calrel Namespace Reference

Namespace calrel. More...

Functions
double	calibrateCamera (InputArrayOfArrays imagePoints, Size imageSize, InputArray objectPoints, int fixedObjPt, InputOutputArray cameraMatrix, InputOutputArray distCoeffs, OutputArrayOfArrays rvecs, OutputArrayOfArrays tvecs, OutputArray newObjPoints, OutputArray stdDeviationsIntrinsics, OutputArray stdDeviationsExtrinsics, OutputArray stdDeviationsObjectPoints, OutputArray perViewErrors, int flags=0, TermCriteria criteria=TermCriteria(TermCriteria::COUNT+TermCriteria::EPS, 60, DBL_EPSILON *30))
	Finds the camera intrinsic and extrinsic parameters from several views of a calibration pattern. More...
double	calibrateCamera (InputArrayOfArrays imagePoints, Size imageSize, InputArray objectPoints, int fixedObjPt, InputOutputArray cameraMatrix, InputOutputArray distCoeffs, OutputArrayOfArrays rvecs, OutputArrayOfArrays tvecs, OutputArray newObjPoints, int flags=0, TermCriteria criteria=TermCriteria(TermCriteria::COUNT+TermCriteria::EPS, 60, DBL_EPSILON *30))

Detailed Description

Namespace calrel.

Function Documentation

calibrateCamera() [1/2]

double calrel::calibrateCamera	(	InputArrayOfArrays	imagePoints,
		Size	imageSize,
		InputArray	objectPoints,
		int	fixedObjPt,
		InputOutputArray	cameraMatrix,
		InputOutputArray	distCoeffs,
		OutputArrayOfArrays	rvecs,
		OutputArrayOfArrays	tvecs,
		OutputArray	newObjPoints,
		OutputArray	stdDeviationsIntrinsics,
		OutputArray	stdDeviationsExtrinsics,
		OutputArray	stdDeviationsObjectPoints,
		OutputArray	perViewErrors,
		int	flags = 0,
		TermCriteria	criteria = TermCriteria(TermCriteria::COUNT+TermCriteria::EPS, 60, DBL_EPSILON *30)
	)

Finds the camera intrinsic and extrinsic parameters from several views of a calibration pattern.

