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Improve solvePnP doc, add assert >= 4 in solvePnP, escape underscore character for Scalar_ documentation.

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Add reference to SOLVEPNP_ITERATIVE in the doc.
This commit is contained in:
catree 2017-05-27 00:31:09 +02:00
parent ee257ffe9e
commit 542cdb2c39
3 changed files with 17 additions and 11 deletions
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24
modules/calib3d/include/opencv2/calib3d.hpp
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@ -534,10 +534,10 @@ where N is the number of points. vector\<Point2f\> can be also passed here.
\f$(k_1, k_2, p_1, p_2[, k_3[, k_4, k_5, k_6 [, s_1, s_2, s_3, s_4[, \tau_x, \tau_y]]]])\f$ of
4, 5, 8, 12 or 14 elements. If the vector is NULL/empty, the zero distortion coefficients are
assumed.
@param rvec Output rotation vector (see Rodrigues ) that, together with tvec , brings points from
@param rvec Output rotation vector (see @ref Rodrigues ) that, together with tvec , brings points from
the model coordinate system to the camera coordinate system.
@param tvec Output translation vector.
@param useExtrinsicGuess Parameter used for SOLVEPNP_ITERATIVE. If true (1), the function uses
@param useExtrinsicGuess Parameter used for #SOLVEPNP_ITERATIVE. If true (1), the function uses
the provided rvec and tvec values as initial approximations of the rotation and translation
vectors, respectively, and further optimizes them.
@param flags Method for solving a PnP problem:
@ -546,15 +546,18 @@ this case the function finds such a pose that minimizes reprojection error, that
of squared distances between the observed projections imagePoints and the projected (using
projectPoints ) objectPoints .
- **SOLVEPNP_P3P** Method is based on the paper of X.S. Gao, X.-R. Hou, J. Tang, H.-F. Chang
"Complete Solution Classification for the Perspective-Three-Point Problem". In this case the
function requires exactly four object and image points.
"Complete Solution Classification for the Perspective-Three-Point Problem" (@cite gao2003complete).
In this case the function requires exactly four object and image points.
- **SOLVEPNP_AP3P** Method is based on the paper of T. Ke, S. Roumeliotis
"An Efficient Algebraic Solution to the Perspective-Three-Point Problem" (@cite Ke17).
In this case the function requires exactly four object and image points.
- **SOLVEPNP_EPNP** Method has been introduced by F.Moreno-Noguer, V.Lepetit and P.Fua in the
paper "EPnP: Efficient Perspective-n-Point Camera Pose Estimation".
paper "EPnP: Efficient Perspective-n-Point Camera Pose Estimation" (@cite lepetit2009epnp).
- **SOLVEPNP_DLS** Method is based on the paper of Joel A. Hesch and Stergios I. Roumeliotis.
"A Direct Least-Squares (DLS) Method for PnP".
"A Direct Least-Squares (DLS) Method for PnP" (@cite hesch2011direct).
- **SOLVEPNP_UPNP** Method is based on the paper of A.Penate-Sanchez, J.Andrade-Cetto,
F.Moreno-Noguer. "Exhaustive Linearization for Robust Camera Pose and Focal Length
Estimation". In this case the function also estimates the parameters \f$f_x\f$ and \f$f_y\f$
Estimation" (@cite penate2013exhaustive). In this case the function also estimates the parameters \f$f_x\f$ and \f$f_y\f$
assuming that both have the same value. Then the cameraMatrix is updated with the estimated
focal length.
@ -575,8 +578,11 @@ projections, as well as the camera matrix and the distortion coefficients.
it as, e.g., imagePoints, one must effectively copy it into a new array: imagePoints =
np.ascontiguousarray(D[:,:2]).reshape((N,1,2))
- The methods **SOLVEPNP_DLS** and **SOLVEPNP_UPNP** cannot be used as the current implementations are
unstable and sometimes give completly wrong results. If you pass one of these two flags,
**SOLVEPNP_EPNP** method will be used instead.
unstable and sometimes give completly wrong results. If you pass one of these two
flags, **SOLVEPNP_EPNP** method will be used instead.
- The minimum number of points is 4. In the case of **SOLVEPNP_P3P** and **SOLVEPNP_AP3P**
methods, it is required to use exactly 4 points (the first 3 points are used to estimate all the solutions
of the P3P problem, the last one is used to retain the best solution that minimizes the reprojection error).
*/
CV_EXPORTS_W bool solvePnP( InputArray objectPoints, InputArray imagePoints,
InputArray cameraMatrix, InputArray distCoeffs,

2
modules/calib3d/src/solvepnp.cpp
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@ -61,7 +61,7 @@ bool solvePnP( InputArray _opoints, InputArray _ipoints,
Mat opoints = _opoints.getMat(), ipoints = _ipoints.getMat();
int npoints = std::max(opoints.checkVector(3, CV_32F), opoints.checkVector(3, CV_64F));
CV_Assert( npoints >= 0 && npoints == std::max(ipoints.checkVector(2, CV_32F), ipoints.checkVector(2, CV_64F)) );
CV_Assert( npoints >= 4 && npoints == std::max(ipoints.checkVector(2, CV_32F), ipoints.checkVector(2, CV_64F)) );
Mat rvec, tvec;
if( flags != SOLVEPNP_ITERATIVE )

2
modules/core/include/opencv2/core/types.hpp
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@ -572,7 +572,7 @@ public:
/** @brief Template class for a 4-element vector derived from Vec.
Being derived from Vec\<_Tp, 4\> , Scalar_ and Scalar can be used just as typical 4-element
Being derived from Vec\<_Tp, 4\> , Scalar\_ and Scalar can be used just as typical 4-element
vectors. In addition, they can be converted to/from CvScalar . The type Scalar is widely used in
OpenCV to pass pixel values.
*/