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Merge branch 4.x
This commit is contained in:
Alexander Alekhin
@@ -51,7 +51,7 @@ respectively) by the same factor.
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The joint rotation-translation matrix \f$[R|t]\f$ is the matrix product of a projective
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transformation and a homogeneous transformation. The 3-by-4 projective transformation maps 3D points
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represented in camera coordinates to 2D poins in the image plane and represented in normalized
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represented in camera coordinates to 2D points in the image plane and represented in normalized
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camera coordinates \f$x' = X_c / Z_c\f$ and \f$y' = Y_c / Z_c\f$:
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\f[Z_c \begin{bmatrix}
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@@ -538,15 +538,15 @@ or vector\<Point2f\> .
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a vector\<Point2f\> .
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@param method Method used to compute a homography matrix. The following methods are possible:
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- **0** - a regular method using all the points, i.e., the least squares method
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- **RANSAC** - RANSAC-based robust method
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- **LMEDS** - Least-Median robust method
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- **RHO** - PROSAC-based robust method
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- @ref RANSAC - RANSAC-based robust method
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- @ref LMEDS - Least-Median robust method
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- @ref RHO - PROSAC-based robust method
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@param ransacReprojThreshold Maximum allowed reprojection error to treat a point pair as an inlier
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(used in the RANSAC and RHO methods only). That is, if
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\f[\| \texttt{dstPoints} _i - \texttt{convertPointsHomogeneous} ( \texttt{H} * \texttt{srcPoints} _i) \|_2 > \texttt{ransacReprojThreshold}\f]
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then the point \f$i\f$ is considered as an outlier. If srcPoints and dstPoints are measured in pixels,
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it usually makes sense to set this parameter somewhere in the range of 1 to 10.
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@param mask Optional output mask set by a robust method ( RANSAC or LMEDS ). Note that the input
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@param mask Optional output mask set by a robust method ( RANSAC or LMeDS ). Note that the input
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mask values are ignored.
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@param maxIters The maximum number of RANSAC iterations.
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@param confidence Confidence level, between 0 and 1.
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@@ -781,37 +781,37 @@ the model coordinate system to the camera coordinate system.
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the provided rvec and tvec values as initial approximations of the rotation and translation
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vectors, respectively, and further optimizes them.
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@param flags Method for solving a PnP problem:
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- **SOLVEPNP_ITERATIVE** Iterative method is based on a Levenberg-Marquardt optimization. In
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- @ref SOLVEPNP_ITERATIVE Iterative method is based on a Levenberg-Marquardt optimization. In
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this case the function finds such a pose that minimizes reprojection error, that is the sum
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of squared distances between the observed projections imagePoints and the projected (using
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@ref projectPoints ) objectPoints .
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- **SOLVEPNP_P3P** Method is based on the paper of X.S. Gao, X.-R. Hou, J. Tang, H.-F. Chang
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- @ref SOLVEPNP_P3P Method is based on the paper of X.S. Gao, X.-R. Hou, J. Tang, H.-F. Chang
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"Complete Solution Classification for the Perspective-Three-Point Problem" (@cite gao2003complete).
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In this case the function requires exactly four object and image points.
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- **SOLVEPNP_AP3P** Method is based on the paper of T. Ke, S. Roumeliotis
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- @ref SOLVEPNP_AP3P Method is based on the paper of T. Ke, S. Roumeliotis
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"An Efficient Algebraic Solution to the Perspective-Three-Point Problem" (@cite Ke17).
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In this case the function requires exactly four object and image points.
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- **SOLVEPNP_EPNP** Method has been introduced by F. Moreno-Noguer, V. Lepetit and P. Fua in the
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- @ref SOLVEPNP_EPNP Method has been introduced by F. Moreno-Noguer, V. Lepetit and P. Fua in the
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paper "EPnP: Efficient Perspective-n-Point Camera Pose Estimation" (@cite lepetit2009epnp).
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- **SOLVEPNP_DLS** **Broken implementation. Using this flag will fallback to EPnP.** \n
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- @ref SOLVEPNP_DLS **Broken implementation. Using this flag will fallback to EPnP.** \n
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Method is based on the paper of J. Hesch and S. Roumeliotis.
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"A Direct Least-Squares (DLS) Method for PnP" (@cite hesch2011direct).
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- **SOLVEPNP_UPNP** **Broken implementation. Using this flag will fallback to EPnP.** \n
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- @ref SOLVEPNP_UPNP **Broken implementation. Using this flag will fallback to EPnP.** \n
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Method is based on the paper of A. Penate-Sanchez, J. Andrade-Cetto,
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F. Moreno-Noguer. "Exhaustive Linearization for Robust Camera Pose and Focal Length
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Estimation" (@cite penate2013exhaustive). In this case the function also estimates the parameters \f$f_x\f$ and \f$f_y\f$
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assuming that both have the same value. Then the cameraMatrix is updated with the estimated
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focal length.