Parameters

imagePoints	It is a vector of vectors of the projections of calibration pattern points (e.g. std::vector<std::vector<cv::Vec2f>>). imagePoints[i].size() must be equal to objectPoints.size() for each i.
imageSize	Size of the image used only to initialize the intrinsic camera matrix.
objectPoints	It is a vector of calibration pattern points in the calibration pattern coordinate space (e.g. std::vector<cv::Vec3f>). The same calibration pattern must be shown in each view and it is fully visible. The points are 3D, but since they are in a pattern coordinate system, then, if the rig is planar, it may make sense to put the model to a XY coordinate plane so that Z-coordinate of each input object point is 0. On output, the refined pattern points are returned for imperfect planar target.
fixedObjPt	The index of the 3D object point to be set as (d, 0, 0) as in Strobl's paper. Usually it is the top-right corner point of the calibration board grid.
cameraMatrix	Output 3x3 floating-point camera matrix \(\begin{bmatrix} f_x & 0 & c_x\\ 0 & f_y & c_y\\ 0 & 0 & 1 \end{bmatrix}\). If CV_CALIB_USE_INTRINSIC_GUESS and/or CALIB_FIX_ASPECT_RATIO are specified, some or all of fx, fy, cx, cy must be initialized before calling the function.
distCoeffs	Output vector of distortion coefficients \((k_1, k_2, p_1, p_2[, k_3[, k_4, k_5, k_6 [, s_1, s_2, s_3, s_4[, \tau_x, \tau_y]]]])\) of 4, 5, 8, 12 or 14 elements.
rvecs	Output vector of rotation vectors (see Rodrigues ) estimated for each pattern view (e.g. std::vector<cv::Mat>>). That is, each k-th rotation vector together with the corresponding k-th translation vector (see the next output parameter description) brings the calibration pattern from the model coordinate space (in which object points are specified) to the world coordinate space, that is, a real position of the calibration pattern in the k-th pattern view (k=0.. M -1).
tvecs	Output vector of translation vectors estimated for each pattern view.
newObjPoints	The updated output vector of pattern points.
stdDeviationsIntrinsics	Output vector of standard deviations estimated for intrinsic parameters. Order of deviations values: \((f_x, f_y, c_x, c_y, k_1, k_2, p_1, p_2, k_3, k_4, k_5, k_6, s_1, s_2, s_3, s_4, \tau_x, \tau_y)\) If one of parameters is not estimated, it's deviation is equals to zero.
stdDeviationsExtrinsics	Output vector of standard deviations estimated for extrinsic parameters. Order of deviations values: \((R_1, T_1, \dotsc , R_M, T_M)\) where M is number of pattern views, \(R_i, T_i\) are concatenated 1x3 vectors.
stdDeviationsObjectPoints	Output vector of standard deviations estimated for refined coordinates of calibration pattern points. It has the same size and order as objectPoints vector.
perViewErrors	Output vector of the RMS re-projection error estimated for each pattern view.
flags	Different flags that may be zero or a combination of the following values: CALIB_USE_INTRINSIC_GUESS cameraMatrix contains valid initial values of 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. 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 CALIB_USE_INTRINSIC_GUESS is set too. CALIB_FIX_ASPECT_RATIO The functions considers only fy as a free parameter. The ratio fx/fy stays the same as in the input cameraMatrix. When CALIB_USE_INTRINSIC_GUESS is not set, the actual input values of fx and fy are ignored, only their ratio is computed and used further. CALIB_ZERO_TANGENT_DIST Tangential distortion coefficients \((p_1, p_2)\) are set to zeros and stay zero. CALIB_FIX_K1,...,CALIB_FIX_K6 The corresponding radial distortion coefficient is not changed during the optimization. If CALIB_USE_INTRINSIC_GUESS is set, the coefficient from the supplied distCoeffs matrix is used. Otherwise, it is set to 0. 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. 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. CALIB_FIX_S1_S2_S3_S4 The thin prism distortion coefficients are not changed during the optimization. If CALIB_USE_INTRINSIC_GUESS is set, the coefficient from the supplied distCoeffs matrix is used. Otherwise, it is set to 0. 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. CALIB_FIX_TAUX_TAUY The coefficients of the tilted sensor model are not changed during the optimization. If CALIB_USE_INTRINSIC_GUESS is set, the coefficient from the supplied distCoeffs matrix is used. Otherwise, it is set to 0.
criteria	Termination criteria for the iterative optimization algorithm.

Returns

the overall RMS re-projection error.

The function estimates the intrinsic camera parameters and extrinsic parameters for each of the views. The algorithm is based on Klaus H. Strobl and Gerd Hirzinger's paper "More Accurate Pinhole Camera Calibration with Imperfect Planar Target". The coordinates of 3D object points and their corresponding 2D projections in each view must be specified. That may be achieved by using an object with a known geometry and easily detectable feature points. Such an object is called a calibration rig or calibration pattern. Currently, initialization of intrinsic parameters (when CALIB_USE_INTRINSIC_GUESS is not set) is only implemented for planar calibration patterns (where Z-coordinates of the object points must be all zeros).

The algorithm performs the following steps:

• Compute the initial intrinsic parameters (the option only available for planar calibration patterns) or read them from the input parameters. The distortion coefficients are all set to 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.
• 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.

calibrateCamera() [2/2]

double calrel::calibrateCamera	(	InputArrayOfArrays	imagePoints,
		Size	imageSize,
		InputArray	objectPoints,
		int	fixedObjPt,
		InputOutputArray	cameraMatrix,
		InputOutputArray	distCoeffs,
		OutputArrayOfArrays	rvecs,
		OutputArrayOfArrays	tvecs,
		OutputArray	newObjPoints,
		int	flags = 0,
		TermCriteria	criteria = TermCriteria(TermCriteria::COUNT+TermCriteria::EPS, 60, DBL_EPSILON *30)
	)

This is an overloaded member function, provided for convenience. It differs from the above function only in what argument(s) it accepts.