浅析ORB、SURF、SIFT特征点提取方法以及ICP匹配方法

更新时间:2023-04-04 05:23:04 阅读：评论：0

在进行编译视觉slam时，书中提到了orb、surf、sift提取方法，以及特征提取方法暴力匹配(brute-force matcher)和快速近邻匹配(flann)。以及7.9讲述的3d-3d：迭代最近点(iterative clost point,icp)方法，icp 的求解方式有两种：利用线性代数求解(主要是svd)，以及利用非线性优化方式求解。

完整代码代码如下：

链接：https://pan.baidu.com/s/1rlh9jtg_awtuyzmphqij3q提取码：8888

main.cpp

#include <iostream>#include "opencv2/opencv.hpp"#include "opencv2/core/core.hpp"#include "opencv2/features2d/features2d.hpp"#include "opencv2/highgui/highgui.hpp"#include <opencv2/xfeatures2d.hpp>#include <iostream>#include <vector>#include <time.h>#include <chrono>#include <math.h>#include<bits/stdc++.h>using namespace std;using namespace cv;using namespace cv::xfeatures2d;double picture1_size_change=1;double picture2_size_change=1;bool show_picture = true;void extract_orb2(string picture1, string picture2){//-- 读取图像mat img_1 = imread(picture1, cv_load_image_color);mat img_2 = imread(picture2, cv_load_image_color);asrt(img_1.data != nullptr && img_2.data != nullptr);resize(img_1, img_1, size(),  picture1_size_change, picture1_size_change);resize(img_2, img_2, size(), picture2_size_change, picture2_size_change);//-- 初始化std::vector<keypoint> keypoints_1, keypoints_2;mat descriptors_1, descriptors_2;ptr<featuredetector> detector = orb::create(2000,(1.200000048f), 8, 100);ptr<descriptorextractor> descriptor = orb::create(5000);ptr<descriptormatcher> matcher = descriptormatcher::create("bruteforce-hamming");//-- 第一步:检测 oriented fast 角点位置chrono::steady_clock::time_point t1 = chrono::steady_clock::now();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);chrono::steady_clock::time_point t2 = chrono::steady_clock::now();chrono::duration<double> time_ud = chrono::duration_cast<chrono::duration<double>>(t2 - t1);// cout << "extract orb cost = " << time_ud.count() * 1000 << " ms " << endl;cout << "detect " << keypoints_1.size() << " and " << keypoints_2.size() << " keypoints " << endl;if (show_picture){mat outimg1;drawkeypoints(img_1, keypoints_1, outimg1, scalar::all(-1), drawmatchesflags::default);imshow("orb features", 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;// t1 = chrono::steady_clock::now();matcher->match(descriptors_1, descriptors_2, matches);t2 = chrono::steady_clock::now();time_ud = chrono::duration_cast<chrono::duration<double>>(t2 - t1);cout << "extract and match orb cost = " << time_ud.count() * 1000 << " ms " << endl;//-- 第四步:匹配点对筛选// 计算最小距离和最大距离auto min_max = minmax_element(matches.begin(), matches.end(),[](const dmatch &m1, const dmatch &m2){ return m1.distance < m2.distance; });double min_dist = min_max.first->distance;double max_dist = min_max.cond->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]);}}cout << "match " << good_matches.size() << " keypoints " << endl;//-- 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);if (show_picture)imshow("good matches", img_goodmatch);if (show_picture)waitkey(0);}void extract_sift(string picture1, string picture2){// double t = (double)gettickcount();mat temp = imread(picture1, imread_grayscale);mat image_check_changed = imread(picture2, imread_grayscale);if (!temp.data || !image_check_changed.data){printf("could not load images...\n");return;}resize(temp, temp, size(),  picture1_size_change, picture1_size_change);resize(image_check_changed, image_check_changed, size(), picture2_size_change, picture2_size_change);//mat image_check_changed = change_image(image_check);//("temp", temp);if (show_picture) imshow("image_check_changed", image_check_changed);int minhessian = 500;// ptr<surf> detector = surf::create(minhessian);    // surfptr<sift> detector = sift::create(); // siftvector<keypoint> keypoints_obj;vector<keypoint> keypoints_scene;mat descriptor_obj, descriptor_scene;clock_t starttime, endtime;starttime = clock();chrono::steady_clock::time_point t1 = chrono::steady_clock::now();// cout << "extract orb cost = " << time_ud.count() * 1000 << " ms " << endl;detector->detectandcompute(temp, mat(), keypoints_obj, descriptor_obj);detector->detectandcompute(image_check_changed, mat(), keypoints_scene, descriptor_scene);cout << "detect " << keypoints_obj.size() << " and " << keypoints_scene.size() << " keypoints " << endl;// matchingflannbadmatcher matcher;vector<dmatch> matches;matcher.match(descriptor_obj, descriptor_scene, matches);chrono::steady_clock::time_point t2 = chrono::steady_clock::now();chrono::duration<double> time_ud = chrono::duration_cast<chrono::duration<double>>(t2 - t1);cout << "extract and match cost = " << time_ud.count() * 1000 << " ms " << endl;//求最小最大距离double mindist = 1000;double maxdist = 0;//row--行 col--列for (int i = 0; i < descriptor_obj.rows; i++){double dist = matches[i].distance;if (dist > maxdist){maxdist = dist;}if (dist < mindist){mindist = dist;}}// printf("max distance : %f\n", maxdist);// printf("min distance : %f\n", mindist);// find good matched pointsvector<dmatch> goodmatches;for (int i = 0; i < descriptor_obj.rows; i++){double dist = matches[i].distance;if (dist < max(5 * mindist, 1.0)){goodmatches.push_back(matches[i]);}}//rectangle(temp, point(1, 1), point(177, 157), scalar(0, 0, 255), 8, 0);cout << "match " << goodmatches.size() << " keypoints " << endl;endtime = clock();// cout << "took time : " << (double)(endtime - starttime) / clocks_per_c * 1000 << " ms" << endl;mat matchesimg;drawmatches(temp, keypoints_obj, image_check_changed, keypoints_scene, goodmatches, matchesimg, scalar::all(-1),scalar::all(-1), vector<char>(), drawmatchesflags::not_draw_single_points);if (show_picture) imshow("flann matching result01", matchesimg);// imwrite("c:/urs/administrator/desktop/matchesimg04.jpg", matchesimg);//求hstd::vector<point2f> points1, points2;//保存对应点for (size_t i = 0; i < goodmatches.size(); i++){//queryidx是对齐图像的描述子和特征点的下标。points1.push_back(keypoints_obj[goodmatches[i].queryidx].pt);//queryidx是是样本图像的描述子和特征点的下标。points2.push_back(keypoints_scene[goodmatches[i].trainidx].pt);}// find homography 计算homography，ransac随机抽样一致性算法mat h = findhomography(points1, points2, ransac);//imwrite("c:/urs/administrator/desktop/c-train/c-train/result/sift/image4_surf_minhessian1000_ mindist1000_a0.9b70.jpg", matchesimg);vector<point2f> obj_corners(4);vector<point2f> scene_corners(4);obj_corners[0] = point(0, 0);obj_corners[1] = point(temp.cols, 0);obj_corners[2] = point(temp.cols, temp.rows);obj_corners[3] = point(妈妈的节日0, temp.rows);//透视变换(把斜的图片扶正)perspectivetransform(obj_corners, scene_corners, h);//mat dst;cvtcolor(image_check_changed, image_check_changed, color_gray2bgr);line(image_check_changed, scene_corners[0], scene_corners[1], scalar(0, 0, 255), 2, 8, 0);line(image_check_changed, scene_corners[1], scene_corners[2], scalar(0, 0, 255), 2, 8, 0);line(image_check_changed, scene_corners[2], scene_corners[3], scalar(0, 0, 255), 2, 8, 0)西汉东汉;line(image_check_changed, scene_corners[3], scene_corners[0], scalar(0, 0, 255), 2, 8, 0);if (show_picture){mat outimg1;mat temp_color = imread(picture1, cv_load_image_color);drawkeypoints(temp_color, keypoints_obj, outimg1, scalar::all(-1), drawmatchesflags::default);imshow("sift features", outimg1);}if (show_picture) imshow("draw object", image_check_changed);// imwrite("c:/urs/administrator/desktop/image04.jpg", image_check_changed);// t = ((double)gettickcount() - t) / gettickfrequency();// printf("averagetime:%f\n", t);if (show_picture) waitkey(0);}void extract_surf(string picture1, string picture2){// double t = (double)gettickcount();mat temp = imread(picture1, imread_grayscale);mat image_check_changed = imread(picture2, imread_grayscale);if (!temp.data || !image_check_changed.data){printf("could not load images...