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

Normalize whitespace in documentation and text files

Browse Source
This commit is contained in:
Andrey Kamaev
2012-10-17 21:42:09 +04:00
parent 9337246867
commit 0e7ca71dcc
95 changed files with 1238 additions and 1238 deletions
Download Patch File Download Diff File Expand all files Collapse all files

10
doc/tutorials/features2d/detection_of_planar_objects/detection_of_planar_objects.rst
View File

@@ -5,9 +5,9 @@ Detection of planar objects
.. highlight:: cpp
The goal of this tutorial is to learn how to use *features2d* and *calib3d* modules for detecting known planar objects in scenes.
The goal of this tutorial is to learn how to use *features2d* and *calib3d* modules for detecting known planar objects in scenes.
*Test data*: use images in your data folder, for instance, ``box.png`` and ``box_in_scene.png``.
*Test data*: use images in your data folder, for instance, ``box.png`` and ``box_in_scene.png``.
#.
Create a new console project. Read two input images. ::
@@ -22,7 +22,7 @@ The goal of this tutorial is to learn how to use *features2d* and *calib3d* modu
FastFeatureDetector detector(15);
vector<KeyPoint> keypoints1;
detector.detect(img1, keypoints1);
... // do the same for the second image
#.
@@ -32,7 +32,7 @@ The goal of this tutorial is to learn how to use *features2d* and *calib3d* modu
SurfDescriptorExtractor extractor;
Mat descriptors1;
extractor.compute(img1, keypoints1, descriptors1);
... // process keypoints from the second image as well
#.
@@ -69,4 +69,4 @@ The goal of this tutorial is to learn how to use *features2d* and *calib3d* modu
perspectiveTransform(Mat(points1), points1Projected, H);
#.
Use ``drawMatches`` for drawing inliers.
Use ``drawMatches`` for drawing inliers.

104
doc/tutorials/features2d/table_of_content_features2d/table_of_content_features2d.rst
View File

