鱼眼摄像头标定与畸变校正（OPENCV版）

转载请注明作者和出处：http://blog.csdn.net/u011475210
代码地址：https://github.com/WordZzzz/fisheye_calibration
软件版本：VS2013+OPENCV2.4.13 OR VS2013+OPENCV3.4.0
编者：WordZzzz

我的代码
可供参考资料
- FishEye模型的畸变校正
- 普通相机模型的畸变校正

我的代码

最近在整理自己以前做过的一些东西，这是基于opencv的鱼眼摄像头畸变校正程序的github地址。

其中：

normal_calibrate：基于OPENCV2与OPENCV3通用的函数实现，可实现USB摄像头实时畸变校正；
fishey_calibrate：基于OPENCV3独有的fishyey结构体实现，可实现USB摄像头实时畸变校正；
fishey_calibrate_img：基于OPENCV3独有的fishyey结构体实现，可实现单张图片畸变校正；

opencv1.0 2.0版只有一种摄像机标定模型，就是普通的小孔成像模型，在cv：：空间下。而从opencv3.0开始，新增了一种鱼眼相机标定模型，在fisheye::空间下。两种模型的主要区别在于像与物的投影关系不同，具体的文献资料依然是数不胜数，这里就不赘述。根据opencv官方文档的建议，在畸变程度较大的广角镜头（比如：鱼眼镜头）上进行摄像机标定和畸变校正，最好是用fisheye模型，该模型在图像边缘畸变程度很大的地方比普通相机模型的效果要好。

当然，还是要贴上官方文档的：

Camera calibration With OpenCV2；
Camera calibration With OpenCV3；

华丽的分割线

可供参考资料

以下是在写这篇博客的时候偶然发现的对opencv两个版本标定过程的讲解，这里直接copy过来并稍微做了一下排版，因为自己忙于为找工作做准备，实在是没时间自己整理了。原文链接。

图像算法中会经常用到摄像机的畸变校正，有必要总结分析OpenCV中畸变校正方法，其中包括普通针孔相机模型和鱼眼相机模型fisheye两种畸变校正方法。

普通相机模型畸变校正函数针对OpenCV中的cv::initUndistortRectifyMap()，鱼眼相机模型畸变校正函数对应OpenCV中的cv::fisheye::initUndistortRectifyMap()。两种方法算出映射Mapx和Mapy后，统一用cv::Remap()函数进行插值得到校正后的图像。

FishEye模型的畸变校正。

方便起见，直接贴出OpenCV源码，我在里面加了注释说明。建议参考OpenCV官方文档看畸变模型原理会更清楚。

简要流程就是：

1.求内参矩阵的逆，由于摄像机坐标系的三维点到二维图像平面，需要乘以旋转矩阵R和内参矩阵K。那么反向投影回去则是二维图像坐标乘以 K*R的逆矩阵。
2.将目标图像中的每一个像素点坐标(j,i)，乘以1中求出的逆矩阵iR，转换到摄像机坐标系（_x,_y,_w）,并归一化得到z=1平面下的三维坐标(x,y,1)。
3.求出平面模型下像素点对应鱼眼半球模型下的极坐标(r, theta)。
4.利用鱼眼畸变模型求出拥有畸变时像素点对应的theta_d。

5.利用求出的theta_d值将三维坐标点重投影到二维图像平面得到(u,v)，(u,v)即为目标图像对应的畸变图像中像素点坐标。
6.使用cv::Remap（）函数，根据mapx,mapy取出对应坐标位置的像素值赋值给目标图像，一般采用双线性插值法，得到畸变校正后的目标图像。