\n");return;}resize(temp, temp, size(),  picture1_size_change, picture1_size_change);resize(image_check_changed, image_check_changed, size(), picture2_size_change, picture2_size_change);//mat image_check_changed = change_image(image_check);//("temp", temp);if (show_picture) imshow("image_check_changed", image_check_changed);int minhessian = 500;ptr<surf> detector = surf::create(minhessian);    // surf// ptr<sift> detector = sift::create(minhessian); // siftvector<keypoint> keypoints_obj;vector<keypoint> keypoints_scene;mat descriptor_obj, descriptor_scene;clock_t starttime, endtime;starttime = clock();chrono::steady_clock::time_point t1 = chrono::steady_clock::now();// cout << "extract orb cost = " << time_ud.count() * 1000 << " ms " << endl;detector->detectandcompute(temp, mat(), keypoints_obj, descriptor_obj);detector->detectandcompute(image_check_changed, mat(), keypoints_scene, descriptor_scene);cout << "detect " << keypoints_obj.size() << " and " << keypoints_scene.size() << " keypoints " << endl;// matchingflannbadmatcher matcher;vector<dmatch> matches;matcher.match(descriptor_obj, descriptor_scene, matches);chrono::steady_clock::time_point t2 = chrono::steady_clock::now();chrono::duration<double> time_ud = chrono::duration_cast<chrono::duration<double>>(t2 - t1);cout << "extract and match cost = " << time_ud.count() * 1000 << " ms " << endl;//求最小最大距离double mindist = 1000;double maxdist = 0;//row--行 col--列for (int i = 0; i < descriptor_obj.rows; i++){double dist = matches[i].distance;if (dist > maxdist){maxdist = dist;}if (dist < mindist){mindist = dist;}}// printf("max distance : %f\n", maxdist);// printf("min distance : %f\n", mindist);// find good matched pointsvector<dmatch> goodmatches;for (int i = 0; i < descriptor_obj.rows; i++){double dist = matches[i].distance;if (dist < max(2 * mindist, 0.15)){goodmatches.push_back(matches[i]);}}//rectangle(temp, point(1, 1), point(177, 157), scalar(0, 0, 255), 8, 0);cout << "match " << goodmatches.size() << " keypoints " << endl;endtime = clock();// cout << "took time : " << (double)(endtime - starttime) / clocks_per_c * 1000 << " ms" << endl;mat matchesimg;drawmatches(temp, keypoints_obj, image_check_changed, keypoints_scene, goodmatches, matchesimg, scalar::all(-1),scalar::all(-1), vector<char>(), drawmatchesflags::not_draw_single_points);if (show_picture) imshow("flann matching result01", matchesimg);// imwrite("c:/urs/administrator/desktop/matchesimg04.jpg", matchesimg);//求hstd::vector<point2f> points1, points2;//保存对应点for (size_t i = 0; i < goodmatches.size(); i++){//queryidx是对齐图像的描述子和特征点的下标。points1.push_back(keypoints_obj[goodmatches[i].queryidx].pt);//queryidx是是样本图像的描述子和特征点的下标。points2.push_back(keypoints_scene[goodmatches[i].trainidx].pt);}// find homography 计算homography，ransac随机抽样一致性算法mat h = findhomography(points1, points2, ransac);//imwrite("c:/urs/administrator/desktop/c-train/c-train/result/sift/image4_surf_minhessian1000_ mindist1000_a0.9b70.jpg", matchesimg);vector<point2f> obj_corners(4);vector<point2f> scene_corners(4);obj_corners[0] = point(0, 0);obj_corners[1] = point(temp.cols, 0);obj_corners[2] = point(temp.cols, temp.rows);obj_corners[3] = point(0, temp.rows);//透视变换(把斜的图片扶正)perspectivetransform(obj_corners, scene_corners, h);//mat dst;cvtcolor(image_check_changed, image_check_changed, color_gray2bgr);line(image_check_changed, scene_corners[0], scene_corners[1], scalar(0, 0, 255), 2, 8, 0);line(image_check_changed, scene_corners[1], scene_corners[2], scalar(0, 0, 255), 2, 8, 0);line(image_check_changed, scene_corners[2], scene_corners[3], scalar(0, 0, 255), 2, 8, 0);line(image_check_changed, scene_corners[3], scene_corners[0], scalar(0, 0, 255), 2, 8, 0);if (show_picture){mat outimg1;mat temp_color = imread(picture1, cv_load_image_color);drawkeypoints(temp_color, keypoints_obj, outimg1, scalar::all(-1), drawmatchesflags::default);imshow("surf features", outimg1);}if (show_picture) imshow("draw object", image_check_changed);// imwrite("c:/urs/administrator/desktop/image04.jpg", image_check_changed);// t = ((double)gettickcount() - t) / gettickfrequency();// printf("averagetime:%f\n", t);if (show_picture) waitkey(0);}void extract_akaze(string picture1,string picture2){//读取图片mat temp = imread(picture1,imread_grayscale);mat image_check_changed = imread(picture2,imread_grayscale);//如果不能读到其中任何一张图片，则打印不能下载图片if(!temp.data || !image_check_changed.data){printf("could not load iamges...