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doc/tutorials/features2d/akaze_matching/akaze_matching.markdown

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+AKAZE local features matching {#tutorial_akaze_matching}
+=============================
+
+@tableofcontents
+
+@prev_tutorial{tutorial_detection_of_planar_objects}
+@next_tutorial{tutorial_akaze_tracking}
+
+|    |    |
+| -: | :- |
+| Original author | Fedor Morozov |
+| Compatibility | OpenCV >= 3.0 |
+
+Introduction
+------------
+
+In this tutorial we will learn how to use AKAZE @cite ANB13 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](http://www.robots.ox.ac.uk/~vgg/data/data-aff.html).
+
+![](images/graf.png)
+
+Homography is given by a 3 by 3 matrix:
+@code{.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
+@endcode
+You can find the images (*graf1.png*, *graf3.png*) and homography (*H1to3p.xml*) in
+*opencv/samples/data/*.
+
+### Source Code
+
+@add_toggle_cpp
+-   **Downloadable code**: Click
+    [here](https://raw.githubusercontent.com/opencv/opencv/master/samples/cpp/tutorial_code/features2D/AKAZE_match.cpp)
+
+-   **Code at glance:**
+    @include samples/cpp/tutorial_code/features2D/AKAZE_match.cpp
+@end_toggle
+
+@add_toggle_java
+-   **Downloadable code**: Click
+    [here](https://raw.githubusercontent.com/opencv/opencv/master/samples/java/tutorial_code/features2D/akaze_matching/AKAZEMatchDemo.java)
+
+-   **Code at glance:**
+    @include samples/java/tutorial_code/features2D/akaze_matching/AKAZEMatchDemo.java
+@end_toggle
+
+@add_toggle_python
+-   **Downloadable code**: Click
+    [here](https://raw.githubusercontent.com/opencv/opencv/master/samples/python/tutorial_code/features2D/akaze_matching/AKAZE_match.py)
+
+-   **Code at glance:**
+    @include samples/python/tutorial_code/features2D/akaze_matching/AKAZE_match.py
+@end_toggle
+
+### Explanation
+
+-   **Load images and homography**
+
+@add_toggle_cpp
+@snippet samples/cpp/tutorial_code/features2D/AKAZE_match.cpp load
+@end_toggle
+
+@add_toggle_java
+@snippet samples/java/tutorial_code/features2D/akaze_matching/AKAZEMatchDemo.java load
+@end_toggle
+
+@add_toggle_python
+@snippet samples/python/tutorial_code/features2D/akaze_matching/AKAZE_match.py load
+@end_toggle
+
+We are loading grayscale images here. Homography is stored in the xml created with FileStorage.
+
+-   **Detect keypoints and compute descriptors using AKAZE**
+
+@add_toggle_cpp
+@snippet samples/cpp/tutorial_code/features2D/AKAZE_match.cpp AKAZE
+@end_toggle
+
+@add_toggle_java
+@snippet samples/java/tutorial_code/features2D/akaze_matching/AKAZEMatchDemo.java AKAZE
+@end_toggle
+
+@add_toggle_python
+@snippet samples/python/tutorial_code/features2D/akaze_matching/AKAZE_match.py AKAZE
+@end_toggle
+
+We create AKAZE and detect and compute AKAZE keypoints and descriptors. Since we don't need the *mask*
+parameter, *noArray()* is used.
+
+-   **Use brute-force matcher to find 2-nn matches**
+
+@add_toggle_cpp
+@snippet samples/cpp/tutorial_code/features2D/AKAZE_match.cpp 2-nn matching
+@end_toggle
+
+@add_toggle_java
+@snippet samples/java/tutorial_code/features2D/akaze_matching/AKAZEMatchDemo.java 2-nn matching
+@end_toggle
+
+@add_toggle_python
+@snippet samples/python/tutorial_code/features2D/akaze_matching/AKAZE_match.py 2-nn matching
+@end_toggle
+
+We use Hamming distance, because AKAZE uses binary descriptor by default.
+
+-   **Use 2-nn matches and ratio criterion to find correct keypoint matches**
+@add_toggle_cpp
+@snippet samples/cpp/tutorial_code/features2D/AKAZE_match.cpp ratio test filtering
+@end_toggle
+
+@add_toggle_java
+@snippet samples/java/tutorial_code/features2D/akaze_matching/AKAZEMatchDemo.java ratio test filtering
+@end_toggle
+
+@add_toggle_python
+@snippet samples/python/tutorial_code/features2D/akaze_matching/AKAZE_match.py ratio test filtering
+@end_toggle
+
+If the closest match distance is significantly lower than the second closest one, then the match is correct (match is not ambiguous).
+
+-   **Check if our matches fit in the homography model**
+
+@add_toggle_cpp
+@snippet samples/cpp/tutorial_code/features2D/AKAZE_match.cpp homography check
+@end_toggle
+
+@add_toggle_java
+@snippet samples/java/tutorial_code/features2D/akaze_matching/AKAZEMatchDemo.java homography check
+@end_toggle
+
+@add_toggle_python
+@snippet samples/python/tutorial_code/features2D/akaze_matching/AKAZE_match.py homography check
+@end_toggle
+
+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.
+
+-   **Output results**
+
+@add_toggle_cpp
+@snippet samples/cpp/tutorial_code/features2D/AKAZE_match.cpp draw final matches
+@end_toggle
+
+@add_toggle_java
+@snippet samples/java/tutorial_code/features2D/akaze_matching/AKAZEMatchDemo.java draw final matches
+@end_toggle
+
+@add_toggle_python
+@snippet samples/python/tutorial_code/features2D/akaze_matching/AKAZE_match.py draw final matches
+@end_toggle
+
+Here we save the resulting image and print some statistics.
+
+Results
+-------
+
+### Found matches
+
+![](images/res.png)
+
+Depending on your OpenCV version, you should get results coherent with:
+
+@code{.none}
+ Keypoints 1:   2943
+ Keypoints 2:   3511
+ Matches:       447
+ Inliers:       308
+ Inlier Ratio: 0.689038
+@endcode