1. ORB特征匹配 BruteForce-Hamming匹配
//使用ORB特征匹配两张图片，并进行运行时间，对称性测试，ratio测试
#include <iostream>
#include <ctime>
//#include <dirent.h>
#include <opencv2/core/core.hpp>
#include <opencv2/highgui/highgui.hpp>
#include <opencv2/nonfree/features2d.hpp>
#include <opencv2/nonfree/nonfree.hpp>
using namespace cv;
using namespace std;
/*
int main(int argc, const char *argv[])
{
if (argc != 4){
cout << "usage:match <image1> <image2> <ratio>\n";
exit(-1);
}
double ratio = (double)atof(argv[3]);
string image1_name = string(argv[1]), image2_name = string(argv[2]);
Mat image1 = imread(image1_name, 1);
Mat image2 = imread(image2_name, 1);
*/
int main()
{
Mat image1 = imread("img_1.bmp", 1);
Mat image2 = imread("img_2.bmp", 1);
Ptr<FeatureDetector> detector;
Ptr<DescriptorExtractor> extractor;
float ratio = 0.8; // 修改获得不同实验结果
// ORB特征点、描述子检测器
detector = FeatureDetector::create("ORB");
extractor = DescriptorExtractor::create("ORB");
cout << "ORB特征点、描述子、BruteForce-Hamming匹配" << endl;
cout << "ratio = " << ratio << endl;
clock_t begin = clock();
// 特征点
vector<KeyPoint> keypoints1, keypoints2;
detector->detect(image1, keypoints1);
detector->detect(image2, keypoints2);
cout << "# keypoints of image1 :" << keypoints1.size() << endl;
cout << "# keypoints of image2 :" << keypoints2.size() << endl;
// 计算描述子
Mat descriptors1, descriptors2;
extractor->compute(image1, keypoints1, descriptors1);
extractor->compute(image2, keypoints2, descriptors2);
cout << "Descriptors size :" << descriptors1.cols << ":" << descriptors1.rows << endl;
vector< vector<DMatch> > matches12, matches21;
Ptr<DescriptorMatcher> matcher = DescriptorMatcher::create("BruteForce-Hamming");
// knn:K最近邻，flann: 快速最近邻逼近搜索
matcher->knnMatch(descriptors1, descriptors2, matches12, 2); // 最近邻匹配
matcher->knnMatch(descriptors2, descriptors1, matches21, 2);
// BFMatcher bfmatcher(NORM_L2, true);
// vector<DMatch> matches;
// bfmatcher.match(descriptors1, descriptors2, matches);
cout << "Matches1-2:" << matches12.size() << endl;
cout << "Matches2-1:" << matches21.size() << endl;
// ratio test proposed by David Lowe paper = 0.8
std::vector<DMatch> good_matches1, good_matches2;
// Yes , the code here is redundant, it is easy to reconstruct it ....
for (int i = 0; i < matches12.size(); i++){
if (matches12[i][0].distance < ratio * matches12[i][1].distance)
good_matches1.push_back(matches12[i][0]);
}
for (int i = 0; i < matches21.size(); i++){
if (matches21[i][0].distance < ratio * matches21[i][1].distance)
good_matches2.push_back(matches21[i][0]);
}
cout << "Good matches1:" << good_matches1.size() << endl;
cout << "Good matches2:" << good_matches2.size() << endl;
// Symmetric Test 对称性测试
std::vector<DMatch> better_matches;
for (int i = 0; i<good_matches1.size(); i++){
for (int j = 0; j<good_matches2.size(); j++){
if (good_matches1[i].queryIdx == good_matches2[j].trainIdx && good_matches2[j].queryIdx == good_matches1[i].trainIdx){
better_matches.push_back(DMatch(good_matches1[i].queryIdx, good_matches1[i].trainIdx, good_matches1[i].distance));
break;
}
}
}
cout << "Better matches:" << better_matches.size() << endl;
// 计时结束
clock_t end = clock();
double elapsed_secs = double(end - begin) / CLOCKS_PER_SEC;
