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Doxygen tutorials: cpp done

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Maksim Shabunin
2014-11-28 16:21:28 +03:00
parent c5536534d8
commit 36a04ef8de
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doc/tutorials/features2d/akaze_matching/akaze_matching.markdown
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@@ -4,10 +4,10 @@ AKAZE local features matching {#tutorial_akaze_matching}
Introduction
------------
In this tutorial we will learn how to use [AKAZE]_ local features to detect and match keypoints on
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:
@@ -18,7 +18,7 @@ Data
We are going to use images 1 and 3 from *Graffity* sequence of Oxford dataset.
![image](images/graf.png)
![](images/graf.png)
Homography is given by a 3 by 3 matrix:
@code{.none}
@@ -35,92 +35,92 @@ You can find the images (*graf1.png*, *graf3.png*) and homography (*H1to3p.xml*)
### Explanation
1. **Load images and homography**
@code{.cpp}
Mat img1 = imread("graf1.png", IMREAD_GRAYSCALE);
Mat img2 = imread("graf3.png", IMREAD_GRAYSCALE);
-# **Load images and homography**
@code{.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;
@endcode
We are loading grayscale images here. Homography is stored in the xml created with FileStorage.
Mat homography;
FileStorage fs("H1to3p.xml", FileStorage::READ);
fs.getFirstTopLevelNode() >> homography;
@endcode
We are loading grayscale images here. Homography is stored in the xml created with FileStorage.
1. **Detect keypoints and compute descriptors using AKAZE**
@code{.cpp}
vector<KeyPoint> kpts1, kpts2;
Mat desc1, desc2;
-# **Detect keypoints and compute descriptors using AKAZE**
@code{.cpp}
vector<KeyPoint> kpts1, kpts2;
Mat desc1, desc2;
AKAZE akaze;
akaze(img1, noArray(), kpts1, desc1);
akaze(img2, noArray(), kpts2, desc2);
@endcode
We create AKAZE object and use it's *operator()* functionality. Since we don't need the *mask*
parameter, *noArray()* is used.
AKAZE akaze;
akaze(img1, noArray(), kpts1, desc1);
akaze(img2, noArray(), kpts2, desc2);
@endcode
We create AKAZE object and use it's *operator()* functionality. Since we don't need the *mask*
parameter, *noArray()* is used.
1. **Use brute-force matcher to find 2-nn matches**
@code{.cpp}
BFMatcher matcher(NORM_HAMMING);
vector< vector<DMatch> > nn_matches;
matcher.knnMatch(desc1, desc2, nn_matches, 2);
@endcode
We use Hamming distance, because AKAZE uses binary descriptor by default.
-# **Use brute-force matcher to find 2-nn matches**
@code{.cpp}
BFMatcher matcher(NORM_HAMMING);
vector< vector<DMatch> > nn_matches;
matcher.knnMatch(desc1, desc2, nn_matches, 2);
@endcode
We use Hamming distance, because AKAZE uses binary descriptor by default.
1. **Use 2-nn matches to find correct keypoint matches**
@code{.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;
-# **Use 2-nn matches to find correct keypoint matches**
@code{.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(dist1 < nn_match_ratio * dist2) {
matched1.push_back(kpts1[first.queryIdx]);
matched2.push_back(kpts2[first.trainIdx]);
}
}
}
@endcode
If the closest match is *ratio* closer than the second closest one, then the match is correct.
@endcode
If the closest match is *ratio* closer than the second closest one, then the match is correct.
1. **Check if our matches fit in the homography model**
@code{.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;
-# **Check if our matches fit in the homography model**
@code{.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));
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(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));
}
}
}
@endcode
If the distance from first keypoint's projection to the second keypoint is less than threshold,
then it it fits in the homography.
@endcode
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.
We create a new set of matches for the inliers, because it is required by the drawing function.
1. **Output results**
@code{.cpp}
Mat res;
drawMatches(img1, inliers1, img2, inliers2, good_matches, res);
imwrite("res.png", res);
...
@endcode
Here we save the resulting image and print some statistics.
-# **Output results**
@code{.cpp}
Mat res;
drawMatches(img1, inliers1, img2, inliers2, good_matches, res);
imwrite("res.png", res);
...
@endcode
Here we save the resulting image and print some statistics.
### Results
Found matches
-------------
![image](images/res.png)
![](images/res.png)
A-KAZE Matching Results
-----------------------

11
doc/tutorials/features2d/akaze_matching/akaze_matching.rst
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@@ -152,8 +152,9 @@ A-KAZE Matching Results
--------------------------
.. code-block:: none
Keypoints 1: 2943
Keypoints 2: 3511
Matches: 447
Inliers: 308
Inlier Ratio: 0.689038
Keypoints 1 2943
Keypoints 2 3511
Matches 447
Inliers 308
Inlier Ratio 0.689038