直接上代码：https://github.com/LixinLu42/fisheye_calibrate 欢迎骚扰我的github哈～～～
使用方法，需要使用鱼眼相机拍摄棋盘格的一组照片，尽量每个角度都要拍，拍个三四十张，保存在本地，使用本程序读取并且标定，会输出一个txt文件，相机畸变参数和内参可以去这里找。
//运行环境 VS2012+opencv3.0已测试 VS2015+opencv3.2已测试
#include <opencv2/opencv.hpp>
#include <fstream>
using namespace std;
using namespace cv;
int main()
{
ofstream fout("caliberation_result.txt"); /** 保存定标结果的文件 **/
/************************************************************************
读取每一幅图像，从中提取出角点，然后对角点进行亚像素精确化
*************************************************************************/
cout << "开始提取角点………………" << endl;
int image_count = 43; /**** 图像数量 ****/
Size board_size = Size(7, 6); /**** 定标板上每行、列的角点数 ****/
vector<Point2f> corners; /**** 缓存每幅图像上检测到的角点 ****/
vector<vector<Point2f> > corners_Seq; /**** 保存检测到的所有角点 ****/
vector<Mat> image_Seq;
int successImageNum = 0; /**** 成功提取角点的棋盘图数量 ****/
int count = 0;
for (int i = 0; i != image_count; i++)
{
cout << "Frame #" << i + 1 << "..." << endl;
string imageFileName;
std::stringstream StrStm;
StrStm << i + 1;
StrStm >> imageFileName;
imageFileName += ".jpg";
cout<< imageFileName << endl;
cv::Mat image = imread("../../img_src/" + imageFileName);
/* 提取角点 */
Mat imageGray;
cvtColor(image, imageGray, CV_RGB2GRAY);
bool patternfound = findChessboardCorners(image, board_size, corners, CALIB_CB_ADAPTIVE_THRESH + CALIB_CB_NORMALIZE_IMAGE +
CALIB_CB_FAST_CHECK);
if (!patternfound)
{
imshow("i",image);
waitKey(30);
cout << "找不到角点，需删除图片文件" << imageFileName << "重新排列文件名，再次标定" << endl;
getchar();
continue;
//exit(1);
}
else
{
/* 亚像素精确化 */
cornerSubPix(imageGray, corners, Size(11, 11), Size(-1, -1), TermCriteria(CV_TERMCRIT_EPS + CV_TERMCRIT_ITER, 30, 0.1));
/* 绘制检测到的角点并保存 */
Mat imageTemp = image.clone();
for (int j = 0; j < corners.size(); j++)
{
circle(imageTemp, corners[j], 10, Scalar(0, 0, 255), 2, 8, 0);
}
string imageFileName;
std::stringstream StrStm;
StrStm << i + 1;
StrStm >> imageFileName;
imageFileName += "_corner.jpg";
imwrite(imageFileName, imageTemp);
cout << "Frame corner#" << i + 1 << "...end" << endl;
count = count + corners.size();
successImageNum = successImageNum + 1;
corners_Seq.push_back(corners);
}
image_Seq.push_back(image);
}
cout << "角点提取完成！\n";
/************************************************************************
摄像机定标
*************************************************************************/
cout << "开始定标………………" << endl;
Size square_size = Size(20, 20);
vector<vector<Point3f> > object_Points; /**** 保存定标板上角点的三维坐标 ****/
Mat image_points = Mat(1, count, CV_32FC2, Scalar::all(0)); /***** 保存提取的所有角点 *****/
vector<int> point_counts;
/* 初始化定标板上角点的三维坐标 */
for (int t = 0; t<successImageNum; t++)
{
vector<Point3f> tempPointSet;
for (int i = 0; i<board_size.height; i++)
{
for (int j = 0; j<board_size.width; j++)
{
/* 假设定标板放在世界坐标系中z=0的平面上 */
Point3f tempPoint;
tempPoint.x = i*square_size.width;
tempPoint.y = j*square_size.height;
tempPoint.z = 0;
tempPointSet.push_back(tempPoint);
}
}
object_Points.push_back(tempPointSet);
}
for (int i = 0; i< successImageNum; i++)
{
point_counts.push_back(board_size.width*board_size.height);
}
/* 开始定标 */
Size image_size = image_Seq[0].size();
cv::Matx33d intrinsic_matrix; /***** 摄像机内参数矩阵 ****/
cv::Vec4d distortion_coeffs; /* 摄像机的4个畸变系数：k1,k2,k3,k4*/
std::vector<cv::Vec3d> rotation_vectors; /* 每幅图像的旋转向量 */
std::vector<cv::Vec3d> translation_vectors; /* 每幅图像的平移向量 */
int flags = 0;
flags |= cv::fisheye::CALIB_RECOMPUTE_EXTRINSIC;
flags |= cv::fisheye::CALIB_CHECK_COND;
flags |= cv::fisheye::CALIB_FIX_SKEW;
fisheye::calibrate(object_Points, corners_Seq, image_size, intrinsic_matrix, distortion_coeffs, rotation_vectors, translation_vectors, flags, cv::TermCriteria(3, 20, 1e-6));
cout << "定标完成！\n";
/************************************************************************
对定标结果进行评价
*************************************************************************/
cout << "开始评价定标结果………………" << endl;
double total_err = 0.0; /* 所有图像的平均误差的总和 */
double err = 0.0; /* 每幅图像的平均误差 */
vector<Point2f> image_points2; /**** 保存重新计算得到的投影点 ****/
cout << "每幅图像的定标误差：" << endl;
cout << "每幅图像的定标误差：" << endl << endl;
