Cascade Classifier

Goal

In this tutorial you will learn how to:

Use the @ref cv::CascadeClassifier class to detect objects in a video stream. Particularly, we will use the functions:
- @ref cv::CascadeClassifier::load to load a .xml classifier file. It can be either a Haar or a LBP classifer
- @ref cv::CascadeClassifier::detectMultiScale to perform the detection.

Theory

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 @code{.cpp} #include "opencv2/objdetect.hpp" #include "opencv2/highgui.hpp" #include "opencv2/imgproc.hpp"

#include #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";

/* @function main */ int main( void ) { VideoCapture capture; Mat frame;

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

//-- 2. Read the video stream
capture.open( -1 );
if ( ! capture.isOpened() ) { printf("--(!)Error opening video capture\n"); return -1; }

while (  capture.read(frame) )
{
    if( frame.empty() )
    {
        printf(" --(!) No captured frame -- Break!");
        break;
    }

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

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

}

/* @function detectAndDisplay */ void detectAndDisplay( Mat frame ) { std::vector faces; Mat frame_gray;

cvtColor( frame, frame_gray, COLOR_BGR2GRAY );
equalizeHist( frame_gray, frame_gray );

//-- Detect faces
face_cascade.detectMultiScale( frame_gray, faces, 1.1, 2, 0|CASCADE_SCALE_IMAGE, Size(30, 30) );

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, 8, 0 );

    Mat faceROI = frame_gray( faces[i] );
    std::vector<Rect> eyes;

    //-- In each face, detect eyes
    eyes_cascade.detectMultiScale( faceROI, eyes, 1.1, 2, 0 |CASCADE_SCALE_IMAGE, Size(30, 30) );

    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, 8, 0 );
    }
}
//-- Show what you got
imshow( window_name, frame );

} @endcode Explanation

Result

-# Here is the result of running the code above and using as input the video stream of a build-in webcam:

![](images/Cascade_Classifier_Tutorial_Result_Haar.jpg)

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*

-# 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.

![](images/Cascade_Classifier_Tutorial_Result_LBP.jpg)

4.0 KiB Raw Blame History