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  • OpenCV定制化创建角点检测子

    定制化创建角点检测子

    目标

    在这个教程中我们将涉及:

    • 使用 OpenCV 函数 cornerEigenValsAndVecs 来计算像素对应的本征值和本征向量来确定其是否是角点。
    • 使用OpenCV 函数 cornerMinEigenVal 通过最小化本征值来进行角点检测。
    • 用上述两个函数实现一个定制化的Harris detector,类似Shi-Tomasi检测子。

    解释

    代码

    这个教程的代码如下所示。源代码还可以从 这个链接下载得到

    #include "opencv2/highgui/highgui.hpp"
    #include "opencv2/imgproc/imgproc.hpp"
    #include <iostream>
    #include <stdio.h>
    #include <stdlib.h>
    
    using namespace cv;
    using namespace std;
    
    /// Global variables
    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);
    
    char* myHarris_window = "My Harris corner detector";
    char* myShiTomasi_window = "My Shi Tomasi corner detector";
    
    /// Function headers
    void myShiTomasi_function( int, void* );
    void myHarris_function( int, void* );
    
    /** @function main */
    int main( int argc, char** argv )
    {
      /// Load source image and convert it to gray
      src = imread( argv[1], 1 );
      cvtColor( src, src_gray, CV_BGR2GRAY );
    
      /// Set some parameters
      int blockSize = 3; int apertureSize = 3;
    
      /// My Harris matrix -- Using cornerEigenValsAndVecs
      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<float>( j, i, 0 );
                float lambda_2 = myHarris_dst.at<float>( j, i, 1 );
                Mc.at<float>(j,i) = lambda_1*lambda_2 - 0.04*pow( ( lambda_1 + lambda_2 ), 2 );
              }
         }
    
      minMaxLoc( Mc, &myHarris_minVal, &myHarris_maxVal, 0, 0, Mat() );
    
      /* Create Window and Trackbar */
      namedWindow( myHarris_window, CV_WINDOW_AUTOSIZE );
      createTrackbar( " Quality Level:", myHarris_window, &myHarris_qualityLevel, max_qualityLevel,
                        myHarris_function );
      myHarris_function( 0, 0 );
    
      /// My Shi-Tomasi -- Using cornerMinEigenVal
      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, CV_WINDOW_AUTOSIZE );
      createTrackbar( " Quality Level:", myShiTomasi_window, &myShiTomasi_qualityLevel, max_qualityLevel,
                         myShiTomasi_function );
      myShiTomasi_function( 0, 0 );
    
      waitKey(0);
      return(0);
    }
    
    /** @function myShiTomasi_function  */
    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 );
    }
    
    /** @function myHarris_function */
    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 );
    }
    

    解释

    结果

    ../../../../../_images/My_Harris_corner_detector_Result.jpg../../../../../_images/My_Shi_Tomasi_corner_detector_Result.jpg

    翻译者

    Shuai Zheng, <kylezheng04@gmail.com>, http://www.cbsr.ia.ac.cn/users/szheng/

    from: http://www.opencv.org.cn/opencvdoc/2.3.2/html/doc/tutorials/features2d/trackingmotion/generic_corner_detector/generic_corner_detector.html#generic-corner-detector

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  • 原文地址:https://www.cnblogs.com/GarfieldEr007/p/5292247.html
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