opencv 实现两幅图片特征匹配

ID:401842 · 发表于 2018-10-19 09:28

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

	using namespace std;
	using namespace cv;

	int main ( int argc, char** argv )
	{
	if ( argc != 3 )
	{
	cout<<"usage: feature_extraction img1 img2"<<endl;
	return 1;
	}
	//-- 读取图像
	Mat img_1 = imread ( argv[1], CV_LOAD_IMAGE_COLOR );
	Mat img_2 = imread ( argv[2], CV_LOAD_IMAGE_COLOR );

	//-- 初始化
	std::vector<KeyPoint> keypoints_1, keypoints_2;
	Mat descriptors_1, descriptors_2;
	Ptr<FeatureDetector> detector = ORB::create();
	Ptr<DescriptorExtractor> descriptor = ORB::create();
	// Ptr<FeatureDetector> detector = FeatureDetector::create(detector_name);
	// Ptr<DescriptorExtractor> descriptor = DescriptorExtractor::create(descriptor_name);
	Ptr<DescriptorMatcher> matcher = DescriptorMatcher::create ( "BruteForce-Hamming" );

	//-- 第一步:检测 Oriented FAST 角点位置
	detector->detect ( img_1,keypoints_1 );
	detector->detect ( img_2,keypoints_2 );

	//-- 第二步:根据角点位置计算 BRIEF 描述子
	descriptor->compute ( img_1, keypoints_1, descriptors_1 );
	descriptor->compute ( img_2, keypoints_2, descriptors_2 );

	Mat outimg1;
	drawKeypoints( img_1, keypoints_1, outimg1, Scalar::all(-1), DrawMatchesFlags::DEFAULT );
	imshow("ORB特征点",outimg1);

	//-- 第三步:对两幅图像中的BRIEF描述子进行匹配，使用 Hamming 距离
	vector<DMatch> matches;
	//BFMatcher matcher ( NORM_HAMMING );
	matcher->match ( descriptors_1, descriptors_2, matches );

	//-- 第四步:匹配点对筛选
	double min_dist=10000, max_dist=0;

	//找出所有匹配之间的最小距离和最大距离, 即是最相似的和最不相似的两组点之间的距离
	for ( int i = 0; i < descriptors_1.rows; i++ )
	{
	double dist = matches.distance;
	if ( dist < min_dist ) min_dist = dist;
	if ( dist > max_dist ) max_dist = dist;
	}

	// 仅供娱乐的写法
	min_dist = min_element( matches.begin(), matches.end(), [](const DMatch& m1, const DMatch& m2) {return m1.distance<m2.distance;} )->distance;
	max_dist = max_element( matches.begin(), matches.end(), [](const DMatch& m1, const DMatch& m2) {return m1.distance<m2.distance;} )->distance;

	printf ( "-- Max dist : %f \n", max_dist );
	printf ( "-- Min dist : %f \n", min_dist );

	//当描述子之间的距离大于两倍的最小距离时,即认为匹配有误.但有时候最小距离会非常小,设置一个经验值30作为下限.
	std::vector< DMatch > good_matches;
	for ( int i = 0; i < descriptors_1.rows; i++ )
	{
	if ( matches.distance <= max ( 2*min_dist, 30.0 ) )
	{
	good_matches.push_back ( matches );
	}
	}

	//-- 第五步:绘制匹配结果
	Mat img_match;
	Mat img_goodmatch;
	drawMatches ( img_1, keypoints_1, img_2, keypoints_2, matches, img_match );
	drawMatches ( img_1, keypoints_1, img_2, keypoints_2, good_matches, img_goodmatch );
	imshow ( "所有匹配点对", img_match );
	imshow ( "优化后匹配点对", img_goodmatch );
	waitKey(0);

	return 0;
	}

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