\n");return;}resize(temp,temp,size(),picture1_size_change,picture1_size_change);resize(image_check_changed,image_check_changed,size(),picture2_size_change,picture2_size_change);//mat image_check_changed = change_image(image_check);//("temp", temp);if(show_picture){imshow("image_checked_changed",image_check_changed);}int minhessian=500;ptr<akaze> detector=akaze::create();//akazevector<keypoint> keypoints_obj;vector<keypoint> keypoints_scene;mat descriptor_obj,descriptor_scene;clock_t starttime,endtime;starttime=clock();chrono::steady_clock::time_point t1=chrono::steady_clock::now();detector->detectandcompute(temp,mat(),keypoints_obj,descriptor_obj);detector->detectandcompute(image_check_changed,mat(),keypoints_scene,descriptor_scene);cout<<" detect "<<keypoints_obj.size()<<" and "<<keypoints_scene.size<<" keypoints "<<endl;//matchingflannbadmatcher matcher;vector<dmatch> matches;matcher.match(descriptor_obj,descriptor_scene,matches);chrono::steady_clock::time_point t2 = chrono::steady_clock::now();chrono::duration<double> time_ud = chrono::duration_cast<chrono::duration<double>>(t2-t1);cout << "extract and match cost = " << time_ud.count()*1000<<" ms "<<endl;//求最小最大距离double mindist = 1000;double max_dist = 0;//row--行 col--列for(int i=0;i<descriptor_obj.rows;i++){double dist = match[i].distance;if(dist > maxdist){maxdist = dist;}if(dist<mindist){mindist = dist;}}// printf("max distance : %f\n", maxdist);// printf("min distance : %f\n", mindist);// find good matched pointsvector<dmatch> goodmatches;for(imt i=0;i<descriptor_obj.rows;i++){double dist = matches[i].distance;if(dist < max(5 * mindist,1.0)){goodmatches.push_back(matches[i]);}}//rectangle(temp, point(1, 1), point(177, 157), scalar(0, 0, 255), 8, 0);cout<<" match "<<goodmatches.size()<<" keypoints "<<endl;endtime = clock();// cout << "took time : " << (double)(endtime - starttime) / clocks_per_c * 1000 << " ms" << endl;mat matchesimg;drawmatches(temp,keypoints_obj,image_check_changed,keypoints_scene,goodmatches,matchesimg,scalar::all(-1),scalar::all(-1),vector<char>(),drawmatchesflags::not_draw_single_points);if(show_picture)imshow("flann matching result01",matchesimg);// imwrite("c:/urs/administrator/desktop/matchesimg04.jpg", matchesimg);//求h   std::vector<point2f> points1,points2;//保存对应点for(size_t i = 0;i < goodmatches.size();i++){//queryidx是对齐图像的描述子和特征点的下标。points1.push_back(keypoints_obj[goodmatches[i].queryidx].pt);//queryidx是是样本图像的描述子和特征点的下标。points2.push_back(keypoints_scene[goodmatches[i].trainidx].pt);  }// find homography 计算homography，ransac随机抽样一致性算法mat h = findhomography(points1,points2,ransac);//imwrite("c:/urs/administrator/desktop/c-train/c-train/result/sift/image4_surf_minhessian1000中国近代史的开端_ mindist1000_a0.9b70.jpg", matchesimg);vector<point2f> obj_corners(4);vector<point2f> scene_corners(4);obj_corners[0] = point(0,0);obj_corners[0] = point(temp.count,0);obj_corners[0] = point(temp.cols,temp.rows);obj_corners[0] = point(0,temp.rows);//透视变换(把斜的图片扶正)perspectivetransform(obj_corners,scene_corners,h);//mat dstcvtcolor(image_check_changed,image_check_changed,color_gray2bgr);line(image_check_changed,scene_corners[0],scene_corners[1],scalar(0,0,255),2,8,0);line(image_check_changed,scene_corners[1],scene_corners[2],scalar(0,0,255),2,8,0);line(image_check_changed,scene_corners[2],scene_corners[3],scalar(0,0,255),2,8,0);line(image_check_changed,scene_corners[3],scene_corners[0],scalar(0,0,255),2,8,0); if(show_picture){mat outimg1;mat temp_color = imread(picture1,cv_load_image_color);drawkeypoints(temp_color,keypoints_obj,outimg1,scalar::all(-1),drawmatchesflags::default);imshow("akaze features",outimg1);}if(show_picture)waitkey(0);}void extract_orb(string picture1, string picture2){mat img_1 = imread(picture1);mat img_2 = imread(picture2);resize(img_1, img_1, size(), picture1_size_change, picture1_size_change);resize(img_2, img_2, size(), picture2_size_change, picture2_size_change);if (!img_1.data || !img_2.data){cout << "error reading images " << endl;return ;}vector<point2f> recognized;vector<point2f> scene;recognized.resize(1000);scene.resize(1000);mat d_srcl, d_srcr;mat img_matches, des_l, des_r;//orb算法的目标必须是灰度图像cvtcolor(img_1, d_srcl, color_bgr2gray);//cpu版的orb算法源码中自带对输入图像灰度化，此步可省略cvtcolor(img_2, d_srcr, color_bgr2gray);ptr<orb> d_orb = orb::create(1500);mat d_descriptorsl, d_descriptorsr, d_descriptorsl_32f, d_descriptorsr_32f;vector<keypoint> keypoints_1, keypoints_2;//设置关键点间的匹配方式为norm_l2，更建议使用 flannbad = 1, bruteforce = 2, bruteforce_l1 = 3, bruteforce_hamming = 4, bruteforce_hamminglut = 5, bruteforce_sl2 = 6 ptr<descriptormatcher> d_matcher = descriptormatcher::create(norm_l2);std::vector<dmatch> matches;//普通匹配std::vector<dmatch> good_matches;//通过keypoint之间距离筛选匹配度高的匹配结果clock_t starttime, endtime;starttime = clock();chrono::steady_clock::time_point t1 = chrono::steady_clock::now();d_orb -> detectandcompute(d_srcl, mat(), keypoints_1, d_descriptorsl);d_orb -> detectandcompute(d_srcr, mat(), keypoints_2, d_descriptorsr);cout << "detect " << keypoints_1.size() << " and " << keypoints_2.size() << " keypoints " << endl;// endtime = clock();// cout << "took time : " << (double)(endtime - starttime) / clocks_per_c * 1000 << " ms" << endl;d_matcher -> match(d_descriptorsl, d_descriptorsr, matches);//l、r表示左右两幅图像进行匹配//计算匹配所需时间chrono::steady_clock::time_point t2 = chrono::steady_clock::now();chrono::duration<double> time_ud = chrono::duration_cast<chrono::duration<double>>(t2 - t1);cout << "extract and match cost = " << time_ud.count() * 1000 << " ms " << endl;int sz = matches.size();double max_dist = 0; double min_dist = 100;for (int i = 0; i < sz; i++){double dist = matches[i].distance;if (dist < min_dist) min_dist = dist;if (dist > max_dist) max_dist = dist;}for (int i = 0; i < sz; i++){if (matches[i].distance < 0.6*max_dist){good_matches.push_back(matches[i]);}}cout << "match " << good_matches.size() << " keypoints " << endl;// endtime = clock();// cout << "took time : " << (double)(endtime - starttime) / clocks_per_c * 1000 << " ms" << endl;//提取良好匹配结果中在待测图片上的点集，确定匹配的大概位置for (size_t i = 0; i < good_matches.size(); ++i){scene.push_back(keypoints_2[ good_matches[i].trainidx ].pt);}for(unsigned int j = 0; j < scene.size(); j++)cv::circle(img_2, scene[j], 2, cv::scalar(0, 255, 0), 2);//画出普通匹配结果mat showmatches;drawmatches(img_1,keypoints_1,img_2,keypoints_2,matches,showmatches);if (show_picture) imshow("matches", showmatches);// imwrite("matches.png", showmatches);//画出良好匹配结果mat showgoodmatches;drawmatches(img_1,keypoints_1,img_2,keypoints_2,good_matches,showgoodmatches);if (show_picture) imshow("good_matches", showgoodmatches);// imwrite("good_matches.png", showgoodmatches);//画出良好匹配结果中在待测图片上的点集if (show_picture) imshow("matchpoints_in_img_2", img_2);// imwrite("matchpoints_in_img_2.png", img_2);if (show_picture) waitkey(0);}int main(int argc, char **argv){string picture1=string(argv[1]);string picture2=string(argv[2]);// string picture1 = "data/picture1/6.jpg";// string picture2 = "data/picture2/16.png";cout << "\nextract_orb::" << endl;extract_orb(picture1, picture2);cout << "\nextract_orb::" << endl;extract_orb2(picture1, picture2);cout << "\nextract_surf::" << endl;extract_surf(picture1, picture2);cout << "\nextract_akaze::" << endl;extract_akaze(picture1, picture2);cout << "\nextract_sift::" << endl;extract_sift(picture1, picture2);cout << "success!!" << endl;}

