AKAZE local features matching

Introduction

In this tutorial we will learn how to use [AKAZE] local features to detect and match keypoints on two images.

We will find keypoints on a pair of images with given homography matrix, match them and count the number of inliers (i. e. matches that fit in the given homography).

You can find expanded version of this example here: https://github.com/pablofdezalc/test_kaze_akaze_opencv

[AKAZE]

Fast Explicit Diffusion for Accelerated Features in Nonlinear Scale Spaces. Pablo F. Alcantarilla, Jesús Nuevo and Adrien Bartoli. In British Machine Vision Conference (BMVC), Bristol, UK, September 2013.

Data

We are going to use images 1 and 3 from Graffity sequence of Oxford dataset.

Homography is given by a 3 by 3 matrix:

7.6285898e-01  -2.9922929e-01   2.2567123e+02
3.3443473e-01   1.0143901e+00  -7.6999973e+01
3.4663091e-04  -1.4364524e-05   1.0000000e+00

You can find the images (graf1.png, graf3.png) and homography (H1to3p.xml) in opencv/samples/cpp.

Source Code

#include <opencv2/features2d.hpp>
#include <opencv2/imgcodecs.hpp>
#include <opencv2/opencv.hpp>
#include <vector>
#include <iostream>

using namespace std;
using namespace cv;

const float inlier_threshold = 2.5f; // Distance threshold to identify inliers
const float nn_match_ratio = 0.8f;   // Nearest neighbor matching ratio

int main(void)
{
    Mat img1 = imread("graf1.png", IMREAD_GRAYSCALE);
    Mat img2 = imread("graf3.png", IMREAD_GRAYSCALE);

    Mat homography;
    FileStorage fs("H1to3p.xml", FileStorage::READ);
    fs.getFirstTopLevelNode() >> homography;

    vector<KeyPoint> kpts1, kpts2;
    Mat desc1, desc2;

    AKAZE akaze;
    akaze(img1, noArray(), kpts1, desc1);
    akaze(img2, noArray(), kpts2, desc2);

    BFMatcher matcher(NORM_HAMMING);
    vector< vector<DMatch> > nn_matches;
    matcher.knnMatch(desc1, desc2, nn_matches, 2);

    vector<KeyPoint> matched1, matched2, inliers1, inliers2;
    vector<DMatch> good_matches;
    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]);
        }
    }

    for(unsigned 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));
        }
    }

    Mat res;
    drawMatches(img1, inliers1, img2, inliers2, good_matches, res);
    imwrite("res.png", res);

    double inlier_ratio = inliers1.size() * 1.0 / matched1.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;

    return 0;
}

Explanation

1. Load images and homography

Mat img1 = imread("graf1.png", IMREAD_GRAYSCALE);
Mat img2 = imread("graf3.png", IMREAD_GRAYSCALE);

Mat homography;
FileStorage fs("H1to3p.xml", FileStorage::READ);
fs.getFirstTopLevelNode() >> homography;

We are loading grayscale images here. Homography is stored in the xml created with FileStorage.

2. Detect keypoints and compute descriptors using AKAZE

vector<KeyPoint> kpts1, kpts2;
Mat desc1, desc2;

AKAZE akaze;
akaze(img1, noArray(), kpts1, desc1);
akaze(img2, noArray(), kpts2, desc2);

We create AKAZE object and use it’s operator() functionality. Since we don’t need the mask parameter, noArray() is used.

3. Use brute-force matcher to find 2-nn matches

BFMatcher matcher(NORM_HAMMING);
vector< vector<DMatch> > nn_matches;
matcher.knnMatch(desc1, desc2, nn_matches, 2);

We use Hamming distance, because AKAZE uses binary descriptor by default.

4. Use 2-nn matches to find correct keypoint matches

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]);
    }
}

If the closest match is ratio closer than the second closest one, then the match is correct.

5. Check if our matches fit in the homography model

for(int 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);
    float 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 = inliers1.size();
        inliers1.push_back(matched1[i]);
        inliers2.push_back(matched2[i]);
        good_matches.push_back(DMatch(new_i, new_i, 0));
    }
}

If the distance from first keypoint’s projection to the second keypoint is less than threshold, then it it fits in the homography.

We create a new set of matches for the inliers, because it is required by the drawing function.

6. Output results

Mat res;
drawMatches(img1, inliers1, img2, inliers2, good_matches, res);
imwrite("res.png", res);
...

Here we save the resulting image and print some statistics.

Results

Found matches

A-KAZE Matching Results

Keypoints 1: 2943

Keypoints 2: 3511

Matches: 447

Inliers: 308

Inliers Ratio: 0.689038