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

C++

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

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

#include <iostream>

using namespace std;
using namespace cv;

void detectAndDisplay( Mat frame );

CascadeClassifier face_cascade;
CascadeClassifier eyes_cascade;

int main( int argc, const char** argv )
{
    CommandLineParser parser(argc, argv,
                             "{help h||}"
                             "{face_cascade|../../data/haarcascades/haarcascade_frontalface_alt.xml|Path to face cascade.}"
                             "{eyes_cascade|../../data/haarcascades/haarcascade_eye_tree_eyeglasses.xml|Path to eyes cascade.}"
                             "{camera|0|Camera device number.}");

    parser.about( "\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();

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

    //-- 1. Load the cascades
    if( !face_cascade.load( face_cascade_name ) )
    {
        cout << "--(!)Error loading face cascade\n";
        return -1;
    };
    if( !eyes_cascade.load( eyes_cascade_name ) )
    {
        cout << "--(!)Error loading eyes cascade\n";
        return -1;
    };

    int camera_device = parser.get<int>("camera");
    VideoCapture capture;
    //-- 2. Read the video stream
    capture.open( camera_device );
    if ( ! capture.isOpened() )
    {
        cout << "--(!)Error opening video capture\n";
        return -1;
    }

    Mat frame;
    while ( capture.read(frame) )
    {
        if( frame.empty() )
        {
            cout << "--(!) No captured frame -- Break!\n";
            break;
        }

        //-- 3. Apply the classifier to the frame
        detectAndDisplay( frame );

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

void detectAndDisplay( Mat frame )
{
    Mat frame_gray;
    cvtColor( frame, frame_gray, COLOR_BGR2GRAY );
    equalizeHist( frame_gray, frame_gray );

    //-- Detect faces
    std::vector<Rect> faces;
    face_cascade.detectMultiScale( frame_gray, faces );

    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 );

        Mat faceROI = frame_gray( faces[i] );

        //-- In each face, detect eyes
        std::vector<Rect> eyes;
        eyes_cascade.detectMultiScale( faceROI, eyes );

        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 );
        }
    }

    //-- Show what you got
    imshow( "Capture - Face detection", frame );
}

Java

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

import java.util.List;

import org.opencv.core.Core;
import org.opencv.core.Mat;
import org.opencv.core.MatOfRect;
import org.opencv.core.Point;
import org.opencv.core.Rect;
import org.opencv.core.Scalar;
import org.opencv.core.Size;
import org.opencv.highgui.HighGui;
import org.opencv.imgproc.Imgproc;
import org.opencv.objdetect.CascadeClassifier;
import org.opencv.videoio.VideoCapture;

class ObjectDetection {
    public void detectAndDisplay(Mat frame, CascadeClassifier faceCascade, CascadeClassifier eyesCascade) {
        Mat frameGray = new Mat();
        Imgproc.cvtColor(frame, frameGray, Imgproc.COLOR_BGR2GRAY);
        Imgproc.equalizeHist(frameGray, frameGray);

        // -- Detect faces
        MatOfRect faces = new MatOfRect();
        faceCascade.detectMultiScale(frameGray, faces);

        List<Rect> listOfFaces = faces.toList();
        for (Rect face : listOfFaces) {
            Point center = new Point(face.x + face.width / 2, face.y + face.height / 2);
            Imgproc.ellipse(frame, center, new Size(face.width / 2, face.height / 2), 0, 0, 360,
                    new Scalar(255, 0, 255));

            Mat faceROI = frameGray.submat(face);

            // -- In each face, detect eyes
            MatOfRect eyes = new MatOfRect();
            eyesCascade.detectMultiScale(faceROI, eyes);

            List<Rect> listOfEyes = eyes.toList();
            for (Rect eye : listOfEyes) {
                Point eyeCenter = new Point(face.x + eye.x + eye.width / 2, face.y + eye.y + eye.height / 2);
                int radius = (int) Math.round((eye.width + eye.height) * 0.25);
                Imgproc.circle(frame, eyeCenter, radius, new Scalar(255, 0, 0), 4);
            }
        }

        //-- Show what you got
        HighGui.imshow("Capture - Face detection", frame );
    }

    public void run(String[] args) {
        String filenameFaceCascade = args.length > 2 ? args[0] : "../../data/haarcascades/haarcascade_frontalface_alt.xml";
        String filenameEyesCascade = args.length > 2 ? args[1] : "../../data/haarcascades/haarcascade_eye_tree_eyeglasses.xml";
        int cameraDevice = args.length > 2 ? Integer.parseInt(args[2]) : 0;

