提交 2e950eb4 编写于 作者: V Vadim Pisarevsky

Merge pull request #3196 from PhilLab:patch-4

......@@ -740,7 +740,7 @@ where
:math:`E` is an essential matrix,
:math:`p_1` and
:math:`p_2` are corresponding points in the first and the second images, respectively.
The result of this function may be passed further to ``decomposeEssentialMat()`` or ``recoverPose()`` to recover the relative pose between cameras.
The result of this function may be passed further to :ocv:func:`decomposeEssentialMat` or :ocv:func:`recoverPose` to recover the relative pose between cameras.
decomposeEssentialMat
-------------------------
......@@ -812,11 +812,11 @@ Returns the number of inliers which pass the check.
Only these inliers will be used to recover pose.
In the output mask only inliers which pass the cheirality check.
This function decomposes an essential matrix using ``decomposeEssentialMat()`` and then verifies possible pose hypotheses by doing cheirality check.
The cheirality check basically means that the triangulated 3D points should have positive depth. Some details can be found from [Nister03]_.
This function decomposes an essential matrix using :ocv:func:`decomposeEssentialMat` and then verifies possible pose hypotheses by doing cheirality check.
The cheirality check basically means that the triangulated 3D points should have positive depth. Some details can be found in [Nister03]_.
This function can be used to process output ``E`` and ``mask`` from ``findEssentialMat()``.
In this scenario, ``points1`` and ``points2`` are the same input for ``findEssentialMat()``. ::
This function can be used to process output ``E`` and ``mask`` from :ocv:func:`findEssentialMat`.
In this scenario, ``points1`` and ``points2`` are the same input for :ocv:func:`findEssentialMat`. ::
// Example. Estimation of fundamental matrix using the RANSAC algorithm
int point_count = 100;
......
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