cout << "Time Costs : " << elapsed_secs << endl;
// 输出
Mat output;
drawMatches(image1, keypoints1, image2, keypoints2, better_matches, output);
imshow("Matches result", output);
waitKey(0);
return 0;
}
2. surf特征点、描述子、Flann算法匹配描述子
// surf特征点匹配：surf特征点、描述子、Flann算法匹配描述子
#include "opencv2/core/core.hpp"
#include "opencv2/features2d/features2d.hpp"
#include "opencv2/highgui/highgui.hpp"
#include <opencv2/nonfree/nonfree.hpp>
#include<opencv2/legacy/legacy.hpp>
#include <iostream>
#include <ctime>
using namespace cv;
using namespace std;
int main(int argc, char** argv)
{
Mat img_1 = imread("img_1.bmp", 1);
Mat img_2 = imread("img_2.bmp", 1);
if (!img_1.data || !img_2.data) {
printf("读取图片image错误！ \n"); return false;
}
cout << "SURF特征点、描述子、FLANN描述子匹配" << endl;
cout << "筛选条件：5倍最小距离" << endl;
clock_t begin = clock();
// 特征点
int minHessian = 300; // surf算法中的hessian阈值
SURF detector(minHessian);
std::vector<KeyPoint> keypoints_1, keypoints_2;
detector.detect(img_1, keypoints_1);
detector.detect(img_2, keypoints_2);
cout << "# keypoints of image1 :" << keypoints_1.size() << endl;
cout << "# keypoints of image2 :" << keypoints_2.size() << endl;
// 描述子/特征向量
SURF extractor;
Mat descriptors_1, descriptors_2;
extractor.compute(img_1, keypoints_1, descriptors_1);
extractor.compute(img_2, keypoints_2, descriptors_2);
cout << "Descriptors size :" << descriptors_1.cols << ":" << descriptors_1.rows << endl;
// 匹配描述子
FlannBasedMatcher matcher;
std::vector< DMatch > matches;
matcher.match(descriptors_1, descriptors_2, matches);
double max_dist = 0; double min_dist = 100;
// 特征点最大最小距离
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;
}
// 输出
printf("> 最大距离（Max dist） : %f \n", max_dist);
printf("> 最小距离（Min dist） : %f \n", min_dist);
// 筛选
std::vector< DMatch > good_matches;
for (int i = 0; i < descriptors_1.rows; i++) {
if (matches[i].distance < 5 * min_dist) {
good_matches.push_back(matches[i]);
}
}
cout << "Good_matches:" << good_matches.size() << endl;
// 计时结束
clock_t end = clock();
double elapsed_secs = double(end - begin) / CLOCKS_PER_SEC;
cout << "Time Costs : " << elapsed_secs << endl;
// 绘制
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);
/*
// 输出 每个匹配的特征点
for (int i = 0; i < good_matches.size(); i++)
{
printf(">符合条件的匹配点 [%d] 特征点1: %d -- 特征点2: %d \n", i, good_matches[i].queryIdx, good_matches[i].trainIdx);
}
*/
// 显示
imshow("匹配效果图", img_matches);
// 任意键退出
waitKey(0);
return 0;
}
3. sift特征点、描述子+FLANN算法
#include <opencv2/core/core.hpp>
#include <opencv2/highgui/highgui.hpp>
#include <opencv2/features2d/features2d.hpp>
#include <opencv2/nonfree/nonfree.hpp>
#include <iostream>
//#include <dirent.h>
#include <ctime>
using namespace cv;
using namespace std;
int main(int argc, const char *argv[]){
/*
if (argc != 3){
cout << "usage:match <level> <image1> <image2>\n";
exit(-1);
}
string arg2 = string(argv[2]);
int level = atoi(arg2.c_str());
string image1_name = string(argv[1]), image2_name = string(argv[2]);
// getline(cin, image1_name);
// getline(cin, image2_name);
*/
Mat image1 = imread("img_1.bmp", 1);
Mat image2 = imread("img_2.bmp", 1);
Ptr<FeatureDetector> detector;
Ptr<DescriptorExtractor> extractor;
initModule_nonfree();
/*
* SIFT,SURF, ORB
*/
detector = FeatureDetector::create("SIFT");
