opencv中sift特征提取的步骤
- 使用SiftFeatureDetector的detect方法检测特征存入一个向量里,并使用drawKeypoints在图中标识出来
- SiftDescriptorExtractor 的compute方法提取特征描述符,特征描述符是一个矩阵
- 使用匹配器matcher对描述符进行匹配,匹配结果保存由DMatch的组成的向量里
- 设置距离阈值,使得匹配的向量距离小于最小距离的2被才能进入最终的结果,用DrawMatch可以显示
代码
// 使用Flann进行特征点匹配.cpp : 定义控制台应用程序的入口点。
//
#include "stdafx.h"
#include <opencv2/opencv.hpp>
#include <highgui/highgui.hpp>
#include <features2d/features2d.hpp>
#include <nonfree/nonfree.hpp>
#include <vector>
using namespace cv;
using namespace std;
int _tmain(int argc, _TCHAR* argv[])
{
Mat input1 = imread("E://code//test//image//box.png", 1);
Mat input2 = imread("E://code//test//image//box_in_scene.jpg", 1);
if (input1.empty()||input2.empty())
{
cout << "不能正常加载图片" << endl;
system("pause");
return -1;
}
/************************************************************************/
/*下面进行提取特征点*/
/************************************************************************/
SiftFeatureDetector feature;
vector<KeyPoint> kerpoints1;
feature.detect(input1, kerpoints1);
Mat output1;
drawKeypoints(input1, kerpoints1, output1);
vector<KeyPoint> kerpoints2;
feature.detect(input2, kerpoints2);
Mat output2;
drawKeypoints(input2, kerpoints2, output2);
imshow("提取特征点后的box.png", output1);
imshow("提取特征点后的box_in_scene.png", output2);
imwrite("提取特征点后的box.png", output1);
imwrite("提取特征点后的box_in_scene.png", output2);
cout << "box提取的特征点数为:" << kerpoints1.size() << endl;
cout << "box_in_scene的特征点数为:" << kerpoints2.size() << endl;
/************************************************************************/
/* 下面进行特征向量提取 */
/************************************************************************/
SiftDescriptorExtractor descript;
Mat description1;
descript.compute(input1, kerpoints1, description1);
Mat description2;
descript.compute(input2, kerpoints2, description2);
/************************************************************************/
/* 下面进行特征向量临近匹配 */
/************************************************************************/
vector<DMatch> matches;
FlannBasedMatcher matcher;
Mat image_match;
matcher.match(description1, description2, matches);
/************************************************************************/
/* 下面计算向量距离的最大值与最小值 */
/************************************************************************/
double max_dist = 0, min_dist = 100;
for (int i = 0; i < description1.rows; i++)
{
if (matches.at(i).distance>max_dist)
{
max_dist = matches[i].distance;
}
if (matches[i].distance<min_dist)
{
min_dist = matches[i].distance;
}
}
cout << "最小距离为" << min_dist << endl;
cout << "最大距离为" << max_dist << endl;
/************************************************************************/
/* 得到距离小于而V诶最小距离的匹配 */
/************************************************************************/
vector<DMatch> good_matches;
for (int i = 0; i < matches.size(); i++)
{
if (matches[i].distance<2*min_dist)
{
good_matches.push_back(matches[i]);
cout <<"第一个图中的"<< matches[i].queryIdx<<"匹配了第二个图中的"<<matches[i].trainIdx<<endl;
}
}
drawMatches(input1, kerpoints1, input2, kerpoints2, good_matches, image_match);
imshow("匹配后的图片", image_match);
imwrite("匹配后的图片.png", image_match);
cout << "匹配的特征点数为:" << good_matches.size() << endl;
waitKey(0);
return 0;
}
程序运行前的原始图片
提取特征点后
进行匹配后
相关代码介绍
double max_dist = 0, min_dist = 100;
for (int i = 0; i < description1.rows; i++)
{
if (matches.at(i).distance>max_dist)
{
max_dist = matches[i].distance;
}
if (matches[i].distance<min_dist)
{
min_dist = matches[i].distance;
}
}
设置阈值,当特征向量的距离在最小距离的二倍范围内的,匹配为好的匹配;
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时间: 2024-10-09 02:19:17