相机参数标定与畸变校正
（把之前做的笔记搬运到这里来）
一、 原理与公式推导
二、代码实现
1.棋盘格生成：
void chessboard()
{
int perBoardPixel = 30;
int nx = 10;
int ny = 7;
Size boardSize(nx, ny);
Size imgSize(nx * perBoardPixel, ny * perBoardPixel);
Mat image(imgSize, CV_8UC1, Scalar(0));
int flag;
int basisHeight = 0;
int basisWidth = 0;
for (int j = 0; j < boardSize.height; j++)
{
for (int i = 0; i < boardSize.width; i++)
{
flag = (i + j) % 2;
if (flag == 0)
{
for (int n = j * perBoardPixel; n < (j + 1) * perBoardPixel; n++)
for (int m = i * perBoardPixel; m < (i + 1) * perBoardPixel; m++)
image.at(n + basisHeight, m + basisWidth) = 255;
}
}
}
imwrite("chessboard.jpg", image);
}
2.标定
void calibrate()
{
ifstream fin("calibdata.txt"); /* 标定所用图像文件的路径 */
ofstream fout("caliberation_result.txt"); /* 保存标定结果的文件 */
// 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
int image_count = 0; /* 图像数量 */
Size image_size; /* 图像的尺寸 */
Size board_size = Size(9, 6); /* 标定板上每行、列的角点数 */
vector image_points_buf; /* 缓存每幅图像上检测到的角点 */
vector> image_points_seq; /* 保存检测到的所有角点 */
string filename; // 图片名
vector filenames;
while (getline(fin, filename))
{
++image_count;
Mat imageInput = imread(filename);
filenames.push_back(filename);
// 读入第一张图片时获取图片大小
if (image_count == 1)
{
image_size.width = imageInput.cols;
image_size.height = imageInput.rows;
}
/* 提取角点 */
if (0 == findChessboardCorners(imageInput, board_size, image_points_buf))
{
cout << "can not find chessboard corners!\n"; // 找不到角点
exit(1);
}
else
{
Mat view_gray;
cvtColor(imageInput, view_gray, CV_RGB2GRAY); // 转灰度图
/* 亚像素精确化 */
// image_points_buf 初始的角点坐标向量，同时作为亚像素坐标位置的输出
// Size(5,5) 搜索窗口大小
// （-1，-1）表示没有死区
// TermCriteria 角点的迭代过程的终止条件, 可以为迭代次数和角点精度两者的组合
cornerSubPix(view_gray, image_points_buf, Size(5, 5), Size(-1, -1), TermCriteria(CV_TERMCRIT_EPS + CV_TERMCRIT_ITER, 30, 0.1));
image_points_seq.push_back(image_points_buf); // 保存亚像素角点
/* 在图像上显示角点位置 */
drawChessboardCorners(view_gray, board_size, image_points_buf, false); // 用于在图片中标记角点
imshow("Camera Calibration", view_gray); // 显示图片
waitKey(500); //暂停0.5S
}
}
int CornerNum = board_size.width * board_size.height; // 每张图片上总的角点数
//-------------以下是摄像机标定------------------
/*棋盘三维信息*/
Size square_size = Size(10, 10); /* 实际测量得到的标定板上每个棋盘格的大小 */
vector> object_points; /* 保存标定板上角点的三维坐标 */
/*内外参数*/
Mat cameraMatrix = Mat(3, 3, CV_32FC1, Scalar::all(0)); /* 摄像机内参数矩阵 */
vector point_counts; // 每幅图像中角点的数量
Mat distCoeffs = Mat(1, 5, CV_32FC1, Scalar::all(0)); /* 摄像机的5个畸变系数：k1,k2,p1,p2,k3 */
vector tvecsMat; /* 每幅图像的旋转向量 */
vector rvecsMat; /* 每幅图像的平移向量 */
/* 初始化标定板上角点的三维坐标 */
int i, j, t;
for (t = 0; t tempPointSet;
for (i = 0; i image_points2; /* 保存重新计算得到的投影点 */
fout << "每幅图像的标定误差：\n";
for (i = 0; i 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 */
projectPoints(tempPointSet, rvecsMat[i], tvecsMat[i], cameraMatrix, distCoeffs, image_points2);
/* 计算新的投影点和旧的投影点之间的误差*/
vector tempImagePoint = image_points_seq[i];
Mat tempImagePointMat = Mat(1, tempImagePoint.size(), CV_32FC2);
Mat image_points2Mat = Mat(1, image_points2.size(), CV_32FC2);
for (int j = 0; j < tempImagePoint.size(); j++)
{
image_points2Mat.at(0, j) = Vec2f(image_points2[j].x, image_points2[j].y);
tempImagePointMat.at(0, j) = Vec2f(tempImagePoint[j].x, tempImagePoint[j].y);
}
err = norm(image_points2Mat, tempImagePointMat, NORM_L2);
total_err += err /= point_counts[i];
fout << "第" << i + 1 << "幅图像的平均误差：" << err << "像素" << endl;
}
fout << "总体平均误差：" << total_err / image_count << "像素" << endl << endl;
//-------------------------评价完成---------------------------------------------
//-----------------------保存定标结果-------------------------------------------
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 << cameraMatrix << endl << endl;
fout << "畸变系数：\n";
fout << distCoeffs << endl << endl << endl;
for (int i = 0; i> imageFileName;
imageFileName += "_d.jpg";
imwrite(imageFileName, newimage);
}
fin.close();
fout.close();
return;
}
三、已有工具
GML C++ Camera Calibration Toolbox：http://graphics.cs.msu.ru/en/node/909
使用注意：输入图像不能过大，会崩