#include <opencv2/opencv.hpp>

#import "OpenCVTool.h"
using namespace std;
using namespace cv;
const floatInlier_threshold = 2.5 f; // Distance threshold to identify inliers with homography check constfloatNn_match_ratio = 0.8 f; +(BOOL)checkImage:(NSString *)path1 withImage:(NSString *)path2 { const char * cpath = [path1 cStringUsingEncoding:NSUTF8StringEncoding]; const char * cpath1 = [path2 cStringUsingEncoding:NSUTF8StringEncoding]; Mat img1 = imread(cpath, IMREAD_GRAYSCALE); Mat img2 = imread(cpath1, IMREAD_GRAYSCALE); Mat homography; vector<KeyPoint> kpts1, kpts2; Mat desc1, desc2; Ptr<AKAZE> akaze = AKAZE::create(); akaze->detectAndCompute(img1, noArray(), kpts1, desc1); akaze->detectAndCompute(img2, noArray(), kpts2, desc2); BFMatcher matcher(NORM_HAMMING); vector< vector<DMatch> > nn_matches; matcher.knnMatch(desc1, desc2, nn_matches, 2); //-------------------- vector<KeyPoint> matched1, matched2; vector<Point2f> obj, scene;for(size_t i = 0; i < nn_matches.size(); i++) {
        DMatch first = nn_matches[i][0];
        float dist1 = nn_matches[i][0].distance;
        float dist2 = nn_matches[i][1].distance;
        if(dist1 < nn_match_ratio * dist2) {
            matched1.push_back(kpts1[first.queryIdx]);
            matched2.push_back(kpts2[first.trainIdx]);
            //-- Get the keypoints from the good matches
            obj.push_back( kpts1[first.queryIdx].pt );
            scene.push_back( kpts2[first.trainIdx].pt );
        }
    }
    homography = findHomography( obj, scene, RANSAC );
    
    
    vector<DMatch> good_matches;
    vector<KeyPoint> inliers1, inliers2;
    for(size_t i = 0; i < matched1.size(); i++) {
        Mat col = Mat::ones(3, 1, CV_64F);
        col.at<double>(0) = matched1[i].pt.x;
        col.at<double>(1) = matched1[i].pt.y;
        col = homography * col;
        col /= col.at<double>(2);
        double dist = sqrt( pow(col.at<double>(0) - matched2[i].pt.x, 2) +
                           pow(col.at<double>(1) - matched2[i].pt.y, 2));
        if(dist < inlier_threshold) {
            int new_i = static_cast<int>(inliers1.size());
            inliers1.push_back(matched1[i]);
            inliers2.push_back(matched2[i]);
            good_matches.push_back(DMatch(new_i, new_i, 0));
        }
    }
    double inlier_ratio = inliers1.size() / (double) matched1.size();
    double match =  (double) matched1.size()/inliers2.size() ;

    cout << "A-KAZE Matching Results" << endl;
    cout << "* * * * * * * * * * * * * * * * * * * * * * * * * * * * * * *" << endl;
    cout << "# Keypoints 1: \t" << kpts1.size() << endl;
    cout << "# Keypoints 2: \t" << kpts2.size() << endl;
    cout << "# Matches: \t" << matched1.size() << endl;
    cout << "# Inliers: \t" << inliers1.size() << endl;
    cout << "# Inliers Ratio: \t" << inlier_ratio << endl;
    cout << endl;
    if(inlier_ratio > = 0.7 && match > 0.15) {return YES;
    }else
    {
        returnNO; }}Copy the code

Example 1 Example 2 Homography needs to be calculated by itself