surf与sift特征点检测代码实现

本文转自： http://blog.csdn.net/holybin/article/details/17247657

概述

       在opencv的features2d中实现了SIFT和SURF算法，可以用于图像特征点的自动检测。具体实现是采用SurfFeatureDetector/SiftFeatureDetector类的detect函数检测SURF/SIFT特征的关键点，并保存在vector容器中，最后使用drawKeypoints函数绘制出特征点。

       实验所用环境是opencv2.4.0+vs2008+win7，测试图片是：

SURF特征点检测

实验代码如下。这里需要注意SurfFeatureDetector是包含在opencv2/nonfree/features2d.hpp中，所以应该include这个头文件，并且在“项目属性->链接器->输入->附加依赖项”中加入库文件：opencv_nonfree240d.lib。

/**
* @SURF特征点检测并绘制特征点
* @author holybin
*/

#include <stdio.h>
#include <iostream>
#include "opencv2/core/core.hpp"
#include "opencv2/highgui/highgui.hpp"
//#include "opencv2/features2d/features2d.hpp"
#include "opencv2/nonfree/features2d.hpp"   //SurfFeatureDetector实际在该头文件中
using namespace cv;
using namespace std;

int main( int argc, char** argv )
{
    Mat src = imread( "D:\\opencv_pic\\cat3d120.jpg", 0 );
    //Mat src = imread( "D:\\opencv_pic\\cat0.jpg", 0 );

    if( !src.data )
    {
        cout<< " --(!) Error reading images "<<endl;
        return -1;
    }

    //1--初始化SURF检测算子
    int minHessian = 400;
    SurfFeatureDetector detector( minHessian );

    //2--使用SURF算子检测特征点
    vector<KeyPoint> keypoints;
    detector.detect( src, keypoints );

    //3--绘制特征点
    Mat keypointImg;
    drawKeypoints( src, keypoints, keypointImg, Scalar::all(-1), DrawMatchesFlags::DEFAULT );
    imshow("SURF keypoints", keypointImg );
    cout<<"keypoint number: "<<keypoints.size()<<endl;

    waitKey(0);
    return 0;
}

/**
* @SURF特征点检测并绘制特征点
* @author holybin
*/

#include <stdio.h>
#include <iostream>
#include "opencv2/core/core.hpp"
#include "opencv2/highgui/highgui.hpp"
//#include "opencv2/features2d/features2d.hpp"
#include "opencv2/nonfree/features2d.hpp"	//SurfFeatureDetector实际在该头文件中
using namespace cv;
using namespace std;

int main( int argc, char** argv )
{
	Mat src = imread( "D:\\opencv_pic\\cat3d120.jpg", 0 );
	//Mat src = imread( "D:\\opencv_pic\\cat0.jpg", 0 );

	if( !src.data )
	{
		cout<< " --(!) Error reading images "<<endl;
		return -1;
	}

	//1--初始化SURF检测算子
	int minHessian = 400;
	SurfFeatureDetector detector( minHessian );

	//2--使用SURF算子检测特征点
	vector<KeyPoint> keypoints;
	detector.detect( src, keypoints );

	//3--绘制特征点
	Mat keypointImg;
	drawKeypoints( src, keypoints, keypointImg, Scalar::all(-1), DrawMatchesFlags::DEFAULT );
	imshow("SURF keypoints", keypointImg );
	cout<<"keypoint number: "<<keypoints.size()<<endl;

	waitKey(0);
	return 0;
}

检测结果：

SIFT特征点检测

同样的，使用SIFT特征描述子进行特征点检测的过程类似，只不过换成了SiftFeatureDetector类，实验代码如下：

/**
* @SIFT特征点检测并绘制特征点
* @author holybin
*/

#include <stdio.h>
#include <iostream>
#include "opencv2/core/core.hpp"
#include "opencv2/highgui/highgui.hpp"
//#include "opencv2/features2d/features2d.hpp"
#include "opencv2/nonfree/features2d.hpp"   //SiftFeatureDetector实际在该头文件中
using namespace cv;
using namespace std;

int main( int argc, char** argv )
{
    Mat src = imread( "D:\\opencv_pic\\cat3d120.jpg", 0 );
    //Mat src = imread( "D:\\opencv_pic\\cat0.jpg", 0 );

    if( !src.data )
    {
        cout<< " --(!) Error reading images "<<endl;
        return -1;
    }

    //1--初始化SIFT检测算子
    //int minHessian = 400;
    SiftFeatureDetector detector;//( minHessian );

    //2--使用SIFT算子检测特征点
    vector<KeyPoint> keypoints;
    detector.detect( src, keypoints );

    //3--绘制特征点
    Mat keypointImg;
    drawKeypoints( src, keypoints, keypointImg, Scalar::all(-1), DrawMatchesFlags::DEFAULT );
    imshow("SIFT keypoints", keypointImg );
    cout<<"keypoint number: "<<keypoints.size()<<endl;

    waitKey(0);
    return 0;
}

/**
* @SIFT特征点检测并绘制特征点
* @author holybin
*/

#include <stdio.h>
#include <iostream>
#include "opencv2/core/core.hpp"
#include "opencv2/highgui/highgui.hpp"
//#include "opencv2/features2d/features2d.hpp"
#include "opencv2/nonfree/features2d.hpp"	//SiftFeatureDetector实际在该头文件中
using namespace cv;
using namespace std;

int main( int argc, char** argv )
{
	Mat src = imread( "D:\\opencv_pic\\cat3d120.jpg", 0 );
	//Mat src = imread( "D:\\opencv_pic\\cat0.jpg", 0 );

	if( !src.data )
	{
		cout<< " --(!) Error reading images "<<endl;
		return -1;
	}

	//1--初始化SIFT检测算子
	//int minHessian = 400;
	SiftFeatureDetector detector;//( minHessian );

	//2--使用SIFT算子检测特征点
	vector<KeyPoint> keypoints;
	detector.detect( src, keypoints );

	//3--绘制特征点
	Mat keypointImg;
	drawKeypoints( src, keypoints, keypointImg, Scalar::all(-1), DrawMatchesFlags::DEFAULT );
	imshow("SIFT keypoints", keypointImg );
	cout<<"keypoint number: "<<keypoints.size()<<endl;

	waitKey(0);
	return 0;
}

检测结果：

从检测结果可以看出，SURF算子检测到的特征点远远多于SIFT算子，至于检测的精确度如何，后面试试利用SIFT和SURF算子进行特征点匹配来区分。