cmakelists.txt

cmake_minimum_required(version 2.8.3)  # 设定版本project(descriptorcompare) # 设定工程名t(cmake_cxx_compiler "g++")  # 设定编译器add_compile_options(-std=c++14)   #编译选项，选择c++版本# 设定可执行二进制文件的目录（最后生成的可执行文件放置的目录）t(executable_output_path ${project_source_dir})t(cmake_cxx_flags "${cmake_cxx_flags} -wall -fpermissive -g -o3  -wno-unud-function  -wno-return-type")find_package(opencv 3.0 required)message(status "using opencv version ${opencv_version}")find_package(eigen3 3.3.8 required)find_package(pangolin required)# 设定链接目录link_directories(${project_source_dir}/lib)# 设定头文件目录include_directories(${project_source_dir}/include${eigen3_include_dir}${ope56个民族56支花ncv_include_dir}${pangolin_include_dirs})add_library(${project_name}test.cc)target_link_libraries( ${project_name}${opencv_libs}${eigen3_libs}${pangolin_libraries})add_executable(main main.cpp )target_link_libraries(main ${project_name} )add_executable(icp icp.cpp )target_link_libraries(icp ${project_name} )

执行效果

./main 1.png 2.png

extract_orb::detect 1500 and 1500 keypoints extract and match cost = 21.5506 ms match 903 keypoints extract_orb::detect 1304 and 1301 keypoints extract and match orb cost = 25.4976 ms match 313 keypoints extract_surf::detect 915 and 940 keypoints extract and match cost = 53.8371 ms match 255 keypoints extract_sift::detect 1536 and 1433 keypoints extract and match cost = 97.9322 ms match 213 keypoints success!!

