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This commit is contained in:
Andrey Kamaev
2012-08-07 13:29:43 +04:00
parent a3527fc4d8
commit 5100ca7508
66 changed files with 1180 additions and 1305 deletions
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66
doc/tutorials/features2d/trackingmotion/harris_detector/harris_detector.rst
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@@ -10,7 +10,7 @@ In this tutorial you will learn:
.. container:: enumeratevisibleitemswithsquare
* What features are and why they are important
* What features are and why they are important
* Use the function :corner_harris:`cornerHarris <>` to detect corners using the Harris-Stephens method.
Theory
@@ -56,7 +56,7 @@ How does it work?
.. container:: enumeratevisibleitemswithsquare
* Let's look for corners. Since corners represents a variation in the gradient in the image, we will look for this "variation".
* Let's look for corners. Since corners represents a variation in the gradient in the image, we will look for this "variation".
* Consider a grayscale image :math:`I`. We are going to sweep a window :math:`w(x,y)` (with displacements :math:`u` in the x direction and :math:`v` in the right direction) :math:`I` and will calculate the variation of intensity.
@@ -66,10 +66,10 @@ How does it work?
where:
* :math:`w(x,y)` is the window at position :math:`(x,y)`
* :math:`w(x,y)` is the window at position :math:`(x,y)`
* :math:`I(x,y)` is the intensity at :math:`(x,y)`
* :math:`I(x+u,y+v)` is the intensity at the moved window :math:`(x+u,y+v)`
* Since we are looking for windows with corners, we are looking for windows with a large variation in intensity. Hence, we have to maximize the equation above, specifically the term:
.. math::
@@ -89,36 +89,36 @@ How does it work?
.. math::
E(u,v) \approx \sum _{x,y} u^{2}I_{x}^{2} + 2uvI_{x}I_{y} + v^{2}I_{y}^{2}
* Which can be expressed in a matrix form as:
.. math::
E(u,v) \approx \begin{bmatrix}
u & v
u & v
\end{bmatrix}
\left (
\displaystyle \sum_{x,y}
w(x,y)
\begin{bmatrix}
I_x^{2} & I_{x}I_{y} \\
I_xI_{y} & I_{y}^{2}
I_xI_{y} & I_{y}^{2}
\end{bmatrix}
\right )
\right )
\begin{bmatrix}
u \\
v
\end{bmatrix}
v
\end{bmatrix}
* Let's denote:
.. math::
M = \displaystyle \sum_{x,y}
w(x,y)
w(x,y)
\begin{bmatrix}
I_x^{2} & I_{x}I_{y} \\
I_xI_{y} & I_{y}^{2}
I_xI_{y} & I_{y}^{2}
\end{bmatrix}
* So, our equation now is:
@@ -126,34 +126,34 @@ How does it work?
.. math::
E(u,v) \approx \begin{bmatrix}
u & v
u & v
\end{bmatrix}
M
\begin{bmatrix}
u \\
v
\end{bmatrix}
v
\end{bmatrix}
* A score is calculated for each window, to determine if it can possibly contain a corner:
.. math::
R = det(M) - k(trace(M))^{2}
R = det(M) - k(trace(M))^{2}
where:
* det(M) = :math:`\lambda_{1}\lambda_{2}`
* trace(M) = :math:`\lambda_{1}+\lambda_{2}`
a window with a score :math:`R` greater than a certain value is considered a "corner"
Code
====
This tutorial code's is shown lines below. You can also download it from `here <http://code.opencv.org/svn/opencv/trunk/opencv/samples/cpp/tutorial_code/TrackingMotion/cornerHarris_Demo.cpp>`_
This tutorial code's is shown lines below. You can also download it from `here <http://code.opencv.org/projects/opencv/repository/revisions/master/raw/samples/cpp/tutorial_code/TrackingMotion/cornerHarris_Demo.cpp>`_
.. code-block:: cpp
.. code-block:: cpp
#include "opencv2/highgui/highgui.hpp"
#include "opencv2/imgproc/imgproc.hpp"
@@ -161,7 +161,7 @@ This tutorial code's is shown lines below. You can also download it from `here <
#include <stdio.h>
#include <stdlib.h>
using namespace cv;
using namespace cv;
using namespace std;
/// Global variables
@@ -186,7 +186,7 @@ This tutorial code's is shown lines below. You can also download it from `here <
namedWindow( source_window, CV_WINDOW_AUTOSIZE );
createTrackbar( "Threshold: ", source_window, &thresh, max_thresh, cornerHarris_demo );
imshow( source_window, src );
cornerHarris_demo( 0, 0 );
waitKey(0);
@@ -204,25 +204,25 @@ This tutorial code's is shown lines below. You can also download it from `here <
int blockSize = 2;
int apertureSize = 3;
double k = 0.04;
/// Detecting corners
cornerHarris( src_gray, dst, blockSize, apertureSize, k, BORDER_DEFAULT );
/// Normalizing
normalize( dst, dst_norm, 0, 255, NORM_MINMAX, CV_32FC1, Mat() );
convertScaleAbs( dst_norm, dst_norm_scaled );
convertScaleAbs( dst_norm, dst_norm_scaled );
/// Drawing a circle around corners
for( int j = 0; j < dst_norm.rows ; j++ )
{ for( int i = 0; i < dst_norm.cols; i++ )
{
if( (int) dst_norm.at<float>(j,i) > thresh )
{
circle( dst_norm_scaled, Point( i, j ), 5, Scalar(0), 2, 8, 0 );
{
circle( dst_norm_scaled, Point( i, j ), 5, Scalar(0), 2, 8, 0 );
}
}
}
/// Showing the result
}
}
/// Showing the result
namedWindow( corners_window, CV_WINDOW_AUTOSIZE );
imshow( corners_window, dst_norm_scaled );
}
@@ -237,11 +237,11 @@ Result
The original image:
.. image:: images/Harris_Detector_Original_Image.jpg
:align: center
:align: center
The detected corners are surrounded by a small black circle
.. image:: images/Harris_Detector_Result.jpg
:align: center
:align: center