图像拼接（四）：双摄像头实时视频拼接（平移模型）

假设两个摄像头平行固定，所拍摄的图像视差很小，可以通过“柱面投影+模板匹配+渐入渐出融合”的解决方案实现视频拼接。

关于这种方法的静态图像拼接，参考图像拼接（一）：柱面投影+模板匹配+渐入渐出融合

OpenCV双摄像头捕获视频并实时显示的代码，参见：图像拼接（三）：OpenCV同时打开两个摄像头捕获视频

将代码整合，实现双摄像头实时视频拼接：

#include "opencv2/core/core.hpp"
#include "opencv2/highgui/highgui.hpp"
#include "opencv2/imgproc/imgproc.hpp"
#include<iostream>

using namespace cv;
using namespace std;

//以下3个函数，与系列文章：图像拼接（一）...中的函数，相同
//柱面投影
Mat cylinder(Mat imgIn, int f);
//计算两幅图像之间的平移量
Point2i getOffset(Mat img, Mat img1);
//线性融合
Mat linearFusion(Mat img, Mat img1, Point2i a);

int main()
{
    VideoCapture cap1();
    VideoCapture cap2();

    double rate = ;
    int delay =  / rate;
    bool stop(false);
    Mat frame1;
    Mat frame2;
    Mat frame;
    Point2i a;//存储偏移量
    int k = ;

    namedWindow("cam1", CV_WINDOW_AUTOSIZE);
    namedWindow("cam2", CV_WINDOW_AUTOSIZE);
    namedWindow("stitch", CV_WINDOW_AUTOSIZE);

    if (cap1.isOpened()&&cap2.isOpened())
    {
        cout << "*** ***" << endl;
        cout << "摄像头已启动！"<<endl;
    }
    else
    {
        cout << "*** ***" << endl;
        cout << "警告：摄像头打开不成功或者未检测到有两个摄像头!" << endl;
        cout << "程序结束！" <<endl<< "*** ***" << endl;
        return -;
    }

    //cap1.set(CV_CAP_PROP_FRAME_WIDTH, 640);
    //cap1.set(CV_CAP_PROP_FRAME_HEIGHT, 480);
    //cap2.set(CV_CAP_PROP_FRAME_WIDTH, 640);
    //cap2.set(CV_CAP_PROP_FRAME_HEIGHT, 480);
    cap1.set(CV_CAP_PROP_FOCUS,);
    cap2.set(CV_CAP_PROP_FOCUS, );

    while (!stop)
    {
        if (cap1.read(frame1) && cap2.read(frame2))
        {
            imshow("cam1", frame1);
            imshow("cam2", frame2);

            //彩色帧转灰度
            cvtColor(frame1, frame1, CV_RGB2GRAY);
            cvtColor(frame2, frame2, CV_RGB2GRAY);

            //柱面投影变换
            //frame1 = cylinder(frame1, 1005);
            //frame2 = cylinder(frame2, 1005);
            //匹配和拼接
            /*视频拼接通过while循环实现，下面这个判断的意思是，有两
             *种情形才计算平移参数，一是程序启动时，前3个循环内；二
             *是按下回车键时。这样在场景和摄像头相对固定时，避免了平
             *移量的重复计算，提高了拼接的实时性
             */
            if (k <  || waitKey(delay) == )//按回车键
            {
                cout << "正在匹配..."<<endl;
                a = getOffset(frame1, frame2);
            }
            frame = linearFusion(frame1, frame2, a);

            imshow("stitch", frame);
            k++;

        }
        else
        {
            cout << "----------------------" << endl;
            cout << "waitting..." << endl;
        }
        //按下ESC键，退出循环，程序结束
        if (waitKey() == )
        {
            stop = true;
            cout << "程序结束！" << endl;
            cout << "*** ***" << endl;
        }
    }
    return ;
}

//计算平移参数
Point2i getOffset(Mat img, Mat img1)
{
    Mat templ(img1, Rect(, *img1.rows, *img1.cols, *img1.rows));
    Mat result(img.cols - templ.cols + , img.rows - templ.rows + , CV_8UC1);//result存放匹配位置信息
    matchTemplate(img, templ, result, CV_TM_CCORR_NORMED);
    normalize(result, result, , , NORM_MINMAX, -, Mat());
    double minVal; double maxVal; Point minLoc; Point maxLoc; Point matchLoc;
    minMaxLoc(result, &minVal, &maxVal, &minLoc, &maxLoc, Mat());
    matchLoc = maxLoc;//获得最佳匹配位置
    int dx = matchLoc.x;
    int dy = matchLoc.y - *img1.rows;//右图像相对左图像的位移
    Point2i a(dx, dy);
    return a;
}   

//线性（渐入渐出）融合
Mat linearFusion(Mat img, Mat img1, Point2i a)
{
    int d = img.cols - a.x;//过渡区宽度
    int ms = img.rows - abs(a.y);//拼接图行数
    int ns = img.cols + a.x;//拼接图列数
    Mat stitch = Mat::zeros(ms, ns, CV_8UC1);
    //拼接
    Mat_<uchar> ims(stitch);
    Mat_<uchar> im(img);
    Mat_<uchar> im1(img1);

    if (a.y >= )
    {
        Mat roi1(stitch, Rect(, , a.x, ms));
        img(Range(a.y, img.rows), Range(, a.x)).copyTo(roi1);
        Mat roi2(stitch, Rect(img.cols, , a.x, ms));
        img1(Range(, ms), Range(d, img1.cols)).copyTo(roi2);
        for (int i = ; i < ms; i++)
            for (int j = a.x; j < img.cols; j++)
                ims(i, j) = uchar((img.cols - j) / float(d)*im(i + a.y, j) + (j - a.x) / float(d)*im1(i, j - a.x));

    }
    else
    {
        Mat roi1(stitch, Rect(, , a.x, ms));
        img(Range(, ms), Range(, a.x)).copyTo(roi1);
        Mat roi2(stitch, Rect(img.cols, , a.x, ms));
        img1(Range(-a.y, img.rows), Range(d, img1.cols)).copyTo(roi2);
        for (int i = ; i < ms; i++)
            for (int j = a.x; j < img.cols; j++)
                ims(i, j) = uchar((img.cols - j) / float(d)*im(i, j) + (j - a.x) / float(d)*im1(i + abs(a.y), j - a.x));
    }


    return stitch;
}

//柱面投影校正
Mat cylinder(Mat imgIn, int f)
{
    int colNum, rowNum;
    colNum =  * f*atan(*imgIn.cols / f);//柱面图像宽
    rowNum = *imgIn.rows*f / sqrt(pow(f, )) + *imgIn.rows;//柱面图像高

    Mat imgOut = Mat::zeros(rowNum, colNum, CV_8UC1);
    Mat_<uchar> im1(imgIn);
    Mat_<uchar> im2(imgOut);

    //正向插值
    int x1(), y1();
    for (int i = ; i < imgIn.rows; i++)
        for (int j = ; j < imgIn.cols; j++)
        {
            x1 = f*atan((j - *imgIn.cols) / f) + f*atan(*imgIn.cols / f);
            y1 = f*(i - *imgIn.rows) / sqrt(pow(j - *imgIn.cols, ) + pow(f, )) + *imgIn.rows;
            if (x1 >=  && x1 < colNum&&y1 >=  && y1<rowNum)
            {
                im2(y1, x1) = im1(i, j);
            }
        }
    return imgOut;
}