Doxygen tutorials: basic structure
This commit is contained in:
Maksim Shabunin
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Detecting corners location in subpixeles {#tutorial_corner_subpixeles}
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========================================
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Goal
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----
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In this tutorial you will learn how to:
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- Use the OpenCV function @ref cv::cornerSubPix to find more exact corner positions (more exact
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than integer pixels).
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Theory
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------
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Code
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----
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This tutorial code's is shown lines below. You can also download it from
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[here](https://github.com/Itseez/opencv/tree/master/samples/cpp/tutorial_code/TrackingMotion/cornerSubPix_Demo.cpp)
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@code{.cpp}
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#include "opencv2/highgui.hpp"
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#include "opencv2/imgproc.hpp"
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#include <iostream>
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#include <stdio.h>
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#include <stdlib.h>
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using namespace cv;
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using namespace std;
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/// Global variables
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Mat src, src_gray;
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int maxCorners = 10;
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int maxTrackbar = 25;
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RNG rng(12345);
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char* source_window = "Image";
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/// Function header
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void goodFeaturesToTrack_Demo( int, void* );
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/* @function main */
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int main( int argc, char** argv )
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{
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/// Load source image and convert it to gray
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src = imread( argv[1], 1 );
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cvtColor( src, src_gray, COLOR_BGR2GRAY );
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/// Create Window
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namedWindow( source_window, WINDOW_AUTOSIZE );
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/// Create Trackbar to set the number of corners
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createTrackbar( "Max corners:", source_window, &maxCorners, maxTrackbar, goodFeaturesToTrack_Demo);
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imshow( source_window, src );
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goodFeaturesToTrack_Demo( 0, 0 );
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waitKey(0);
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return(0);
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}
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/*
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* @function goodFeaturesToTrack_Demo.cpp
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* @brief Apply Shi-Tomasi corner detector
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*/
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void goodFeaturesToTrack_Demo( int, void* )
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{
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if( maxCorners < 1 ) { maxCorners = 1; }
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/// Parameters for Shi-Tomasi algorithm
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vector<Point2f> corners;
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double qualityLevel = 0.01;
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double minDistance = 10;
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int blockSize = 3;
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bool useHarrisDetector = false;
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double k = 0.04;
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/// Copy the source image
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Mat copy;
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copy = src.clone();
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/// Apply corner detection
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goodFeaturesToTrack( src_gray,
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corners,
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maxCorners,
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qualityLevel,
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minDistance,
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Mat(),
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blockSize,
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useHarrisDetector,
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k );
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/// Draw corners detected
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cout<<"** Number of corners detected: "<<corners.size()<<endl;
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int r = 4;
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for( int i = 0; i < corners.size(); i++ )
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{ circle( copy, corners[i], r, Scalar(rng.uniform(0,255), rng.uniform(0,255),
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rng.uniform(0,255)), -1, 8, 0 ); }
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/// Show what you got
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namedWindow( source_window, WINDOW_AUTOSIZE );
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imshow( source_window, copy );
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/// Set the neeed parameters to find the refined corners
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Size winSize = Size( 5, 5 );
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Size zeroZone = Size( -1, -1 );
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TermCriteria criteria = TermCriteria( TermCriteria::EPS + TermCriteria::MAX_ITER, 40, 0.001 );
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/// Calculate the refined corner locations
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cornerSubPix( src_gray, corners, winSize, zeroZone, criteria );
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/// Write them down
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for( int i = 0; i < corners.size(); i++ )
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{ cout<<" -- Refined Corner ["<<i<<"] ("<<corners[i].x<<","<<corners[i].y<<")"<<endl; }
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}
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@endcode
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Explanation
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-----------
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Result
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------
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Here is the result:
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@@ -0,0 +1,36 @@
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Creating yor own corner detector {#tutorial_generic_corner_detector}
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================================
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Goal
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----
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In this tutorial you will learn how to:
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- Use the OpenCV function @ref cv::cornerEigenValsAndVecs to find the eigenvalues and eigenvectors
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to determine if a pixel is a corner.
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- Use the OpenCV function @ref cv::cornerMinEigenVal to find the minimum eigenvalues for corner
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detection.
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- To implement our own version of the Harris detector as well as the Shi-Tomasi detector, by using
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the two functions above.
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Theory
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------
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Code
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----
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This tutorial code's is shown lines below. You can also download it from
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[here](https://github.com/Itseez/opencv/tree/master/samples/cpp/tutorial_code/TrackingMotion/cornerDetector_Demo.cpp)
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@includelineno cpp/tutorial_code/TrackingMotion/cornerDetector_Demo.cpp
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Explanation
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-----------
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Result
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------
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Shi-Tomasi corner detector {#tutorial_good_features_to_track}
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==========================
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Goal
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----
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In this tutorial you will learn how to:
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- Use the function @ref cv::goodFeaturesToTrack to detect corners using the Shi-Tomasi method.
