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}.

diff --git a/modules/calib3d/doc/camera_calibration_and_3d_reconstruction.rst b/modules/calib3d/doc/camera_calibration_and_3d_reconstruction.rst
index 201f9bd..60264b2 100644 (file)
--- 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
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