Doxygen tutorials: cpp done

doc/tutorials/features2d/akaze_tracking/akaze_tracking.markdown

@@ -11,16 +11,17 @@ The algorithm is as follows:
 
 -   Detect and describe keypoints on the first frame, manually set object boundaries
 -   For every next frame:
-    1.  Detect and describe keypoints
-    2.  Match them using bruteforce matcher
-    3.  Estimate homography transformation using RANSAC
-    4.  Filter inliers from all the matches
-    5.  Apply homography transformation to the bounding box to find the object
-    6.  Draw bounding box and inliers, compute inlier ratio as evaluation metric
+    -#  Detect and describe keypoints
+    -#  Match them using bruteforce matcher
+    -#  Estimate homography transformation using RANSAC
+    -#  Filter inliers from all the matches
+    -#  Apply homography transformation to the bounding box to find the object
+    -#  Draw bounding box and inliers, compute inlier ratio as evaluation metric
 
-![image](images/frame.png)
+![](images/frame.png)
 
-### Data
+Data
+----
 
 To do the tracking we need a video and object position on the first frame.
 
@@ -31,14 +32,16 @@ To run the code you have to specify input and output video path and object bound
 @code{.none}
 ./planar_tracking blais.mp4 result.avi blais_bb.xml.gz
 @endcode
-### Source Code
+
+Source Code
+-----------
 
 @includelineno cpp/tutorial_code/features2D/AKAZE_tracking/planar_tracking.cpp
 
-### Explanation
+Explanation
+-----------
 
-Tracker class
--------------
+### Tracker class
 
 This class implements algorithm described abobve using given feature detector and descriptor
 matcher.
@@ -63,62 +66,60 @@ matcher.
 
 -   **Processing frames**
 
-    1.  Locate keypoints and compute descriptors
-    @code{.cpp}
-    (*detector)(frame, noArray(), kp, desc);
-    @endcode
-    
-    To find matches between frames we have to locate the keypoints first.
-    
-    In this tutorial detectors are set up to find about 1000 keypoints on each frame.
+    -#  Locate keypoints and compute descriptors
+        @code{.cpp}
+        (*detector)(frame, noArray(), kp, desc);
+        @endcode
 
-    1.  Use 2-nn matcher to find correspondences
-    @code{.cpp}
-    matcher->knnMatch(first_desc, desc, matches, 2);
-    for(unsigned i = 0; i < matches.size(); i++) {
-        if(matches[i][0].distance < nn_match_ratio * matches[i][1].distance) {
-            matched1.push_back(first_kp[matches[i][0].queryIdx]);
-            matched2.push_back(      kp[matches[i][0].trainIdx]);
+        To find matches between frames we have to locate the keypoints first.
+
+        In this tutorial detectors are set up to find about 1000 keypoints on each frame.
+
+    -#  Use 2-nn matcher to find correspondences
+        @code{.cpp}
+        matcher->knnMatch(first_desc, desc, matches, 2);
+        for(unsigned i = 0; i < matches.size(); i++) {
+            if(matches[i][0].distance < nn_match_ratio * matches[i][1].distance) {
+                matched1.push_back(first_kp[matches[i][0].queryIdx]);
+                matched2.push_back(      kp[matches[i][0].trainIdx]);
+            }
         }
-    }
-    @endcode
-    
-    If the closest match is *nn_match_ratio* closer than the second closest one, then it's a
-    match.
+        @endcode
+        If the closest match is *nn_match_ratio* closer than the second closest one, then it's a
+        match.
 
-    2.  Use *RANSAC* to estimate homography transformation
-    @code{.cpp}
-    homography = findHomography(Points(matched1), Points(matched2),
-                                RANSAC, ransac_thresh, inlier_mask);
-    @endcode
-    
-    If there are at least 4 matches we can use random sample consensus to estimate image
-    transformation.
+    -#  Use *RANSAC* to estimate homography transformation
+        @code{.cpp}
+        homography = findHomography(Points(matched1), Points(matched2),
+                                    RANSAC, ransac_thresh, inlier_mask);
+        @endcode
+        If there are at least 4 matches we can use random sample consensus to estimate image
+        transformation.
 
-    3.  Save the inliers
-    @code{.cpp}
-    for(unsigned i = 0; i < matched1.size(); i++) {
-        if(inlier_mask.at<uchar>(i)) {
-            int new_i = static_cast<int>(inliers1.size());
-            inliers1.push_back(matched1[i]);
-            inliers2.push_back(matched2[i]);
-            inlier_matches.push_back(DMatch(new_i, new_i, 0));
+    -#  Save the inliers
+        @code{.cpp}
+        for(unsigned i = 0; i < matched1.size(); i++) {
+            if(inlier_mask.at<uchar>(i)) {
+                int new_i = static_cast<int>(inliers1.size());
+                inliers1.push_back(matched1[i]);
+                inliers2.push_back(matched2[i]);
+                inlier_matches.push_back(DMatch(new_i, new_i, 0));
+            }
         }
-    }
-    @endcode
-    
-    Since *findHomography* computes the inliers we only have to save the chosen points and
-    matches.
+        @endcode
+        Since *findHomography* computes the inliers we only have to save the chosen points and
+        matches.
 
-    4.  Project object bounding box
-    @code{.cpp}
-    perspectiveTransform(object_bb, new_bb, homography);
-    @endcode
-    
-    If there is a reasonable number of inliers we can use estimated transformation to locate the
-    object.
+    -#  Project object bounding box
+        @code{.cpp}
+        perspectiveTransform(object_bb, new_bb, homography);
+        @endcode
 
-### Results
+        If there is a reasonable number of inliers we can use estimated transformation to locate the
+        object.
+
+Results
+-------
 
 You can watch the resulting [video on youtube](http://www.youtube.com/watch?v=LWY-w8AGGhE).
 
@@ -129,6 +130,7 @@ Inliers      410
 Inlier ratio 0.58
 Keypoints    1117
 @endcode
+
 *ORB* statistics:
 @code{.none}
 Matches      504