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Theory
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------
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Code
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----
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This tutorial code's is shown lines below. You can also download it from
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[here](https://github.com/Itseez/opencv/tree/master/samples/cpp/tutorial_code/TrackingMotion/goodFeaturesToTrack_Demo.cpp)
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@code{.cpp}
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#include "opencv2/highgui.hpp"
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#include "opencv2/imgproc.hpp"
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#include <iostream>
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#include <stdio.h>
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#include <stdlib.h>
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using namespace cv;
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using namespace std;
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/// Global variables
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Mat src, src_gray;
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int maxCorners = 23;
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int maxTrackbar = 100;
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RNG rng(12345);
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char* source_window = "Image";
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/// Function header
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void goodFeaturesToTrack_Demo( int, void* );
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/*
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* @function main
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*/
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int main( int argc, char** argv )
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{
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/// Load source image and convert it to gray
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src = imread( argv[1], 1 );
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cvtColor( src, src_gray, COLOR_BGR2GRAY );
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/// Create Window
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namedWindow( source_window, WINDOW_AUTOSIZE );
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/// Create Trackbar to set the number of corners
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createTrackbar( "Max corners:", source_window, &maxCorners, maxTrackbar, goodFeaturesToTrack_Demo );
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imshow( source_window, src );
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goodFeaturesToTrack_Demo( 0, 0 );
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waitKey(0);
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return(0);
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}
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/*
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* @function goodFeaturesToTrack_Demo.cpp
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* @brief Apply Shi-Tomasi corner detector
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*/
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void goodFeaturesToTrack_Demo( int, void* )
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{
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if( maxCorners < 1 ) { maxCorners = 1; }
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/// Parameters for Shi-Tomasi algorithm
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vector<Point2f> corners;
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double qualityLevel = 0.01;
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double minDistance = 10;
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int blockSize = 3;
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bool useHarrisDetector = false;
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double k = 0.04;
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/// Copy the source image
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Mat copy;
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copy = src.clone();
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/// Apply corner detection
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goodFeaturesToTrack( src_gray,
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corners,
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maxCorners,
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qualityLevel,
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minDistance,
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Mat(),
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blockSize,
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useHarrisDetector,
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k );
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/// Draw corners detected
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cout<<"** Number of corners detected: "<<corners.size()<<endl;
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int r = 4;
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for( int i = 0; i < corners.size(); i++ )
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{ circle( copy, corners[i], r, Scalar(rng.uniform(0,255), rng.uniform(0,255),
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rng.uniform(0,255)), -1, 8, 0 ); }
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/// Show what you got
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namedWindow( source_window, WINDOW_AUTOSIZE );
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imshow( source_window, copy );
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}
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@endcode
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Explanation
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-----------
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Result
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------
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Harris corner detector {#tutorial_harris_detector}
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======================
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Goal
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----
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In this tutorial you will learn:
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- What features are and why they are important
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- Use the function @ref cv::cornerHarris to detect corners using the Harris-Stephens method.
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Theory
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------
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### What is a feature?
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- In computer vision, usually we need to find matching points between different frames of an
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environment. Why? If we know how two images relate to each other, we can use *both* images to
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extract information of them.
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- When we say **matching points** we are referring, in a general sense, to *characteristics* in
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the scene that we can recognize easily. We call these characteristics **features**.
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- **So, what characteristics should a feature have?**
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- It must be *uniquely recognizable*
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### Types of Image Features
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To mention a few:
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- Edges
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- **Corners** (also known as interest points)
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- Blobs (also known as regions of interest )
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In this tutorial we will study the *corner* features, specifically.
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### Why is a corner so special?
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- Because, since it is the intersection of two edges, it represents a point in which the
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directions of these two edges *change*. Hence, the gradient of the image (in both directions)
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have a high variation, which can be used to detect it.
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### How does it work?
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- Let's look for corners. Since corners represents a variation in the gradient in the image, we
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will look for this "variation".
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- Consider a grayscale image \f$I\f$. We are going to sweep a window \f$w(x,y)\f$ (with displacements \f$u\f$
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in the x direction and \f$v\f$ in the right direction) \f$I\f$ and will calculate the variation of
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intensity.
