c++两幅影像配准代码

以下是一份基于OpenCV库的C++图像配准代码：

#include <opencv2/opencv.hpp>
#include <iostream>

using namespace cv;
using namespace std;

int main(int argc, char** argv)
{
    // 读取待配准图像
    Mat img1 = imread("img1.jpg");
    Mat img2 = imread("img2.jpg");

    // 转换为灰度图像
    Mat gray1, gray2;
    cvtColor(img1, gray1, COLOR_BGR2GRAY);
    cvtColor(img2, gray2, COLOR_BGR2GRAY);

    // 特征点检测
    vector<KeyPoint> keypoints1, keypoints2;
    Ptr<FeatureDetector> detector = ORB::create();
    detector->detect(gray1, keypoints1);
    detector->detect(gray2, keypoints2);

    // 特征点描述
    Mat descriptors1, descriptors2;
    Ptr<DescriptorExtractor> descriptor = ORB::create();
    descriptor->compute(gray1, keypoints1, descriptors1);
    descriptor->compute(gray2, keypoints2, descriptors2);

    // 特征点匹配
    vector<DMatch> matches;
    Ptr<DescriptorMatcher> matcher = DescriptorMatcher::create("BruteForce-Hamming");
    matcher->match(descriptors1, descriptors2, matches);

    // 筛选匹配点
    double min_dist = 10000, max_dist = 0;
    for (int i = 0; i < descriptors1.rows; i++)
    {
        double dist = matches[i].distance;
        if (dist < min_dist) min_dist = dist;
        if (dist > max_dist) max_dist = dist;
    }
    vector<DMatch> good_matches;
    for (int i = 0; i < descriptors1.rows; i++)
    {
        if (matches[i].distance <= max(2 * min_dist, 30.0))
        {
            good_matches.push_back(matches[i]);
        }
    }

    // 显示匹配结果
    Mat img_matches;
    drawMatches(img1, keypoints1, img2, keypoints2, good_matches, img_matches);
    imshow("matches", img_matches);
    waitKey(0);

    // 计算变换矩阵
    vector<Point2f> points1, points2;
    for (int i = 0; i < good_matches.size(); i++)
    {
        points1.push_back(keypoints1[good_matches[i].queryIdx].pt);
        points2.push_back(keypoints2[good_matches[i].trainIdx].pt);
    }
    Mat H = findHomography(points1, points2, RANSAC);

    // 图像配准
    Mat img_aligned;
    warpPerspective(img1, img_aligned, H, img1.size());
    imshow("aligned", img_aligned);
    waitKey(0);

    return 0;
}

该代码使用ORB算法进行特征点检测和描述，使用汉明距离进行特征点匹配，使用RANSAC算法计算变换矩阵，最终使用透视变换将待配准图像进行配准。你可以根据自己的需求进行修改和优化。