如何使用 openCV 正确校准带有广角镜头的相机?

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问题描述

我正在尝试使用鱼眼镜头校准相机.因此我使用了鱼眼镜头模块，但无论我修复什么失真参数，都会得到奇怪的结果.这是我使用的输入图像:https://i.imgur.com/apBuAwF.png

I am trying to calibrate a camera with a fisheye lens. I therefor used the fisheye lens module, but keep getting strange results no matter what distortion parameters I fix. This is the input image I use: https://i.imgur.com/apBuAwF.png

红色圆圈表示我用来校准相机的角落.

where the red circles indicate the corners I use to calibrate my camera.

这是我能得到的最好的，输出:https://imgur.com/a/XeXk5

This is the best I could get, output: https://imgur.com/a/XeXk5

我目前不知道相机传感器的尺寸是多少，但根据在我的 nitrinsic 矩阵中计算的以像素为单位的焦距，我推断我的传感器尺寸约为 3.3 毫米(假设我的物理焦距是 1.8 毫米)，这对我来说似乎很现实.然而，当不扭曲我的输入图像时，我会胡说八道.有人可以告诉我我可能做错了什么吗?

I currently don't know by heart what the camera sensor dimensions are, but based on the focal length in pixels that is being calculated in my nitrinsic matrix, I deduce my sensor size is approximately 3.3mm (assuming my physical focal length is 1.8mm), which seems realistic to me. Yet, when undistorting my input image I get nonsense. Could someone tell me what I may be doing incorrectly?

校准输出的矩阵和有效值:

the matrices and rms being output by the calibration:

K:[263.7291703200009, 0, 395.1618975493187;
 0, 144.3800397321767, 188.9308218101271;
 0, 0, 1]

D:[0, 0, 0, 0]

rms: 9.27628

我的代码:

#include <opencv2/opencv.hpp>
#include "opencv2/core.hpp"
#include "opencv2/imgcodecs.hpp"
#include "opencv2/imgproc.hpp"
#include "opencv2/highgui.hpp"
#include "opencv2/ccalib/omnidir.hpp"

using namespace std;
using namespace cv;

vector<vector<Point2d> > points2D;
vector<vector<Point3d> > objectPoints;

Mat src;

//so that I don't have to select them manually every time
void initializePoints2D()
{
    points2D[0].push_back(Point2d(234, 128));
    points2D[0].push_back(Point2d(300, 124));
    points2D[0].push_back(Point2d(381, 126));
    points2D[0].push_back(Point2d(460, 127));
    points2D[0].push_back(Point2d(529, 137));
    points2D[0].push_back(Point2d(207, 147));
    points2D[0].push_back(Point2d(280, 147));
    points2D[0].push_back(Point2d(379, 146));
    points2D[0].push_back(Point2d(478, 153));
    points2D[0].push_back(Point2d(551, 165));
    points2D[0].push_back(Point2d(175, 180));
    points2D[0].push_back(Point2d(254, 182));
    points2D[0].push_back(Point2d(377, 185));
    points2D[0].push_back(Point2d(502, 191));
    points2D[0].push_back(Point2d(586, 191));
    points2D[0].push_back(Point2d(136, 223));
    points2D[0].push_back(Point2d(216, 239));
    points2D[0].push_back(Point2d(373, 253));
    points2D[0].push_back(Point2d(534, 248));
    points2D[0].push_back(Point2d(624, 239));
    points2D[0].push_back(Point2d(97, 281));
    points2D[0].push_back(Point2d(175, 322));
    points2D[0].push_back(Point2d(370, 371));
    points2D[0].push_back(Point2d(578, 339));
    points2D[0].push_back(Point2d(662, 298));


    for(int j=0; j<25;j++)
    {   
        circle(src, points2D[0].at(j), 5, Scalar(0, 0, 255), 1, 8, 0);
    }

    imshow("src with circles", src);
    waitKey(0);
}

int main(int argc, char** argv)
{
    Mat srcSaved;

    src = imread("images/frontCar.png");
    resize(src, src, Size(), 0.5, 0.5);
    src.copyTo(srcSaved);

    vector<Point3d> objectPointsRow;
    vector<Point2d> points2DRow;
    objectPoints.push_back(objectPointsRow);
    points2D.push_back(points2DRow);

    for(int i=0; i<5;i++)
    {

        for(int j=0; j<5;j++)
        {
            objectPoints[0].push_back(Point3d(5*j,5*i,1));        
        }
    }

    initializePoints2D();
    cv::Matx33d K;
    cv::Vec4d D;
    std::vector<cv::Vec3d> rvec;
    std::vector<cv::Vec3d> tvec;


    int flag = 0;
    flag |= cv::fisheye::CALIB_RECOMPUTE_EXTRINSIC;
    flag |= cv::fisheye::CALIB_CHECK_COND;
    flag |= cv::fisheye::CALIB_FIX_SKEW; 
    flag |= cv::fisheye::CALIB_FIX_K1; 
    flag |= cv::fisheye::CALIB_FIX_K2; 
    flag |= cv::fisheye::CALIB_FIX_K3; 
    flag |= cv::fisheye::CALIB_FIX_K4; 


    double rms =cv::fisheye::calibrate(
 objectPoints, points2D, src.size(), 
 K, D, rvec, tvec, flag, cv::TermCriteria(3, 20, 1e-6)     
 );

    Mat output;
    cerr<<"K:"<<K<<endl;
    cerr<<"D:"<<D<<endl;
    cv::fisheye::undistortImage(srcSaved, output, K, D);
    cerr<<"rms: "<<rms<<endl;
    imshow("output", output);
    waitKey(0);

    cerr<<"image .size: "<<srcSaved.size()<<endl;

}

如果有人有想法，请随时分享一些 Python 代码或 C++ 代码.不管你的船是什么.

If anybody has an idea, feel free to either share some code in Python either in C++. Whatever floats your boat.

您可能已经注意到，我没有使用黑白棋盘格进行校准，而是使用构成我的地毯的瓷砖的角落.归根结底，我认为的目标是获取角坐标，这些坐标代表来自畸变半径的样本.地毯在某种程度上与棋盘格相同，唯一的区别 - 我再次认为 - 是在地毯上的角落处的高频边缘比在黑白棋盘格上的要少.

As you may have notice I don't use a black and white checkerboard for the calibration, but corners from tiles constituting my carpet. At the end of the day the goal -I think- is to get corner coordinates which represent samples from the distortion radii . The carpet is to some extent the same as the checkerboard, the only difference -once again I think- is the fact that you have less high frequency edges at those eg corners on the carpet than on a black and white checkerboard.

我知道图片的数量非常有限，即只有1张.我希望图像在某种程度上是不失真的，但我也希望不失真做得很好.但在这种情况下，图像输出看起来完全是胡说八道.

I know the number of pictures is very limited, ie only 1. I expect the image to be undistorted to some extent, but I also expect the undistortion to be very well done. But in this case the image output looks like total nonsense.

我最终将这张图片与棋盘一起使用:https://imgur.com/a/WlLBR由本网站提供:https://sites.google.com/site/scarabotix/ocamcalib-toolbox/ocamcalib-toolbox-download-page但是结果仍然很差:像我发布的其他输出图像一样的对角线.

I ended up using this image with a chessboard: https://imgur.com/a/WlLBR provided by this website: https://sites.google.com/site/scarabotix/ocamcalib-toolbox/ocamcalib-toolbox-download-page But results are still very poor: diagonal lines like the other output image I posted.

谢谢

如何使用 openCV 正确校准带有广角镜头的相机?

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