Commit rst tutorial for Cascade Classifiers

doc/conf.py

@@ -377,7 +377,10 @@ extlinks = {'cvt_color': ('http://opencv.willowgarage.com/documentation/cpp/imgp
 	        'perspective_transform' : ('http://opencv.willowgarage.com/documentation/cpp/core_operations_on_arrays.html?#perspectiveTransform%s', None ),
 	        'flann_based_matcher' : ('http://opencv.willowgarage.com/documentation/cpp/features2d_common_interfaces_of_descriptor_matchers.html?#FlannBasedMatcher%s', None),
 	        'brute_force_matcher' : ('http://opencv.willowgarage.com/documentation/cpp/features2d_common_interfaces_of_descriptor_matchers.html?#BruteForceMatcher%s', None ),
-	        'flann' : ('http://opencv.willowgarage.com/documentation/cpp/flann_fast_approximate_nearest_neighbor_search.html?%s', None )			 		 
+	        'flann' : ('http://opencv.willowgarage.com/documentation/cpp/flann_fast_approximate_nearest_neighbor_search.html?%s', None ),
+	        'cascade_classifier' : ('http://opencv.willowgarage.com/documentation/cpp/objdetect_cascade_classification.html#cascadeclassifier%s', None ),
+		'cascade_classifier_load' : ('http://opencv.willowgarage.com/documentation/cpp/objdetect_cascade_classification.html#cv-cascadeclassifier-load%s', None ),
+		'cascade_classifier_detect_multiscale' : ('http://opencv.willowgarage.com/documentation/cpp/objdetect_cascade_classification.html#cv-cascadeclassifier-detectmultiscale%s', None )			 		 
            }
 
 

doc/tutorials/features2d/table_of_content_features2d/table_of_content_features2d.rst

@@ -136,7 +136,7 @@ Learn about how to use the feature points  detectors, descriptors and matching f
                         
                         *Author:* |Author_AnaH|
                         
-                        In this tutorial, you will use *features2d* to detect interest points.
+                        In this tutorial, you will use the FLANN library to make a fast matching.
   
   ===================== ==============================================
   
@@ -198,4 +198,9 @@ Learn about how to use the feature points  detectors, descriptors and matching f
    ../trackingmotion/good_features_to_track/good_features_to_track.rst
    ../trackingmotion/generic_corner_detector/generic_corner_detector
    ../trackingmotion/corner_subpixeles/corner_subpixeles
+   ../feature_detection/feature_detection
+   ../feature_detection/feature_description
+   ../feature_flann_matcher/feature_flann_matcher
+   ../feature_homography/feature_homography
    ../detection_of_planar_objects/detection_of_planar_objects
+