        CascadeClassifier faceCascade = new CascadeClassifier();
        CascadeClassifier eyesCascade = new CascadeClassifier();

        if (!faceCascade.load(filenameFaceCascade)) {
            System.err.println("--(!)Error loading face cascade: " + filenameFaceCascade);
            System.exit(0);
        }
        if (!eyesCascade.load(filenameEyesCascade)) {
            System.err.println("--(!)Error loading eyes cascade: " + filenameEyesCascade);
            System.exit(0);
        }

        VideoCapture capture = new VideoCapture(cameraDevice);
        if (!capture.isOpened()) {
            System.err.println("--(!)Error opening video capture");
            System.exit(0);
        }

        Mat frame = new Mat();
        while (capture.read(frame)) {
            if (frame.empty()) {
                System.err.println("--(!) No captured frame -- Break!");
                break;
            }

            //-- 3. Apply the classifier to the frame
            detectAndDisplay(frame, faceCascade, eyesCascade);

            if (HighGui.waitKey(10) == 27) {
                break;// escape
            }
        }

        System.exit(0);
    }
}

public class ObjectDetectionDemo {
    public static void main(String[] args) {
        // Load the native OpenCV library
        System.loadLibrary(Core.NATIVE_LIBRARY_NAME);

        new ObjectDetection().run(args);
    }
}

Python

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

from __future__ import print_function
import cv2 as cv
import argparse

def detectAndDisplay(frame):
    frame_gray = cv.cvtColor(frame, cv.COLOR_BGR2GRAY)
    frame_gray = cv.equalizeHist(frame_gray)

    #-- Detect faces
    faces = face_cascade.detectMultiScale(frame_gray)
    for (x,y,w,h) in faces:
        center = (x + w//2, y + h//2)
        frame = cv.ellipse(frame, center, (w//2, h//2), 0, 0, 360, (255, 0, 255), 4)

        faceROI = frame_gray[y:y+h,x:x+w]
        #-- In each face, detect eyes
        eyes = eyes_cascade.detectMultiScale(faceROI)
        for (x2,y2,w2,h2) in eyes:
            eye_center = (x + x2 + w2//2, y + y2 + h2//2)
            radius = int(round((w2 + h2)*0.25))
            frame = cv.circle(frame, eye_center, radius, (255, 0, 0 ), 4)

    cv.imshow('Capture - Face detection', frame)

parser = argparse.ArgumentParser(description='Code for Cascade Classifier tutorial.')
parser.add_argument('--face_cascade', help='Path to face cascade.', default='../../data/haarcascades/haarcascade_frontalface_alt.xml')
parser.add_argument('--eyes_cascade', help='Path to eyes cascade.', default='../../data/haarcascades/haarcascade_eye_tree_eyeglasses.xml')
parser.add_argument('--camera', help='Camera devide number.', type=int, default=0)
args = parser.parse_args()

face_cascade_name = args.face_cascade
eyes_cascade_name = args.eyes_cascade

face_cascade = cv.CascadeClassifier()
eyes_cascade = cv.CascadeClassifier()

#-- 1. Load the cascades
if not face_cascade.load(face_cascade_name):
    print('--(!)Error loading face cascade')
    exit(0)
if not eyes_cascade.load(eyes_cascade_name):
    print('--(!)Error loading eyes cascade')
    exit(0)

camera_device = args.camera
#-- 2. Read the video stream
cap = cv.VideoCapture(camera_device)
if not cap.isOpened:
    print('--(!)Error opening video capture')
    exit(0)

while True:
    ret, frame = cap.read()
    if frame is None:
        print('--(!) No captured frame -- Break!')
        break

    detectAndDisplay(frame)

    if cv.waitKey(10) == 27:
        break

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.

   

Additional Resources

1. Paul Viola and Michael J. Jones. Robust real-time face detection. International Journal of Computer Vision, 57(2):137–154, 2004. [195]
2. Rainer Lienhart and Jochen Maydt. An extended set of haar-like features for rapid object detection. In Image Processing. 2002. Proceedings. 2002 International Conference on, volume 1, pages I–900. IEEE, 2002. [112]
3. Video Lecture on Face Detection and Tracking
4. An interesting interview regarding Face Detection by Adam Harvey
5. OpenCV Face Detection: Visualized on Vimeo by Adam Harvey