#include <opencv2\opencv.hpp>void cv::fisheye::initUndistortRectifyMap( InputArray K, InputArray D, InputArray R, InputArray P,const cv::Size& size, int m1type, OutputArray map1, OutputArray map2 )
{CV_Assert( m1type == CV_16SC2 || m1type == CV_32F || m1type <=0 );map1.create( size, m1type <= 0 ? CV_16SC2 : m1type );map2.create( size, map1.type() == CV_16SC2 ? CV_16UC1 : CV_32F );CV_Assert((K.depth() == CV_32F || K.depth() == CV_64F) && (D.depth() == CV_32F || D.depth() == CV_64F));CV_Assert((P.empty() || P.depth() == CV_32F || P.depth() == CV_64F) && (R.empty() || R.depth() == CV_32F || R.depth() == CV_64F));CV_Assert(K.size() == Size(3, 3) && (D.empty() || D.total() == 4));CV_Assert(R.empty() || R.size() == Size(3, 3) || R.total() * R.channels() == 3);CV_Assert(P.empty() || P.size() == Size(3, 3) || P.size() == Size(4, 3));//从内参矩阵K中取出归一化焦距fx,fy; cx,cycv::Vec2d f, c;if (K.depth() == CV_32F){Matx33f camMat = K.getMat();f = Vec2f(camMat(0, 0), camMat(1, 1));c = Vec2f(camMat(0, 2), camMat(1, 2));}else{Matx33d camMat = K.getMat();f = Vec2d(camMat(0, 0), camMat(1, 1));c = Vec2d(camMat(0, 2), camMat(1, 2));}//从畸变系数矩阵D中取出畸变系数k1,k2,k3,k4Vec4d k = Vec4d::all(0);if (!D.empty())k = D.depth() == CV_32F ? (Vec4d)*D.getMat().ptr<Vec4f>(): *D.getMat().ptr<Vec4d>();//旋转矩阵RR转换数据类型为CV_64F，如果不需要旋转，则RR为单位阵cv::Matx33d RR  = cv::Matx33d::eye();if (!R.empty() && R.total() * R.channels() == 3){cv::Vec3d rvec;R.getMat().convertTo(rvec, CV_64F);RR = Affine3d(rvec).rotation();}else if (!R.empty() && R.size() == Size(3, 3))R.getMat().convertTo(RR, CV_64F);//新的内参矩阵PP转换数据类型为CV_64Fcv::Matx33d PP = cv::Matx33d::eye();if (!P.empty())P.getMat().colRange(0, 3).convertTo(PP, CV_64F);//关键一步：新的内参矩阵*旋转矩阵，然后利用SVD分解求出逆矩阵iR，后面用到cv::Matx33d iR = (PP * RR).inv(cv::DECOMP_SVD);//反向映射，遍历目标图像所有像素位置，找到畸变图像中对应位置坐标(u,v)，并分别保存坐标(u,v)到mapx和mapy中for( int i = 0; i < size.height; ++i){float* m1f = map1.getMat().ptr<float>(i);float* m2f = map2.getMat().ptr<float>(i);short*  m1 = (short*)m1f;ushort* m2 = (ushort*)m2f;//二维图像平面坐标系->摄像机坐标系double _x = i*iR(0, 1) + iR(0, 2),_y = i*iR(1, 1) + iR(1, 2),_w = i*iR(2, 1) + iR(2, 2);for( int j = 0; j < size.width; ++j){//归一化摄像机坐标系，相当于假定在Z=1平面上double x = _x/_w, y = _y/_w;//求鱼眼半球体截面半径rdouble r = sqrt(x*x + y*y);//求鱼眼半球面上一点与光心的连线和光轴的夹角Thetadouble theta = atan(r);//畸变模型求出theta_d，相当于有畸变的角度值double theta2 = theta*theta, theta4 = theta2*theta2, theta6 = theta4*theta2, theta8 = theta4*theta4;double theta_d = theta * (1 + k[0]*theta2 + k[1]*theta4 + k[2]*theta6 + k[3]*theta8);//利用有畸变的Theta值，将摄像机坐标系下的归一化三维坐标，重投影到二维图像平面，得到(j,i)对应畸变图像中的(u,v)double scale = (r == 0) ? 1.0 : theta_d / r;double u = f[0]*x*scale + c[0];double v = f[1]*y*scale + c[1];//保存(u,v)坐标到mapx,mapyif( m1type == CV_16SC2 ){int iu = cv::saturate_cast<int>(u*cv::INTER_TAB_SIZE);int iv = cv::saturate_cast<int>(v*cv::INTER_TAB_SIZE);m1[j*2+0] = (short)(iu >> cv::INTER_BITS);m1[j*2+1] = (short)(iv >> cv::INTER_BITS);m2[j] = (ushort)((iv & (cv::INTER_TAB_SIZE-1))*cv::INTER_TAB_SIZE + (iu & (cv::INTER_TAB_SIZE-1)));}else if( m1type == CV_32FC1 ){m1f[j] = (float)u;m2f[j] = (float)v;}//这三条语句是上面 ”//二维图像平面坐标系->摄像机坐标系“的一部分，是矩阵iR的第一列，这样写能够简化计算_x += iR(0, 0);_y += iR(1, 0);_w += iR(2, 0);}}
}

