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来确定像素是否是一个角。
• 使用OpenCV函数cv :: cornerMinEigenVal来找到拐角检测的最小特征值。
• 通过使用上述两个功能来实现我们自己的Harris检测器版本以及Shi-Tomasi检测器。

Code

本教程代码如下所示。您也可以从这里下载

#include "opencv2/imgcodecs.hpp"
#include "opencv2/highgui.hpp"
#include "opencv2/imgproc.hpp"
#include <iostream>
using namespace cv;
using namespace std;
Mat src, src_gray;
Mat myHarris_dst; Mat myHarris_copy; Mat Mc;
Mat myShiTomasi_dst; Mat myShiTomasi_copy;
int myShiTomasi_qualityLevel = 50;
int myHarris_qualityLevel = 50;
int max_qualityLevel = 100;
double myHarris_minVal; double myHarris_maxVal;
double myShiTomasi_minVal; double 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, char** argv )
{
  src = imread( argv[1], IMREAD_COLOR );
  cvtColor( src, src_gray, COLOR_BGR2GRAY );
  int blockSize = 3; int apertureSize = 3;
  myHarris_dst = Mat::zeros( src_gray.size(), CV_32FC(6) );
  Mc = Mat::zeros( src_gray.size(), CV_32FC1 );
  cornerEigenValsAndVecs( src_gray, myHarris_dst, blockSize, apertureSize, BORDER_DEFAULT );
  /* calculate Mc */
  for( int j = 0; j < src_gray.rows; j++ )
     { for( int i = 0; i < src_gray.cols; i++ )
          {
            float lambda_1 = myHarris_dst.at<Vec6f>(j, i)[0];
            float lambda_2 = myHarris_dst.at<Vec6f>(j, i)[1];
            Mc.at<float>(j,i) = lambda_1*lambda_2 - 0.04f*pow( ( lambda_1 + lambda_2 ), 2 );
          }
     }
  minMaxLoc( Mc, &myHarris_minVal, &myHarris_maxVal, 0, 0, Mat() );
  /* Create Window and Trackbar */
  namedWindow( myHarris_window, WINDOW_AUTOSIZE );
  createTrackbar( " Quality Level:", myHarris_window, &myHarris_qualityLevel, max_qualityLevel, myHarris_function );
  myHarris_function( 0, 0 );
  myShiTomasi_dst = Mat::zeros( src_gray.size(), CV_32FC1 );
  cornerMinEigenVal( src_gray, myShiTomasi_dst, blockSize, apertureSize, BORDER_DEFAULT );
  minMaxLoc( myShiTomasi_dst, &myShiTomasi_minVal, &myShiTomasi_maxVal, 0, 0, Mat() );
  /* Create Window and Trackbar */
  namedWindow( myShiTomasi_window, WINDOW_AUTOSIZE );
  createTrackbar( " Quality Level:", myShiTomasi_window, &myShiTomasi_qualityLevel, max_qualityLevel, myShiTomasi_function );
  myShiTomasi_function( 0, 0 );
  waitKey(0);
  return(0);
}
void myShiTomasi_function( int, void* )
{
  myShiTomasi_copy = src.clone();
  if( myShiTomasi_qualityLevel < 1 ) { myShiTomasi_qualityLevel = 1; }
  for( int j = 0; j < src_gray.rows; j++ )
     { for( int i = 0; i < src_gray.cols; i++ )
          {
            if( myShiTomasi_dst.at<float>(j,i) > myShiTomasi_minVal + ( myShiTomasi_maxVal - myShiTomasi_minVal )*myShiTomasi_qualityLevel/max_qualityLevel )
              { circle( myShiTomasi_copy, Point(i,j), 4, Scalar( rng.uniform(0,255), rng.uniform(0,255), rng.uniform(0,255) ), -1, 8, 0 ); }
          }
     }
  imshow( myShiTomasi_window, myShiTomasi_copy );
}
void myHarris_function( int, void* )
{
  myHarris_copy = src.clone();
  if( myHarris_qualityLevel < 1 ) { myHarris_qualityLevel = 1; }
  for( int j = 0; j < src_gray.rows; j++ )
     { for( int i = 0; i < src_gray.cols; i++ )
          {
            if( Mc.at<float>(j,i) > myHarris_minVal + ( myHarris_maxVal - myHarris_minVal )*myHarris_qualityLevel/max_qualityLevel )
              { circle( myHarris_copy, Point(i,j), 4, Scalar( rng.uniform(0,255), rng.uniform(0,255), rng.uniform(0,255) ), -1, 8, 0 ); }
          }
     }
  imshow( myHarris_window, myHarris_copy );
}

结果