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Fix documentation for findEssentialMat to reflect how it actually works. #25488

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merged 2 commits into from
Apr 25, 2024
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Fix documentation for findEssentialMat to reflect how it actually works. #25488

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75163be
Fix documentation for findEssentialMat to reflect how it actually works.
inkredibl Apr 24, 2024
245c861
Code review fixes.
asmorkalov Apr 25, 2024
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30 changes: 10 additions & 20 deletions modules/calib3d/include/opencv2/calib3d.hpp
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Original file line number Diff line number Diff line change
Expand Up @@ -2492,13 +2492,13 @@ CV_EXPORTS_W Mat findFundamentalMat( InputArray points1, InputArray points2,

@param points1 Array of N (N \>= 5) 2D points from the first image. The point coordinates should
be floating-point (single or double precision).
@param points2 Array of the second image points of the same size and format as points1 .
@param points2 Array of the second image points of the same size and format as points1.
@param cameraMatrix Camera intrinsic matrix \f$\cameramatrix{A}\f$ .
Note that this function assumes that points1 and points2 are feature points from cameras with the
same camera intrinsic matrix. If this assumption does not hold for your use case, use
#undistortPoints with `P = cv::NoArray()` for both cameras to transform image points
to normalized image coordinates, which are valid for the identity camera intrinsic matrix. When
passing these coordinates, pass the identity matrix for this parameter.
same camera intrinsic matrix. If this assumption does not hold for your use case, use another
function overload or #undistortPoints with `P = cv::NoArray()` for both cameras to transform image
points 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.
- @ref RANSAC for the RANSAC algorithm.
- @ref LMEDS for the LMedS algorithm.
Expand Down Expand Up @@ -2590,23 +2590,13 @@ Mat findEssentialMat(

@param points1 Array of N (N \>= 5) 2D points from the first image. The point coordinates should
be floating-point (single or double precision).
@param points2 Array of the second image points of the same size and format as points1 .
@param cameraMatrix1 Camera matrix \f$K = \vecthreethree{f_x}{0}{c_x}{0}{f_y}{c_y}{0}{0}{1}\f$ .
Note that this function assumes that points1 and points2 are feature points from cameras with the
same camera matrix. If this assumption does not hold for your use case, use
#undistortPoints with `P = cv::NoArray()` for both cameras to transform image points
to normalized image coordinates, which are valid for the identity camera matrix. When
passing these coordinates, pass the identity matrix for this parameter.
@param cameraMatrix2 Camera matrix \f$K = \vecthreethree{f_x}{0}{c_x}{0}{f_y}{c_y}{0}{0}{1}\f$ .
Note that this function assumes that points1 and points2 are feature points from cameras with the
same camera matrix. If this assumption does not hold for your use case, use
#undistortPoints with `P = cv::NoArray()` for both cameras to transform image points
to normalized image coordinates, which are valid for the identity camera matrix. When
passing these coordinates, pass the identity matrix for this parameter.
@param distCoeffs1 Input vector of distortion coefficients
@param points2 Array of the second image points of the same size and format as points1.
@param cameraMatrix1 Camera matrix for the first camera \f$K = \vecthreethree{f_x}{0}{c_x}{0}{f_y}{c_y}{0}{0}{1}\f$ .
@param cameraMatrix2 Camera matrix for the second camera \f$K = \vecthreethree{f_x}{0}{c_x}{0}{f_y}{c_y}{0}{0}{1}\f$ .
@param distCoeffs1 Input vector of distortion coefficients for the first camera
\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 distCoeffs2 Input vector of distortion coefficients
@param distCoeffs2 Input vector of distortion coefficients for the second camera
\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.
Expand Down