首先放上一张效果动图：如果你需要这样的Demo,请下载：海康威视标定Demo
软件配置环境：VS2013+OpenCV2.49+海康威视相关SDK导入，Release下编译运行
标定部分核心代码：
m_progress.SetPos(0);
CString PIC = "";
CStdioFile picpath("calibdata.ini", CFile::modeRead);
picpath.ReadString(PIC);
picpath.Close();
// TODO: 在此添加控件通知处理程序代码
ifstream fin("calibdata.ini"); /* 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
m_progress.SetPos(20);
cout << "开始提取角点………………";
int image_count = 0; /* 图像数量 */
/* 图像的尺寸 */
Size board_size = Size(6, 8); /* 标定板上每行、列的角点数 */
vector<Point2f> image_points_buf; /* 缓存每幅图像上检测到的角点 */
vector<vector<Point2f>> image_points_seq; /* 保存检测到的所有角点 */
string filename;
int count = -1;//用于存储角点个数。
while (getline(fin, filename))
{
image_count++;
// 用于观察检验输出
cout << "image_count = " << image_count << endl;
/* 输出检验*/
cout << "-->count = " << count;
Mat imageInput = imread(filename);
if (image_count == 1) //读入第一张图片时获取图像宽高信息
{
image_size.width = imageInput.cols;
image_size.height = imageInput.rows;
cout << "image_size.width = " << image_size.width << endl;
cout << "image_size.height = " << image_size.height << endl;
}
/* 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);
/* 亚像素精确化 */
find4QuadCornerSubpix(view_gray, image_points_buf, Size(5, 5)); //对粗提取的角点进行精确化
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, true); //用于在图片中标记角点
//imshow("Camera Calibration", view_gray);//显示图片
imwrite("2.bmp", view_gray);
CImage image;
CString showJD = "2.bmp";
int cx, cy;
CRect rect;
//根据路径载入图片
//char strPicPath[] = PicName;
image.Load(showJD);
//获取图片的宽 高
cx = image.GetWidth();
cy = image.GetHeight();
CWnd *pWnd = NULL;
pWnd = GetDlgItem(IDC_STATIC_JD);//获取控件句柄
//获取Picture Control控件的客户区
pWnd->GetClientRect(&rect);
CDC *pDc = NULL;
pDc = pWnd->GetDC();//获取picture control的DC
//设置指定设备环境中的位图拉伸模式
int ModeOld = SetStretchBltMode(pDc->m_hDC, STRETCH_HALFTONE);
//从源矩形中复制一个位图到目标矩形，按目标设备设置的模式进行图像的拉伸或压缩
image.StretchBlt(pDc->m_hDC, rect, SRCCOPY);
SetStretchBltMode(pDc->m_hDC, ModeOld);
ReleaseDC(pDc);
//waitKey(500);//暂停0.5S
}
}
int total = image_points_seq.size();
cout << "total = " << total << endl;
int CornerNum = board_size.width*board_size.height; //每张图片上总的角点数
for (int ii = 0; ii < total; ii++)
{
if (0 == ii%CornerNum)// 24 是每幅图片的角点个数。此判断语句是为了输出 图片号，便于控制台观看
{
int i = -1;
i = ii / CornerNum;
int j = i + 1;
cout << "--> 第 " << j << "图片的数据 --> : " << endl;
}
if (0 == ii % 3) // 此判断语句，格式化输出，便于控制台查看
{
cout << endl;
}
else
{
cout.width(10);
}
//输出所有的角点
cout << " -->" << image_points_seq[ii][0].x;
cout << " -->" << image_points_seq[ii][0].y;
}
cout << "角点提取完成！\n";
m_progress.SetPos(50);
//以下是摄像机标定
cout << "开始标定………………";
/*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<vector<Point3f>> object_points; /* 保存标定板上角点的三维坐标 */
/*内外参数*/
/* 摄像机内参数矩阵 */
vector<int> point_counts; // 每幅图像中角点的数量
vector<Mat> tvecsMat; /* 每幅图像的旋转向量 */
vector<Mat> 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 < image_count; t++)
{
vector<Point3f> tempPointSet;
for (i = 0; i < board_size.height; i++)
{
for (j = 0; j < board_size.width; j++)
{
Point3f realPoint;
/* 假设标定板放在世界坐标系中z=0的平面上 */
realPoint.x = i*square_size.width;
realPoint.y = j*square_size.height;
realPoint.z = 0;
tempPointSet.push_back(realPoint);
}
}
object_points.push_back(tempPointSet);
}
/* 初始化每幅图像中的角点数量，假定每幅图像中都可以看到完整的标定板 */
for (i = 0; i < image_count; i++)
{
