Doxygen tutorials: basic structure

doc/tutorials/objdetect/cascade_classifier/cascade_classifier.markdown

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+Cascade Classifier {#tutorial_cascade_classifier}
+==================
+
+Goal
+----
+
+In this tutorial you will learn how to:
+
+-   Use the @ref cv::CascadeClassifier class to detect objects in a video stream. Particularly, we
+    will use the functions:
+    -   @ref cv::load to load a .xml classifier file. It can be either a Haar or a LBP classifer
+    -   @ref cv::detectMultiScale to perform the detection.
+
+Theory
+------
+
+Code
+----
+
+This tutorial code's is shown lines below. You can also download it from
+[here](https://github.com/Itseez/opencv/tree/master/samples/cpp/tutorial_code/objectDetection/objectDetection.cpp)
+. The second version (using LBP for face detection) can be [found
+here](https://github.com/Itseez/opencv/tree/master/samples/cpp/tutorial_code/objectDetection/objectDetection2.cpp)
+@code{.cpp}
+#include "opencv2/objdetect.hpp"
+#include "opencv2/highgui.hpp"
+#include "opencv2/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";
+
+/* @function main */
+int main( void )
+{
+    VideoCapture capture;
+    Mat frame;
+
+    //-- 1. Load the cascades
+    if( !face_cascade.load( face_cascade_name ) ){ printf("--(!)Error loading face cascade\n"); return -1; };
+    if( !eyes_cascade.load( eyes_cascade_name ) ){ printf("--(!)Error loading eyes cascade\n"); return -1; };
+
+    //-- 2. Read the video stream
+    capture.open( -1 );
+    if ( ! capture.isOpened() ) { printf("--(!)Error opening video capture\n"); return -1; }
+
+    while (  capture.read(frame) )
+    {
+        if( frame.empty() )
+        {
+            printf(" --(!) No captured frame -- Break!");
+            break;
+        }
+
+        //-- 3. Apply the classifier to the frame
+        detectAndDisplay( frame );
+
+        int c = waitKey(10);
+        if( (char)c == 27 ) { break; } // escape
+    }
+    return 0;
+}
+
+/* @function detectAndDisplay */
+void detectAndDisplay( Mat frame )
+{
+    std::vector<Rect> faces;
+    Mat frame_gray;
+
+    cvtColor( frame, frame_gray, COLOR_BGR2GRAY );
+    equalizeHist( frame_gray, frame_gray );
+
+    //-- Detect faces
+    face_cascade.detectMultiScale( frame_gray, faces, 1.1, 2, 0|CASCADE_SCALE_IMAGE, Size(30, 30) );
+
+    for( size_t i = 0; i < faces.size(); i++ )
+    {
+        Point center( faces[i].x + faces[i].width/2, faces[i].y + faces[i].height/2 );
+        ellipse( frame, center, Size( faces[i].width/2, faces[i].height/2), 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 |CASCADE_SCALE_IMAGE, Size(30, 30) );
+
+        for( size_t j = 0; j < eyes.size(); j++ )
+        {
+            Point eye_center( faces[i].x + eyes[j].x + eyes[j].width/2, faces[i].y + eyes[j].y + eyes[j].height/2 );
+            int radius = cvRound( (eyes[j].width + eyes[j].height)*0.25 );
+            circle( frame, eye_center, radius, Scalar( 255, 0, 0 ), 4, 8, 0 );
+        }
+    }
+    //-- Show what you got
+    imshow( window_name, frame );
+}
+@endcode
+Explanation
+-----------
+
+Result
+------
+
+1.  Here is the result of running the code above and using as input the video stream of a build-in
+    webcam:
+
+    ![image](images/Cascade_Classifier_Tutorial_Result_Haar.jpg)
+
+    Remember to copy the files *haarcascade_frontalface_alt.xml* and
+    *haarcascade_eye_tree_eyeglasses.xml* in your current directory. They are located in
+    *opencv/data/haarcascades*
+
+2.  This is the result of using the file *lbpcascade_frontalface.xml* (LBP trained) for the face
+    detection. For the eyes we keep using the file used in the tutorial.
+
+    ![image](images/Cascade_Classifier_Tutorial_Result_LBP.jpg)
+
+

doc/tutorials/objdetect/table_of_content_objdetect/table_of_content_objdetect.markdown

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+Object Detection (objdetect module) {#tutorial_table_of_content_objdetect}
+===================================
+
+Ever wondered how your digital camera detects peoples and faces? Look here to find out!
+
+-   @subpage tutorial_cascade_classifier
+
+    *Compatibility:* \> OpenCV 2.0
+
+    *Author:* Ana Huamán
+
+    Here we learn how to use *objdetect* to find objects in our images or videos