Question

我有照片和匹配的相机位置（x，y，z），方向（偏航，俯仰和滚动），相机矩阵（Cx，Cy，Fx，Fy）以及径向和切向校正参数。我想在相同坐标系中提供的照片上叠加一些额外的3D信息。看一下类似的帖子here我觉得我应该能够做到这个OpenCV projectPoints函数如下;

#include "opencv2/core/core.hpp"
#include "opencv2/imgproc/imgproc.hpp"
#include "opencv2/calib3d/calib3d.hpp"
#include "opencv2/highgui/highgui.hpp"

#include <iostream>
#include <string>

int ProjectMyPoints()
{
    std::vector<cv::Point3d> objectPoints;
    std::vector<cv::Point2d> imagePoints;

    // Camera position
    double CameraX = 709095.949, CameraY = 730584.110, CameraZ = 64.740;

    // Camera orientation (converting from Grads to radians)
    double PI = 3.14159265359;
    double Pitch = -99.14890023 * (PI / 200.0),
        Yaw = PI + 65.47067336 * (PI / 200.0),
        Roll = 194.92713428 * (PI / 200.0);

    // Input data in real world coordinates 
    double x, y, z;
    x = 709092.288; y = 730582.891; z = 62.837; objectPoints.push_back(cv::Point3d(x, y, z));
    x = 709091.618; y = 730582.541; z = 62.831; objectPoints.push_back(cv::Point3d(x, y, z));
    x = 709092.131; y = 730581.602; z = 62.831; objectPoints.push_back(cv::Point3d(x, y, z));
    x = 709092.788; y = 730581.973; z = 62.843; objectPoints.push_back(cv::Point3d(x, y, z));

    // Coefficients for camera matrix
    double CV_CX = 1005.1951672908998,
        CV_CY = 1010.36740512214021,
        CV_FX = 1495.61455114326009,
        CV_FY = 1495.61455114326009,

        // Distortion co-efficients

        CV_K1 = -1.74729071186991E-1,
        CV_K2 = 1.18342592220238E-1,
        CV_K3 = -2.29972026710921E-2,
        CV_K4 = 0.00000000000000E+0,
        CV_K5 = 0.00000000000000E+0,
        CV_K6 = 0.00000000000000E+0,
        CV_P1 = -3.46272954067614E-4,
        CV_P2 = -4.45389772269491E-4;

    // Intrisic matrix / camera matrix

    cv::Mat intrisicMat(3, 3, cv::DataType<double>::type); 
    intrisicMat.at<double>(0, 0) = CV_FX;
    intrisicMat.at<double>(1, 0) = 0;
    intrisicMat.at<double>(2, 0) = 0;

    intrisicMat.at<double>(0, 1) = 0;
    intrisicMat.at<double>(1, 1) = CV_FY;
    intrisicMat.at<double>(2, 1) = 0;

    intrisicMat.at<double>(0, 2) = CV_CX;
    intrisicMat.at<double>(1, 2) = CV_CY;
    intrisicMat.at<double>(2, 2) = 1;

    // Rotation matrix created from orientation
    rRot.at<double>(0, 0) = cos(Yaw)*cos(Pitch);
    rRot.at<double>(1, 0) = sin(Yaw)*cos(Pitch);
    rRot.at<double>(2, 0) = -sin(Pitch);

    rRot.at<double>(0, 1) = cos(Yaw)*sin(Pitch)*sin(Roll) - sin(Yaw)*cos(Roll);
    rRot.at<double>(1, 1) = sin(Yaw)*sin(Pitch)*sin(Roll) + cos(Yaw)*cos(Roll);
    rRot.at<double>(2, 1) = cos(Pitch)*sin(Roll);

    rRot.at<double>(0, 2) = cos(Yaw)*sin(Pitch)*cos(Roll) + sin(Yaw)*sin(Roll);
    rRot.at<double>(1, 2) = sin(Yaw)*sin(Pitch)*cos(Roll) - cos(Yaw)*sin(Roll);;
    rRot.at<double>(2, 2) = cos(Pitch)*cos(Roll);

    // Convert 3x3 rotation matrix to 1x3 rotation vector    
    cv::Mat rVec(3, 1, cv::DataType<double>::type); // Rotation vector
    cv::Rodrigues(rRot, rVec);

    cv::Mat tVec(3, 1, cv::DataType<double>::type); // Translation vector
    tVec.at<double>(0) = CameraX;
    tVec.at<double>(1) = CameraY;
    tVec.at<double>(2) = CameraZ;

    cv::Mat distCoeffs(5, 1, cv::DataType<double>::type);   // Distortion vector
    distCoeffs.at<double>(0) = CV_K1;
    distCoeffs.at<double>(1) = CV_K2;
    distCoeffs.at<double>(2) = CV_P1;
    distCoeffs.at<double>(3) = CV_P2;
    distCoeffs.at<double>(4) = CV_K3;

    std::cout << "Intrisic matrix: " << intrisicMat << std::endl << std::endl;
    std::cout << "Rotation vector: " << rVec << std::endl << std::endl;
    std::cout << "Translation vector: " << tVec << std::endl << std::endl;
    std::cout << "Distortion coef: " << distCoeffs << std::endl << std::endl;

    std::vector<cv::Point2f> projectedPoints;

    cv::projectPoints(objectPoints, rVec, tVec, intrisicMat, distCoeffs, imagePoints);

    for (unsigned int i = 0; i < imagePoints.size(); ++i)
         std::cout << "Image point: " << imagePoints[i] << std::endl;

    std::cout << "Press any key to exit.";
    std::cin.ignore();
    std::cin.get();

    return 0;
}

旋转矩阵取自this post，如下

并如here所述转换为Rodriques向量。我假设翻译是为了移除相机位置，因此将相机放在翻译矢量中。问题是，它不起作用。如果我将平移归零，我得到的坐标接近像素坐标但不完全相似（即使用保形变换来比较形状显示高残差）。任何想法如何解决这个问题。 FWIW，目标坐标应为

1 1448 1680， 2 1393 1578， 3 1052 1605， 4 1053 1702

如下图所示;

我的感觉是我错过了一些显而易见的东西，但任何帮助都非常感激

使用OpenCV projectPoints覆盖照片上的真实世界数据

0 个答案: