@ -674,15 +674,15 @@ or vector\<Point2f\> .
a vector \ < Point2f \ > .
@ param method Method used to compute a homography matrix . The following methods are possible :
- * * 0 * * - a regular method using all the points , i . e . , the least squares method
- * * RANSAC * * - RANSAC - based robust method
- * * LMEDS * * - Least - Median robust method
- * * RHO * * - PROSAC - based robust method
- @ ref RANSAC - RANSAC - based robust method
- @ ref LMEDS - Least - Median robust method
- @ ref RHO - PROSAC - based robust method
@ param ransacReprojThreshold Maximum allowed reprojection error to treat a point pair as an inlier
( used in the RANSAC and RHO methods only ) . That is , if
\ f [ \ | \ texttt { dstPoints } _i - \ texttt { convertPointsHomogeneous } ( \ texttt { H } * \ texttt { srcPoints } _i ) \ | _2 > \ texttt { ransacReprojThreshold } \ f ]
then the point \ f $ i \ f $ is considered as an outlier . If srcPoints and dstPoints are measured in pixels ,
it usually makes sense to set this parameter somewhere in the range of 1 to 10.
@ param mask Optional output mask set by a robust method ( RANSAC or LME DS ) . Note that the input
@ param mask Optional output mask set by a robust method ( RANSAC or LMe DS ) . Note that the input
mask values are ignored .
@ param maxIters The maximum number of RANSAC iterations .
@ param confidence Confidence level , between 0 and 1.
@ -917,37 +917,37 @@ 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
@ ref 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 J . Hesch and S . 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 ]
- point 2 : [ squareLength / 2 , - squareLength / 2 , 0 ]
- point 3 : [ - squareLength / 2 , - squareLength / 2 , 0 ]
- * * SOLVEPNP_SQPNP * * Method is based on the paper " A Consistently Fast and Globally Optimal Solution to the
- @ ref SOLVEPNP_SQPNP Method is based on the paper " A Consistently Fast and Globally Optimal Solution to the
Perspective - n - Point Problem " by G. Terzakis and M.Lourakis (@cite Terzakis20). It requires 3 or more points.
@ -1057,23 +1057,23 @@ a 3D point expressed in the world frame into the camera frame:
- 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 ]
- point 2 : [ squareLength / 2 , - squareLength / 2 , 0 ]
- point 3 : [ - squareLength / 2 , - squareLength / 2 , 0 ]
- With * * SOLVEPNP_SQPNP * * input points must be > = 3
- With @ ref SOLVEPNP_SQPNP input points must be > = 3
*/
CV_EXPORTS_W bool solvePnP ( InputArray objectPoints , InputArray imagePoints ,
InputArray cameraMatrix , InputArray distCoeffs ,
@ -1152,9 +1152,9 @@ assumed.
the model coordinate system to the camera coordinate system . A P3P problem has up to 4 solutions .
@ param tvecs Output translation vectors .
@ 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 ) .
The function estimates the object pose given 3 object points , their corresponding image
@ -1254,39 +1254,39 @@ 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 ]
- point 2 : [ squareLength / 2 , - squareLength / 2 , 0 ]
- point 3 : [ - squareLength / 2 , - squareLength / 2 , 0 ]
@ param rvec Rotation vector used to initialize an iterative PnP refinement algorithm , when flag is SOLVEPNP_ITERATIVE
@ param rvec Rotation vector used to initialize an iterative PnP refinement algorithm , when flag is @ ref SOLVEPNP_ITERATIVE
and useExtrinsicGuess is set to true .
@ param tvec Translation vector used to initialize an iterative PnP refinement algorithm , when flag is SOLVEPNP_ITERATIVE
@ param tvec Translation vector used to initialize an iterative PnP refinement algorithm , when flag is @ ref SOLVEPNP_ITERATIVE
and useExtrinsicGuess is set to true .
@ param reprojectionError Optional vector of reprojection error , that is the RMS error
( \ f $ \ text { RMSE } = \ sqrt { \ frac { \ sum_ { i } ^ { N } \ left ( \ hat { y_i } - y_i \ right ) ^ 2 } { N } } \ f $ ) between the input image points
@ -1398,17 +1398,17 @@ a 3D point expressed in the world frame into the camera frame:
- 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 ]
@ -1448,13 +1448,13 @@ CV_EXPORTS_W Mat initCameraMatrix2D( InputArrayOfArrays objectPoints,
( patternSize = cv : : Size ( points_per_row , points_per_colum ) = cv : : Size ( columns , rows ) ) .
@ param corners Output array of detected corners .
@ param flags Various operation flags that can be zero or a combination of the following values :
- * * CALIB_CB_ADAPTIVE_THRESH * * Use adaptive thresholding to convert the image to black
- @ ref CALIB_CB_ADAPTIVE_THRESH Use adaptive thresholding to convert the image to black
and white , rather than a fixed threshold level ( computed from the average image brightness ) .
- * * CALIB_CB_NORMALIZE_IMAGE * * Normalize the image gamma with equalizeHist before
- @ ref CALIB_CB_NORMALIZE_IMAGE Normalize the image gamma with equalizeHist before
applying fixed or adaptive thresholding .
- * * CALIB_CB_FILTER_QUADS * * Use additional criteria ( like contour area , perimeter ,
- @ ref CALIB_CB_FILTER_QUADS Use additional criteria ( like contour area , perimeter ,
square - like shape ) to filter out false quads extracted at the contour retrieval stage .
- * * CALIB_CB_FAST_CHECK * * Run a fast check on the image that looks for chessboard corners ,
- @ ref CALIB_CB_FAST_CHECK Run a fast check on the image that looks for chessboard corners ,
and shortcut the call if none is found . This can drastically speed up the call in the
degenerate condition when no chessboard is observed .
@ -1665,9 +1665,9 @@ typedef CirclesGridFinderParameters CirclesGridFinderParameters2;
( patternSize = Size ( points_per_row , points_per_colum ) ) .
@ param centers output array of detected centers .
@ param flags various operation flags that can be one of the following values :
- * * CALIB_CB_SYMMETRIC_GRID * * uses symmetric pattern of circles .
- * * CALIB_CB_ASYMMETRIC_GRID * * uses asymmetric pattern of circles .
- * * CALIB_CB_CLUSTERING * * uses a special algorithm for grid detection . It is more robust to
- @ ref CALIB_CB_SYMMETRIC_GRID uses symmetric pattern of circles .
- @ ref CALIB_CB_ASYMMETRIC_GRID uses asymmetric pattern of circles .
- @ ref CALIB_CB_CLUSTERING uses a special algorithm for grid detection . It is more robust to
perspective distortions but much more sensitive to background clutter .
@ param blobDetector feature detector that finds blobs like dark circles on light background .
If ` blobDetector ` is NULL then ` image ` represents Point2f array of candidates .
@ -1681,7 +1681,7 @@ row). Otherwise, if the function fails to find all the corners or reorder them,
Sample usage of detecting and drawing the centers of circles : :
@ code
Size patternsize ( 7 , 7 ) ; //number of centers
Mat gray = . . . . ; //source image
Mat gray = . . . ; //source image
vector < Point2f > centers ; //this will be filled by the detected centers
bool patternfound = findCirclesGrid ( gray , patternsize , centers ) ;
@ -1720,8 +1720,8 @@ respectively. In the old interface all the vectors of object points from differe
concatenated together .
@ param imageSize Size of the image used only to initialize the camera intrinsic matrix .
@ param cameraMatrix Input / output 3 x3 floating - point camera intrinsic matrix
\ f $ \ cameramatrix { A } \ f $ . If CV \ _CALIB \ _USE \ _INTRINSIC \ _GUESS
and / or CALIB_FIX_ASPECT_RATIO are specified , some or all of fx , fy , cx , cy must be
\ f $ \ cameramatrix { A } \ f $ . If @ ref CALIB_USE_INTRINSIC _GUESS
and / or @ ref CALIB_FIX_ASPECT_RATIO are specified , some or all of fx , fy , cx , cy must be
initialized before calling the function .
@ param distCoeffs Input / output vector of distortion coefficients
\ f $ \ distcoeffs \ f $ .
@ -1744,40 +1744,40 @@ parameters. Order of deviations values: \f$(R_0, T_0, \dotsc , R_{M - 1}, T_{M -
the number of pattern views . \ f $ R_i , T_i \ f $ are concatenated 1 x3 vectors .
@ param perViewErrors Output vector of the RMS re - projection error estimated for each pattern view .
@ param flags Different flags that may be zero or a combination of the following values :
- * * CALIB_USE_INTRINSIC_GUESS * * cameraMatrix contains valid initial values of
- @ ref 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
- @ ref 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 consider only fy as a free parameter . The
@ ref CALIB_USE_INTRINSIC_GUESS is set too .
- @ ref CALIB_FIX_ASPECT_RATIO The functions consider 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
@ ref 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 \ f $ ( p_1 , p_2 ) \ f $ are set
- @ ref CALIB_ZERO_TANGENT_DIST Tangential distortion coefficients \ f $ ( p_1 , p_2 ) \ f $ 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
- @ ref CALIB_FIX_K1 , . . . , @ ref CALIB_FIX_K6 The corresponding radial distortion
coefficient is not changed during the optimization . If @ ref 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
- @ ref 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
- @ ref 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
- @ ref CALIB_FIX_S1_S2_S3_S4 The thin prism distortion coefficients are not changed during
the optimization . If @ ref 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
- @ ref 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
- @ ref CALIB_FIX_TAUX_TAUY The coefficients of the tilted sensor model are not changed during
the optimization . If @ ref CALIB_USE_INTRINSIC_GUESS is set , the coefficient from the
supplied distCoeffs matrix is used . Otherwise , it is set to 0.
@ param criteria Termination criteria for the iterative optimization algorithm .
@ -1789,7 +1789,7 @@ points and their corresponding 2D projections in each view must be specified. Th
by using an object with known geometry and easily detectable feature points . Such an object is
called a calibration rig or calibration pattern , and OpenCV has built - in support for a chessboard as
a calibration rig ( see @ ref findChessboardCorners ) . Currently , initialization of intrinsic
parameters ( when CALIB_USE_INTRINSIC_GUESS is not set ) is only implemented for planar calibration
parameters ( when @ ref 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 ) . 3 D calibration rigs can also
be used as long as initial cameraMatrix is provided .
@ -1972,39 +1972,39 @@ second camera coordinate system.
@ param F Output fundamental matrix .
@ param perViewErrors Output vector of the RMS re - projection error estimated for each pattern view .
@ param flags Different flags that may be zero or a combination of the following values :
- * * CALIB_FIX_INTRINSIC * * Fix cameraMatrix ? and distCoeffs ? so that only R , T , E , and F
- @ ref CALIB_FIX_INTRINSIC Fix cameraMatrix ? and distCoeffs ? so that only R , T , E , and F
matrices are estimated .
- * * CALIB_USE_INTRINSIC_GUESS * * Optimize some or all of the intrinsic parameters
- @ ref CALIB_USE_INTRINSIC_GUESS Optimize some or all of the intrinsic parameters
according to the specified flags . Initial values are provided by the user .
- * * CALIB_USE_EXTRINSIC_GUESS * * R and T contain valid initial values that are optimized further .
- @ ref CALIB_USE_EXTRINSIC_GUESS R and T contain valid initial values that are optimized further .
Otherwise R and T are initialized to the median value of the pattern views ( each dimension separately ) .
- * * CALIB_FIX_PRINCIPAL_POINT * * Fix the principal points during the optimization .
- * * CALIB_FIX_FOCAL_LENGTH * * Fix \ f $ f ^ { ( j ) } _x \ f $ and \ f $ f ^ { ( j ) } _y \ f $ .
- * * CALIB_FIX_ASPECT_RATIO * * Optimize \ f $ f ^ { ( j ) } _y \ f $ . Fix the ratio \ f $ f ^ { ( j ) } _x / f ^ { ( j ) } _y \ f $
- @ ref CALIB_FIX_PRINCIPAL_POINT Fix the principal points during the optimization .
- @ ref CALIB_FIX_FOCAL_LENGTH Fix \ f $ f ^ { ( j ) } _x \ f $ and \ f $ f ^ { ( j ) } _y \ f $ .
- @ ref CALIB_FIX_ASPECT_RATIO Optimize \ f $ f ^ { ( j ) } _y \ f $ . Fix the ratio \ f $ f ^ { ( j ) } _x / f ^ { ( j ) } _y \ f $
.
- * * CALIB_SAME_FOCAL_LENGTH * * Enforce \ f $ f ^ { ( 0 ) } _x = f ^ { ( 1 ) } _x \ f $ and \ f $ f ^ { ( 0 ) } _y = f ^ { ( 1 ) } _y \ f $ .
- * * CALIB_ZERO_TANGENT_DIST * * Set tangential distortion coefficients for each camera to
- @ ref CALIB_SAME_FOCAL_LENGTH Enforce \ f $ f ^ { ( 0 ) } _x = f ^ { ( 1 ) } _x \ f $ and \ f $ f ^ { ( 0 ) } _y = f ^ { ( 1 ) } _y \ f $ .
- @ ref CALIB_ZERO_TANGENT_DIST Set tangential distortion coefficients for each camera to
zeros and fix there .
- * * CALIB_FIX_K1 , . . . , CALIB_FIX_K6 * * Do not change the corresponding radial
distortion coefficient during the optimization . If CALIB_USE_INTRINSIC_GUESS is set ,
- @ ref CALIB_FIX_K1 , . . . , @ ref CALIB_FIX_K6 Do not change the corresponding radial
distortion coefficient during the optimization . If @ ref CALIB_USE_INTRINSIC_GUESS is set ,
the coefficient from the supplied distCoeffs matrix is used . Otherwise , it is set to 0.
- * * CALIB_RATIONAL_MODEL * * Enable coefficients k4 , k5 , and k6 . To provide the backward
- @ ref CALIB_RATIONAL_MODEL Enable coefficients k4 , k5 , and k6 . 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
- @ ref 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
- @ ref CALIB_FIX_S1_S2_S3_S4 The thin prism distortion coefficients are not changed during
the optimization . If @ ref 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
- @ ref 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
- @ ref CALIB_FIX_TAUX_TAUY The coefficients of the tilted sensor model are not changed during
the optimization . If @ ref CALIB_USE_INTRINSIC_GUESS is set , the coefficient from the
supplied distCoeffs matrix is used . Otherwise , it is set to 0.
@ param criteria Termination criteria for the iterative optimization algorithm .
@ -2052,10 +2052,10 @@ Besides the stereo-related information, the function can also perform a full cal
the two cameras . However , due to the high dimensionality of the parameter space and noise in the
input data , the function can diverge from the correct solution . If the intrinsic parameters can be
estimated with high accuracy for each of the cameras individually ( for example , using
calibrateCamera ) , you are recommended to do so and then pass CALIB_FIX_INTRINSIC flag to the
calibrateCamera ) , you are recommended to do so and then pass @ ref CALIB_FIX_INTRINSIC flag to the
function along with the computed intrinsic parameters . Otherwise , if all the parameters are
estimated at once , it makes sense to restrict some parameters , for example , pass
CALIB_SAME_FOCAL_LENGTH and CALIB_ZERO_TANGENT_DIST flags , which is usually a
@ ref CALIB_SAME_FOCAL_LENGTH and @ ref CALIB_ZERO_TANGENT_DIST flags , which is usually a
reasonable assumption .
Similarly to calibrateCamera , the function minimizes the total re - projection error for all the
@ -2105,7 +2105,7 @@ rectified first camera's image.
camera , i . e . it projects points given in the rectified first camera coordinate system into the
rectified second camera ' s image .
@ param Q Output \ f $ 4 \ times 4 \ f $ disparity - to - depth mapping matrix ( see @ ref reprojectImageTo3D ) .
@ param flags Operation flags that may be zero or CALIB_ZERO_DISPARITY . If the flag is set ,
@ param flags Operation flags that may be zero or @ ref CALIB_ZERO_DISPARITY . If the flag is set ,
the function makes the principal points of each camera have the same pixel coordinates in the
rectified views . And if the flag is not set , the function may still shift the images in the
horizontal or vertical direction ( depending on the orientation of epipolar lines ) to maximize the
@ -2152,7 +2152,7 @@ coordinates. The function distinguishes the following two cases:
\ end { bmatrix } , \ f ]
where \ f $ T_x \ f $ is a horizontal shift between the cameras and \ f $ cx_1 = cx_2 \ f $ if
CALIB_ZERO_DISPARITY is set .
@ ref CALIB_ZERO_DISPARITY is set .
- * * Vertical stereo * * : the first and the second camera views are shifted relative to each other
mainly in the vertical direction ( and probably a bit in the horizontal direction too ) . The epipolar
@ -2171,7 +2171,7 @@ coordinates. The function distinguishes the following two cases:
\ end { bmatrix } , \ f ]
where \ f $ T_y \ f $ is a vertical shift between the cameras and \ f $ cy_1 = cy_2 \ f $ if
CALIB_ZERO_DISPARITY is set .
@ ref CALIB_ZERO_DISPARITY is set .
As you can see , the first three columns of P1 and P2 will effectively be the new " rectified " camera
matrices . The matrices , together with R1 and R2 , can then be passed to initUndistortRectifyMap to
@ -2680,8 +2680,8 @@ same camera intrinsic matrix. If this assumption does not hold for your use case
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
@ -2713,8 +2713,8 @@ 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
@ -3221,8 +3221,8 @@ b_2\\
@ param to Second input 2 D 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 .
@ -3271,8 +3271,8 @@ two 2D point sets.
@ param to Second input 2 D 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 .
@ -3772,7 +3772,8 @@ namespace fisheye
CALIB_FIX_K3 = 1 < < 6 ,
CALIB_FIX_K4 = 1 < < 7 ,
CALIB_FIX_INTRINSIC = 1 < < 8 ,
CALIB_FIX_PRINCIPAL_POINT = 1 < < 9
CALIB_FIX_PRINCIPAL_POINT = 1 < < 9 ,
CALIB_ZERO_DISPARITY = 1 < < 10
} ;
/** @brief Projects points using fisheye model
@ -3905,7 +3906,7 @@ namespace fisheye
@ param image_size Size of the image used only to initialize the camera intrinsic matrix .
@ param K Output 3 x3 floating - point camera intrinsic matrix
\ f $ \ cameramatrix { A } \ f $ . If
fisheye : : CALIB_USE_INTRINSIC_GUESS / is specified , some or all of fx , fy , cx , cy must be
@ ref fisheye : : CALIB_USE_INTRINSIC_GUESS is specified , some or all of fx , fy , cx , cy must be
initialized before calling the function .
@ param D Output vector of distortion coefficients \ f $ \ distcoeffsfisheye \ f $ .
@ param rvecs Output vector of rotation vectors ( see Rodrigues ) estimated for each pattern view .
@ -3915,17 +3916,17 @@ namespace fisheye
position of the calibration pattern in the k - th pattern view ( k = 0. . * M * - 1 ) .
@ param tvecs Output vector of translation vectors estimated for each pattern view .
@ param flags Different flags that may be zero or a combination of the following values :
- * * fisheye : : CALIB_USE_INTRINSIC_GUESS * * cameraMatrix contains valid initial values of
- @ ref fisheye : : 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 .
- * * fisheye : : CALIB_RECOMPUTE_EXTRINSIC * * Extrinsic will be recomputed after each iteration
- @ ref fisheye : : CALIB_RECOMPUTE_EXTRINSIC Extrinsic will be recomputed after each iteration
of intrinsic optimization .
- * * fisheye : : CALIB_CHECK_COND * * The functions will check validity of condition number .
- * * fisheye : : CALIB_FIX_SKEW * * Skew coefficient ( alpha ) is set to zero and stay zero .
- * * fisheye : : CALIB_FIX_K1 . . fisheye : : CALIB_FIX_K4 * * Selected distortion coefficients
- @ ref fisheye : : CALIB_CHECK_COND The functions will check validity of condition number .
- @ ref fisheye : : CALIB_FIX_SKEW Skew coefficient ( alpha ) is set to zero and stay zero .
- @ ref fisheye : : CALIB_FIX_K1 , . . . , @ ref fisheye : : CALIB_FIX_K4 Selected distortion coefficients
are set to zeros and stay zero .
- * * fisheye : : 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 .
- @ ref fisheye : : 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 @ ref fisheye : : CALIB_USE_INTRINSIC_GUESS is set too .
@ param criteria Termination criteria for the iterative optimization algorithm .
*/
CV_EXPORTS_W double calibrate ( InputArrayOfArrays objectPoints , InputArrayOfArrays imagePoints , const Size & image_size ,
@ -3949,7 +3950,7 @@ optimization. It stays at the center or at a different location specified when C
@ param P2 Output 3 x4 projection matrix in the new ( rectified ) coordinate systems for the second
camera .
@ param Q Output \ f $ 4 \ times 4 \ f $ disparity - to - depth mapping matrix ( see reprojectImageTo3D ) .
@ param flags Operation flags that may be zero or CALIB_ZERO_DISPARITY . If the flag is set ,
@ param flags Operation flags that may be zero or @ ref fisheye : : CALIB_ZERO_DISPARITY . If the flag is set ,
the function makes the principal points of each camera have the same pixel coordinates in the
rectified views . And if the flag is not set , the function may still shift the images in the
horizontal or vertical direction ( depending on the orientation of epipolar lines ) to maximize the
@ -3975,7 +3976,7 @@ optimization. It stays at the center or at a different location specified when C
observed by the second camera .
@ param K1 Input / output first camera intrinsic matrix :
\ f $ \ vecthreethree { f_x ^ { ( j ) } } { 0 } { c_x ^ { ( j ) } } { 0 } { f_y ^ { ( j ) } } { c_y ^ { ( j ) } } { 0 } { 0 } { 1 } \ f $ , \ f $ j = 0 , \ , 1 \ f $ . If
any of fisheye : : CALIB_USE_INTRINSIC_GUESS , fisheye : : CALIB_FIX_INTRINSIC are specified ,
any of @ ref fisheye : : CALIB_USE_INTRINSIC_GUESS , @ ref fisheye : : CALIB_FIX_INTRINSIC are specified ,
some or all of the matrix components must be initialized .
@ param D1 Input / output vector of distortion coefficients \ f $ \ distcoeffsfisheye \ f $ of 4 elements .
@ param K2 Input / output second camera intrinsic matrix . The parameter is similar to K1 .
@ -3985,16 +3986,16 @@ optimization. It stays at the center or at a different location specified when C
@ param R Output rotation matrix between the 1 st and the 2 nd camera coordinate systems .
@ param T Output translation vector between the coordinate systems of the cameras .
@ param flags Different flags that may be zero or a combination of the following values :
- * * fisheye : : CALIB_FIX_INTRINSIC * * Fix K1 , K2 ? and D1 , D2 ? so that only R , T matrices
- @ ref fisheye : : CALIB_FIX_INTRINSIC Fix K1 , K2 ? and D1 , D2 ? so that only R , T matrices
are estimated .
- * * fisheye : : CALIB_USE_INTRINSIC_GUESS * * K1 , K2 contains valid initial values of
- @ ref fisheye : : CALIB_USE_INTRINSIC_GUESS K1 , K2 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 .
- * * fisheye : : CALIB_RECOMPUTE_EXTRINSIC * * Extrinsic will be recomputed after each iteration
- @ ref fisheye : : CALIB_RECOMPUTE_EXTRINSIC Extrinsic will be recomputed after each iteration
of intrinsic optimization .
- * * fisheye : : CALIB_CHECK_COND * * The functions will check validity of condition number .
- * * fisheye : : CALIB_FIX_SKEW * * Skew coefficient ( alpha ) is set to zero and stay zero .
- * * fisheye : : CALIB_FIX_K1 . .4 * * Selected distortion coefficients are set to zeros and stay
- @ ref fisheye : : CALIB_CHECK_COND The functions will check validity of condition number .
- @ ref fisheye : : CALIB_FIX_SKEW Skew coefficient ( alpha ) is set to zero and stay zero .
- @ ref fisheye : : CALIB_FIX_K1 , . . . , @ ref fisheye : : CALIB_FIX_K4 Selected distortion coefficients are set to zeros and stay
zero .
@ param criteria Termination criteria for the iterative optimization algorithm .
*/
Alexander Alekhin
4 years ago