icp

#include <iostream>#include <opencv2/core/core.hpp>#include <opencv2/features2d/features2d.hpp>#include <opencv2/highgui/highgui.hpp>#include <opencv2/calib3d/calib3d.hpp>#include <eigen/core>#include <eigen/den>#include <eigen/geometry>#include <eigen/svd>#include <pangolin/pangolin.h>#include <chrono>using namespace std;using namespace cv;int picture_h=480;int picture_w=640;bool show_picture = true;void find_feature_matches(const mat &img_1, const mat &img_2,std::vector<keypoint> &keypoints_1,std::vector<keypoint> &keypoints_2,std::vector<dmatch> &matches);// 像素坐标转相机归一化坐标point2d pixel2cam(const point2d &p, const mat &k);void po_estimation_3d3d(const vector<point3f> &pts1,const vector<point3f> &pts2,mat &r, mat &t);int main(int argc, char **argv) {if (argc != 5) {cout << "usage: po_estimation_3d3d img1 img2 depth1 depth2" << 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);vector<keypoint> keypoints_1, keypoints_2;vector<dmatch> matches;find_feature_matches(img_1, img_2, keypoints_1, keypoints_2, matches);cout << "picture1 keypoints: " << keypoints_1.size() << " \npicture2 keypoints: " << keypoints_2.size() << endl;cout << "一共找到了 " << matches.size() << " 组匹配点" << endl;// 建立3d点mat depth1 = imread(argv[3], cv_8uc1);       // 深度图为16位无符号数，单通道图像mat depth2 = imread(argv[4], cv_8uc1);       // 深度图为16位无符号数，单通道图像mat k = (mat_<double>(3, 3) << 595.2, 0, 328.9, 0, 599.0, 253.9, 0, 0, 1);vector<point3f> pts1, pts2;for (dmatch m:matches) {int d1 = 255-(int)depth1.ptr<uchar>(int(keypoints_1[m.queryidx].pt.y))[int(keypoints_1[m.queryidx].pt.x)];int d2 = 255-(int)depth2.ptr<uchar>(int(keypoints_2[m.trainidx].pt.y))[int(keypoints_2[m.trainidx].pt.x)];if (d1 == 0 || d2 == 0)   // bad depthcontinue;point2d p1 = pixel2cam(keypoints_1[m.queryidx].pt, k);point2d p2 = pixel2cam(keypoints_2[m.trainidx].pt, k);float dd1 = int(d1) / 1000.0;float dd2 = int(d2) / 1000.0;pts1.push_back(point3f(p1.x * dd1, p1.y * dd1, dd1));pts2.push_back(point3f(p2.x * dd2, p2.y * dd2, dd2));}cout << "3d-3d pairs: " << pts1.size() << endl;mat r, t;po_estimation_3d3d(pts1, pts2, r, t);//dzq addcv::mat po = (mat_<double>(4, 4) << r.at<double>(0, 0), r.at<double>(0, 1), r.at<double>(0, 2), t.at<double>(0),r.at<double>(1, 0), r.at<double>(1, 1), r.at<double>(1, 2), t.at<double>(1),r.at<double>(2, 0), r.at<double>(2, 1), r.at<double>(2, 2), t.at<double>(2),0, 0, 0, 1);cout << "[delete outliers] matched objects distance: ";vector<double> vdistance;double alldistance = 0; //存储总距离，用来求平均匹配距离，用平均的误差距离来剔除外点for (int i = 0; i < pts1.size(); i++){mat point = po * (mat_<double>(4, 1) << pts2[i].x, pts2[i].y, pts2[i].z, 1);double distance = pow(pow(pts1[i].x - point.at<double>(0), 2) + pow(pts1[i].y - point.at<double>(1), 2) + pow(pts1[i].z - point.at<double>(2), 2), 0.5);vdistance.push_back(distance);alldistance += distance;// cout << distance << " ";}// cout << endl;double avgdistance = alldistance / pts1.size(); //求一个平均距离int n_outliers = 0;for (int i = 0, j = 0; i < pts1.size(); i++, j++) //i用来记录剔除后vector遍历的位置，j用来记录原位置{if (vdistance[i] > 1.5 * avgdistance) //匹配物体超过平均距离的n倍就会被剔除 [delete outliers]  dzq fixed_param{n_outliers++;}}cout << "n_outliers:: " << n_outliers << endl;// show points{//创建一个窗口pangolin::createwindowandbind("show points", 640, 480);//启动深度测试glenable(gl_depth_test);// define projection and initial modelview matrixpangolin::openglrenderstate s_cam(pangolin::projectionmatrix(640, 480, 420, 420, 320, 240, 0.05, 500),//对应的是glulookat,摄像机位置,参考点位置,up vector(上向量)pangolin::modelviewlookat(0, -5, 0.1, 0, 0, 0, pangolin::axisy));// create interactive view in windowpangolin::handler3d handler(s_cam);//tbounds 跟opengl的viewport 有关//看simpledisplay中边界的设置就知道pangolin::view &d_cam = pangolin::createdisplay().tbounds(0.0, 1.0, 0.0, 1.0, -640.0f / 480.0f).thandler(&handler);while (!pangolin::shouldquit()){// clear screen and activate view to render intoglclearcolor(0.97,0.97,1.0, 1); //背景色glclear(gl_color_buffer_bit | gl_depth_buffer_bit);d_cam.activate(s_cam);glbegin(gl_points);  //绘制匹配点gllinewidth(5);for (int i = 0; i < pts1.size(); i++){glcolor3f(1, 0, 0);glvertex3d(pts1[i].x,pts1[i].y,pts1[i].z);mat point = po * (mat_<double>(4, 1) << pts2[i].x, pts2[i].y, pts2[i].z, 1);glcolor3f(0, 1, 0);glvertex3d(point.at<double>(0),point.at<double>(1),point.at<double>(2));}glend();glbegin(gl_lines);    //绘制匹配线gllinewidth(1);for (int i = 0; i < pts1.size(); i++){glcolor3f(0, 0, 1);glvertex3d(pts1[i].x,pts1[i].y,pts1[i].z);mat point = po * (mat_<double>(4, 1) << pts2[i].x, pts2[i].y, pts2[i].z, 