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. [198]
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. [114]
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