cvGoodFeaturesToTrack()与goodFeaturesToTrack()进行harris角点检测

1、角点检测函数和参数说明

cvGoodFeaturesToTrack()函数主要是处理IplImage数据格式的图像，而goodFeaturesToTrack()函数主要是处理Mat数据格式的图像。参数quality_level ：特征值最大值最小值乘法因子；参数minDistance：角点之间最小距离；均对图像中harris角点检测的个数有影响。

2、具体代码如下

（1）harris角点检测

#include "opencv2/core/core.hpp"
#include "opencv2/flann/miniflann.hpp"
#include "opencv2/imgproc/imgproc.hpp"
#include "opencv2/video/video.hpp"
#include "opencv2/features2d/features2d.hpp"
#include "opencv2/objdetect/objdetect.hpp"
#include "opencv2/calib3d/calib3d.hpp"
#include "opencv2/ml/ml.hpp"
#include "opencv2/highgui/highgui.hpp"
#include "opencv2/contrib/contrib.hpp"

#include <iostream>
#include <stdio.h>
using namespace std;
using namespace cv;

int main()
{
//FILE* fp=fopen("E:\\实验室项目\\ImagePro\\GoodFeatures\\result.txt","w+");
//char* filename="E:\\实验室项目\\ImagePro\\GoodFeatures\\j10.jpg";
IplImage* img=cvLoadImage("F22.jpg",CV_LOAD_IMAGE_COLOR);
if (!img)
{
cout<<"读取图像失败！"<<endl;
}
IplImage* img_copy=cvCloneImage(img);
IplImage* img_gray=cvCreateImage(cvGetSize(img),IPL_DEPTH_8U,1);
IplImage* eig_img=cvCreateImage(cvGetSize(img),IPL_DEPTH_32F,1);
IplImage* temp_img=cvCloneImage(eig_img);

cvCvtColor(img,img_gray,CV_BGR2GRAY);
const int MAX_CORNERS=1000;//定义角点个数最大值
CvPoint2D32f* corners=new CvPoint2D32f[MAX_CORNERS];//分配保存角点的空间
int corner_count=MAX_CORNERS;
double quality_level=0.01;//or 0.01
double min_distance=10;
cvGoodFeaturesToTrack(img_gray,eig_img,temp_img,corners,&corner_count,quality_level,min_distance);

//画角点
for(int i=0;i<corner_count;i++)
{
cvCircle(img_copy,cvPoint((int)corners[i].x,(int)corners[i].y),1,CV_RGB(255,0,0),2,8);
//fprintf(fp,"\t%f,%f\n",corners[i].x,corners[i].y);
}
cout<<"检测到角点个数为："<<corner_count;
cvNamedWindow("角点检测",CV_WINDOW_AUTOSIZE);
cvShowImage("角点检测",img_copy);
cvWaitKey(0);
cvReleaseImage(&img);
cvReleaseImage(&img_copy);
cvReleaseImage(&img_gray);
cvReleaseImage(&eig_img);
cvReleaseImage(&temp_img);
cvDestroyWindow("角点检测");
return 0;
}

(2)shi-Tomas角点检测 #include "opencv2/core/core.hpp" #include "opencv2/flann/miniflann.hpp" #include "opencv2/imgproc/imgproc.hpp" #include "opencv2/video/video.hpp" #include "opencv2/features2d/features2d.hpp" #include "opencv2/objdetect/objdetect.hpp" #include "opencv2/calib3d/calib3d.hpp" #include "opencv2/ml/ml.hpp" #include "opencv2/highgui/highgui.hpp" #include "opencv2/contrib/contrib.hpp" #include <iostream> #include <stdio.h> #include <stdlib.h> //#include <vector> using namespace std; using namespace cv; Mat src,src_gray; int maxCorners=1000; int maxTrackbar=100; RNG rng(12345); char* source_window="Image"; void goodFeaturesToTrack_demo(int,void*); int main() { src=imread("F22.jpg",1); cvtColor(src,src_gray,CV_BGR2GRAY); namedWindow(source_window,CV_WINDOW_AUTOSIZE); createTrackbar("角点个数：",source_window,&maxCorners,maxTrackbar,goodFeaturesToTrack_demo); imshow(source_window,src); goodFeaturesToTrack_demo(0,0); waitKey(0); return 0; } void goodFeaturesToTrack_demo(int,void*) { if (maxCorners<1) { maxCorners=1; } //Shi-Tomasi 角点算法参数定义 vector<Point2f> corners; double qualityLevel=0.01;//最大最小特征值乘法因子 double minDistance=10;//角点之间最小距离 int blockSize=3; bool useHarrisDetector=false; double k=0.04; Mat copy; copy=src.clone(); goodFeaturesToTrack(src_gray,corners,maxCorners,qualityLevel,minDistance,Mat(),blockSize,useHarrisDetector,k); cout<<"检测到角点数:"<<corners.size()<<endl; int r=1; 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)),2,8,0); } namedWindow(source_window,CV_WINDOW_AUTOSIZE); imshow(source_window,copy); } 测试结果：

题目: Shi-Tomasi角点检测子