AKAZE fixes and tracking tutorial

doc/tutorials/features2d/akaze_matching/akaze_matching.rst

@@ -46,7 +46,7 @@ Source Code
 Explanation
 ===========
 
-1. **Load images and homography**
+#. **Load images and homography**
 
   .. code-block:: cpp
 
@@ -59,7 +59,7 @@ Explanation
 
   We are loading grayscale images here. Homography is stored in the xml created with FileStorage.
 
-2. **Detect keypoints and compute descriptors using AKAZE**
+#. **Detect keypoints and compute descriptors using AKAZE**
 
   .. code-block:: cpp
 
@@ -72,7 +72,7 @@ Explanation
 
   We create AKAZE object and use it's *operator()* functionality. Since we don't need the *mask* parameter, *noArray()* is used.
 
-3. **Use brute-force matcher to find 2-nn matches**
+#. **Use brute-force matcher to find 2-nn matches**
 
   .. code-block:: cpp
 
@@ -82,7 +82,7 @@ Explanation
 
   We use Hamming distance, because AKAZE uses binary descriptor by default.
 
-4. **Use 2-nn matches to find correct keypoint matches**
+#. **Use 2-nn matches to find correct keypoint matches**
 
   .. code-block:: cpp
 
@@ -99,7 +99,7 @@ Explanation
 
   If the closest match is *ratio* closer than the second closest one, then the match is correct.
 
-5. **Check if our matches fit in the homography model**
+#. **Check if our matches fit in the homography model**
 
   .. code-block:: cpp
 
@@ -125,7 +125,7 @@ Explanation
 
   We create a new set of matches for the inliers, because it is required by the drawing function.
 
-6. **Output results**
+#. **Output results**
 
   .. code-block:: cpp
 
@@ -150,12 +150,10 @@ Found matches
 
 A-KAZE Matching Results
 --------------------------
-Keypoints 1:                        	2943
 
-Keypoints 2:                        	3511
-
-Matches:                            	447
-
-Inliers:                            	308
-
-Inliers Ratio:                      	0.689038
+  ::code-block:: none
+    Keypoints 1:   2943
+    Keypoints 2:   3511
+    Matches:       447
+    Inliers:       308
+    Inlier Ratio: 0.689038

doc/tutorials/features2d/akaze_tracking/akaze_tracking.rst

@@ -0,0 +1,155 @@
+.. _akazeTracking:
+
+
+AKAZE and ORB planar tracking
+******************************
+
+Introduction
+------------------
+
+In this tutorial we will compare *AKAZE* and *ORB* local features
+using them to find matches between video frames and track object movements.
+
+The algorithm is as follows:
+
+* Detect and describe keypoints on the first frame, manually set object boundaries
+* For every next frame:
+
+  #. 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
+  :height: 480pt
+  :width:  640pt
+  :alt: Result frame example
+  :align: center
+
+Data
+===========
+To do the tracking we need a video and object position on the first frame.
+
+You can download our example video and data from `here <https://docs.google.com/file/d/0B72G7D4snftJandBb0taLVJHMFk>`_.
+
+To run the code you have to specify input and output video path and object bounding box.
+
+.. code-block:: none
+
+  ./planar_tracking blais.mp4 result.avi blais_bb.xml.gz
+
+Source Code
+===========
+.. literalinclude:: ../../../../samples/cpp/tutorial_code/features2D/AKAZE_tracking/planar_tracking.cpp
+   :language: cpp
+   :linenos:
+   :tab-width: 4
+
+Explanation
+===========
+
+Tracker class
+--------------
+
+  This class implements algorithm described abobve
+  using given feature detector and descriptor matcher.
+
+* **Setting up the first frame**
+
+  .. code-block:: cpp
+
+    void Tracker::setFirstFrame(const Mat frame, vector<Point2f> bb, string title, Stats& stats)
+    {
+        first_frame = frame.clone();
+        (*detector)(first_frame, noArray(), first_kp, first_desc);
+        stats.keypoints = (int)first_kp.size();
+        drawBoundingBox(first_frame, bb);
+        putText(first_frame, title, Point(0, 60), FONT_HERSHEY_PLAIN, 5, Scalar::all(0), 4);
+        object_bb = bb;
+    }
+
+  We compute and store keypoints and descriptors from the first frame and prepare it for the output.
+
+  We need to save number of detected keypoints to make sure both detectors locate roughly the same number of those.
+
+* **Processing frames**
+
+  #. Locate keypoints and compute descriptors
+
+    .. code-block:: cpp
+
+      (*detector)(frame, noArray(), kp, desc);
+
+    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-block:: 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]);
+          }
+      }
+
+    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-block:: cpp
+
+      homography = findHomography(Points(matched1), Points(matched2),
+                                  RANSAC, ransac_thresh, inlier_mask);
+
+    If there are at least 4 matches we can use random sample consensus to estimate image transformation.
+
+  3. Save the inliers
+
+    .. code-block:: 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));
+            }
+        }
+
+    Since *findHomography* computes the inliers we only have to save the chosen points and matches.
+
+  4. Project object bounding box
+
+    .. code-block:: cpp
+
+        perspectiveTransform(object_bb, new_bb, homography);
+
+    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>`_.
+
+*AKAZE* statistics:
+
+  .. code-block:: none
+
+    Matches      626
+    Inliers      410
+    Inlier ratio 0.58
+    Keypoints    1117
+
+*ORB* statistics:
+
+  .. code-block:: none
+
+    Matches      504
+    Inliers      319
+    Inlier ratio 0.56
+    Keypoints    1112

doc/tutorials/features2d/table_of_content_features2d/table_of_content_features2d.rst

@@ -194,7 +194,7 @@ Learn about how to use the feature points  detectors, descriptors and matching f
 
                         *Author:* Fedor Morozov
 
-                        Use *AKAZE* local features to find correspondence between two images.
+                        Using *AKAZE* local features to find correspondence between two images.
 
   ===================== ==============================================
 
@@ -202,6 +202,21 @@ Learn about how to use the feature points  detectors, descriptors and matching f
                      :height: 90pt
                      :width:  90pt
 
+  ===================== ==============================================
+   |AkazeTracking|         **Title:** :ref:`akazeTracking`
+
+                           *Compatibility:* > OpenCV 3.0
+
+                           *Author:* Fedor Morozov
+
+                           Using *AKAZE* and *ORB* for planar object tracking.
+
+  ===================== ==============================================
+
+  .. |AkazeTracking| image:: images/AKAZE_Tracking_Tutorial_Cover.png
+                     :height: 90pt
+                     :width:  90pt
+
 .. raw:: latex
 
    \pagebreak
@@ -221,3 +236,4 @@ Learn about how to use the feature points  detectors, descriptors and matching f
    ../feature_homography/feature_homography
    ../detection_of_planar_objects/detection_of_planar_objects
    ../akaze_matching/akaze_matching
+   ../akaze_tracking/akaze_tracking