OpenCV实现车辆识别和运动目标检测

//1 灰度处理 目的 RGB三通道转灰度单通道 压缩到原图片三分之一大小
    cvtColor(frontFrame,frontGray,CV_RGB2GRAY);//前一帧灰度化处理
    cvtColor(afterFrame,afterGray,CV_RGB2GRAY);//后一帧灰度化处理
    //imshow("frontGray",frontGray);//测试
    //imshow("afterGray",afterGray);//测试

 //2 帧差处理 目的 找到帧与帧之间的差异(正在运动的物体)
    Mat diff;
    Mat frontGray,afterGray;
    absdiff(frontGray,afterGray,diff);//前后帧对比存于diff中
    imshow("diff",diff);//测试

    //3 二值化处理 目的 将灰度图继续识别转换为黑白分明的图像
    threshold(diff,diff,25,255,CV_THRESH_BINARY);
    imshow("threshold",diff);//测试

    //4 图像降噪
    //4-1 腐蚀处理 目的 去除白色噪点
    Mat element = cv::getStructuringElement(MORPH_RECT,Size(3,3));//小于3*3方块的白色噪点都会被腐蚀
    erode(diff,diff,element);
    imshow("erode",diff);//测试

    //4-2 膨胀 目的 把白色区域变大
    Mat element2=cv::getStructuringElement(MORPH_RECT,Size(20,20));
    dilate(diff,diff,element2);
    imshow("dilate",diff);//测试

    //5 提取关键点
    //5-1 查找特征点
    vector<vector<Point>>contours;
    findContours(diff,contours,CV_RETR_EXTERNAL,CV_CHAIN_APPROX_SIMPLE,Point(0,0));
 
    //5-2 提取关键点
    vector<vector<Point>>contours_poly(contours.size());
    vector<Rect>boundRect(contours.size());
 
    //5-3 确定下四个点来用于框选目标物体
    int x,y,w,h;
    int num=contours.size();
    for(int i = 0;i < num;i++)
    {
        approxPolyDP(Mat(contours[i]),contours_poly[i],3,true);
        //多边拟合
        boundRect[i]=boundingRect(Mat(contours_poly[i]));
        x=boundRect[i].x;
        y=boundRect[i].y;
        w=boundRect[i].width;
        h=boundRect[i].height;
        //绘制矩形
        rectangle(resFrame,Point(x,y),Point(x+w,y+h),Scalar(0,0,255),2);
    }

#include <iostream>
#include <opencv2/opencv.hpp>
 
using namespace std;
using namespace cv;
 
Mat moveCheck(Mat &frontFrame,Mat &afterFrame)
{
    Mat resFrame,diff;
    Mat frontGray,afterGray;
 
    //克隆当前帧画面 返回最终结果
    resFrame = afterFrame.clone();
 
    //1 灰度处理 目的 RGB三通道转灰度单通道 压缩到原图片三分之一大小
    cvtColor(frontFrame,frontGray,CV_RGB2GRAY);
    cvtColor(afterFrame,afterGray,CV_RGB2GRAY);
 
    //imshow("frontGray",frontGray);
    //imshow("afterGray",afterGray);
 
    //2 帧差处理 目的 找到帧与帧之间的差异(正在运动的物体)
    absdiff(frontGray,afterGray,diff);
    //imshow("diff",diff);
 
    //3 二值化处理 目的 将灰度图继续识别转换为黑白分明的图像
    threshold(diff,diff,25,255,CV_THRESH_BINARY);
    //imshow("threshold",diff);
 
    //4 图像降噪
    //4-1 腐蚀处理 目的 去除白色噪点
    Mat element = cv::getStructuringElement(MORPH_RECT,Size(3,3));//小于3*3方块的白色噪点都会被腐蚀
    erode(diff,diff,element);
    //imshow("erode",diff);
 
    //4-2 膨胀 目的 把白色区域变大
    Mat element2=cv::getStructuringElement(MORPH_RECT,Size(20,20));
    dilate(diff,diff,element2);
    //imshow("dilate",diff);
 
    //5 提取关键点
    //5-1 查找特征点
    vector<vector<Point>>contours;
    findContours(diff,contours,CV_RETR_EXTERNAL,CV_CHAIN_APPROX_SIMPLE,Point(0,0));
 
    //5-2 提取关键点
    vector<vector<Point>>contours_poly(contours.size());
    vector<Rect>boundRect(contours.size());
 
    //5-3 确定下四个点来用于框选目标物体
    int x,y,w,h;
    int num=contours.size();
    for(int i = 0;i < num;i++)
    {
        approxPolyDP(Mat(contours[i]),contours_poly[i],3,true);
        //多边拟合
        boundRect[i]=boundingRect(Mat(contours_poly[i]));
        x=boundRect[i].x;
        y=boundRect[i].y;
        w=boundRect[i].width;
        h=boundRect[i].height;
        //绘制矩形
        rectangle(resFrame,Point(x,y),Point(x+w,y+h),Scalar(0,0,255),2);
    }
 
    return resFrame;
}
 
int main(int argc, char *argv[])
{
    Mat frame;
    Mat temp;
    Mat res;
    int count = 0;
 
    VideoCapture cap("D:/00000000000003jieduanshipincailliao/carMove.mp4");//视频路径
 
    while (cap.read(frame))
    {
        count++;
        if(count == 1)
        {
            res = moveCheck(frame,frame);
        }
        else
        {
            res = moveCheck(temp,frame);
        }
        imshow("frame",frame);
        imshow("res",res);//最终车辆识别成果
        temp = frame.clone();
        waitKey(15);
    }
    return 0;
}