图像拼接stitching是OpenCV2.4.0出现的一个新模块，所有的相关函数都被封装在Stitcher类当中。关于Stitcher类的详细介绍，可以参考： http://docs.opencv.org/2.4.2/modules/stitching/doc/high_level.html?highlight=stitcher#stitcher。
这个类当中我们主要用到的成员函数有createDefault，用于创建缺省参数的stitcher；estimateTransform，用于 生成最后的拼接图像；而对于composePanorama和stitch，文档中提示如果对stitching的整过过程不熟悉的话，最好不要使用以上 两个函数，直接使用stitch就行了。整个拼接的算法实现过程十分复杂，其中涉及到图像特征点的提取和匹配、摄像机的校准、图像融合、图像的变形、曝光 补偿等算法的结合。
说得这么复杂，但实际上这些模块的接口调用，OpenCV都为我们搞定了，我们只需要调用createDefault函数生成默认的参数，再使用stitch函数进行拼接就ok了。
图像拼接的实例代码如下，在VS2013平台上运行成功：
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#include <iostream>
#include <fstream>
#include "opencv2/highgui/highgui.hpp"
#include "opencv2/stitching/stitcher.hpp"
using namespace std;
using namespace cv;
bool try_use_gpu = false;
vector<Mat> imgs;
string result_name = "result.jpg"; // 默认输出文件名及格式
void printUsage();
int parseCmdArgs(int argc, char** argv);
int main(int argc, char* argv[])
{
int retval = parseCmdArgs(argc, argv);
if (retval) return -;
Mat pano;
Stitcher stitcher = Stitcher::createDefault(try_use_gpu);
Stitcher::Status status = stitcher.stitch(imgs, pano);
if (status != Stitcher::OK)
{
cout << "Can't stitch images, error code = " << status << endl;
return -;
}
imwrite(result_name, pano);
return ;
}
void printUsage()
{
cout <<
"Rotation model images stitcher.\n\n"
"stitching img1 img2 [...imgN]\n\n"
"Flags:\n"
" --try_use_gpu (yes|no)\n"
" Try to use GPU. The default value is 'no'. All default values\n"
" are for CPU mode.\n"
" --output <result_img>\n"
" The default is 'result.jpg'.\n";
}
int parseCmdArgs(int argc, char** argv)
{
if (argc == )
{
printUsage();
return -;
}
for (int i = ; i < argc; ++i)
{
if (string(argv[i]) == "--help" || string(argv[i]) == "/?")
{
printUsage();
return -;
}
else if (string(argv[i]) == "--try_use_gpu") // 默认不使用gpu加速
{
if (string(argv[i + ]) == "no")
try_use_gpu = false;
else if (string(argv[i + ]) == "yes")
try_use_gpu = true;
else
{
cout << "Bad --try_use_gpu flag value\n";
return -;
}
i++;
}
else if (string(argv[i]) == "--output") // 若定义了输出图像名，则更改result_name
{
result_name = argv[i + ];
i++;
}
else
{
Mat img = imread(argv[i]);
if (img.empty())
{
cout << "Can't read image '" << argv[i] << "'\n";
return -;
}
imgs.push_back(img);
}
}
return ;
}
这里在自己的机子上实现简单的图像拼接，程序生成的可执行文件名为imageStitching.exe，如果直接运行程序将直接退出，需要在cmd中进行以下操作：
找到imageStitching.exe所在的目录，在终端里输入imageStitching+被拼接的图像路径（若与imageStitching.exe在同个文件夹，直接输入图像的名字和后缀名即可），如这里输入:imageStitching 1.jpg 2.jpg 3.jpg。
若输入：–output 4.jpg，就会把1.jpg、2.jpg和3.jpg 进行拼接，而输出的文件是imageStitching.exe路径下的4.jpg，由于在例程默认输出为result.jpg，可以不用设置output。
由于选取的图像在拍摄时并不平行，能拼接出这种效果的图像已是不错：
1.jpg：
2.jpg：
3.jpg：
拼接输出结果result.jpg：
代码中使用函数：
Stitcher::Status status = stitcher.stitch(imgs, pano);
就能得出一个傻瓜拼接结果…如之前所提到的，这其中涉及到很多算法的实现过程，可以看到图像拼接算法是一个值得深入的领域。更多的算法可参考：
http://academy.nearsoft.com/project-updates/makingapanoramapicture