摄像头标定

void calRealPoint(std::vector<std::vector<cv::Point3f>>& obj, int boardwidth, int boardheight, int imgNumber, int squaresize)
{
 //  Mat imgpoint(boardheight, boardwidth, CV_32FC3,Scalar(0,0,0)); 
 std::vector<cv::Point3f> imgpoint;
 for (int rowIndex = 0; rowIndex < boardheight; rowIndex++)
 {
  for (int colIndex = 0; colIndex < boardwidth; colIndex++)
  {
   //  imgpoint.at<Vec3f>(rowIndex, colIndex) = Vec3f(rowIndex * squaresize, colIndex*squaresize, 0); 
   imgpoint.push_back(cv::Point3f(rowIndex * squaresize, colIndex * squaresize, 0));
  }
 }
 for (int imgIndex = 0; imgIndex < imgNumber; imgIndex++)
 {
  obj.push_back(imgpoint);
 }
}
void Camera::initCameraparam()
{
 intrinsic.create(3,3, CV_64FC1);
 distortion_coeff.create(5,1,CV_64FC1);

 intrinsic.at<double>(0, 0) = 256.8093262;   //fx        
 intrinsic.at<double>(0, 2) = 160.2826538;   //cx 
 intrinsic.at<double>(1, 1) = 254.7511139;   //fy 
 intrinsic.at<double>(1, 2) = 127.6264572;   //cy 

 intrinsic.at<double>(0, 1) = 0;
 intrinsic.at<double>(1, 0) = 0;
 intrinsic.at<double>(2, 0) = 0;
 intrinsic.at<double>(2, 1) = 0;
 intrinsic.at<double>(2, 2) = 1;

 /*
 k1 k2 p1 p2 p3
 */
 distortion_coeff.at<double>(0, 0) = -0.193740;  //k1 
 distortion_coeff.at<double>(1, 0) = -0.378588;  //k2 
 distortion_coeff.at<double>(2, 0) = 0.028980;   //p1 
 distortion_coeff.at<double>(3, 0) = 0.008136;   //p2 
 distortion_coeff.at<double>(4, 0) = 0;          //p3 
}
void Camera::outputCameraParam()
{
 std::cout << "fx :" << intrinsic.at<double>(0, 0) << std::endl << "fy :" << intrinsic.at<double>(1, 1) << std::endl;
 std::cout << "cx :" << intrinsic.at<double>(0, 2) << std::endl << "cy :" << intrinsic.at<double>(1, 2) << std::endl;

 std::cout << "k1 :" << distortion_coeff.at<double>(0, 0) << std::endl;
 std::cout << "k2 :" << distortion_coeff.at<double>(1, 0) << std::endl;
 std::cout << "p1 :" << distortion_coeff.at<double>(2, 0) << std::endl;
 std::cout << "p2 :" << distortion_coeff.at<double>(3, 0) << std::endl;
 std::cout << "p3 :" << distortion_coeff.at<double>(4, 0) << std::endl;
}
void Camera::CameraInit()
{
 cv::Mat img;
 int goodFeatNums = 0;
 cv::namedWindow("chessboard");
 //std::cout << "Plaeas input" << std::endl;
 //std::string
 std::string filename, filedir;
 int i = 0;
  while (goodFeatNums < imageNums)
 {
  filedir = "E:\Code_zoom\OpenCVcode\CV\CV\Camera_pic\Lpic\Lpic_";
  filename = filedir + std::to_string(i) + ".jpg";
  i++;
  rgbImage = cv::imread(filename, CV_LOAD_IMAGE_COLOR);
  cv::cvtColor(rgbImage,grayImage,CV_RGB2GRAY);
  cv::imshow("Reload Image",grayImage);
  bool isFind = findChessboardCorners(rgbImage, boardSize, corner, 0);
   if (isFind == true) //所有角点都被找到 说明这幅图像是可行的 
   {
    /*
    Size(5,5) 搜索窗口的一半大小
    Size(-1,-1) 死区的一半尺寸
    TermCriteria(CV_TERMCRIT_EPS | CV_TERMCRIT_ITER, 20, 0.1)迭代终止条件
    */
    cornerSubPix(grayImage, corner, cv::Size(5, 5), cv::Size(-1, -1), cv::TermCriteria(CV_TERMCRIT_EPS | CV_TERMCRIT_ITER, 20, 0.1));
    drawChessboardCorners(rgbImage, boardSize, corner, isFind);
    cv::imshow("chessboard", rgbImage);
    corners.push_back(corner);
    goodFeatNums++;
    std::cout << "The image is good" << std::endl;
   }
  else
  {
   std::cout << "The image is bad feature" << std::endl;
  }
  if (cv::waitKey(10) == 'q')
   break;
 }

 initCameraparam();
 print("initCamera successed");
 calRealPoint(objRealPoint, boardWidth, boardHeight, imageNums, squareSize);//计算世界坐标值
 print("cal real successed");
 cv::calibrateCamera(objRealPoint, corners, cv::Size(imageWidth, imageHeight), intrinsic, distortion_coeff, rvecs, tvecs, 0);
 //相机标定计算标定矩阵和偏移矩阵
 print("calibrateCamera successed");
 outputCameraParam();//打印矩阵值
 print("output successed");
 cv::Mat uImage;
 cv::undistort(rgbImage,uImage,intrinsic,distortion_coeff);
 //rgbImage 转换成一个无偏移的图像uImage，intriinsic是内参数，distortion_coeff 为相机外参数（畸变）
 cv::imshow("undistort image",uImage);
 cv::imshow("initial image",rgbImage);
 print("imshow OK");
 cv::waitKey(0);
}

 

 

参考地址：https://blog.csdn.net/xiaoxuebajie/article/details/78213573