基于全景投影的拼接 c++ 代码

全景投影的拼接可以使用OpenCV库来实现，在C++中可以使用以下代码：

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

using namespace std;
using namespace cv;

int main(){

    // 读取图像
    Mat img1 = imread("image1.jpg", IMREAD_COLOR);
    Mat img2 = imread("image2.jpg", IMREAD_COLOR);

    // 判断图像是否读取成功
    if(img1.empty() || img2.empty()){
        cout << "读取图像失败" << endl;
        return -1;
    }

    // 特征点匹配
    vector<KeyPoint> keypoints1, keypoints2;
    Mat descriptors1, descriptors2;
    Ptr<ORB> orb = ORB::create();
    orb->detectAndCompute(img1, Mat(), keypoints1, descriptors1);
    orb->detectAndCompute(img2, Mat(), keypoints2, descriptors2);
    BFMatcher matcher(NORM_HAMMING, true);
    vector<DMatch> matches;
    matcher.match(descriptors1, descriptors2, matches);

    // 筛选匹配点
    double min_dist = 100, 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]);
        }
    }

    // 计算图像配准矩阵
    vector<Point2f> img1_pts, img2_pts;
    for(int i = 0; i < good_matches.size(); i++){
        img1_pts.push_back(keypoints1[good_matches[i].queryIdx].pt);
        img2_pts.push_back(keypoints2[good_matches[i].trainIdx].pt);
    }
    Mat H = findHomography(img1_pts, img2_pts, RANSAC);

    // 拼接图像
    Mat dst;
    warpPerspective(img1, dst, H, Size(img1.cols + img2.cols, img1.rows));
    Mat roi(dst, Rect(img1.cols, 0, img2.cols, img2.rows));
    img2.copyTo(roi);

    // 显示结果
    namedWindow("拼接结果", WINDOW_NORMAL);
    imshow("拼接结果", dst);
    waitKey(0);
    return 0;
}

需要注意的是，这里使用的是ORB特征点检测和匹配算法，可以根据实际情况选择其他的算法。此外，还需要根据实际情况调整匹配点的筛选条件，以获得更好的拼接效果。