opencv/doc/tutorials/features2d/akaze_matching/akaze_matching.rst

.. _akazeMatching:


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.

.. image:: images/graf.png
  :height: 200pt
  :width:  320pt
  :alt: Graffity
  :align: center

Homography is given by a 3 by 3 matrix:

.. code-block:: none

    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
===========
.. literalinclude:: ../../../../samples/cpp/tutorial_code/features2D/AKAZE_match.cpp
   :language: cpp
   :linenos:
   :tab-width: 4

Explanation
===========

#. **Load images and homography**

  .. code-block:: cpp

    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.

#. **Detect keypoints and compute descriptors using AKAZE**

  .. code-block:: cpp

    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.

#. **Use brute-force matcher to find 2-nn matches**

  .. code-block:: cpp

    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.

#. **Use 2-nn matches to find correct keypoint matches**

  .. code-block:: cpp

    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.

#. **Check if our matches fit in the homography model**

  .. code-block:: cpp

    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.

#. **Output results**

  .. code-block:: cpp

    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
--------------

.. image:: images/res.png
  :height: 200pt
  :width:  320pt
  :alt: Matches
  :align: center

A-KAZE Matching Results
--------------------------

.. code-block:: none
    Keypoints 1:   2943
    Keypoints 2:   3511
    Matches:       447
    Inliers:       308
    Inlier Ratio: 0.689038