Cascade Classifier

Goal

In this tutorial you will learn how to:

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

Theory

Code

This tutorial code’s is shown lines below. You can also download it from here

#include "opencv2/objdetect.hpp"
#include "opencv2/videoio.hpp"
#include "opencv2/highgui.hpp"
#include "opencv2/imgproc.hpp"

#include <iostream>
#include <stdio.h>

using namespace std;
using namespace cv;

void detectAndDisplay( Mat frame );

String face_cascade_name, eyes_cascade_name;
CascadeClassifier face_cascade;
CascadeClassifier eyes_cascade;
String window_name = "Capture - Face detection";

int main( int argc, const char** argv )
{
    CommandLineParser parser(argc, argv,
        "{help h||}"
        "{face_cascade|../../data/haarcascades/haarcascade_frontalface_alt.xml|}"
        "{eyes_cascade|../../data/haarcascades/haarcascade_eye_tree_eyeglasses.xml|}");

    cout << "\nThis program demonstrates using the cv::CascadeClassifier class to detect objects (Face + eyes) in a video stream.\n"
            "You can use Haar or LBP features.\n\n";
    parser.printMessage();

    face_cascade_name = parser.get<string>("face_cascade");
    eyes_cascade_name = parser.get<string>("eyes_cascade");
    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( 0 );
    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 );

        char c = (char)waitKey(10);
        if( c == 27 ) { break; } // escape
    }
    return 0;
}

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

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

   

   Be sure the program will find the path of files haarcascade_frontalface_alt.xml and haarcascade_eye_tree_eyeglasses.xml. 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.