extractor = DescriptorExtractor::create("SIFT");
cout << "Sift特征点、描述子、FLANN匹配" << endl;
// cout << "ratio = " << ratio << endl;
clock_t begin = clock();
vector<KeyPoint> keypoints1, keypoints2;
detector->detect(image1, keypoints1);
detector->detect(image2, keypoints2);
cout << "# keypoints of image1 :" << keypoints1.size() << endl;
cout << "# keypoints of image2 :" << keypoints2.size() << endl;
Mat descriptors1, descriptors2;
extractor->compute(image1, keypoints1, descriptors1);
extractor->compute(image2, keypoints2, descriptors2);
cout << "Descriptors size :" << descriptors1.cols << ":" << descriptors1.rows << endl;
vector< vector<DMatch> > matches12, matches21;
Ptr<DescriptorMatcher> matcher = DescriptorMatcher::create("FlannBased");
matcher->knnMatch(descriptors1, descriptors2, matches12, 2);
matcher->knnMatch(descriptors2, descriptors1, matches21, 2);
cout << "Matches1-2:" << matches12.size() << endl;
cout << "Matches2-1:" << matches21.size() << endl;
// ratio test proposed by David Lowe paper = 0.8
std::vector<DMatch> good_matches1, good_matches2;
for (int i = 0; i < matches12.size(); i++){
const float ratio = 0.8;
if (matches12[i][0].distance < ratio * matches12[i][1].distance)
good_matches1.push_back(matches12[i][0]);
}
for (int i = 0; i < matches21.size(); i++){
const float ratio = 0.8;
if (matches21[i][0].distance < ratio * matches21[i][1].distance)
good_matches2.push_back(matches21[i][0]);
}
cout << "Good matches1:" << good_matches1.size() << endl;
cout << "Good matches2:" << good_matches2.size() << endl;
// Symmetric Test
std::vector<DMatch> better_matches;
for (int i = 0; i<good_matches1.size(); i++){
for (int j = 0; j<good_matches2.size(); j++){
if (good_matches1[i].queryIdx == good_matches2[j].trainIdx && good_matches2[j].queryIdx == good_matches1[i].trainIdx){
better_matches.push_back(DMatch(good_matches1[i].queryIdx, good_matches1[i].trainIdx, good_matches1[i].distance));
break;
}
}
}
cout << "Better matches:" << better_matches.size() << endl;
clock_t end = clock();
double elapsed_secs = double(end - begin) / CLOCKS_PER_SEC;
cout << "Time Costs : " << elapsed_secs << endl;
// show it on an image
Mat output;
drawMatches(image1, keypoints1, image2, keypoints2, better_matches, output);
imshow("Matches result", output);
waitKey(0);
return 0;
}
4. SIFT特征点、描述子+暴力匹配
#include <opencv2/core/core.hpp>
#include <opencv2/highgui/highgui.hpp>
#include <opencv2/nonfree/features2d.hpp> //
#include <opencv2/nonfree/nonfree.hpp>
#include <iostream>
//#include <dirent.h>
#include <ctime>
using namespace cv;
using namespace std;
int main(int argc, const char *argv[]){
/*
if (argc != 3){
cout << "usage:match <level> <image1> <image2>\n";
exit(-1);
}
string arg2 = string(argv[2]);
int level = atoi(arg2.c_str());
string image1_name = string(argv[1]), image2_name = string(argv[2]);
// getline(cin, image1_name);
// getline(cin, image2_name);
*/
Mat image1 = imread("img_1.bmp", 1);
Mat image2 = imread("img_2.bmp", 1);
Ptr<FeatureDetector> detector;
Ptr<DescriptorExtractor> extractor;
initModule_nonfree();
/*
* SIFT,SURF, ORB
*/
detector = FeatureDetector::create("SIFT");
extractor = DescriptorExtractor::create("SIFT");
