.. _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://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-block:: 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 );
    }

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