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OpenCV C++ Program for Face Detection

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This program uses the OpenCV library to detect faces in a live stream from webcam or in a video file stored in the local machine. This program detects faces in real time and tracks it. It uses pre-trained XML classifiers for the same. The classifiers used in this program have facial features trained in them. Different classifiers can be used to detect different objects. Requirements for running the program: 1) OpenCV must be installed on the local machine. 2) Paths to the classifier XML files must be given before the execution of the program. These XML files can be found in the OpenCV directory “opencv/data/haarcascades”. 3) Use 0 in capture.open(0) to play webcam feed. 4) For detection in a local video provide the path to the video.(capture.open(“path_to_video”)).  Implementation:
 

CPP

// CPP program to detects face in a video
 
// Include required header files from OpenCV directory
#include "/usr/local/include/opencv2/objdetect.hpp"
#include "/usr/local/include/opencv2/highgui.hpp"
#include "/usr/local/include/opencv2/imgproc.hpp"
#include <iostream>
 
using namespace std;
using namespace cv;
 
// Function for Face Detection
void detectAndDraw( Mat& img, CascadeClassifier& cascade,
                CascadeClassifier& nestedCascade, double scale );
string cascadeName, nestedCascadeName;
 
int main( int argc, const char** argv )
{
    // VideoCapture class for playing video for which faces to be detected
    VideoCapture capture;
    Mat frame, image;
 
    // PreDefined trained XML classifiers with facial features
    CascadeClassifier cascade, nestedCascade;
    double scale=1;
 
    // Load classifiers from "opencv/data/haarcascades" directory
    nestedCascade.load( "../../haarcascade_eye_tree_eyeglasses.xml" ) ;
 
    // Change path before execution
    cascade.load( "../../haarcascade_frontalcatface.xml" ) ;
 
    // Start Video..1) 0 for WebCam 2) "Path to Video" for a Local Video
    capture.open(0);
    if( capture.isOpened() )
    {
        // Capture frames from video and detect faces
        cout << "Face Detection Started...." << endl;
        while(1)
        {
            capture >> frame;
            if( frame.empty() )
                break;
            Mat frame1 = frame.clone();
            detectAndDraw( frame1, cascade, nestedCascade, scale );
            char c = (char)waitKey(10);
         
            // Press q to exit from window
            if( c == 27 || c == 'q' || c == 'Q' )
                break;
        }
    }
    else
        cout<<"Could not Open Camera";
    return 0;
}
 
void detectAndDraw( Mat& img, CascadeClassifier& cascade,
                    CascadeClassifier& nestedCascade,
                    double scale)
{
    vector<Rect> faces, faces2;
    Mat gray, smallImg;
 
    cvtColor( img, gray, COLOR_BGR2GRAY ); // Convert to Gray Scale
    double fx = 1 / scale;
 
    // Resize the Grayscale Image
    resize( gray, smallImg, Size(), fx, fx, INTER_LINEAR );
    equalizeHist( smallImg, smallImg );
 
    // Detect faces of different sizes using cascade classifier
    cascade.detectMultiScale( smallImg, faces, 1.1,
                            2, 0|CASCADE_SCALE_IMAGE, Size(30, 30) );
 
    // Draw circles around the faces
    for ( size_t i = 0; i < faces.size(); i++ )
    {
        Rect r = faces[i];
        Mat smallImgROI;
        vector<Rect> nestedObjects;
        Point center;
        Scalar color = Scalar(255, 0, 0); // Color for Drawing tool
        int radius;
 
        double aspect_ratio = (double)r.width/r.height;
        if( 0.75 < aspect_ratio && aspect_ratio < 1.3 )
        {
            center.x = cvRound((r.x + r.width*0.5)*scale);
            center.y = cvRound((r.y + r.height*0.5)*scale);
            radius = cvRound((r.width + r.height)*0.25*scale);
            circle( img, center, radius, color, 3, 8, 0 );
        }
        else
            rectangle( img, cvPoint(cvRound(r.x*scale), cvRound(r.y*scale)),
                    cvPoint(cvRound((r.x + r.width-1)*scale),
                    cvRound((r.y + r.height-1)*scale)), color, 3, 8, 0);
        if( nestedCascade.empty() )
            continue;
        smallImgROI = smallImg( r );
         
        // Detection of eyes in the input image
        nestedCascade.detectMultiScale( smallImgROI, nestedObjects, 1.1, 2,
                                        0|CASCADE_SCALE_IMAGE, Size(30, 30) );
         
        // Draw circles around eyes
        for ( size_t j = 0; j < nestedObjects.size(); j++ )
        {
            Rect nr = nestedObjects[j];
            center.x = cvRound((r.x + nr.x + nr.width*0.5)*scale);
            center.y = cvRound((r.y + nr.y + nr.height*0.5)*scale);
            radius = cvRound((nr.width + nr.height)*0.25*scale);
            circle( img, center, radius, color, 3, 8, 0 );
        }
    }
 
    // Show Processed Image with detected faces
    imshow( "Face Detection", img );
}

                    

Output: 

Face Detection

Next Article: Opencv Python Program for face detection References: 1) http://docs.opencv.org/2.4/modules/contrib/doc/facerec/facerec_tutorial.html 2) http://docs.opencv.org/2.4/doc/tutorials/objdetect/cascade_classifier/cascade_classifier.html



Last Updated : 12 Apr, 2023
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