From ce1b6e2137a3f330792cedc7acf1f1e963c55f86 Mon Sep 17 00:00:00 2001 From: Daniel Angelov Date: Fri, 30 May 2014 23:59:32 +0100 Subject: [PATCH] Fixed inconsistency with flag names Fixed inconsistency with flag names for solvePnP. The default value for the function lacks the CV_ prefix. The code checks against "ITERATIVE". The suggested values for the parameters *include* the prefix. Even though the enum CV_ITERATIVE (+ CV_P3P, CV_EPNP) = ITERATIVE (& P3P, EPNP), lets show to the users only one of them. Now the user sees only {ITERATIVE, P3P, EPNP}. --- .../doc/camera_calibration_and_3d_reconstruction.rst | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/modules/calib3d/doc/camera_calibration_and_3d_reconstruction.rst b/modules/calib3d/doc/camera_calibration_and_3d_reconstruction.rst index 201f9bd47..60264b2ff 100644 --- a/modules/calib3d/doc/camera_calibration_and_3d_reconstruction.rst +++ b/modules/calib3d/doc/camera_calibration_and_3d_reconstruction.rst @@ -584,15 +584,15 @@ Finds an object pose from 3D-2D point correspondences. :param flags: Method for solving a PnP problem: - * **CV_ITERATIVE** Iterative method is based on 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 :ocv:func:`projectPoints` ) ``objectPoints`` . - * **CV_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. - * **CV_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". + * **ITERATIVE** Iterative method is based on 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 :ocv:func:`projectPoints` ) ``objectPoints`` . + * **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. + * **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". The function estimates the object pose given a set of object points, their corresponding image projections, as well as the camera matrix and the distortion coefficients. .. note:: - * An example of how to use solvePNP for planar augmented reality can be found at opencv_source_code/samples/python2/plane_ar.py + * An example of how to use solvePnP for planar augmented reality can be found at opencv_source_code/samples/python2/plane_ar.py solvePnPRansac ------------------