opencv学习之路(35)、SURF特征点提取与匹配(三)

一、简介

opencv学习之路(35)、SURF特征点提取与匹配(三)

opencv学习之路(35)、SURF特征点提取与匹配(三)

opencv学习之路(35)、SURF特征点提取与匹配(三)

二、opencv中的SURF算法接口

opencv学习之路(35)、SURF特征点提取与匹配(三)

三、特征点匹配方法

opencv学习之路(35)、SURF特征点提取与匹配(三)

四、代码

1.特征点提取

#include "opencv2/opencv.hpp"
#include <opencv2/nonfree/nonfree.hpp>
#include <vector>
#include<iostream>
using namespace std;
using namespace cv; void main()
{
Mat srcImg1 = imread("E://1.jpg");
Mat srcImg2 = imread("E://2.jpg");
//定义SURF特征检测类对象
SurfFeatureDetector surfDetector();//SIFT有默认值,SURF没有默认值,需要赋初值 hessianThreshold
//定义KeyPoint变量
vector<KeyPoint>keyPoints1;
vector<KeyPoint>keyPoints2;
//特征点检测
surfDetector.detect(srcImg1, keyPoints1);
surfDetector.detect(srcImg2, keyPoints2);
//绘制特征点(关键点)
Mat feature_pic1, feature_pic2;
drawKeypoints(srcImg1, keyPoints1, feature_pic1, Scalar(,,));
//drawKeypoints(srcImg2, keyPoints2, feature_pic2, Scalar::all(-1));
//drawKeypoints(srcImg1, keyPoints1, feature_pic1, Scalar::all(-1), DrawMatchesFlags::DRAW_RICH_KEYPOINTS);
drawKeypoints(srcImg2, keyPoints2, feature_pic2, Scalar::all(-), DrawMatchesFlags::DRAW_RICH_KEYPOINTS);
//显示原图
imshow("src1", srcImg1);
imshow("src2", srcImg2);
//显示结果
imshow("feature1", feature_pic1);
imshow("feature2", feature_pic2); waitKey();
}

opencv学习之路(35)、SURF特征点提取与匹配(三)

2.暴力匹配(尽量避免使用“nth_element前多少个”筛选)

#include "opencv2/opencv.hpp"
#include <opencv2/nonfree/nonfree.hpp>
#include <opencv2/legacy/legacy.hpp>
#include <vector>
#include<iostream>
using namespace std;
using namespace cv; void main()
{
Mat srcImg1 = imread("E://11.jpg");
Mat srcImg2 = imread("E://22.jpg");
//定义SURF特征检测类对象
SurfFeatureDetector surfDetector(); //HessianThreshold //定义KeyPoint变量
vector<KeyPoint>keyPoints1;
vector<KeyPoint>keyPoints2;
//特征点检测
surfDetector.detect(srcImg1, keyPoints1);
surfDetector.detect(srcImg2, keyPoints2);
//绘制特征点(关键点)
Mat feature_pic1, feature_pic2;
drawKeypoints(srcImg1, keyPoints1, feature_pic1, Scalar::all(-));
drawKeypoints(srcImg2, keyPoints2, feature_pic2, Scalar::all(-));
//显示原图
imshow("src1", srcImg1);
imshow("src2", srcImg2);
//显示结果
imshow("feature1", feature_pic1);
imshow("feature2", feature_pic2); //计算特征点描述符 / 特征向量提取
SurfDescriptorExtractor descriptor;
Mat description1;
descriptor.compute(srcImg1, keyPoints1, description1);
Mat description2;
descriptor.compute(srcImg2, keyPoints2, description2);
cout<<description1.cols<<endl;
cout<<description1.rows<<endl; //进行BFMatch暴力匹配
BruteForceMatcher<L2<float>>matcher; //实例化暴力匹配器
vector<DMatch>matches; //定义匹配结果变量
matcher.match(description1, description2, matches); //实现描述符之间的匹配 //计算向量距离的最大值与最小值
double max_dist=, min_dist=;
for(int i=; i<description1.rows; i++)
{
if(matches.at(i).distance > max_dist)
max_dist = matches[i].distance;
if(matches.at(i).distance < min_dist)
min_dist = matches[i].distance;
}
cout<<"min_distance="<<min_dist<<endl;
cout<<"max_distance="<<max_dist<<endl; //匹配结果筛选
vector<DMatch>good_matches;
for(int i=; i<matches.size(); i++)
{
if(matches[i].distance < *min_dist)
good_matches.push_back(matches[i]);
} Mat result;
//drawMatches(srcImg1, keyPoints1, srcImg2, keyPoints2, matches, result, Scalar::all(-1), Scalar::all(-1));
drawMatches(srcImg1, keyPoints1, srcImg2, keyPoints2, good_matches, result, Scalar(, , ), Scalar::all(-));
imshow("Match_Result", result); waitKey();
}

opencv学习之路(35)、SURF特征点提取与匹配(三)

因为surf检测到的角点比较少,所以不适合做小目标匹配。

同样代码,使用sift作对比

opencv学习之路(35)、SURF特征点提取与匹配(三)

3.FlannBasedMatcher匹配

   //BruteForceMatcher<L2<float>>matcher;    //实例化暴力匹配器
FlannBasedMatcher matcher; //实例化FLANN匹配器
vector<DMatch>matches; //定义匹配结果变量
matcher.match(description1, description2, matches); //实现描述符之间的匹配

其余代码相同

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