本文实例为大家分享了opencv2实现两张图像拼接融合的具体代码，供大家参考，具体内容如下

要用到两个文件，estimate.cpp和matcher.h(在有关鲁棒匹配这篇博文中有）

estimate.cpp的头文件也需要添加一些东西才行，以下是对的，已经成功运行。

加了using namespace std;之后，cv::可以去掉了。

estimate.cpp:

#include <iostream>#include <vector>#include <opencv2/core/core.hpp>#include <opencv2/imgproc/imgproc.hpp>#include <opencv2/highgui/highgui.hpp>#include <opencv2/features2d/features2d.hpp>#include <opencv2/calib3d/calib3d.hpp>#include<opencv2/nonfree/nonfree.hpp>#include<opencv2\legacy\legacy.hpp> #include "matcher.h"using namespace std;using namespace cv;int main(){// Read input images读入图像cv::Mat image1= cv::imread("parliament1.bmp",0);cv::Mat image2= cv::imread("parliament2.bmp",0);if (!image1.data || !image2.data)return 0; // Display the images显示图像cv::namedWindow("Image 1");cv::imshow("Image 1",image1);cv::namedWindow("Image 2");cv::imshow("Image 2",image2);// Prepare the matcher准备匹配RobustMatcher rmatcher;rmatcher.setConfidenceLevel(0.98);rmatcher.setMinDistanceToEpipolar(1.0);rmatcher.setRatio(0.65f);cv::Ptr<cv::FeatureDetector> pfd= new cv::SurfFeatureDetector(10); rmatcher.setFeatureDetector(pfd);// Match the two imagesstd::vector<cv::DMatch> matches;std::vector<cv::KeyPoint> keypoints1, keypoints2;cv::Mat fundemental= rmatcher.match(image1,image2,matches, keypoints1, keypoints2);// draw the matches画匹配结果cv::Mat imageMatches;cv::drawMatches(image1,keypoints1, // 1st image and its keypoints第一张图像及其关键点 image2,keypoints2, // 2nd image and its keypoints第二张图像及其关键点matches, // the matches匹配结果imageMatches, // the image produced产生的图像cv::Scalar(255,255,255)); // color of the lines线的颜色cv::namedWindow("Matches");cv::imshow("Matches",imageMatches);// Convert keypoints into Point2f将关键点转换为Point2fstd::vector<cv::Point2f> points1, points2;for (std::vector<cv::DMatch>::const_iterator it= matches.begin();it!= matches.end(); ++it) {H// Get the position of left keypoints得到左图关键点位置float x= keypoints1[it->queryIdx].pt.x;float y= keypoints1[it->queryIdx].pt.y;points1.push_back(cv::Point2f(x,y));// Get the position of right keypoints得到右图关键点位置x= keypoints2[it->trainIdx].pt.x;y= keypoints2[it->trainIdx].pt.y;points2.push_back(cv::Point2f(x,y));}std::cout << points1.size() << " " << points2.size() << std::endl; // Find the homography between image 1 and image 2找到图像1和图像2之间的单应性矩阵std::vector<uchar> inliers(points1.size(),0);cv::Mat homography= cv::findHomography(cv::Mat(points1),cv::Mat(points2), // corresponding points对应点inliers, // outputed inliers matches 输出内点匹配CV_RANSAC, // RANSAC method RANSAC 方法1.); // max distance to reprojection point到对应点的最大距离// Draw the inlier points画内点std::vector<cv::Point2f>::const_iterator itPts= points1.begin();std::vector<uchar>::const_iterator itIn= inliers.begin();while (itPts!=points1.end()) {// draw a circle at each inlier location在每一个内点画一个圈if (*itIn) cv::circle(image1,*itPts,3,cv::Scalar(255,255,255),2);++itPts;++itIn;}itPts= points2.begin();itIn= inliers.begin();while (itPts!=points2.end()) {// draw a circle at each inlier location在每一个内点画一个圈if (*itIn) cv::circle(image2,*itPts,3,cv::Scalar(255,255,255),2);++itPts;++itIn;} // Display the images with points显示画点的图像cv::namedWindow("Image 1 Homography Points");cv::imshow("Image 1 Homography Points",image1);cv::namedWindow("Image 2 Homography Points");cv::imshow("Image 2 Homography Points",image2);// Warp image 1 to image 2变形图像1到图像2cv::Mat result;cv::warpPerspective(image1, // input image输入的图像result, // output image输出的图像homography, // homography单应性矩阵cv::Size(2*image1.cols,image1.rows)); // size of output image输出图像的大小// Copy image 1 on the first half of full image复制图像1的上一部分cv::Mat half(result,cv::Rect(0,0,image2.cols,image2.rows));image2.copyTo(half); // Display the warp image显示变形后图像cv::namedWindow("After warping");cv::imshow("After warping",result);cv::waitKey();return 0;}

以上就是本文的全部内容，希望对大家的学习有所帮助，也希望大家多多支持。