for (int i = 0; i<image_count; i++)
{
vector<Point3f> tempPointSet = object_Points[i];
/**** 通过得到的摄像机内外参数，对空间的三维点进行重新投影计算，得到新的投影点 ****/
fisheye::projectPoints(tempPointSet, image_points2, rotation_vectors[i], translation_vectors[i], intrinsic_matrix, distortion_coeffs);
/* 计算新的投影点和旧的投影点之间的误差*/
vector<Point2f> tempImagePoint = corners_Seq[i];
Mat tempImagePointMat = Mat(1, tempImagePoint.size(), CV_32FC2);
Mat image_points2Mat = Mat(1, image_points2.size(), CV_32FC2);
for (size_t i = 0; i != tempImagePoint.size(); i++)
{
image_points2Mat.at<Vec2f>(0, i) = Vec2f(image_points2[i].x, image_points2[i].y);
tempImagePointMat.at<Vec2f>(0, i) = Vec2f(tempImagePoint[i].x, tempImagePoint[i].y);
}
err = norm(image_points2Mat, tempImagePointMat, NORM_L2);
total_err += err /= point_counts[i];
cout << "第" << i + 1 << "幅图像的平均误差：" << err << "像素" << endl;
fout << "第" << i + 1 << "幅图像的平均误差：" << err << "像素" << endl;
}
cout << "总体平均误差：" << total_err / image_count << "像素" << endl;
fout << "总体平均误差：" << total_err / image_count << "像素" << endl << endl;
cout << "评价完成！" << endl;
/************************************************************************
保存定标结果
*************************************************************************/
cout << "开始保存定标结果………………" << endl;
Mat rotation_matrix = Mat(3, 3, CV_32FC1, Scalar::all(0)); /* 保存每幅图像的旋转矩阵 */
fout << "相机内参数矩阵：" << endl;
fout << intrinsic_matrix << endl;
fout << "畸变系数：\n";
fout << distortion_coeffs << endl;
for (int i = 0; i<image_count; i++)
{
fout << "第" << i + 1 << "幅图像的旋转向量：" << endl;
fout << rotation_vectors[i] << endl;
/* 将旋转向量转换为相对应的旋转矩阵 */
Rodrigues(rotation_vectors[i], rotation_matrix);
fout << "第" << i + 1 << "幅图像的旋转矩阵：" << endl;
fout << rotation_matrix << endl;
fout << "第" << i + 1 << "幅图像的平移向量：" << endl;
fout << translation_vectors[i] << endl;
}
cout << "完成保存" << endl;
fout << endl;
/************************************************************************
显示定标结果
*************************************************************************/
Mat mapx = Mat(Size(1920, 1080), CV_32FC1);
Mat mapy = Mat(Size(1920, 1080), CV_32FC1);
Mat R = Mat::eye(3, 3, CV_32F);
cout << "保存矫正图像" << endl;
for (int i = 0; i != image_count; i++)
{
cout << "Frame #" << i + 1 << "..." << endl;
fisheye::initUndistortRectifyMap(intrinsic_matrix,distortion_coeffs,R,intrinsic_matrix,image_size,CV_32FC1,mapx,mapy);
//fisheye::initUndistortRectifyMap(intrinsic_matrix, distortion_coeffs, R,
//getOptimalNewCameraMatrix(intrinsic_matrix, distortion_coeffs, image_size, 1, image_size, 0), Size(3200, 1800), CV_32FC1, mapx, mapy);
Mat t = image_Seq[i].clone();
cv::remap(image_Seq[i], t, mapx, mapy, INTER_LINEAR);
string imageFileName;
std::stringstream StrStm;
StrStm << i + 1;
StrStm >> imageFileName;
imageFileName += "_d.jpg";
imwrite(imageFileName, t);
}
cout << "保存结束" << endl;
/************************************************************************
测试一张图片
*************************************************************************/
if (1)
{
//cout<<"TestImage ..."<<endl;
//Mat testImage = imread("a.jpg",1);
//fisheye::initUndistortRectifyMap(intrinsic_matrix,distortion_coeffs,R,
// getOptimalNewCameraMatrix(intrinsic_matrix, distortion_coeffs, image_size, 1, image_size, 0),image_size,CV_32FC1,mapx,mapy);
//Mat t = testImage.clone();
//cv::remap(testImage,t,mapx, mapy, INTER_LINEAR);
//imwrite("TestOutput.jpg",t);
//cout<<"保存结束"<<endl;
cout << "TestImage ..." << endl;
Mat distort_img = imread("a.jpg", 1);
Mat undistort_img;
Mat intrinsic_mat(intrinsic_matrix), new_intrinsic_mat;
intrinsic_mat.copyTo(new_intrinsic_mat);
//调节视场大小,乘的系数越小视场越大
new_intrinsic_mat.at<double>(0, 0) *= 0.7;
new_intrinsic_mat.at<double>(1, 1) *= 0.7;
//调节校正图中心，建议置于校正图中心
new_intrinsic_mat.at<double>(0, 2) = 0.5 * distort_img.cols;
new_intrinsic_mat.at<double>(1, 2) = 0.5 * distort_img.rows;
fisheye::undistortImage(distort_img, undistort_img, intrinsic_matrix, distortion_coeffs, new_intrinsic_mat);
imwrite("output.jpg", undistort_img);
cout << "保存结束" << endl;
}
return 0;
}
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