c++代码 实现opencv三维重建和投影

以下是一个简单的C++代码实现OpenCV三维重建和投影的示例：

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

using namespace std;
using namespace cv;

int main()
{
    // 读取图像
    Mat img1 = imread("image1.jpg", IMREAD_GRAYSCALE);
    Mat img2 = imread("image2.jpg", IMREAD_GRAYSCALE);

    // 特征点检测与匹配
    Ptr<FeatureDetector> detector = SIFT::create();
    vector<KeyPoint> keypoints1, keypoints2;
    detector->detect(img1, keypoints1);
    detector->detect(img2, keypoints2);

    Ptr<DescriptorExtractor> extractor = SIFT::create();
    Mat descriptors1, descriptors2;
    extractor->compute(img1, keypoints1, descriptors1);
    extractor->compute(img2, keypoints2, descriptors2);

    Ptr<DescriptorMatcher> matcher = DescriptorMatcher::create("FlannBased");
    vector<DMatch> matches;
    matcher->match(descriptors1, descriptors2, matches);

    // 三维重建
    vector<Point3f> points3d;
    vector<Point2f> points1, points2;
    for (int i = 0; i < matches.size(); i++)
    {
        int queryIdx = matches[i].queryIdx;
        int trainIdx = matches[i].trainIdx;
        points1.push_back(keypoints1[queryIdx].pt);
        points2.push_back(keypoints2[trainIdx].pt);
    }
    Mat R, t;
    Matx33f K(1000, 0, img1.cols / 2,
              0, 1000, img1.rows / 2,
              0, 0, 1);
    Mat E = findEssentialMat(points1, points2, K, RANSAC, 0.999, 1.0);
    recoverPose(E, points1, points2, K, R, t);

    Matx34f P1(1, 0, 0, 0,
               0, 1, 0, 0,
               0, 0, 1, 0);
    Matx34f P2(R.at<double>(0, 0), R.at<double>(0, 1), R.at<double>(0, 2), t.at<double>(0),
               R.at<double>(1, 0), R.at<double>(1, 1), R.at<double>(1, 2), t.at<double>(1),
               R.at<double>(2, 0), R.at<double>(2, 1), R.at<double>(2, 2), t.at<double>(2));
    triangulatePoints(P1, P2, points1, points2, points3d);

    // 投影
    vector<Point2f> projected_points;
    Matx33f K1(1000, 0, img1.cols / 2,
               0, 1000, img1.rows / 2,
               0, 0, 1);
    Matx33f K2(1000, 0, img2.cols / 2,
               0, 1000, img2.rows / 2,
               0, 0, 1);
    projectPoints(points3d, Matx31f(0, 0, 0), Matx31f(0, 0, 0), K1, Mat(), projected_points);

    // 显示结果
    Mat result;
    hconcat(img1, img2, result);
    for (int i = 0; i < projected_points.size(); i++)
    {
        line(result, points1[i], projected_points[i] + Point2f(img1.cols, 0), Scalar(0, 255, 0));
    }
    imshow("Result", result);
    waitKey(0);
    return 0;
}

以上代码实现了从两张图像中提取特征点，计算相机的位姿，并进行三维重建和投影。其中，使用了SIFT算法进行特征点提取和匹配，使用了findEssentialMat()和recoverPose()函数计算相机的位姿，使用了triangulatePoints()函数进行三维重建，使用了projectPoints()函数进行投影。最终，显示出了图像中特征点和重建出的三维点云的对应关系。