使用Stitcher类,通过createDefault()方法创建拼接对象,通过stitch()方法执行默认的自动拼接。自动拼接和07年Brown和Lowe发表的论文描述的步骤基本一致,只不过使用的特征提取算法是ORB,而不是慢吞吞、有专利保护的SIFT和SURF。开源万岁!
代码内容:设置几张图片,扔到向量里面,然后计算全景图。
opencv-3.0.0源码中没有找到测试图片,很蛋碎。到github上找了下,发现都在[https://github.com/Itseez/opencv_extra](opencv_extra)这个项目下。。使用到了boat1.jpg~boat6.jpg
在fedora22+i53210+12G内存+全SSD条件下测试,还是有点慢的,大概5,6秒才出结果。当然,如果只有2张图片,秒出。
代码:
```
//图像拼接
//哦,这个程序是最简单的拼接,最傻瓜的那种,不必知道拼接的pipeline
//只需要调用createDefault()和stitch()方法就可以完成拼接
#include <iostream>
#include <opencv2/opencv.hpp>
#include <opencv2/stitching/stitcher.hpp>
using namespace std;
using namespace cv;
string IMAGE_PATH_PREFIX = "/home/chris/Pictures/";
bool try_use_gpu = false;
vector<Mat> imgs;
string result_name = IMAGE_PATH_PREFIX + "result.jpg";
int main()
{
Mat img = imread(IMAGE_PATH_PREFIX + "boat1.jpg");
imgs.push_back(img);
img=imread(IMAGE_PATH_PREFIX+"boat2.jpg");
imgs.push_back(img);
img=imread(IMAGE_PATH_PREFIX+"boat3.jpg");
imgs.push_back(img);
img=imread(IMAGE_PATH_PREFIX+"boat3.jpg");
imgs.push_back(img);
img=imread(IMAGE_PATH_PREFIX+"boat4.jpg");
imgs.push_back(img);
img=imread(IMAGE_PATH_PREFIX+"boat5.jpg");
imgs.push_back(img);
img=imread(IMAGE_PATH_PREFIX+"boat6.jpg");
imgs.push_back(img);
Mat pano;//拼接结果图片
//Stitcher stitcher = Stitcher::createDefault(try_use_gpu);
Stitcher stitcher = Stitcher::createDefault(true);
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);
}
int main_test_feature_algo(){
#ifdef HAVE_OPENCV_XFEATURES2D
cout << "Surf" << endl;
#else
cout << "Orb" << endl;
#endif
}
```
当然你也可以看下opencv-3.0.0/samples/cpp/stitching.cpp的代码
效果图:
原文:http://www.cnblogs.com/zjutzz/p/4960422.html