Cascade Classifier — OpenCV 2.4.13.0 documentation

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# Cascade Classifier

## Goal

In this tutorial you will learn how to:

- Use the CascadeClassifier class to detect objects in a video stream. Particularly, we will use the functions:
  - load to load a .xml classifier file. It can be either a Haar or a LBP classifer
  - detectMultiScale to perform the detection.

## Theory

## Code

This tutorial code’s is shown lines below. You can also download it from here . The second version (using LBP for face detection) can be found here

     #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( size_t 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( size_t 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[j].height)*0.25 );
           circle( frame, center, radius, Scalar( 255, 0, 0 ), 4, 8, 0 );
         }
      }
      //-- Show what you got
      imshow( window_name, frame );
     }

## Explanation

## Result

1. Here is the result of running the code above and using as input the video stream of a build-in webcam:

   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.

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