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Doxygen tutorials: warnings cleared

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This commit is contained in:
Maksim Shabunin
2014-11-27 19:54:13 +03:00
parent 8375182e34
commit c5536534d8
64 changed files with 889 additions and 1659 deletions
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3
doc/tutorials/calib3d/camera_calibration/camera_calibration.markdown
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@@ -91,7 +91,7 @@ directory mentioned above.
The application starts up with reading the settings from the configuration file. Although, this is
an important part of it, it has nothing to do with the subject of this tutorial: *camera
calibration*. Therefore, I've chosen not to post the code for that part here. Technical background
on how to do this you can find in the @ref fileInputOutputXMLYAML tutorial.
on how to do this you can find in the @ref tutorial_file_input_output_with_xml_yml tutorial.
Explanation
-----------
@@ -486,4 +486,3 @@ here](https://www.youtube.com/watch?v=ViPN810E0SU).
<iframe title=" Camera calibration With OpenCV - Chessboard or asymmetrical circle pattern." width="560" height="349" src="http://www.youtube.com/embed/ViPN810E0SU?rel=0&loop=1" frameborder="0" allowfullscreen align="middle"></iframe>
</div>
\endhtmlonly

34
doc/tutorials/calib3d/real_time_pose/real_time_pose.markdown
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@@ -51,8 +51,7 @@ plane:
\f[s\ \left [ \begin{matrix} u \\ v \\ 1 \end{matrix} \right ] = \left [ \begin{matrix} f_x & 0 & c_x \\ 0 & f_y & c_y \\ 0 & 0 & 1 \end{matrix} \right ] \left [ \begin{matrix} r_{11} & r_{12} & r_{13} & t_1 \\ r_{21} & r_{22} & r_{23} & t_2 \\ r_{31} & r_{32} & r_{33} & t_3 \end{matrix} \right ] \left [ \begin{matrix} X \\ Y \\ Z\\ 1 \end{matrix} \right ]\f]
The complete documentation of how to manage with this equations is in @ref cv::Camera Calibration
and 3D Reconstruction .
The complete documentation of how to manage with this equations is in @ref calib3d .
Source code
-----------
@@ -80,7 +79,7 @@ then uses the mesh along with the [Möller–Trumbore intersection
algorithm](http://http://en.wikipedia.org/wiki/M%C3%B6ller%E2%80%93Trumbore_intersection_algorithm/)
to compute the 3D coordinates of the found features. Finally, the 3D points and the descriptors
are stored in different lists in a file with YAML format which each row is a different point. The
technical background on how to store the files can be found in the @ref fileInputOutputXMLYAML
technical background on how to store the files can be found in the @ref tutorial_file_input_output_with_xml_yml
tutorial.
![image](images/registration.png)
@@ -91,9 +90,9 @@ The aim of this application is estimate in real time the object pose given its 3
The application starts up loading the 3D textured model in YAML file format with the same
structure explained in the model registration program. From the scene, the ORB features and
descriptors are detected and extracted. Then, is used @ref cv::FlannBasedMatcher with @ref
cv::LshIndexParams to do the matching between the scene descriptors and the model descriptors.
Using the found matches along with @ref cv::solvePnPRansac function the @ref cv::R\` and \f$t\f$ of
descriptors are detected and extracted. Then, is used @ref cv::FlannBasedMatcher with
@ref cv::flann::GenericIndex to do the matching between the scene descriptors and the model descriptors.
Using the found matches along with @ref cv::solvePnPRansac function the `R` and `t` of
the camera are computed. Finally, a KalmanFilter is applied in order to reject bad poses.
In the case that you compiled OpenCV with the samples, you can find it in opencv/build/bin/cpp-tutorial-pnp_detection\`.
@@ -242,7 +241,7 @@ implemented a *class* **RobustMatcher** which has a function for keypoints detec
extraction. You can find it in
`samples/cpp/tutorial_code/calib3d/real_time_pose_estimation/src/RobusMatcher.cpp`. In your
*RobusMatch* object you can use any of the 2D features detectors of OpenCV. In this case I used
@ref cv::ORB features because is based on @ref cv::FAST to detect the keypoints and @ref cv::BRIEF
@ref cv::ORB features because is based on @ref cv::FAST to detect the keypoints and @ref cv::xfeatures2d::BriefDescriptorExtractor
to extract the descriptors which means that is fast and robust to rotations. You can find more
detailed information about *ORB* in the documentation.
@@ -265,9 +264,9 @@ It is the first step in our detection algorithm. The main idea is to match the s
with our model descriptors in order to know the 3D coordinates of the found features into the
current scene.
Firstly, we have to set which matcher we want to use. In this case is used @ref
cv::FlannBasedMatcher matcher which in terms of computational cost is faster than the @ref
cv::BruteForceMatcher matcher as we increase the trained collectction of features. Then, for
Firstly, we have to set which matcher we want to use. In this case is used
@ref cv::FlannBasedMatcher matcher which in terms of computational cost is faster than the
@ref cv::BFMatcher matcher as we increase the trained collectction of features. Then, for
FlannBased matcher the index created is *Multi-Probe LSH: Efficient Indexing for High-Dimensional
Similarity Search* due to *ORB* descriptors are binary.
@@ -349,8 +348,8 @@ void RobustMatcher::robustMatch( const cv::Mat& frame, std::vector<cv::DMatch>&
}
@endcode
After the matches filtering we have to subtract the 2D and 3D correspondences from the found scene
keypoints and our 3D model using the obtained *DMatches* vector. For more information about @ref
cv::DMatch check the documentation.
keypoints and our 3D model using the obtained *DMatches* vector. For more information about
@ref cv::DMatch check the documentation.
@code{.cpp}
// -- Step 2: Find out the 2D/3D correspondences
@@ -385,8 +384,8 @@ solution.
For the camera pose estimation I have implemented a *class* **PnPProblem**. This *class* has 4
atributes: a given calibration matrix, the rotation matrix, the translation matrix and the
rotation-translation matrix. The intrinsic calibration parameters of the camera which you are
using to estimate the pose are necessary. In order to obtain the parameters you can check @ref
CameraCalibrationSquareChessBoardTutorial and @ref cameraCalibrationOpenCV tutorials.
using to estimate the pose are necessary. In order to obtain the parameters you can check
@ref tutorial_camera_calibration_square_chess and @ref tutorial_camera_calibration tutorials.
The following code is how to declare the *PnPProblem class* in the main program:
@code{.cpp}
@@ -543,9 +542,9 @@ Filter will be applied after detected a given number of inliers.
You can find more information about what [Kalman
Filter](http://en.wikipedia.org/wiki/Kalman_filter) is. In this tutorial it's used the OpenCV
implementation of the @ref cv::Kalman Filter based on [Linear Kalman Filter for position and
orientation tracking](http://campar.in.tum.de/Chair/KalmanFilter) to set the dynamics and
measurement models.
implementation of the @ref cv::KalmanFilter based on
[Linear Kalman Filter for position and orientation tracking](http://campar.in.tum.de/Chair/KalmanFilter)
to set the dynamics and measurement models.
Firstly, we have to define our state vector which will have 18 states: the positional data (x,y,z)
with its first and second derivatives (velocity and acceleration), then rotation is added in form
@@ -796,4 +795,3 @@ here](http://www.youtube.com/user/opencvdev/videos).
<iframe title="Pose estimation of textured object using OpenCV in cluttered background" width="560" height="349" src="http://www.youtube.com/embed/YLS9bWek78k?rel=0&loop=1" frameborder="0" allowfullscreen align="middle"></iframe>
</div>
\endhtmlonly