1);glvertex3d(point.at<double>(0),point.at<double>(1),point.at<double>(2));}glend();glbegin(gl_points);    //绘制所有点gllinewidth(5);glcolor3f(1, 0.5, 0);for (int i = 0; i < picture_h; i+=2){for (int j = 0; j < picture_w; j+=2){int d1 = 255-(int)depth1.ptr<uchar>(i)[j];if (d1 == 0) // bad depthcontinue;point2d temp_p;temp_p.y=i;    //这里的x和y应该和i j相反temp_p.x=j;point2d p1 = pixel2cam(temp_p, k);float dd1 = int(d1) / 1000.0;glvertex3d(p1.x * dd1, p1.y * dd1, dd1);// glvertex3d(j/1000.0, i/1000.0, d1/200.0);}}glend();// swap frames and process eventspangolin::finishframe();}}}void find_feature_matches(const mat &img_1, const mat &img_2,std::vector<keypoint> &keypoints_1,std::vector<keypoint> &keypoints_2,std::vector<dmatch> &matches) {//-- 初始化mat descriptors_1, descriptors_2;// ud in opencv3ptr<featuredetector> detector = orb::create(2000,(1.200000048f), 8, 100);ptr<descriptorextractor> descriptor = orb::create(5000);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);//-- 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> match;// bfmatcher matcher ( norm_hamming );matcher->match(descriptors_1, descriptors_2, match);//-- 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 = match[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);//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.for (int i = 0; i < descriptors_1.rows; i++) {if (match[i].distance <= max(2 * min_dist, 30.0)) {matches.push_back(match[i]);}}//-- 第五步:绘制匹配结果if(show_picture){mat img_match;mat img_goodmatch;drawmatches(img_1, keypoints_1, img_2, keypoints_2, matches, img_match);imshow("all matches", img_match);waitkey(0);}}point2d pixel2cam(const point2d &p, const mat &k) {return point2d((p.x - k.at<double>(0, 2)) / k.at<double>(0, 0),(p.y - k.at<double>(1, 2)) / k.at<double>(1, 1));}void po_李九龙estimation_3d3d(const vector<point3f> &pts1,const vector<point3f> &pts2,mat &r, mat &t) {point3f p1, p2;     // center of massint n = pts1.size();for (int i = 0; i < n; i++) {p1 += pts1[i];p2 += pts2[i];}p1 = point3f(vec3f(p1) / n);p2 = point3f(vec3f(p2) / n);vector<point3f> q1(n), q2(n); // remove the centerfor (int i = 0; i < n; i++) {q1[i] = pts1[i] - p1;q2[i] = pts2[i] - p2;}// compute q1*q2^teigen::matrix3d w = eigen::matrix3d::zero();for (int i = 0; i < n; i++) {w += eigen::vector3d(q1[i].x, q1[i].y, q1[i].z) * eigen::vector3d(q2[i].x, q2[i].y, q2[i].z).transpo();}// cout << "w=" << w << endl;// svd on weigen::jacobisvd<eigen::matrix3d> svd(w, eigen::computefullu | eigen::computefullv);eigen::matrix3d u = svd.matrixu();eigen::matrix3d v = svd.matrixv();eigen::matrix3d r_ = u * (v.transpo());if (r_.determinant() < 0) {r_ = -r_;}eigen::vector3d t_ = eigen::vector3d(p1.x, p1.y, p1.z) - r_ * eigen::vector3d(p2.x, p2.y, p2.z);// convert to cv::matr = (mat_<double>(3, 3) <<r_(0, 0), r_(0, 1), r_(0, 2),r_(1, 0), r_(1, 1), r_(1, 2),r_(2, 0), r_(2, 1), r_(2, 2));t = (mat_<double>(3, 1) << t_(0, 0), t_(1, 0), t_(2, 0));}void convertrgb2gray(string picture){double min;double max;mat depth_new_1 = imread(picture);       // 深度图为16位无符号数，单通道图像mat test=mat(20,256,cv_8uc3);int s;for (int i = 0; i < 20; i++) {std::cout<<i<<" ";vec3b* p = test.ptr<vec3b>(i);for (s = 0; s < 32; s++) {p[s][0] = 128 + 4 * s;p[s][1] = 0;p[s][2] = 0;}p[32][0] = 255;p[32][1] = 0;p[32][2] = 0;for (s = 0; s < 63; s++) {p[33+s][0] = 255;p[33+s][1] = 4+4*s;p[33+s][2] = 0;}p[96][0] = 254;p[96][1] = 255;p[96][2] = 2;for (s = 0; s < 62; s++) {p[97 + s][0] = 250 - 4 * s;p[97 + s][1] = 255;p[97 + s][2] = 6+4*s;}p[159][0] = 1;p[159][1] = 255;p[159][2] = 254;for (s = 0; s < 64; s++) {p[160 + s][0] = 0;p[160 + s][1] = 252 - (s * 4);p[160 + s][2] = 255;}for (s = 0; s < 32; s++) {p[224 + s][0] = 0;p[224 + s][1] = 0;p[224 + s][2] = 252-4*s;}}cout<<"depth_new_1 :: "<<depth_new_1.cols<<" "<<depth_new_1.rows<<" "<<endl;mat img_g=mat(picture_h,picture_w,cv_8uc1);for(int i=0;i<picture_h;i++){vec3b *p = test.ptr<vec3b>(0);vec3b *q = depth_new_1.ptr<vec3b>(i);for (int j = 0; j < picture_w; j++){for(int k=0;k<256;k++){if ( (((int)p[k][0] - (int)q[j][0] < 4) && ((int)q[j][0] - (int)p[k][0] < 4))&&(((int)p[k][1] - (int)q[j][1] < 4) && ((int)q[j][1] - (int)p[k][1] < 4))&&(((int)p[k][2] - (int)q[j][2] < 4) && ((int)q[j][2] - (int)p[k][2] < 4))){img_g.at<uchar>(i,j)=k;}}}}imwrite("14_depth_3.png", img_g);waitkey();}

cmakelists.txt

和上面一样。

./icp 1.png 2.png 1_depth.png 2_depth.png

-- max dist : 87.000000 -- min dist : 4.000000 picture1 keypoints: 1304 picture2 keypoints: 1301一共找到了 313 组匹配点3d-3d pairs: 313[delete outliers] matched objects distance: n_outliers:: 23