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( 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[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.