doc/tutorials/objdetect/cascade_classifier/cascade_classifier.rst

@@ -0,0 +1,134 @@
+.. _cascade_classifier:
+
+Cascade Classifier
+*******************
+
+Goal
+=====
+
+In this tutorial you will learn how to:
+
+.. container:: enumeratevisibleitemswithsquare
+
+   * Use the :cascade_classifier:`CascadeClassifier <>` class to detect objects in a video stream. Particularly, we will use the functions:
+
+     * :cascade_classifier_load:`load <>` to load a .xml classifier file. It can be either a Haar or a LBP classifer
+     * :cascade_classifier_detect_multiscale:`detectMultiScale <>` to perform the detection.
+           
+
+Theory
+======
+
+Code
+====
+
+This tutorial code's is shown lines below. You can also download it from `here <https://code.ros.org/svn/opencv/trunk/opencv/samples/cpp/tutorial_code/objectDetection/objectDetection.cpp>`_ . The second version (using LBP for face detection) can be found `here <https://code.ros.org/svn/opencv/trunk/opencv/samples/cpp/tutorial_code/objectDetection/objectDetection2.cpp>`_ 
+
+.. code-block:: cpp 
+
+   #include "opencv2/objdetect/objdetect.hpp"
+   #include "opencv2/highgui/highgui.hpp"
+   #include "opencv2/imgproc/imgproc.hpp"
+
+   #include <iostream>
+   #include <stdio.h>
+
+   using namespace std;
+   using namespace cv;
+
+   /** Function Headers */
+   void detectAndDisplay( Mat frame );
+
+   /** Global variables */
+   String face_cascade_name = "haarcascade_frontalface_alt.xml";
+   String eyes_cascade_name = "haarcascade_eye_tree_eyeglasses.xml";
+   CascadeClassifier face_cascade;
+   CascadeClassifier eyes_cascade;
+   string window_name = "Capture - Face detection";
+   RNG rng(12345);
+
+   /** @function main */
+   int main( int argc, const char** argv )
+   {
+     CvCapture* capture;
+     Mat frame;
+
+     //-- 1. Load the cascades
+     if( !face_cascade.load( face_cascade_name ) ){ printf("--(!)Error loading\n"); return -1; };
+     if( !eyes_cascade.load( eyes_cascade_name ) ){ printf("--(!)Error loading\n"); return -1; };
+ 
+     //-- 2. Read the video stream
+     capture = cvCaptureFromCAM( -1 );
+     if( capture )
+     {
+       while( true )
+       {
+	 frame = cvQueryFrame( capture );
+  
+ 	 //-- 3. Apply the classifier to the frame
+      	 if( !frame.empty() )
+       	 { detectAndDisplay( frame ); }
+      	 else
+       	 { printf(" --(!) No captured frame -- Break!"); break; }
+      
+         int c = waitKey(10);
+         if( (char)c == 'c' ) { break; } 
+        }
+     }
+     return 0;
+   }
+
+  /** @function detectAndDisplay */
+  void detectAndDisplay( Mat frame )
+  {
+    std::vector<Rect> faces;
+    Mat frame_gray;
+
+    cvtColor( frame, frame_gray, CV_BGR2GRAY );
+    equalizeHist( frame_gray, frame_gray );
+
+    //-- Detect faces
+    face_cascade.detectMultiScale( frame_gray, faces, 1.1, 2, 0|CV_HAAR_SCALE_IMAGE, Size(30, 30) );
+
+    for( int i = 0; i < faces.size(); i++ )
+    {
+      Point center( faces[i].x + faces[i].width*0.5, faces[i].y + faces[i].height*0.5 );
+      ellipse( frame, center, Size( faces[i].width*0.5, faces[i].height*0.5), 0, 0, 360, Scalar( 255, 0, 255 ), 4, 8, 0 );
+
+      Mat faceROI = frame_gray( faces[i] );
+      std::vector<Rect> eyes;
+
+      //-- In each face, detect eyes
+      eyes_cascade.detectMultiScale( faceROI, eyes, 1.1, 2, 0 |CV_HAAR_SCALE_IMAGE, Size(30, 30) );
+
+      for( int j = 0; j < eyes.size(); j++ )
+       {
+         Point center( faces[i].x + eyes[j].x + eyes[j].width*0.5, faces[i].y + eyes[j].y + eyes[j].height*0.5 ); 
+         int radius = cvRound( (eyes[j].width + eyes[i].height)*0.25 );
+         circle( frame, center, radius, Scalar( 255, 0, 0 ), 4, 8, 0 );
+       }
+    } 
+    //-- Show what you got
+    imshow( window_name, frame );
+   }
+
+Explanation
+============
+
+Result
+======
+
+#. Here it is:
+
+   ========== ==========  
+    |CCH|      |CCLBP|   
+   ========== ==========   
+
+   .. |CCH|  image:: images/Cascade_Classifier_Tutorial_Result_Haar.jpg
+             :align: middle
+	     :height: 200pt
+
+   .. |CCLBP|  image:: images/Cascade_Classifier_Tutorial_Result_LBP.jpg
+               :align: middle
+               :height: 200pt   
+

doc/tutorials/objdetect/table_of_content_objdetect/table_of_content_objdetect.rst

@@ -5,4 +5,32 @@
 
 Ever wondered how your digital camera detects peoples and faces? Look here to find out!
 
-.. include:: ../../definitions/noContent.rst
+.. include:: ../../definitions/tocDefinitions.rst 
+
++ 
+  .. tabularcolumns:: m{100pt} m{300pt}
+  .. cssclass:: toctableopencv
+
+  ===================== ==============================================
+   |CascadeClassif|     **Title:** :ref:`cascade_classifier`
+  
+                        *Compatibility:* > OpenCV 2.0
+                        
+                        *Author:* |Author_AnaH|
+                        
+                        Here we learn how to use *objdetect* to find objects in our images or videos
+  
+  ===================== ==============================================
+  
+  .. |CascadeClassif| image:: images/Cascade_Classifier_Tutorial_Cover.jpg
+                      :height: 90pt
+                      :width:  90pt
+
+.. raw:: latex
+
+   \pagebreak
+
+.. toctree::
+   :hidden:
+
+   ../cascade_classifier/cascade_classifier