二、OpenCV3.4.2 实现图像拼接与融合
参考大神的帖子：
使用OpenCV3进行SURF特征提取和暴力匹配代码详解：https://blog.csdn.net/zilanpotou182/article/details/68061929
OpenCV探索之路（二十四）图像拼接与图像融合技术：http://www.cnblogs.com/skyfsm/p/7411961.html
最终完整代码如下：
#include
#include
#include "opencv2/core.hpp"
#include "opencv2/core/utility.hpp"
#include "opencv2/core/ocl.hpp"
#include "opencv2/imgcodecs.hpp"
#include "opencv2/highgui.hpp"
#include "opencv2/features2d.hpp"
#include "opencv2/calib3d.hpp"
#include "opencv2/imgproc.hpp"
#include"opencv2/flann.hpp"
#include"opencv2/xfeatures2d.hpp"
#include"opencv2/ml.hpp"
using namespace cv;
using namespace std;
using namespace cv::xfeatures2d;
using namespace cv::ml;
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];
}
int main()
{
Mat a = imread("2.jpg", 1);//右图
Mat b = imread("1.jpg", 1);//左图
Ptr surf; //创建方式和OpenCV2中的不一样,并且要加上命名空间xfreatures2d
//否则即使配置好了还是显示SURF为未声明的标识符
surf = SURF::create(800);
BFMatcher matcher; //实例化一个暴力匹配器
Mat c, d;
vectorkey1, key2;
vector matches; //DMatch是用来描述匹配好的一对特征点的类，包含这两个点之间的相关信息
//比如左图有个特征m，它和右图的特征点n最匹配，这个DMatch就记录它俩最匹配，并且还记录m和n的
//特征向量的距离和其他信息，这个距离在后面用来做筛选
surf->detectAndCompute(a, Mat(), key1, c);//输入图像，输入掩码，输入特征点，输出Mat，存放所有特征点的描述向量
surf->detectAndCompute(b, Mat(), key2, d);//这个Mat行数为特征点的个数，列数为每个特征向量的尺寸，SURF是64（维）
matcher.match(d, c, matches); //匹配，数据来源是特征向量，结果存放在DMatch类型里面
//sort函数对数据进行升序排列
sort(matches.begin(), matches.end()); //筛选匹配点，根据match里面特征对的距离从小到大排序
vector< DMatch > good_matches;
int ptsPairs = std::min(50, (int)(matches.size() * 0.15));
cout << ptsPairs << endl;
for (int i = 0; i < ptsPairs; i++)
{
good_matches.push_back(matches[i]);//距离最小的50个压入新的DMatch
}
Mat outimg; //drawMatches这个函数直接画出摆在一起的图
drawMatches(b, key2, a, key1, good_matches, outimg, Scalar::all(-1), Scalar::all(-1), vector(), DrawMatchesFlags::NOT_DRAW_SINGLE_POINTS); //绘制匹配点
imshow("桌面", outimg);
///////////////////////图像配准及融合////////////////////////
vector imagePoints1, imagePoints2;
for (int i = 0; i(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);
}
}
} 匹配结果：
右图经过透射投影变换结果：
优化后的拼接融合效果：
备注：此拼接效果是相对于左图做的变换，应将两个图均相对于中心坐标做变换可得到更友好舒服的拼接效果，未完待续。
下面给出opencv2/stitching图像拼接函数的运行效果（具体原理还在研究）：
#include
#include
#include "opencv2/core.hpp"
#include "opencv2/core/utility.hpp"
#include "opencv2/core/ocl.hpp"
#include "opencv2/imgcodecs.hpp"
#include "opencv2/highgui.hpp"
#include "opencv2/features2d.hpp"
#include "opencv2/calib3d.hpp"
#include "opencv2/imgproc.hpp"
#include"opencv2/flann.hpp"
#include"opencv2/xfeatures2d.hpp"
#include"opencv2/ml.hpp"
#include
using namespace cv;
using namespace std;
using namespace cv::xfeatures2d;
using namespace cv::ml;
bool try_use_gpu = false;
vector imgs;
string result_name = "dst1.jpg";
int main(int argc, char * argv[])
{
Mat img1 = imread("2.jpg");
Mat img2 = imread("1.jpg");
imshow("p1", img1);
imshow("p2", img2);
if (img1.empty() || img2.empty())
{
cout << "Can't read image" << endl;
return -1;
}
imgs.push_back(img1);
imgs.push_back(img2);
Stitcher stitcher = Stitcher::createDefault(try_use_gpu);
// 使用stitch函数进行拼接
Mat pano;
Stitcher::Status status = stitcher.stitch(imgs, pano);
if (status != Stitcher::OK)
{
cout << "Can't stitch images, error code = " << int(status) << endl;
return -1;
}
imwrite(result_name, pano);
Mat pano2 = pano.clone();
// 显示源图像，和结果图像
imshow("全景图像", pano);
if (waitKey() == 27)
return 0;
} 原始图像如下：
拼接后效果（符合正常的视觉效果，中心对称）：
猜测：分别相对于中心坐标做变换，或者相对于左图变换后做了个旋转