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- **SOLVEPNP_IPPE** Method is based on the paper of T. Collins and A. Bartoli.
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- @ref SOLVEPNP_IPPE Method is based on the paper of T. Collins and A. Bartoli.
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"Infinitesimal Plane-Based Pose Estimation" (@cite Collins14). This method requires coplanar object points.
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- **SOLVEPNP_IPPE_SQUARE** Method is based on the paper of Toby Collins and Adrien Bartoli.
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- @ref SOLVEPNP_IPPE_SQUARE Method is based on the paper of Toby Collins and Adrien Bartoli.
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"Infinitesimal Plane-Based Pose Estimation" (@cite Collins14). This method is suitable for marker pose estimation.
|
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It requires 4 coplanar object points defined in the following order:
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- point 0: [-squareLength / 2, squareLength / 2, 0]
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- point 1: [ squareLength / 2, squareLength / 2, 0]
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- point 2: [ squareLength / 2, -squareLength / 2, 0]
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- point 3: [-squareLength / 2, -squareLength / 2, 0]
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- **SOLVEPNP_SQPNP** Method is based on the paper "A Consistently Fast and Globally Optimal Solution to the
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- @ref SOLVEPNP_SQPNP Method is based on the paper "A Consistently Fast and Globally Optimal Solution to the
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Perspective-n-Point Problem" by G. Terzakis and M.Lourakis (@cite Terzakis20). It requires 3 or more points.
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@@ -921,23 +921,23 @@ a 3D point expressed in the world frame into the camera frame:
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- Thus, given some data D = np.array(...) where D.shape = (N,M), in order to use a subset of
|
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it as, e.g., imagePoints, one must effectively copy it into a new array: imagePoints =
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np.ascontiguousarray(D[:,:2]).reshape((N,1,2))
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- The methods **SOLVEPNP_DLS** and **SOLVEPNP_UPNP** cannot be used as the current implementations are
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- The methods @ref SOLVEPNP_DLS and @ref SOLVEPNP_UPNP cannot be used as the current implementations are
|
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unstable and sometimes give completely wrong results. If you pass one of these two
|
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flags, **SOLVEPNP_EPNP** method will be used instead.
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- The minimum number of points is 4 in the general case. In the case of **SOLVEPNP_P3P** and **SOLVEPNP_AP3P**
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flags, @ref SOLVEPNP_EPNP method will be used instead.
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- The minimum number of points is 4 in the general case. In the case of @ref SOLVEPNP_P3P and @ref SOLVEPNP_AP3P
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methods, it is required to use exactly 4 points (the first 3 points are used to estimate all the solutions
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of the P3P problem, the last one is used to retain the best solution that minimizes the reprojection error).
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- With **SOLVEPNP_ITERATIVE** method and `useExtrinsicGuess=true`, the minimum number of points is 3 (3 points
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- With @ref SOLVEPNP_ITERATIVE method and `useExtrinsicGuess=true`, the minimum number of points is 3 (3 points
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are sufficient to compute a pose but there are up to 4 solutions). The initial solution should be close to the
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global solution to converge.
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- With **SOLVEPNP_IPPE** input points must be >= 4 and object points must be coplanar.
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- With **SOLVEPNP_IPPE_SQUARE** this is a special case suitable for marker pose estimation.
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- With @ref SOLVEPNP_IPPE input points must be >= 4 and object points must be coplanar.
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- With @ref SOLVEPNP_IPPE_SQUARE this is a special case suitable for marker pose estimation.
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Number of input points must be 4. Object points must be defined in the following order:
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- point 0: [-squareLength / 2, squareLength / 2, 0]
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- point 1: [ squareLength / 2, squareLength / 2, 0]
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- point 2: [ squareLength / 2, -squareLength / 2, 0]
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- point 3: [-squareLength / 2, -squareLength / 2, 0]
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- With **SOLVEPNP_SQPNP** input points must be >= 3
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- With @ref SOLVEPNP_SQPNP input points must be >= 3
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*/
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CV_EXPORTS_W bool solvePnP( InputArray objectPoints, InputArray imagePoints,
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InputArray cameraMatrix, InputArray distCoeffs,
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@@ -1015,9 +1015,9 @@ assumed.
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the model coordinate system to the camera coordinate system. A P3P problem has up to 4 solutions.
