图像拼接（四）：双摄像头实时视频拼接（平移模型）

原文地址：https://blog.csdn.net/czl389/article/details/54604677
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(0);
VideoCapture cap2(1);

double rate = 60;
int delay = 1000 / rate;
bool stop(false);
Mat frame1;
Mat frame2;
Mat frame;
Point2i a;//存储偏移量
int k = 0;

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 -1;
}

//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,0);
cap2.set(CV_CAP_PROP_FOCUS, 0);

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 < 3 || waitKey(delay) == 13)//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(1) == 27)
{
stop = true;
cout << "程序结束！" << endl;
cout << "*** ***" << endl;
}
}
return 0;
}

//计算平移参数
Point2i getOffset(Mat img, Mat img1)
{
Mat templ(img1, Rect(0, 0.4*img1.rows, 0.2*img1.cols, 0.2*img1.rows));
Mat result(img.cols - templ.cols + 1, img.rows - templ.rows + 1, CV_8UC1);//result存放匹配位置信息
matchTemplate(img, templ, result, CV_TM_CCORR_NORMED);
normalize(result, result, 0, 1, NORM_MINMAX, -1, 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 - 0.4*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)
{

4000
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 >= 0)
{
Mat roi1(stitch, Rect(0, 0, a.x, ms));
img(Range(a.y, img.rows), Range(0, a.x)).copyTo(roi1);
Mat roi2(stitch, Rect(img.cols, 0, a.x, ms));
img1(Range(0, ms), Range(d, img1.cols)).copyTo(roi2);
for (int i = 0; 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(0, 0, a.x, ms));
img(Range(0, ms), Range(0, a.x)).copyTo(roi1);
Mat roi2(stitch, Rect(img.cols, 0, a.x, ms));
img1(Range(-a.y, img.rows), Range(d, img1.cols)).copyTo(roi2);
for (int i = 0; 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 = 2 * f*atan(0.5*imgIn.cols / f);//柱面图像宽
rowNum = 0.5*imgIn.rows*f / sqrt(pow(f, 2)) + 0.5*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(0), y1(0);
for (int i = 0; i < imgIn.rows; i++)
for (int j = 0; j < imgIn.cols; j++)
{
x1 = f*atan((j - 0.5*imgIn.cols) / f) + f*atan(0.5*imgIn.cols / f);
y1 = f*(i - 0.5*imgIn.rows) / sqrt(pow(j - 0.5*imgIn.cols, 2) + pow(f, 2)) + 0.5*imgIn.rows;
if (x1 >= 0 && x1 < colNum&&y1 >= 0 && y1<rowNum)
{
im2(y1, x1) = im1(i, j);
}
}
return imgOut;
}