ORB特征提取匹配opencv3代码实现

#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 )
{

//-- 读取图像
Mat img_1 = imread ( "1.png", CV_LOAD_IMAGE_COLOR );
Mat img_2 = imread ( "2.png", 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[i].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[i].distance <= max ( 2*min_dist, 30.0 ) )
{
good_matches.push_back ( matches[i] );
}
}

//-- 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;
}

1.png

2.png