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@param tvecs Output translation vectors.
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@param flags Method for solving a P3P problem:
|
||||
- **SOLVEPNP_P3P** Method is based on the paper of X.S. Gao, X.-R. Hou, J. Tang, H.-F. Chang
|
||||
- @ref 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" (@cite gao2003complete).
|
||||
- **SOLVEPNP_AP3P** Method is based on the paper of T. Ke and S. Roumeliotis.
|
||||
- @ref SOLVEPNP_AP3P Method is based on the paper of T. Ke and S. Roumeliotis.
|
||||
"An Efficient Algebraic Solution to the Perspective-Three-Point Problem" (@cite Ke17).
|
||||
|
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The function estimates the object pose given 3 object points, their corresponding image
|
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@@ -1119,39 +1119,39 @@ the model coordinate system to the camera coordinate system.
|
||||
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:
|
||||
- **SOLVEPNP_ITERATIVE** Iterative method is based on a Levenberg-Marquardt optimization. In
|
||||
- @ref SOLVEPNP_ITERATIVE Iterative method is based on a Levenberg-Marquardt optimization. In
|
||||
this case the function finds such a pose that minimizes reprojection error, that is the sum
|
||||
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
|
||||
- @ref 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" (@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
|
||||
- @ref 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
|
||||
- @ref 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" (@cite lepetit2009epnp).
|
||||
- **SOLVEPNP_DLS** **Broken implementation. Using this flag will fallback to EPnP.** \n
|
||||
- @ref SOLVEPNP_DLS **Broken implementation. Using this flag will fallback to EPnP.** \n
|
||||
Method is based on the paper of Joel A. Hesch and Stergios I. Roumeliotis.
|
||||
"A Direct Least-Squares (DLS) Method for PnP" (@cite hesch2011direct).
|
||||
- **SOLVEPNP_UPNP** **Broken implementation. Using this flag will fallback to EPnP.** \n
|
||||
- @ref SOLVEPNP_UPNP **Broken implementation. Using this flag will fallback to EPnP.** \n
|
||||
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" (@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.
|
||||
- **SOLVEPNP_IPPE** Method is based on the paper of T. Collins and A. Bartoli.
|
||||
- @ref SOLVEPNP_IPPE Method is based on the paper of T. Collins and A. Bartoli.
|
||||
"Infinitesimal Plane-Based Pose Estimation" (@cite Collins14). This method requires coplanar object points.
|
||||
- **SOLVEPNP_IPPE_SQUARE** Method is based on the paper of Toby Collins and Adrien Bartoli.
|
||||
- @ref SOLVEPNP_IPPE_SQUARE Method is based on the paper of Toby Collins and Adrien Bartoli.
|
||||
"Infinitesimal Plane-Based Pose Estimation" (@cite Collins14). This method is suitable for marker pose estimation.
|
||||
It requires 4 coplanar object points defined in the following order:
|
||||
- point 0: [-squareLength / 2, squareLength / 2, 0]
|
||||
- point 1: [ squareLength / 2, squareLength / 2, 0]
|
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- point 2: [ squareLength / 2, -squareLength / 2, 0]
|
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- point 3: [-squareLength / 2, -squareLength / 2, 0]
|
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@param rvec Rotation vector used to initialize an iterative PnP refinement algorithm, when flag is SOLVEPNP_ITERATIVE
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@param rvec Rotation vector used to initialize an iterative PnP refinement algorithm, when flag is @ref SOLVEPNP_ITERATIVE
|
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and useExtrinsicGuess is set to true.
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@param tvec Translation vector used to initialize an iterative PnP refinement algorithm, when flag is SOLVEPNP_ITERATIVE
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@param tvec Translation vector used to initialize an iterative PnP refinement algorithm, when flag is @ref SOLVEPNP_ITERATIVE
|
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and useExtrinsicGuess is set to true.
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@param reprojectionError Optional vector of reprojection error, that is the RMS error
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(\f$ \text{RMSE} = \sqrt{\frac{\sum_{i}^{N} \left ( \hat{y_i} - y_i \right )^2}{N}} \f$) between the input image points
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@@ -1263,17 +1263,17 @@ a 3D point expressed in the world frame into the camera frame:
|
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- Thus, given some data D = np.array(...) where D.shape = (N,M), in order to use a subset of
|
||||
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
|
||||
- The methods @ref SOLVEPNP_DLS and @ref SOLVEPNP_UPNP cannot be used as the current implementations are
|
||||
unstable and sometimes give completely 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 general case. In the case of **SOLVEPNP_P3P** and **SOLVEPNP_AP3P**
|
||||
flags, @ref SOLVEPNP_EPNP method will be used instead.
