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.

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 cascadesif( !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 streamcapture = cvCaptureFromCAM( -1 );if( capture ){while( true ){frame = cvQueryFrame( capture );//-- 3. Apply the classifier to the frameif( !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 facesface_cascade.detectMultiScale( frame_gray, faces, 1.1, 2, 0|CV_HAAR_SCALE_IMAGE, Size(30, 30) );for( size_t 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 eyeseyes_cascade.detectMultiScale( faceROI, eyes, 1.1, 2, 0 |CV_HAAR_SCALE_IMAGE, Size(30, 30) );for( size_t 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 gotimshow( window_name, frame );}

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.