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Merge pull request #20018 from lpea:doc_fixes_calib3d
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@ -1510,8 +1510,8 @@ concatenated together.
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@param imageSize Size of the image used only to initialize the camera intrinsic matrix.
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@param cameraMatrix Input/output 3x3 floating-point camera intrinsic matrix
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\f$\cameramatrix{A}\f$ . If @ref CALIB_USE_INTRINSIC_GUESS
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and/or @ref CALIB_FIX_ASPECT_RATIO are specified, some or all of fx, fy, cx, cy must be
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initialized before calling the function.
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and/or @ref CALIB_FIX_ASPECT_RATIO, @ref CALIB_FIX_PRINCIPAL_POINT or @ref CALIB_FIX_FOCAL_LENGTH
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are specified, some or all of fx, fy, cx, cy must be initialized before calling the function.
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@param distCoeffs Input/output vector of distortion coefficients
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\f$\distcoeffs\f$.
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@param rvecs Output vector of rotation vectors (@ref Rodrigues ) estimated for each pattern view
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@ -1537,7 +1537,7 @@ the number of pattern views. \f$R_i, T_i\f$ are concatenated 1x3 vectors.
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fx, fy, cx, cy that are optimized further. Otherwise, (cx, cy) is initially set to the image
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center ( imageSize is used), and focal distances are computed in a least-squares fashion.
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Note, that if intrinsic parameters are known, there is no need to use this function just to
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estimate extrinsic parameters. Use solvePnP instead.
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estimate extrinsic parameters. Use @ref solvePnP instead.
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- @ref CALIB_FIX_PRINCIPAL_POINT The principal point is not changed during the global
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optimization. It stays at the center or at a different location specified when
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@ref CALIB_USE_INTRINSIC_GUESS is set too.
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@ -1547,24 +1547,23 @@ ratio fx/fy stays the same as in the input cameraMatrix . When
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ignored, only their ratio is computed and used further.
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- @ref CALIB_ZERO_TANGENT_DIST Tangential distortion coefficients \f$(p_1, p_2)\f$ are set
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to zeros and stay zero.
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- @ref CALIB_FIX_FOCAL_LENGTH The focal length is not changed during the global optimization if
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@ref CALIB_USE_INTRINSIC_GUESS is set.
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- @ref CALIB_FIX_K1,..., @ref CALIB_FIX_K6 The corresponding radial distortion
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coefficient is not changed during the optimization. If @ref CALIB_USE_INTRINSIC_GUESS is
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set, the coefficient from the supplied distCoeffs matrix is used. Otherwise, it is set to 0.
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- @ref CALIB_RATIONAL_MODEL Coefficients k4, k5, and k6 are enabled. To provide the
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backward compatibility, this extra flag should be explicitly specified to make the
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calibration function use the rational model and return 8 coefficients. If the flag is not
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set, the function computes and returns only 5 distortion coefficients.
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calibration function use the rational model and return 8 coefficients or more.
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- @ref CALIB_THIN_PRISM_MODEL Coefficients s1, s2, s3 and s4 are enabled. To provide the
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backward compatibility, this extra flag should be explicitly specified to make the
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calibration function use the thin prism model and return 12 coefficients. If the flag is not
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set, the function computes and returns only 5 distortion coefficients.
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calibration function use the thin prism model and return 12 coefficients or more.
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- @ref CALIB_FIX_S1_S2_S3_S4 The thin prism distortion coefficients are not changed during
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the optimization. If @ref CALIB_USE_INTRINSIC_GUESS is set, the coefficient from the
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supplied distCoeffs matrix is used. Otherwise, it is set to 0.
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- @ref CALIB_TILTED_MODEL Coefficients tauX and tauY are enabled. To provide the
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backward compatibility, this extra flag should be explicitly specified to make the
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calibration function use the tilted sensor model and return 14 coefficients. If the flag is not
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set, the function computes and returns only 5 distortion coefficients.
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calibration function use the tilted sensor model and return 14 coefficients.
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- @ref CALIB_FIX_TAUX_TAUY The coefficients of the tilted sensor model are not changed during
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the optimization. If @ref CALIB_USE_INTRINSIC_GUESS is set, the coefficient from the
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supplied distCoeffs matrix is used. Otherwise, it is set to 0.
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@ -1589,12 +1588,12 @@ The algorithm performs the following steps:
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zeros initially unless some of CALIB_FIX_K? are specified.
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- Estimate the initial camera pose as if the intrinsic parameters have been already known. This is
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done using solvePnP .
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done using @ref solvePnP .
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- Run the global Levenberg-Marquardt optimization algorithm to minimize the reprojection error,
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that is, the total sum of squared distances between the observed feature points imagePoints and
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the projected (using the current estimates for camera parameters and the poses) object points
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objectPoints. See projectPoints for details.
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objectPoints. See @ref projectPoints for details.
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@note
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If you use a non-square (i.e. non-N-by-N) grid and @ref findChessboardCorners for calibration,
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