【图像识别与处理】特征点检测之ORB特征提取_lvi-sam中orb特征点提取-优快云博客

本文通过使用ORB算法进行图像特征匹配，展示了如何从两幅图像中提取关键点并计算描述符，然后利用BruteForceMatcher进行匹配，筛选出优质匹配项，并绘制匹配结果。

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#include <iostream>
#include "opencv2/core/core.hpp"
#include "opencv2/features2d/features2d.hpp"
#include "opencv2/highgui/highgui.hpp"
#include <iostream>
#include <vector>
using namespace cv;
using namespace std;
int main()
{
	Mat img_1 = imread("D:\\image\\img1.jpg");
	Mat img_2 = imread("D:\\image\\img2.jpg");
	if (!img_1.data || !img_2.data)
	{
		cout << "error reading images " << endl;
		return -1;
	}

	ORB orb;
	vector<KeyPoint> keyPoints_1, keyPoints_2;
	Mat descriptors_1, descriptors_2;

	orb(img_1, Mat(), keyPoints_1, descriptors_1);
	orb(img_2, Mat(), keyPoints_2, descriptors_2);
	
	BruteForceMatcher<HammingLUT> matcher;
	vector<DMatch> matches;
	matcher.match(descriptors_1, descriptors_2, matches);

	double max_dist = 0; double min_dist = 100;
	//-- Quick calculation of max and min distances between keypoints
	for( int i = 0; i < descriptors_1.rows; i++ )
	{ 
		double dist = matches[i].distance;
		if( dist < min_dist ) min_dist = dist;
		if( dist > max_dist ) max_dist = dist;
	}
	printf("-- Max dist : %f \n", max_dist );
	printf("-- Min dist : %f \n", min_dist );
	//-- Draw only "good" matches (i.e. whose distance is less than 0.6*max_dist )
	//-- PS.- radiusMatch can also be used here.
	std::vector< DMatch > good_matches;
	for( int i = 0; i < descriptors_1.rows; i++ )
	{ 
		if( matches[i].distance < 0.6*max_dist )
		{ 
			good_matches.push_back( matches[i]); 
		}
	}

	Mat img_matches;
	drawMatches(img_1, keyPoints_1, img_2, keyPoints_2,
		good_matches, img_matches, Scalar::all(-1), Scalar::all(-1),
		vector<char>(), DrawMatchesFlags::NOT_DRAW_SINGLE_POINTS);
	imshow( "Match", img_matches);
	cvWaitKey();
	return 0;