opencv 图像配准c++代码

以下是使用OpenCV进行基于特征点匹配的图像配准的C++代码示例：

#include <opencv2/opencv.hpp>

using namespace std;
using namespace cv;

int main(int argc, char** argv)
{
    // 加载需要进行配准的图像
    Mat img1 = imread("img1.jpg", IMREAD_GRAYSCALE);
    Mat img2 = imread("img2.jpg", IMREAD_GRAYSCALE);

    // 提取图像中的特征点
    Ptr<FeatureDetector> detector = ORB::create();
    vector<KeyPoint> kp1, kp2;
    detector->detect(img1, kp1);
    detector->detect(img2, kp2);

    // 对提取出的特征点进行描述
    Ptr<DescriptorExtractor> extractor = ORB::create();
    Mat desc1, desc2;
    extractor->compute(img1, kp1, desc1);
    extractor->compute(img2, kp2, desc2);

    // 对两张图像中的特征点进行匹配
    Ptr<DescriptorMatcher> matcher = DescriptorMatcher::create("BruteForce-Hamming");
    vector<DMatch> matches;
    matcher->match(desc1, desc2, matches);

    // 筛选匹配的特征点
    double min_dist = DBL_MAX, max_dist = 0;
    for (int i = 0; i < desc1.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 < desc1.rows; i++) {
        if (matches[i].distance <= max(2 * min_dist, 30.0)) {
            good_matches.push_back(matches[i]);
        }
    }

    // 计算变换矩阵
    vector<Point2f> pts1, pts2;
    for (int i = 0; i < good_matches.size(); i++) {
        pts1.push_back(kp1[good_matches[i].queryIdx].pt);
        pts2.push_back(kp2[good_matches[i].trainIdx].pt);
    }
    Mat H = findHomography(pts1, pts2, RANSAC);

    // 应用变换矩阵进行配准
    Mat img_aligned;
    warpPerspective(img1, img_aligned, H, img1.size());

    // 显示结果
    imshow("img1", img1);
    imshow("img2", img2);
    imshow("img_aligned", img_aligned);
    waitKey(0);

    return 0;
}

以上代码中使用了ORB特征点检测和描述算法、BruteForce-Hamming特征点匹配算法以及RANSAC算法进行变换矩阵计算。在实际应用中，可能需要根据具体问题选用不同的算法并进行参数优化。