普通相机模型的畸变校正

  同样建议参考OpenCV官方文档阅读代码。  主要流程和上面Fisheye模型差不多，只有第4部分的畸变模型不一样，普通相机的畸变模型如下：


  同样把源代码贴上，并加上注解：

#include <opencv2\opencv.hpp>void cv::initUndistortRectifyMap( InputArray _cameraMatrix, InputArray _distCoeffs,InputArray _matR, InputArray _newCameraMatrix,Size size, int m1type, OutputArray _map1, OutputArray _map2 )
{Mat cameraMatrix = _cameraMatrix.getMat(), distCoeffs = _distCoeffs.getMat();Mat matR = _matR.getMat(), newCameraMatrix = _newCameraMatrix.getMat();if( m1type <= 0 )m1type = CV_16SC2;CV_Assert( m1type == CV_16SC2 || m1type == CV_32FC1 || m1type == CV_32FC2 );_map1.create( size, m1type );Mat map1 = _map1.getMat(), map2;if( m1type != CV_32FC2 ){_map2.create( size, m1type == CV_16SC2 ? CV_16UC1 : CV_32FC1 );map2 = _map2.getMat();}else_map2.release();Mat_<double> R = Mat_<double>::eye(3, 3);Mat_<double> A = Mat_<double>(cameraMatrix), Ar;if( !newCameraMatrix.empty() )Ar = Mat_<double>(newCameraMatrix);elseAr = getDefaultNewCameraMatrix( A, size, true );if( !matR.empty() )R = Mat_<double>(matR);if( !distCoeffs.empty() )distCoeffs = Mat_<double>(distCoeffs);else{distCoeffs.create(14, 1, CV_64F);distCoeffs = 0.;}CV_Assert( A.size() == Size(3,3) && A.size() == R.size() );CV_Assert( Ar.size() == Size(3,3) || Ar.size() == Size(4, 3));//LU分解求新的内参矩阵Ar与旋转矩阵R乘积的逆矩阵iRMat_<double> iR = (Ar.colRange(0,3)*R).inv(DECOMP_LU);const double* ir = &iR(0,0);//从旧的内参矩阵中取出光心位置u0,v0,和归一化焦距fx,fydouble u0 = A(0, 2),  v0 = A(1, 2);double fx = A(0, 0),  fy = A(1, 1);//尼玛14个畸变系数，不过大多用到的只有(k1,k2,p1,p2)，最多加一个k3，用不到的置为0CV_Assert( distCoeffs.size() == Size(1, 4) || distCoeffs.size() == Size(4, 1) ||distCoeffs.size() == Size(1, 5) || distCoeffs.size() == Size(5, 1) ||distCoeffs.size() == Size(1, 8) || distCoeffs.size() == Size(8, 1) ||distCoeffs.size() == Size(1, 12) || distCoeffs.size() == Size(12, 1) ||distCoeffs.size() == Size(1, 14) || distCoeffs.size() == Size(14, 1));if( distCoeffs.rows != 1 && !distCoeffs.isContinuous() )distCoeffs = distCoeffs.t();const double* const distPtr = distCoeffs.ptr<double>();double k1 = distPtr[0];double k2 = distPtr[1];double p1 = distPtr[2];double p2 = distPtr[3];double k3 = distCoeffs.cols + distCoeffs.rows - 1 >= 5 ? distPtr[4] : 