generic-library/opencv
1
0
Fork 0
Code Issues Pull Requests Releases Wiki Activity

Fixed inconsistency with flag names

Browse Source 
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}.
This commit is contained in:
Daniel Angelov 2014-05-30 23:59:32 +01:00
parent d4a1936c2d
commit ce1b6e2137
1 changed files with 4 additions and 4 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

8
modules/calib3d/doc/camera_calibration_and_3d_reconstruction.rst
View File

@ -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
------------------