opencv实现双摄像头视频拼接

#include "stdafx.h"
#include "highgui/highgui.hpp"    
#include "opencv2/nonfree/nonfree.hpp"    
#include "opencv2/legacy/legacy.hpp"   
#include 


using namespace cv;
using namespace std;

void OptimizeSeam(Mat& img1, Mat& trans, Mat& dst);

typedef struct
{
	Point2f left_top;
	Point2f left_bottom;
	Point2f right_top;
	Point2f right_bottom;
}four_corners_t;

four_corners_t corners;

void CalcCorners(const Mat& H, const Mat& src)
{
	double v2[] = { 0, 0, 1 };//左上角
	double v1[3];//变换后的坐标值
	Mat V2 = Mat(3, 1, CV_64FC1, v2);  //列向量
	Mat V1 = Mat(3, 1, CV_64FC1, v1);  //列向量

	V1 = H * V2;
	//左上角(0,0,1)
	cout << "V2: " << V2 << endl;
	cout << "V1: " << V1 << endl;
	corners.left_top.x = v1[0] / v1[2];
	corners.left_top.y = v1[1] / v1[2];

	//左下角(0,src.rows,1)
	v2[0] = 0;
	v2[1] = src.rows;
	v2[2] = 1;
	V2 = Mat(3, 1, CV_64FC1, v2);  //列向量
	V1 = Mat(3, 1, CV_64FC1, v1);  //列向量
	V1 = H * V2;
	corners.left_bottom.x = v1[0] / v1[2];
	corners.left_bottom.y = v1[1] / v1[2];

	//右上角(src.cols,0,1)
	v2[0] = src.cols;
	v2[1] = 0;
	v2[2] = 1;
	V2 = Mat(3, 1, CV_64FC1, v2);  //列向量
	V1 = Mat(3, 1, CV_64FC1, v1);  //列向量
	V1 = H * V2;
	corners.right_top.x = v1[0] / v1[2];
	corners.right_top.y = v1[1] / v1[2];

	//右下角(src.cols,src.rows,1)
	v2[0] = src.cols;
	v2[1] = src.rows;
	v2[2] = 1;
	V2 = Mat(3, 1, CV_64FC1, v2);  //列向量
	V1 = Mat(3, 1, CV_64FC1, v1);  //列向量
	V1 = H * V2;
	corners.right_bottom.x = v1[0] / v1[2];
	corners.right_bottom.y = v1[1] / v1[2];

}

//优化两图的连接处，使得拼接自然
void OptimizeSeam(Mat& img1, Mat& trans, Mat& dst)
{
	int start = MIN(corners.left_top.x, corners.left_bottom.x);//开始位置，即重叠区域的左边界  

	double processWidth = img1.cols - start;//重叠区域的宽度  
	int rows = dst.rows;
	int cols = img1.cols; //注意，是列数*通道数
	double alpha = 1;//img1中像素的权重  
	for (int i = 0; i < rows; i++)
	{
		uchar* p = img1.ptr(i);  //获取第i行的首地址
		uchar* t = trans.ptr(i);
		uchar* d = dst.ptr(i);
		for (int j = start; j < cols; j++)
		{
			//如果遇到图像trans中无像素的黑点，则完全拷贝img1中的数据
			if (t[j * 3] == 0 && t[j * 3 + 1] == 0 && t[j * 3 + 2] == 0)
			{
				alpha = 1;
			}
			else
			{
				//img1中像素的权重，与当前处理点距重叠区域左边界的距离成正比，实验证明，这种方法确实好  
				alpha = (processWidth - (j - start)) / processWidth;
			}

			d[j * 3] = p[j * 3] * alpha + t[j * 3] * (1 - alpha);
			d[j * 3 + 1] = p[j * 3 + 1] * alpha + t[j * 3 + 1] * (1 - alpha);
			d[j * 3 + 2] = p[j * 3 + 2] * alpha + t[j * 3 + 2] * (1 - alpha);

		}
	}

}


int _tmain(int argc, _TCHAR* argv[])
{
	VideoCapture cap1(0);//左
	VideoCapture cap2(1);//右

	double rate = 60;
	int delay = 1000 / rate;
	bool stop(false);
	Mat frame1;
	Mat frame2;
	Mat frame3;
	Mat frame4;
	int k = 100;
	int n = 50;
	Mat image01;
	Mat image02;
	Mat image03;
	Mat image04;
	Mat imageTransform1, imageTransform2;
	Mat homo;

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

	if (cap1.isOpened() && cap2.isOpened())
	{
		cout << "*** ***" << endl;
		cout << "摄像头已启动！" << endl;
	}
	else
	{
		cout << "*** ***" << endl;
		cout << "警告：请检查摄像头是否安装好!" << 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);

	//获取两幅图像，通过这两幅图像来估计摄像机参数

	while (n--)
	{
		if (cap1.read(frame1) && cap2.read(frame2))
		{
			imshow("cam1", frame1);
			imshow("cam2", frame2);
			imwrite("frame1.bmp", frame1);
			imwrite("frame2.bmp", frame2);
		}
		if (waitKey(1) == 27)//按ESC键
		{
			stop = true;
			cout << "程序结束！" << endl;
			cout << "*** ***" << endl;
		}
	}

	image02 = imread("frame1.bmp", 1);    //左图
	image01 = imread("frame2.bmp", 1);    //右图

	//灰度图转换  
	Mat image1, image2;
	cvtColor(image01, image1, CV_RGB2GRAY);
	cvtColor(image02, image2, CV_RGB2GRAY);


	//提取特征点    
	SurfFeatureDetector Detector(2000);
	vector keyPoint1, keyPoint2;
	Detector.detect(image1, keyPoint1);
	Detector.detect(image2, keyPoint2);

	//特征点描述，为下边的特征点匹配做准备    
	SurfDescriptorExtractor Descriptor;
	Mat imageDesc1, imageDesc2;
	Descriptor.compute(image1, keyPoint1, imageDesc1);
	Descriptor.compute(image2, keyPoint2, imageDesc2);

	FlannBasedMatcher matcher;
	vector > matchePoints;
	vector GoodMatchePoints;

	vector train_desc(1, imageDesc1);
	matcher.add(train_desc);
	matcher.train();

	matcher.knnMatch(imageDesc2, matchePoints, 2);
	cout << "total match points: " << matchePoints.size() << endl;

	// Lowe's algorithm,获取优秀匹配点
	for (int i = 0; i < matchePoints.size(); i++)
	{
		if (matchePoints[i][0].distance < 0.4 * matchePoints[i][1].distance)
		{
			GoodMatchePoints.push_back(matchePoints[i][0]);
		}
	}

	Mat first_match;
	drawMatches(image02, keyPoint2, image01, keyPoint1, GoodMatchePoints, first_match);

	vector imagePoints1, imagePoints2;

	for (int i = 0; i