车灯检测并绘制矩形框

最新推荐文章于 2023-12-22 22:53:16 发布

rj_monck

最新推荐文章于 2023-12-22 22:53:16 发布

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本文链接：https://blog.youkuaiyun.com/rj1457365980/article/details/88593056

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本文档展示了如何使用OpenCV库在C++中实现车灯检测并绘制矩形框的过程。首先，代码读取视频，然后进行图像预处理，包括灰度化和图像裁剪。接着，通过颜色和形态学操作提取红色区域，再利用SimpleBlobDetector进行Blob检测，找到车灯特征点。最后，根据特征点绘制矩形框，以便于进一步分析和识别。

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// Starfeatures_detector2.cpp : 定义控制台应用程序的入口点。
//

#include "stdafx.h"
#include <iostream>
#include <string.h>
#include <opencv2/opencv.hpp>
#include <opencv2/highgui/highgui.hpp>
#include <opencv2/features2d/features2d.hpp>
#include <opencv2/features2d.hpp>
using namespace cv;
using namespace std;

int main()
{
   //读取视频
   VideoCapture cap;
   cap.open("C:\\Users\\lbn\\Desktop\\夜间车辆\\6.mp4");

   if (!cap.isOpened()) {
       cout << "Failed to load the video!" << endl;
       return -1;
   }

   Mat image;
   while (1) {
       cap >> image;//等同于cap.read(frame);
       if (image.empty())break;//如果某帧为空则退出循坏

                               //重置图片大小
                               //Mat Image(480, 640, CV_8UC3);
                               //const int nTimes = 100;
                               //for (int i = 0; i < nTimes; i++)
                               //{
                               //   //resize(srcImage, Image, dstImage.size(), 0, 0);
                               //   resize(image, Image, Image.size(), 0, 0);
                               //}
       Mat Image;
       image.copyTo(Image);

       //灰度化
       Mat image_gray;
       cvtColor(Image, image_gray, CV_RGB2GRAY);

//将图像最上面的1/3区域置零

       Size img_size = Image.size();
       Mat image_t = Image.clone();
       Mat Mask(cvFloor(image_t.rows / 3), image_t.cols, CV_8UC3, Scalar(0));
       Mat roi = image_t(Rect(0, 0, image_t.cols, cvFloor(image_t.rows / 3)));
       Mask.copyTo(roi);

       //double t1 = (double)getTickCount();
       //for (int i = 0; i < cvFloor(image_t.rows / 3); i++) {
       //   for (int j = 0; j < image_t.cols; j++) {
       //       image_t.at<Vec3b>(i, j)[0] = 0;
       //       image_t.at<Vec3b>(i, j)[1] = 0;
       //       image_t.at<Vec3b>(i, j)[2] = 0;
       //   }
       //}
       //double t2 = (double)getTickCount();
       //cout << "time:" << (t2 - t1) * 1000 / getTickFrequency() << endl;

//imshow("【原图】", image_t);

       //=============================================================在RGB空间分割红色和白色区域================================================================//
       //将RGB图像分离通道
       vector<Mat> imageRGB;
       Mat dst;
       split(image_t, imageRGB);
       merge(imageRGB, dst);
       //显示图像
       //imshow("dst",dst);

       //分离RGB通道并分别显示
       vector<Mat> b, g, r;
       Mat tmp(image_t.size(), CV_8U, Scalar(0));
       for (int i = 0; i < 3; i++) {

           if (i == 0)
               b.push_back(imageRGB[0]);
           else
               b.push_back(tmp);

           if (i == 1)
               g.push_back(imageRGB[1]);
           else
               g.push_back(tmp);

           if (i == 2)
               r.push_back(imageRGB[2]);
           else
               r.push_back(tmp);

       }
       //merge(b,dst);
       //imshow("b",dst);

       //merge(g,dst);
       //imshow("g",dst);
       merge(r, dst);
       //imshow("r",dst);

       //检测红色强度较高的区域
       Mat imgdst;
       dst.copyTo(imgdst);
       for (int i = 0; i < image_t.rows; i++) {
           for (int j = 0; j < image_t.cols; j++) {
               //检测红色区域
               if (dst.at<Vec3b>(i, j)[2] >= 200 && dst.at<Vec3b>(i, j)[2] <= 255)
               {
                   imgdst.at<Vec3b>(i, j)[0] = 0;
                   imgdst.at<Vec3b>(i, j)[1] = 0;
                   imgdst.at<Vec3b>(i, j)[2] = 255;
               }
               else
               {
                   imgdst.at<Vec3b>(i, j)[0] = 0;
                   imgdst.at<Vec3b>(i, j)[1] = 0;
                   imgdst.at<Vec3b>(i, j)[2] = 0;
               }
               /*   if (dst.at<Vec3b>(i, j)[0] == 255 && dst.at<Vec3b>(i, j)[1] == 255 && dst.at<Vec3b>(i, j)[2] == 255) {
               imgdst.at<Vec3b>(i, j)[0] = 0;
               imgdst.at<Vec3b>(i, j)[1] = 0;
               imgdst.at<Vec3b>(i, j)[2] = 255;
               }
               else
               {
               imgdst.at<Vec3b>(i, j)[0] = 0;
               imgdst.at<Vec3b>(i, j)[1] = 0;
               imgdst.at<Vec3b>(i, j)[2] = 0;
               }*/
           }
       }
       //   imshow("红色提取RGB", imgdst);

//进行形态学腐蚀膨胀，以去除小区域

int g_nStructElementSize = 2;
Mat element = getStructuringElement(MORPH_ELLIPSE, Size(2 * g_nStructElementSize + 1, 2 * g_nStructElementSize + 1), Point(g_nStructElementSi

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