)hull.size();
            // 保存凸包的點(diǎn)
            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;
}
 
//確定兩個(gè)矩形區(qū)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);
 
    if (minX < maxX && minY < maxY)
        return true;
    else
        return false;
}

三、總結(jié)

本文主要講解OpenCV輪廓識(shí)別原理以及OpenCV紅綠燈識(shí)別的實(shí)現(xiàn)具體步驟OpenCV還是有很多識(shí)別的庫函數(shù)可以用，接下來繼續(xù)探索，結(jié)合生活實(shí)際繼續(xù)做一些有意思的案例

到此這篇關(guān)于C++ OpenCV實(shí)現(xiàn)之實(shí)現(xiàn)紅綠燈識(shí)別的文章就介紹到這了,更多相關(guān)C++ OpenCV紅綠燈識(shí)別內(nèi)容請(qǐng)搜索腳本之家以前的文章或繼續(xù)瀏覽下面的相關(guān)文章希望大家以后多多支持腳本之家！

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C++?OpenCV實(shí)現(xiàn)之實(shí)現(xiàn)紅綠燈識(shí)別

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Step4：紅綠燈提示判斷

Step5：輪廓提取

Step6：判斷是否相交

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最新国产好看的视频,伊人天堂AV在线,国产Aaaaaa视频,蜜臀视频在线观看一区,人妻av色图,密臀久久久精品影片,青青视频免费观看毛片,久草在线观看视,国产三级精品色情在线

C++?OpenCV實(shí)現(xiàn)之實(shí)現(xiàn)紅綠燈識(shí)別

目錄

前言

一、輪廓識(shí)別相關(guān)原理

什么是輪廓檢測(cè)

輪廓提取函數(shù)findContours

二、案例實(shí)現(xiàn)

Step1：初始化配置

Step2：進(jìn)行幀處理

Step3：膨脹腐蝕處理

Step4：紅綠燈提示判斷

Step5：輪廓提取

Step6：判斷是否相交

完整代碼

三、總結(jié)

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一、輪廓識(shí)別相關(guān)原理

二、案例實(shí)現(xiàn)

三、總結(jié)