point_counts.push_back(board_size.width*board_size.height);
}
/* 开始标定 */
calibrateCamera(object_points, image_points_seq, image_size, cameraMatrix, distCoeffs, rvecsMat, tvecsMat, 0);
cout << "标定完成！\n";
m_progress.SetPos(70);
//对标定结果进行评价
cout << "开始评价标定结果………………\n";
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 << "\t每幅图像的标定误差：\n";
fout << "每幅图像的标定误差：\n";
for (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 */
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<Point2f> 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<Vec2f>(0, j) = Vec2f(image_points2[j].x, image_points2[j].y);
tempImagePointMat.at<Vec2f>(0, j) = Vec2f(tempImagePoint[j].x, tempImagePoint[j].y);
}
err = norm(image_points2Mat, tempImagePointMat, NORM_L2);
total_err += err /= point_counts[i];
std::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;
}
std::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;
std::cout << "评价完成！" << endl;
//保存定标结果
std::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 << cameraMatrix << endl << endl;
fout << "畸变系数：\n";
fout << distCoeffs << endl << endl << endl;
for (int i = 0; i < image_count; i++)
{
fout << "第" << i + 1 << "幅图像的旋转向量：" << endl;
fout << tvecsMat[i] << endl;
/* 将旋转向量转换为相对应的旋转矩阵 */
Rodrigues(tvecsMat[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 << rvecsMat[i] << endl << endl;
}
std::cout << "完成保存" << endl;
m_progress.SetPos(80);
fout << endl;
/************************************************************************
显示定标结果
*************************************************************************/
std::cout << "保存矫正图像" << endl;
string imageFileName;
std::stringstream StrStm;
for (int i = 0; i != image_count; i++)
{
std::cout << "Frame #" << i + 1 << "..." << endl;
initUndistortRectifyMap(cameraMatrix, distCoeffs, R, cameraMatrix, image_size, CV_32FC1, mapx, mapy);
func(cameraMatrix, distCoeffs, R, image_size, mapx, mapy);
StrStm.clear();
imageFileName.clear();
string filePath = PIC;
/*StrStm << i + 1;
StrStm >> imageFileName;
filePath += imageFileName;
filePath += ".bmp";*/
Mat imageSource = imread(filePath);
Mat newimage = imageSource.clone();
//另一种不需要转换矩阵的方式
//undistort(imageSource,newimage,cameraMatrix,distCoeffs);
remap(imageSource, newimage, mapx, mapy, INTER_LINEAR);
/*imshow("原始图像", imageSource);
imshow("矫正后图像", newimage);*/
////
CImage image1;
MatToCImage(newimage, image1);
//PIC = PicName;
CImage image;
int cx, cy;
CRect rect;
//根据路径载入图片
//char strPicPath[] = PicName;
image.Load(PIC);
//获取图片的宽 高
cx = image1.GetWidth();
cy = image1.GetHeight();
CWnd *pWnd = NULL;
pWnd = GetDlgItem(IDC_STATIC_JZ);//获取控件句柄
//获取Picture Control控件的客户区
pWnd->GetClientRect(&rect);
CDC *pDc = NULL;
pDc = pWnd->GetDC();//获取picture control的DC
//设置指定设备环境中的位图拉伸模式
int ModeOld = SetStretchBltMode(pDc->m_hDC, STRETCH_HALFTONE);
//从源矩形中复制一个位图到目标矩形，按目标设备设置的模式进行图像的拉伸或压缩
image1.StretchBlt(pDc->m_hDC, rect, SRCCOPY);
SetStretchBltMode(pDc->m_hDC, ModeOld);
ReleaseDC(pDc);
////
waitKey();
StrStm.clear();
filePath.clear();
CString str3 = "_calibrated";
PIC.Insert(14, str3);
imageFileName = PIC;
imwrite(imageFileName, newimage);
file.Open("calibrated.ini", CFile::modeCreate | CFile::modeNoTruncate | CFile::modeWrite);
file.Write(PIC, strlen(PIC));
file.Close();
}
std::cout << "保存结束" << endl;
m_progress.SetPos(100);
return;