cout << "sift特征点、描述子、暴力匹配" << endl;
//cout << "筛选条件：5倍最小距离" << endl;
clock_t begin = clock();
vector<KeyPoint> keypoints1, keypoints2;
detector->detect(image1, keypoints1);
detector->detect(image2, keypoints2);
cout << "# keypoints of image1 :" << keypoints1.size() << endl;
cout << "# keypoints of image2 :" << keypoints2.size() << endl;
Mat descriptors1, descriptors2;
extractor->compute(image1, keypoints1, descriptors1);
extractor->compute(image2, keypoints2, descriptors2);
cout << "Descriptors size :" << descriptors1.cols << ":" << descriptors1.rows << endl;
vector< vector<DMatch> > matches12, matches21;
Ptr<DescriptorMatcher> matcher = DescriptorMatcher::create("BruteForce");
matcher->knnMatch(descriptors1, descriptors2, matches12, 2);
matcher->knnMatch(descriptors2, descriptors1, matches21, 2);
// BFMatcher bfmatcher(NORM_L2, true);
// vector<DMatch> matches;
// bfmatcher.match(descriptors1, descriptors2, matches);
cout << "Matches1-2:" << matches12.size() << endl;
cout << "Matches2-1:" << matches21.size() << endl;
// ratio test proposed by David Lowe paper = 0.8
std::vector<DMatch> good_matches1, good_matches2;
for (int i = 0; i < matches12.size(); i++){
const float ratio = 0.8;
if (matches12[i][0].distance < ratio * matches12[i][1].distance)
good_matches1.push_back(matches12[i][0]);
}
for (int i = 0; i < matches21.size(); i++){
const float ratio = 0.8;
if (matches21[i][0].distance < ratio * matches21[i][1].distance)
good_matches2.push_back(matches21[i][0]);
}
cout << "Good matches1:" << good_matches1.size() << endl;
cout << "Good matches2:" << good_matches2.size() << endl;
// Symmetric Test
std::vector<DMatch> better_matches;
for (int i = 0; i<good_matches1.size(); i++){
for (int j = 0; j<good_matches2.size(); j++){
if (good_matches1[i].queryIdx == good_matches2[j].trainIdx && good_matches2[j].queryIdx == good_matches1[i].trainIdx){
better_matches.push_back(DMatch(good_matches1[i].queryIdx, good_matches1[i].trainIdx, good_matches1[i].distance));
break;
}
}
}
cout << "Better matches:" << better_matches.size() << endl;
clock_t end = clock();
double elapsed_secs = double(end - begin) / CLOCKS_PER_SEC;
cout << "Time Costs : " << elapsed_secs << endl;
// show it on an image
Mat output;
drawMatches(image1, keypoints1, image2, keypoints2, better_matches, output);
imshow("Matches result", output);
waitKey(0);
return 0;
}
5. fast特征点 surf描述子 暴力匹配
#include <opencv2/core/core.hpp>
#include <opencv2/highgui/highgui.hpp>
#include <opencv2/nonfree/features2d.hpp> //
#include <opencv2/nonfree/nonfree.hpp>
#include <iostream>
//#include <dirent.h>
#include <ctime>
using namespace cv;
using namespace std;
#define IMG_DIR "./imgs/"
bool has_suffix(const std::string &str, const std::string &suffix);
int main(int argc, const char *argv[]){
/*
if (argc != 2){
cout << "usage:match <method>\n";
exit(-1);
}
string method = string(argv[1]);
string image1_name, image2_name;
getline(cin, image1_name);
getline(cin, image2_name);
*/
Mat image1 = imread("img_1.bmp", 1);
Mat image2 = imread("img_2.bmp", 1);
FastFeatureDetector fast(40); // 检测的阈值为40
SurfDescriptorExtractor extractor;
//Ptr<DescriptorExtractor> extractor = DescriptorExtractor::create("SIFT");
// WHY CANNOT WORK ???
