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@ -784,7 +784,7 @@ space, that is, a real position of the calibration pattern in the k-th pattern v |
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@param stdDeviationsExtrinsics Output vector of standard deviations estimated for extrinsic parameters. |
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Order of deviations values: \f$(R_1, T_1, \dotsc , R_M, T_M)\f$ where M is number of pattern views, |
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\f$R_i, T_i\f$ are concatenated 1x3 vectors. |
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@param perViewErrors Output vector of average re-projection errors estimated for each pattern view. |
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@param perViewErrors Output vector of the RMS re-projection error estimated for each pattern view. |
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@param flags Different flags that may be zero or a combination of the following values: |
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- **CV_CALIB_USE_INTRINSIC_GUESS** cameraMatrix contains valid initial values of |
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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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@ -823,6 +823,8 @@ the optimization. If CV_CALIB_USE_INTRINSIC_GUESS is set, the coefficient from t |
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supplied distCoeffs matrix is used. Otherwise, it is set to 0. |
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@param criteria Termination criteria for the iterative optimization algorithm. |
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@return the overall RMS re-projection error. |
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The function estimates the intrinsic camera parameters and extrinsic parameters for each of the |
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views. The algorithm is based on @cite Zhang2000 and @cite BouguetMCT . The coordinates of 3D object |
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points and their corresponding 2D projections in each view must be specified. That may be achieved |
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@ -847,8 +849,6 @@ The algorithm performs the following steps: |
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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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The function returns the final re-projection error. |
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@note |
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If you use a non-square (=non-NxN) grid and findChessboardCorners for calibration, and |
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calibrateCamera returns bad values (zero distortion coefficients, an image center very far from |
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