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

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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 );
}

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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);
    }
}

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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(cv.samples.findFile(face_cascade_name)):
    print('--(!)Error loading face cascade')
    exit(0)
if not eyes_cascade.load(cv.samples.findFile(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

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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

1. 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.Viola04
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.Lienhart02
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