ORB特征点提取

#include <opencv2/core/core.hpp>
#include <opencv2/highgui/highgui.hpp>
#include <opencv2/imgproc/imgproc.hpp>
#include <opencv2/features2d/features2d.hpp>
#include <iostream>

using namespace cv;
using namespace std;

int main(int argc, char** argv)
{
Mat img_1 = imread("./1.jpg");
Mat img_2 = imread("./2.jpg");

// -- Step 1: Detect the keypoints using STAR Detector
std::vector<KeyPoint> keypoints_1,keypoints_2;
ORB orb;
orb.detect(img_1, keypoints_1);
orb.detect(img_2, keypoints_2);

// -- Stpe 2: Calculate descriptors (feature vectors)
Mat descriptors_1, descriptors_2;
orb.compute(img_1, keypoints_1, descriptors_1);
orb.compute(img_2, keypoints_2, descriptors_2);

//-- Step 3: Matching descriptor vectors with a brute force matcher
BFMatcher matcher(NORM_HAMMING);
std::vector<DMatch> matches;
matcher.match(descriptors_1, descriptors_2, matches);

//4.对匹配结果进行筛选（依据DMatch结构体中的float类型变量distance进行筛选）
float minDistance = 100;
float maxDistance = 0;
for (int i = 0; i < matches.size(); i++)
{
if (matches[i].distance < minDistance)
minDistance = matches[i].distance;
if (matches[i].distance > maxDistance)
maxDistance = matches[i].distance;
}
cout << "minDistance: " << minDistance << endl;
cout << "maxDistance: " << maxDistance << endl;
vector<DMatch> goodMatches;
for (int i = 0; i < matches.size(); i++)
{
if (matches[i].distance < 2 * minDistance)
{
goodMatches.push_back(matches[i]);
}
}

// -- dwaw matches
Mat img_mathes;
drawMatches(img_1, keypoints_1, img_2, keypoints_2, goodMatches, img_mathes);
// -- show
imshow("Mathces", img_mathes);

waitKey(0);
return 0;
}