0.;double k4 = distCoeffs.cols + distCoeffs.rows - 1 >= 8 ? distPtr[5] : 0.;double k5 = distCoeffs.cols + distCoeffs.rows - 1 >= 8 ? distPtr[6] : 0.;double k6 = distCoeffs.cols + distCoeffs.rows - 1 >= 8 ? distPtr[7] : 0.;double s1 = distCoeffs.cols + distCoeffs.rows - 1 >= 12 ? distPtr[8] : 0.;double s2 = distCoeffs.cols + distCoeffs.rows - 1 >= 12 ? distPtr[9] : 0.;double s3 = distCoeffs.cols + distCoeffs.rows - 1 >= 12 ? distPtr[10] : 0.;double s4 = distCoeffs.cols + distCoeffs.rows - 1 >= 12 ? distPtr[11] : 0.;double tauX = distCoeffs.cols + distCoeffs.rows - 1 >= 14 ? distPtr[12] : 0.;double tauY = distCoeffs.cols + distCoeffs.rows - 1 >= 14 ? distPtr[13] : 0.;//tauX,tauY这个是什么梯形畸变，用不到的话matTilt为单位阵// Matrix for trapezoidal distortion of tilted image sensorcv::Matx33d matTilt = cv::Matx33d::eye();cv::detail::computeTiltProjectionMatrix(tauX, tauY, &matTilt);for( int i = 0; i < size.height; i++ ){float* m1f = map1.ptr<float>(i);float* m2f = map2.empty() ? 0 : map2.ptr<float>(i);short* m1 = (short*)m1f;ushort* m2 = (ushort*)m2f;//利用逆矩阵iR将二维图像坐标(j,i)转换到摄像机坐标系(_x,_y,_w)double _x = i*ir[1] + ir[2], _y = i*ir[4] + ir[5], _w = i*ir[7] + ir[8];for( int j = 0; j < size.width; j++, _x += ir[0], _y += ir[3], _w += ir[6] ){//摄像机坐标系归一化，令Z=1平面double w = 1./_w, x = _x*w, y = _y*w;//这一部分请看OpenCV官方文档，畸变模型部分double x2 = x*x, y2 = y*y;double r2 = x2 + y2, _2xy = 2*x*y;double kr = (1 + ((k3*r2 + k2)*r2 + k1)*r2)/(1 + ((k6*r2 + k5)*r2 + k4)*r2);double xd = (x*kr + p1*_2xy + p2*(r2 + 2*x2) + s1*r2+s2*r2*r2);double yd = (y*kr + p1*(r2 + 2*y2) + p2*_2xy + s3*r2+s4*r2*r2);//根据求取的xd,yd将三维坐标重投影到二维畸变图像坐标(u,v)cv::Vec3d vecTilt = matTilt*cv::Vec3d(xd, yd, 1);double invProj = vecTilt(2) ? 1./vecTilt(2) : 1;double u = fx*invProj*vecTilt(0) + u0;double v = fy*invProj*vecTilt(1) + v0;//保存u,v的值到Mapx,Mapy中if( m1type == CV_16SC2 ){int iu = saturate_cast<int>(u*INTER_TAB_SIZE);int iv = saturate_cast<int>(v*INTER_TAB_SIZE);m1[j*2] = (short)(iu >> INTER_BITS);m1[j*2+1] = (short)(iv >> INTER_BITS);m2[j] = (ushort)((iv & (INTER_TAB_SIZE-1))*INTER_TAB_SIZE + (iu & (INTER_TAB_SIZE-1)));}else if( m1type == CV_32FC1 ){m1f[j] = (float)u;m2f[j] = (float)v;}else{m1f[j*2] = (float)u;m1f[j*2+1] = (float)v;}}}