目标
在本教程中，您将学习如何：
- 使用OpenCV函数cv :: cornerEigenValsAndVecs来查找特征值和特征向量以确定像素是否是拐角。
- 使用OpenCV函数cv :: cornerMinEigenVal查找拐角检测的最小特征值。
- 通过使用上述两个函数，实施我们自己的Harris探测器以及Shi-Tomasi探测器。
理论
程序
#include "opencv2/highgui.hpp"
#include "opencv2/imgproc.hpp"
#include <iostream>
using namespace cv;
using namespace std;
Mat src, src_gray;
Mat myHarris_dst, myHarris_copy, Mc;
Mat myShiTomasi_dst, myShiTomasi_copy;
int myShiTomasi_qualityLevel = 50;
int myHarris_qualityLevel = 50;
int max_qualityLevel = 100;
double myHarris_minVal, myHarris_maxVal;
double myShiTomasi_minVal, myShiTomasi_maxVal;
RNG rng(12345);
const char* myHarris_window = "My Harris corner detector";
const char* myShiTomasi_window = "My Shi Tomasi corner detector";
void myShiTomasi_function( int, void* );
void myHarris_function( int, void* );
int main( int argc, char** argv )
{
CommandLineParser parser( argc, argv, "{@input | lena11.jpg | input image}" );
src = imread( parser.get<String>( "@input" ) );
if ( src.empty() )
{
cout << "Could not open or find the image!\n" << endl;
cout << "Usage: " << argv[0] << " <Input image>" << endl;
return -1;
}
cvtColor( src, src_gray, COLOR_BGR2GRAY );
int blockSize = 3, apertureSize = 3;
//计算特征值和特征向量：输入，输出，邻域大小，孔径大小
cornerEigenValsAndVecs( src_gray, myHarris_dst, blockSize, apertureSize );
/* calculate Mc */
Mc = Mat( src_gray.size(), CV_32FC1 );
for( int i = 0; i < src_gray.rows; i++ )
{
for( int j = 0; j < src_gray.cols; j++ )
{
float lambda_1 = myHarris_dst.at<Vec6f>(i, j)[0];
float lambda_2 = myHarris_dst.at<Vec6f>(i, j)[1];
Mc.at<float>(i, j) = lambda_1*lambda_2 - 0.04f*pow( ( lambda_1 + lambda_2 ), 2 );
}
}
//寻找全局最小值和全局最大值
minMaxLoc( Mc, &myHarris_minVal, &myHarris_maxVal );
/* Create Window and Trackbar */
namedWindow( myHarris_window );
createTrackbar( "Quality Level:", myHarris_window, &myHarris_qualityLevel, max_qualityLevel, myHarris_function );
myHarris_function( 0, 0 );
//计算最小的特征值。输入，输出，邻域大小，孔径参数
cornerMinEigenVal( src_gray, myShiTomasi_dst, blockSize, apertureSize );
//全局最大最小特征值
minMaxLoc( myShiTomasi_dst, &myShiTomasi_minVal, &myShiTomasi_maxVal );
/* Create Window and Trackbar */
namedWindow( myShiTomasi_window );
createTrackbar( "Quality Level:", myShiTomasi_window, &myShiTomasi_qualityLevel, max_qualityLevel, myShiTomasi_function );
myShiTomasi_function( 0, 0 );
waitKey();
return 0;
}
void myShiTomasi_function( int, void* )
{
myShiTomasi_copy = src.clone();
myShiTomasi_qualityLevel = MAX(myShiTomasi_qualityLevel, 1);
for( int i = 0; i < src_gray.rows; i++ )
{
for( int j = 0; j < src_gray.cols; j++ )
{
if( myShiTomasi_dst.at<float>(i,j) > myShiTomasi_minVal + ( myShiTomasi_maxVal - myShiTomasi_minVal )*myShiTomasi_qualityLevel/max_qualityLevel )
{
circle( myShiTomasi_copy, Point(j,i), 4, Scalar( rng.uniform(0,256), rng.uniform(0,256), rng.uniform(0,256) ), FILLED );
}
}
}
imshow( myShiTomasi_window, myShiTomasi_copy );
}
void myHarris_function( int, void* )
{
myHarris_copy = src.clone();
myHarris_qualityLevel = MAX(myHarris_qualityLevel, 1);
for( int i = 0; i < src_gray.rows; i++ )
{
for( int j = 0; j < src_gray.cols; j++ )
{
if( Mc.at<float>(i,j) > myHarris_minVal + ( myHarris_maxVal - myHarris_minVal )*myHarris_qualityLevel/max_qualityLevel )
{
circle( myHarris_copy, Point(j,i), 4, Scalar( rng.uniform(0,256), rng.uniform(0,256), rng.uniform(0,256) ), FILLED );
}
}
}
imshow( myHarris_window, myHarris_copy );
}