|
||||
- The minimum number of points is 4 in the general case. In the case of @ref SOLVEPNP_P3P and @ref 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).
|
||||
- With **SOLVEPNP_ITERATIVE** method and `useExtrinsicGuess=true`, the minimum number of points is 3 (3 points
|
||||
- With @ref SOLVEPNP_ITERATIVE method and `useExtrinsicGuess=true`, the minimum number of points is 3 (3 points
|
||||
are sufficient to compute a pose but there are up to 4 solutions). The initial solution should be close to the
|
||||
global solution to converge.
|
||||
- With **SOLVEPNP_IPPE** input points must be >= 4 and object points must be coplanar.
|
||||
- With **SOLVEPNP_IPPE_SQUARE** this is a special case suitable for marker pose estimation.
|
||||
- With @ref SOLVEPNP_IPPE input points must be >= 4 and object points must be coplanar.
|
||||
- With @ref SOLVEPNP_IPPE_SQUARE this is a special case suitable for marker pose estimation.
|
||||
Number of input points must be 4. Object points must be defined in the following order:
|
||||
- point 0: [-squareLength / 2, squareLength / 2, 0]
|
||||
- point 1: [ squareLength / 2, squareLength / 2, 0]
|
||||
@@ -1427,8 +1427,8 @@ same camera intrinsic matrix. If this assumption does not hold for your use case
|
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to normalized image coordinates, which are valid for the identity camera intrinsic matrix. When
|
||||
passing these coordinates, pass the identity matrix for this parameter.
|
||||
@param method Method for computing an essential matrix.
|
||||
- **RANSAC** for the RANSAC algorithm.
|
||||
- **LMEDS** for the LMedS algorithm.
|
||||
- @ref RANSAC for the RANSAC algorithm.
|
||||
- @ref LMEDS for the LMedS algorithm.
|
||||
@param prob Parameter used for the RANSAC or LMedS methods only. It specifies a desirable level of
|
||||
confidence (probability) that the estimated matrix is correct.
|
||||
@param threshold Parameter used for RANSAC. It is the maximum distance from a point to an epipolar
|
||||
@@ -1437,6 +1437,7 @@ final fundamental matrix. It can be set to something like 1-3, depending on the
|
||||
point localization, image resolution, and the image noise.
|
||||
@param mask Output array of N elements, every element of which is set to 0 for outliers and to 1
|
||||
for the other points. The array is computed only in the RANSAC and LMedS methods.
|
||||
@param maxIters The maximum number of robust method iterations.
|
||||
|
||||
This function estimates essential matrix based on the five-point algorithm solver in @cite Nister03 .
|
||||
@cite SteweniusCFS is also a related. The epipolar geometry is described by the following equation:
|
||||
@@ -1447,10 +1448,22 @@ where \f$E\f$ is an essential matrix, \f$p_1\f$ and \f$p_2\f$ are corresponding
|
||||
second images, respectively. The result of this function may be passed further to
|
||||
decomposeEssentialMat or recoverPose to recover the relative pose between cameras.
|
||||
*/
|
||||
CV_EXPORTS_W Mat findEssentialMat( InputArray points1, InputArray points2,
|
||||
InputArray cameraMatrix, int method = RANSAC,
|
||||
double prob = 0.999, double threshold = 1.0,
|
||||
OutputArray mask = noArray() );
|
||||
CV_EXPORTS_W
|
||||
Mat findEssentialMat(
|
||||
InputArray points1, InputArray points2,
|
||||
InputArray cameraMatrix, int method = RANSAC,
|
||||
double prob = 0.999, double threshold = 1.0,
|
||||
int maxIters = 1000, OutputArray mask = noArray()
|
||||
);
|
||||
|
||||
/** @overload */
|
||||
CV_EXPORTS
|
||||
Mat findEssentialMat(
|
||||
InputArray points1, InputArray points2,
|
||||
InputArray cameraMatrix, int method,
|
||||
double prob, double threshold,
|
||||
OutputArray mask
|
||||
); // TODO remove from OpenCV 5.0
|
||||
|
||||
/** @overload
|
||||
@param points1 Array of N (N \>= 5) 2D points from the first image. The point coordinates should
|
||||
@@ -1460,8 +1473,8 @@ be floating-point (single or double precision).
|
||||
are feature points from cameras with same focal length and principal point.
|
||||
@param pp principal point of the camera.
|
||||
@param method Method for computing a fundamental matrix.
|
||||
- **RANSAC** for the RANSAC algorithm.
|
||||
- **LMEDS** for the LMedS algorithm.
