亚像素级的角点检测

目标

在本教程中我们将涉及以下内容:

使用OpenCV函数 cornerSubPix 寻找更精确的角点位置 (不是整数类型的位置，而是更精确的浮点类型位置).

理论

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;

int maxCorners = 10;

int maxTrackbar = 25;

RNG rng(12345);

char* source_window = "Image";

/// Function header

void goodFeaturesToTrack_Demo( 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 );

  /// Create Window

  namedWindow( source_window, CV_WINDOW_AUTOSIZE );

  /// Create Trackbar to set the number of corners

  createTrackbar( "Max  corners:", source_window, &maxCorners, maxTrackbar, goodFeaturesToTrack_Demo);

  imshow( source_window, src );

  goodFeaturesToTrack_Demo( 0, 0 );

  waitKey(0);

  return(0);

}

/**

 * @function goodFeaturesToTrack_Demo.cpp

 * @brief Apply Shi-Tomasi corner detector

 */

void goodFeaturesToTrack_Demo( int, void* )

{

  if( maxCorners < 1 ) { maxCorners = 1; }

  /// Parameters for Shi-Tomasi algorithm

  vector<Point2f> corners;

  double qualityLevel = 0.01;

  double minDistance = 10;

  int blockSize = 3;

  bool useHarrisDetector = false;

  double k = 0.04;

  /// Copy the source image

  Mat copy;

  copy = src.clone();

  /// Apply corner detection

  goodFeaturesToTrack( src_gray,

                       corners,

                       maxCorners,

                       qualityLevel,

                       minDistance,

                       Mat(),

                       blockSize,

                       useHarrisDetector,

                       k );

  /// Draw corners detected

  cout<<"** Number of corners detected: "<<corners.size()<<endl;

  int r = 4;

  for( int i = 0; i < corners.size(); i++ )

     { circle( copy, corners[i], r, Scalar(rng.uniform(0,255), rng.uniform(0,255),

                                                 rng.uniform(0,255)), -1, 8, 0 ); }

  /// Show what you got

  namedWindow( source_window, CV_WINDOW_AUTOSIZE );

  imshow( source_window, copy );

  /// Set the neeed parameters to find the refined corners

  Size winSize = Size( 5, 5 );

  Size zeroZone = Size( -1, -1 );

  TermCriteria criteria = TermCriteria( CV_TERMCRIT_EPS + CV_TERMCRIT_ITER, 40, 0.001 );

  /// Calculate the refined corner locations

  cornerSubPix( src_gray, corners, winSize, zeroZone, criteria );

  /// Write them down

  for( int i = 0; i < corners.size(); i++ )

     { cout<<" -- Refined Corner ["<<i<<"]  ("<<corners[i].x<<","<<corners[i].y<<")"<<endl; }

}

解释

结果

OpenCV亚像素级的角点检测-LMLPHP

亚像素级的角点检测结果:

OpenCV亚像素级的角点检测-LMLPHP

翻译者

Shuai Zheng, <[email protected]>, http://www.cbsr.ia.ac.cn/users/szheng/

from:　http://www.opencv.org.cn/opencvdoc/2.3.2/html/doc/tutorials/features2d/trackingmotion/corner_subpixeles/corner_subpixeles.html#corner-subpixeles