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\f[E(u,v) = \sum _{x,y} w(x,y)[ I(x+u,y+v) - I(x,y)]^{2}\f]
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where:
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- \f$w(x,y)\f$ is the window at position \f$(x,y)\f$
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- \f$I(x,y)\f$ is the intensity at \f$(x,y)\f$
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- \f$I(x+u,y+v)\f$ is the intensity at the moved window \f$(x+u,y+v)\f$
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- Since we are looking for windows with corners, we are looking for windows with a large variation
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in intensity. Hence, we have to maximize the equation above, specifically the term:
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\f[\sum _{x,y}[ I(x+u,y+v) - I(x,y)]^{2}\f]
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- Using *Taylor expansion*:
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\f[E(u,v) \approx \sum _{x,y}[ I(x,y) + u I_{x} + vI_{y} - I(x,y)]^{2}\f]
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- Expanding the equation and cancelling properly:
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\f[E(u,v) \approx \sum _{x,y} u^{2}I_{x}^{2} + 2uvI_{x}I_{y} + v^{2}I_{y}^{2}\f]
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- Which can be expressed in a matrix form as:
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\f[E(u,v) \approx \begin{bmatrix}
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u & v
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\end{bmatrix}
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\left (
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\displaystyle \sum_{x,y}
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w(x,y)
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\begin{bmatrix}
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I_x^{2} & I_{x}I_{y} \\
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I_xI_{y} & I_{y}^{2}
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\end{bmatrix}
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\right )
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\begin{bmatrix}
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u \\
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v
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\end{bmatrix}\f]
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- Let's denote:
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\f[M = \displaystyle \sum_{x,y}
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w(x,y)
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\begin{bmatrix}
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I_x^{2} & I_{x}I_{y} \\
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I_xI_{y} & I_{y}^{2}
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\end{bmatrix}\f]
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- So, our equation now is:
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\f[E(u,v) \approx \begin{bmatrix}
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u & v
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\end{bmatrix}
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M
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\begin{bmatrix}
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u \\
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v
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\end{bmatrix}\f]
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- A score is calculated for each window, to determine if it can possibly contain a corner:
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\f[R = det(M) - k(trace(M))^{2}\f]
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where:
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- det(M) = \f$\lambda_{1}\lambda_{2}\f$
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- trace(M) = \f$\lambda_{1}+\lambda_{2}\f$
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a window with a score \f$R\f$ greater than a certain value is considered a "corner"
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Code
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----
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This tutorial code's is shown lines below. You can also download it from
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[here](https://github.com/Itseez/opencv/tree/master/samples/cpp/tutorial_code/TrackingMotion/cornerHarris_Demo.cpp)
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@code{.cpp}
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#include "opencv2/highgui.hpp"
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#include "opencv2/imgproc.hpp"
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#include <iostream>
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#include <stdio.h>
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#include <stdlib.h>
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using namespace cv;
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using namespace std;
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/// Global variables
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Mat src, src_gray;
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int thresh = 200;
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int max_thresh = 255;
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char* source_window = "Source image";
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char* corners_window = "Corners detected";
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/// Function header
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void cornerHarris_demo( int, void* );
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/* @function main */
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int main( int argc, char** argv )
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{
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/// Load source image and convert it to gray
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src = imread( argv[1], 1 );
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cvtColor( src, src_gray, COLOR_BGR2GRAY );
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/// Create a window and a trackbar
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namedWindow( source_window, WINDOW_AUTOSIZE );
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createTrackbar( "Threshold: ", source_window, &thresh, max_thresh, cornerHarris_demo );
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imshow( source_window, src );
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cornerHarris_demo( 0, 0 );
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waitKey(0);
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return(0);
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}
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/* @function cornerHarris_demo */
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void cornerHarris_demo( int, void* )
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{
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Mat dst, dst_norm, dst_norm_scaled;
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dst = Mat::zeros( src.size(), CV_32FC1 );
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/// Detector parameters
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int blockSize = 2;
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int apertureSize = 3;
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double k = 0.04;
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/// Detecting corners
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cornerHarris( src_gray, dst, blockSize, apertureSize, k, BORDER_DEFAULT );
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/// Normalizing
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normalize( dst, dst_norm, 0, 255, NORM_MINMAX, CV_32FC1, Mat() );
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convertScaleAbs( dst_norm, dst_norm_scaled );
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/// Drawing a circle around corners
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for( int j = 0; j < dst_norm.rows ; j++ )
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{ for( int i = 0; i < dst_norm.cols; i++ )
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{
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if( (int) dst_norm.at<float>(j,i) > thresh )
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{
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circle( dst_norm_scaled, Point( i, j ), 5, Scalar(0), 2, 8, 0 );
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}
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}
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}
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/// Showing the result
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namedWindow( corners_window, WINDOW_AUTOSIZE );
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imshow( corners_window, dst_norm_scaled );
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}
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@endcode
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Explanation
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-----------
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Result
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------
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The original image:
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The detected corners are surrounded by a small black circle
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