@@ -5,166 +5,166 @@
Learn about how to use the feature points detectors, descriptors and matching framework found inside OpenCV.
.. include:: ../../definitions/tocDefinitions.rst
.. include:: ../../definitions/tocDefinitions.rst
+
+
.. tabularcolumns:: m{100pt} m{300pt}
.. cssclass:: toctableopencv
===================== ==============================================
|Harris| **Title:** :ref:`harris_detector`
*Compatibility:* > OpenCV 2.0
*Author:* |Author_AnaH|
Why is it a good idea to track corners? We learn to use the Harris method to detect corners
===================== ==============================================
.. |Harris| image:: images/trackingmotion/Harris_Detector_Cover.jpg
:height: 90pt
:width: 90pt
+
+
.. tabularcolumns:: m{100pt} m{300pt}
.. cssclass:: toctableopencv
===================== ==============================================
|ShiTomasi| **Title:** :ref:`good_features_to_track`
*Compatibility:* > OpenCV 2.0
*Author:* |Author_AnaH|
Where we use an improved method to detect corners more accuratelyI
===================== ==============================================
.. |ShiTomasi| image:: images/trackingmotion/Shi_Tomasi_Detector_Cover.jpg
:height: 90pt
:width: 90pt
+
+
.. tabularcolumns:: m{100pt} m{300pt}
.. cssclass:: toctableopencv
===================== ==============================================
|GenericCorner| **Title:** :ref:`generic_corner_detector`
*Compatibility:* > OpenCV 2.0
*Author:* |Author_AnaH|
Here you will learn how to use OpenCV functions to make your personalized corner detector!
===================== ==============================================
.. |GenericCorner| image:: images/trackingmotion/Generic_Corner_Detector_Cover.jpg
:height: 90pt
:width: 90pt
+
+
.. tabularcolumns:: m{100pt} m{300pt}
.. cssclass:: toctableopencv
===================== ==============================================
|Subpixel| **Title:** :ref:`corner_subpixeles`
*Compatibility:* > OpenCV 2.0
*Author:* |Author_AnaH|
Is pixel resolution enough? Here we learn a simple method to improve our accuracy.
===================== ==============================================
.. |Subpixel| image:: images/trackingmotion/Corner_Subpixeles_Cover.jpg
:height: 90pt
:width: 90pt
+
+
.. tabularcolumns:: m{100pt} m{300pt}
.. cssclass:: toctableopencv
===================== ==============================================
|FeatureDetect| **Title:** :ref:`feature_detection`
*Compatibility:* > OpenCV 2.0
*Author:* |Author_AnaH|
In this tutorial, you will use *features2d* to detect interest points.
===================== ==============================================
.. |FeatureDetect| image:: images/Feature_Detection_Tutorial_Cover.jpg
:height: 90pt
:width: 90pt
+
+
.. tabularcolumns:: m{100pt} m{300pt}
.. cssclass:: toctableopencv
===================== ==============================================
|FeatureDescript| **Title:** :ref:`feature_description`
*Compatibility:* > OpenCV 2.0
*Author:* |Author_AnaH|
In this tutorial, you will use *features2d* to calculate feature vectors.
===================== ==============================================
.. |FeatureDescript| image:: images/Feature_Description_Tutorial_Cover.jpg
:height: 90pt
:width: 90pt
+
+
.. tabularcolumns:: m{100pt} m{300pt}
.. cssclass:: toctableopencv
===================== ==============================================
|FeatureFlann| **Title:** :ref:`feature_flann_matcher`
*Compatibility:* > OpenCV 2.0
*Author:* |Author_AnaH|
In this tutorial, you will use the FLANN library to make a fast matching.
===================== ==============================================
.. |FeatureFlann| image:: images/Feature_Flann_Matcher_Tutorial_Cover.jpg
:height: 90pt
:width: 90pt
+
+
.. tabularcolumns:: m{100pt} m{300pt}
.. cssclass:: toctableopencv
===================== ==============================================
|FeatureHomo| **Title:** :ref:`feature_homography`
*Compatibility:* > OpenCV 2.0
*Author:* |Author_AnaH|
In this tutorial, you will use *features2d* and *calib3d* to detect an object in a scene.
===================== ==============================================
.. |FeatureHomo| image:: images/Feature_Homography_Tutorial_Cover.jpg
:height: 90pt
:width: 90pt
+
+
.. tabularcolumns:: m{100pt} m{300pt}
.. cssclass:: toctableopencv
@@ -175,7 +175,7 @@ Learn about how to use the feature points detectors, descriptors and matching f
*Author:* |Author_VictorE|
You will use *features2d* and *calib3d* modules for detecting known planar objects in scenes.
You will use *features2d* and *calib3d* modules for detecting known planar objects in scenes.
===================== ==============================================

16
doc/tutorials/features2d/trackingmotion/corner_subpixeles/corner_subpixeles.rst
View File

@@ -87,14 +87,14 @@ This tutorial code's is shown lines below. You can also download it from `here <
/// Apply corner detection
goodFeaturesToTrack( src_gray,
corners,
maxCorners,
qualityLevel,
minDistance,
Mat(),
blockSize,
useHarrisDetector,
k );
corners,
maxCorners,
qualityLevel,
minDistance,
Mat(),
blockSize,
useHarrisDetector,
k );
/// Draw corners detected

26
doc/tutorials/features2d/trackingmotion/harris_detector/harris_detector.rst
View File

@@ -98,16 +98,16 @@ How does it work?
u & v
\end{bmatrix}
\left (
\displaystyle \sum_{x,y}
\displaystyle \sum_{x,y}
w(x,y)
\begin{bmatrix}
I_x^{2} & I_{x}I_{y} \\
I_xI_{y} & I_{y}^{2}
\end{bmatrix}
\right )
\begin{bmatrix}
\end{bmatrix}
\right )
\begin{bmatrix}
u \\
v
v
\end{bmatrix}
* Let's denote:
@@ -115,11 +115,11 @@ How does it work?
.. math::
M = \displaystyle \sum_{x,y}
w(x,y)
\begin{bmatrix}
I_x^{2} & I_{x}I_{y} \\
I_xI_{y} & I_{y}^{2}
\end{bmatrix}
w(x,y)
\begin{bmatrix}
I_x^{2} & I_{x}I_{y} \\
I_xI_{y} & I_{y}^{2}
\end{bmatrix}
* So, our equation now is:
@@ -128,10 +128,10 @@ How does it work?
E(u,v) \approx \begin{bmatrix}
u & v
\end{bmatrix}
M
\begin{bmatrix}
M
\begin{bmatrix}
u \\
v
v
\end{bmatrix}