@ -1510,8 +1510,8 @@ concatenated together.
@ param imageSize Size of the image used only to initialize the camera intrinsic matrix .
@ param cameraMatrix Input / output 3 x3 floating - point camera intrinsic matrix
\ f $ \ cameramatrix { A } \ f $ . If @ ref CALIB_USE_INTRINSIC_GUESS
and / or @ ref CALIB_FIX_ASPECT_RATIO are specified , some or all of fx , fy , cx , cy must be
initialized before calling the function .
and / or @ ref CALIB_FIX_ASPECT_RATIO , @ ref CALIB_FIX_PRINCIPAL_POINT or @ ref CALIB_FIX_FOCAL_LENGTH
are specified , some or all of fx , fy , cx , cy must be initialized before calling the function .
@ param distCoeffs Input / output vector of distortion coefficients
\ f $ \ distcoeffs \ f $ .
@ param rvecs Output vector of rotation vectors ( @ ref Rodrigues ) estimated for each pattern view
@ -1537,7 +1537,7 @@ the number of pattern views. \f$R_i, T_i\f$ are concatenated 1x3 vectors.
fx , fy , cx , cy that are optimized further . Otherwise , ( cx , cy ) is initially set to the image
center ( imageSize is used ) , and focal distances are computed in a least - squares fashion .
Note , that if intrinsic parameters are known , there is no need to use this function just to
estimate extrinsic parameters . Use solvePnP instead .
estimate extrinsic parameters . Use @ ref solvePnP instead .
- @ ref CALIB_FIX_PRINCIPAL_POINT The principal point is not changed during the global
optimization . It stays at the center or at a different location specified when
@ ref CALIB_USE_INTRINSIC_GUESS is set too .
@ -1547,24 +1547,23 @@ ratio fx/fy stays the same as in the input cameraMatrix . When
ignored , only their ratio is computed and used further .
- @ ref CALIB_ZERO_TANGENT_DIST Tangential distortion coefficients \ f $ ( p_1 , p_2 ) \ f $ are set
to zeros and stay zero .
- @ ref CALIB_FIX_FOCAL_LENGTH The focal length is not changed during the global optimization if
@ ref CALIB_USE_INTRINSIC_GUESS is set .
- @ ref CALIB_FIX_K1 , . . . , @ ref CALIB_FIX_K6 The corresponding radial distortion
coefficient is not changed during the optimization . If @ ref CALIB_USE_INTRINSIC_GUESS is
set , the coefficient from the supplied distCoeffs matrix is used . Otherwise , it is set to 0.
- @ ref CALIB_RATIONAL_MODEL Coefficients k4 , k5 , and k6 are enabled . To provide the
backward compatibility , this extra flag should be explicitly specified to make the
calibration function use the rational model and return 8 coefficients . If the flag is not
set , the function computes and returns only 5 distortion coefficients .
calibration function use the rational model and return 8 coefficients or more .
- @ ref CALIB_THIN_PRISM_MODEL Coefficients s1 , s2 , s3 and s4 are enabled . To provide the
backward compatibility , this extra flag should be explicitly specified to make the
calibration function use the thin prism model and return 12 coefficients . If the flag is not
set , the function computes and returns only 5 distortion coefficients .
calibration function use the thin prism model and return 12 coefficients or more .
- @ ref CALIB_FIX_S1_S2_S3_S4 The thin prism distortion coefficients are not changed during
the optimization . If @ ref CALIB_USE_INTRINSIC_GUESS is set , the coefficient from the
supplied distCoeffs matrix is used . Otherwise , it is set to 0.
- @ ref CALIB_TILTED_MODEL Coefficients tauX and tauY are enabled . To provide the
backward compatibility , this extra flag should be explicitly specified to make the
calibration function use the tilted sensor model and return 14 coefficients . If the flag is not
set , the function computes and returns only 5 distortion coefficients .
calibration function use the tilted sensor model and return 14 coefficients .
- @ ref CALIB_FIX_TAUX_TAUY The coefficients of the tilted sensor model are not changed during
the optimization . If @ ref CALIB_USE_INTRINSIC_GUESS is set , the coefficient from the
supplied distCoeffs matrix is used . Otherwise , it is set to 0.
@ -1589,12 +1588,12 @@ The algorithm performs the following steps:
zeros initially unless some of CALIB_FIX_K ? are specified .
- Estimate the initial camera pose as if the intrinsic parameters have been already known . This is
done using solvePnP .
done using @ ref solvePnP .
- Run the global Levenberg - Marquardt optimization algorithm to minimize the reprojection error ,
that is , the total sum of squared distances between the observed feature points imagePoints and
the projected ( using the current estimates for camera parameters and the poses ) object points
objectPoints . See projectPoints for details .
objectPoints . See @ ref projectPoints for details .
@ note
If you use a non - square ( i . e . non - N - by - N ) grid and @ ref findChessboardCorners for calibration ,