cout << "fast特征点、surf描述子、暴力匹配" << endl;
clock_t begin = clock();
vector<KeyPoint> keypoints1, keypoints2;
fast.detect(image1, keypoints1);
fast.detect(image2, keypoints2);
cout << "# keypoints of image1 :" << keypoints1.size() << endl;
cout << "# keypoints of image2 :" << keypoints2.size() << endl;
Mat descriptors1, descriptors2;
extractor.compute(image1, keypoints1, descriptors1);
extractor.compute(image2, keypoints2, descriptors2);
clock_t end = clock();
double elapsed_secs = double(end - begin) / CLOCKS_PER_SEC;
cout << "Time Costs : " << elapsed_secs << endl;
BFMatcher bfmatcher(NORM_L2, true);
vector<DMatch> matches;
bfmatcher.match(descriptors1, descriptors2, matches);
cout << "# matches : " << matches.size() << endl;
// show it on an image
Mat output;
drawMatches(image1, keypoints1, image2, keypoints2, matches, output);
imshow("Matches result", output);
waitKey(0);
return 0;
}
bool has_suffix(const std::string &str, const std::string &suffix)
{
return str.size() >= suffix.size() &&
str.compare(str.size() - suffix.size(), suffix.size(), suffix) == 0;
}
6. fast特征点、orb描述子、BruteForce-Hamming匹配
#include <opencv2/core/core.hpp>
#include <opencv2/highgui/highgui.hpp>
#include <opencv2/nonfree/features2d.hpp> //
#include <opencv2/nonfree/nonfree.hpp>
#include <iostream>
//#include <dirent.h>
#include <ctime>
using namespace cv;
using namespace std;
int main(int argc, const char *argv[]){
/*
if (argc != 3){
cout << "usage:match <image1> <image2>\n";
exit(-1);
}
string image1_name = string(argv[1]), image2_name = string(argv[2]);
// getline(cin, image1_name);
// getline(cin, image2_name);
*/
Mat image1 = imread("img_1.bmp", 1);
Mat image2 = imread("img_2.bmp", 1);
vector<KeyPoint> keypoints_1, keypoints_2;
Mat descriptors_1, descriptors_2;
cout << "fast特征点、orb描述子、BruteForce-Hamming匹配" << endl;
//cout << "筛选条件：5倍最小距离" << endl;
clock_t begin = clock();
Ptr<FeatureDetector> detector;
detector = new DynamicAdaptedFeatureDetector(new FastAdjuster(10, true), 3000, 6000, 8);
detector->detect(image1, keypoints_1);
detector->detect(image2, keypoints_2);
cout << "# keypoints of image1 :" << keypoints_1.size() << endl;
cout << "# keypoints of image2 :" << keypoints_2.size() << endl;
initModule_nonfree();//NB. need this, otherwise get coredump ,oops !!!!!
Ptr<DescriptorExtractor> extractor = DescriptorExtractor::create("ORB");
extractor->compute(image1, keypoints_1, descriptors_1);
extractor->compute(image2, keypoints_2, descriptors_2);
vector< vector<DMatch> > matches;
Ptr<DescriptorMatcher> matcher = DescriptorMatcher::create("BruteForce-Hamming");
matcher->knnMatch(descriptors_1, descriptors_2, matches, 500);
//look whether the match is inside a defined area of the image
//only 25% of maximum of possible distance
double tresholdDist = 0.25 * sqrt(double(image1.size().height*image1.size().height + image1.size().width*image1.size().width));
vector< DMatch > good_matches2;
good_matches2.reserve(matches.size());
for (size_t i = 0; i < matches.size(); ++i){
for (int j = 0; j < matches[i].size(); j++) {
Point2f from = keypoints_1[matches[i][j].queryIdx].pt;
Point2f to = keypoints_2[matches[i][j].trainIdx].pt;
//calculate local distance for each possible match
double dist = sqrt((from.x - to.x) * (from.x - to.x) + (from.y - to.y) * (from.y - to.y));
//save as best match if local distance is in specified area and on same height
if (dist < tresholdDist && abs(from.y - to.y)<5) {
good_matches2.push_back(matches[i][j]);
j = matches[i].size();
}
}
}
cout << "Good matches :" << good_matches2.size() << endl;
clock_t end = clock();
double elapsed_secs = double(end - begin) / CLOCKS_PER_SEC;
cout << "Time Costs : " << elapsed_secs << endl;
// show it on an image
Mat output;
drawMatches(image1, keypoints_1, image2, keypoints_2, good_matches2, output);
imshow("Matches result", output);
waitKey(0);
}
参考资料
1. http://blog.csdn.net/vonzhoufz/article/details/46594369
2. 《opencv3编程入门》
时间： 2024-10-21 13:42:12