OpenCV  3.4.6-dev
Open Source Computer Vision
AKAZE local features matching

Introduction

In this tutorial we will learn how to use AKAZE [5] 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

Data

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

graf.png

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/data/.

Source Code

Explanation

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

We create AKAZE and detect and compute AKAZE keypoints and descriptors. Since we don't need the mask parameter, noArray() is used.

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

If the closest match distance is significantly lower than the second closest one, then the match is correct (match is not ambiguous).

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

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

Here we save the resulting image and print some statistics.

Results

Found matches

res.png

Depending on your OpenCV version, you should get results coherent with:

Keypoints 1: 2943
Keypoints 2: 3511
Matches: 447
Inliers: 308
Inlier Ratio: 0.689038