.. _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 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
      :align: center
      :height: 300pt

   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* 

#. 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
      :align: center
      :height: 300pt   

