opencv4-findHomography-flann.cpp

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#include <vector>
#include <string>
#include <opencv2/opencv.hpp>

using namespace std;
using namespace cv;

//以下のコードを参考にした
//https://whitewell.sakura.ne.jp/OpenCV/py_tutorials/py_feature2d/py_matcher/py_matcher.html
//特徴点のマッチング
//https://qiita.com/wakaba130/items/b566f15a4f2cf1ce414f
//OpenCVのcv::findHomographyについて

int main()
{
    //画像は以下から
    //http://www.ess.ic.kanagawa-it.ac.jp/app_images_j.html
    //標準画像データベース[神奈川工大 信号処理応用研究室]
    Mat src1 = imread("Mandrill.png");
    Mat src2 = imread("Mandrill2.png");

    //SIFTについて
    //https://www.slideshare.net/slideshow/siftsurf/6668292
    //画像認識の初歩、SIFT,SURF特徴量
    //https://mosapui.hatenablog.com/entry/2009/04/25/193525
    //SURF(Speeded Up Robust Features)すごい - もさぷい
    //http://www.atinfinity.info/blog/archives/108
    //OpenCVのCVS版（20081010）で遊んでみた | dandelion's log
    //https://kesin.hatenablog.com/entry/20120810/1344582180
    //OpenCV2でSIFT, SURFによる画像の対応付け
    vector<KeyPoint> key1;
    vector<KeyPoint> key2;
    Mat desc1, desc2;
    auto detector=SIFT::create(500);
    detector->detectAndCompute(src1, Mat(), key1, desc1);
    detector->detectAndCompute(src2, Mat(), key2, desc2);

    //flannによるマッチングについて
    //https://speakerdeck.com/matsui_528/jin-si-zui-jin-bang-tan-suo-falsezui-qian-xian
    //近似最近傍探索の最前線
    //https://docs.opencv.org/4.5.2/db/d39/classcv_1_1DescriptorMatcher.html
    //cv::DescriptorMatcher
    //https://vovkos.github.io/doxyrest-showcase/opencv/sphinxdoc/struct_cv_flann_SearchParams.html
    //struct cv::flann::SearchParams
    //https://stackoverflow.com/questions/44184159/opencv-c-flann-indexparams-and-searchparams-error
    //OpenCV C++ Flann IndexParams and SearchParams error
    //https://qiita.com/dandelion1124/items/ab6004058af8fd393674
    //OpenCVの初期化処理，終了処理
    //https://blog.goo.ne.jp/yoossh/e/a35701fa421903cc78055e3c07c3e1b5
    //kd-tree法で点群データ座標を探索(OpenCVとVisual Studio 2019)
    //https://gist.github.com/kuboyoo/8af6a5eba574a6c9e76af5f39beb5be5
    //nearestNeighbor.cpp
    //https://nokixa.hatenablog.com/entry/2021/08/28/020213
    //OpenCVやってみる-20. 特徴点のマッチング(FLANN)
    vector<vector<DMatch>> knnmatch_points;
    auto params=cv::makePtr<flann::SearchParams>(50);
    auto kd=cv::makePtr<flann::KDTreeIndexParams>(5);
    FlannBasedMatcher match(kd, params);
    desc1.convertTo(desc1, CV_32F);
    desc2.convertTo(desc2, CV_32F);
    match.knnMatch(desc1, desc2, knnmatch_points, 2);

    //二番目にいい点との比較をする
    vector<DMatch> goodMatch;
    vector<Point2f> match_point1;
    vector<Point2f> match_point2;
    for (const auto& m: knnmatch_points) {
        if (m[0].distance <= m[1].distance * 0.75) {
            goodMatch.push_back(m[0]);
            match_point1.push_back(key1[m[0].queryIdx].pt);
            match_point2.push_back(key2[m[0].trainIdx].pt);
        }
    }

    Mat masks;
    Mat H = findHomography(match_point1, match_point2, masks, RANSAC, 3);

    //RANSACによるマスク(ロバストなので一部無視する)
    vector<char> maskx(masks.reshape(1, 1));

    //https://docs.opencv.org/4.x//d4/d5d/group__features2d__draw.html
    //Drawing Function of Keypoints and Matches
    Mat drawMatch;
    drawMatches(src1, key1, src2, key2, goodMatch, drawMatch, 1, Scalar::all(-1), Scalar::all(-1), maskx);
    imwrite("match.jpg", drawMatch);

    return 0;
}