|
||||
- @ref RANSAC for the RANSAC algorithm.
|
||||
- @ref LMEDS for the LMedS algorithm.
|
||||
@param threshold Parameter used for RANSAC. It is the maximum distance from a point to an epipolar
|
||||
line in pixels, beyond which the point is considered an outlier and is not used for computing the
|
||||
final fundamental matrix. It can be set to something like 1-3, depending on the accuracy of the
|
||||
@@ -1470,6 +1483,7 @@ point localization, image resolution, and the image noise.
|
||||
confidence (probability) that the estimated matrix is correct.
|
||||
@param mask Output array of N elements, every element of which is set to 0 for outliers and to 1
|
||||
for the other points. The array is computed only in the RANSAC and LMedS methods.
|
||||
@param maxIters The maximum number of robust method iterations.
|
||||
|
||||
This function differs from the one above that it computes camera intrinsic matrix from focal length and
|
||||
principal point:
|
||||
@@ -1481,10 +1495,23 @@ f & 0 & x_{pp} \\
|
||||
0 & 0 & 1
|
||||
\end{bmatrix}\f]
|
||||
*/
|
||||
CV_EXPORTS_W Mat findEssentialMat( InputArray points1, InputArray points2,
|
||||
double focal = 1.0, Point2d pp = Point2d(0, 0),
|
||||
int method = RANSAC, double prob = 0.999,
|
||||
double threshold = 1.0, OutputArray mask = noArray() );
|
||||
CV_EXPORTS_W
|
||||
Mat findEssentialMat(
|
||||
InputArray points1, InputArray points2,
|
||||
double focal = 1.0, Point2d pp = Point2d(0, 0),
|
||||
int method = RANSAC, double prob = 0.999,
|
||||
double threshold = 1.0, int maxIters = 1000,
|
||||
OutputArray mask = noArray()
|
||||
);
|
||||
|
||||
/** @overload */
|
||||
CV_EXPORTS
|
||||
Mat findEssentialMat(
|
||||
InputArray points1, InputArray points2,
|
||||
double focal, Point2d pp,
|
||||
int method, double prob,
|
||||
double threshold, OutputArray mask
|
||||
); // TODO remove from OpenCV 5.0
|
||||
|
||||
/** @brief Calculates an essential matrix from the corresponding points in two images from potentially two different cameras.
|
||||
|
||||
@@ -1510,8 +1537,8 @@ of 4, 5, 8, 12 or 14 elements. If the vector is NULL/empty, the zero distortion
|
||||
\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 method Method for computing an essential matrix.
|
||||
- **RANSAC** for the RANSAC algorithm.
|
||||
- **LMEDS** for the LMedS algorithm.
|
||||
- @ref RANSAC for the RANSAC algorithm.
|
||||
- @ref LMEDS for the LMedS algorithm.
|
||||
@param prob Parameter used for the RANSAC or LMedS methods only. It specifies a desirable level of
|
||||
confidence (probability) that the estimated matrix is correct.
|
||||
@param threshold Parameter used for RANSAC. It is the maximum distance from a point to an epipolar
|
||||
@@ -1900,8 +1927,8 @@ b_2\\
|
||||
@param to Second input 2D point set containing \f$(x,y)\f$.
|
||||
@param inliers Output vector indicating which points are inliers (1-inlier, 0-outlier).
|
||||
@param method Robust method used to compute transformation. The following methods are possible:
|
||||
- cv::RANSAC - RANSAC-based robust method
|
||||
- cv::LMEDS - Least-Median robust method
|
||||
- @ref RANSAC - RANSAC-based robust method
|
||||
- @ref LMEDS - Least-Median robust method
|
||||
RANSAC is the default method.
|
||||
@param ransacReprojThreshold Maximum reprojection error in the RANSAC algorithm to consider
|
||||
a point as an inlier. Applies only to RANSAC.
|
||||
@@ -1950,8 +1977,8 @@ two 2D point sets.
|
||||
@param to Second input 2D point set.
|
||||
@param inliers Output vector indicating which points are inliers.
|
||||
@param method Robust method used to compute transformation. The following methods are possible:
|
||||
- cv::RANSAC - RANSAC-based robust method
|
||||
- cv::LMEDS - Least-Median robust method
|
||||
- @ref RANSAC - RANSAC-based robust method
|
||||
- @ref LMEDS - Least-Median robust method
|
||||
RANSAC is the default method.
|
||||
@param ransacReprojThreshold Maximum reprojection error in the RANSAC algorithm to consider
|
||||
a point as an inlier. Applies only to RANSAC.
|
||||
|
||||
Reference in New Issue
Block a user