134 lines
4.2 KiB
ReStructuredText
134 lines
4.2 KiB
ReStructuredText
.. _cascade_classifier:
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Cascade Classifier
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*******************
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Goal
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=====
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In this tutorial you will learn how to:
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.. container:: enumeratevisibleitemswithsquare
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* Use the :cascade_classifier:`CascadeClassifier <>` class to detect objects in a video stream. Particularly, we will use the functions:
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* :cascade_classifier_load:`load <>` to load a .xml classifier file. It can be either a Haar or a LBP classifer
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* :cascade_classifier_detect_multiscale:`detectMultiScale <>` to perform the detection.
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Theory
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======
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Code
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====
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This tutorial code's is shown lines below. You can also download it from `here <https://github.com/Itseez/opencv/tree/master/samples/cpp/tutorial_code/objectDetection/objectDetection.cpp>`_ . The second version (using LBP for face detection) can be `found here <https://github.com/Itseez/opencv/tree/master/samples/cpp/tutorial_code/objectDetection/objectDetection2.cpp>`_
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.. code-block:: cpp
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#include "opencv2/objdetect/objdetect.hpp"
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#include "opencv2/highgui/highgui.hpp"
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#include "opencv2/imgproc/imgproc.hpp"
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#include <iostream>
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#include <stdio.h>
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using namespace std;
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using namespace cv;
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/** Function Headers */
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void detectAndDisplay( Mat frame );
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/** Global variables */
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String face_cascade_name = "haarcascade_frontalface_alt.xml";
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String eyes_cascade_name = "haarcascade_eye_tree_eyeglasses.xml";
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CascadeClassifier face_cascade;
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CascadeClassifier eyes_cascade;
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string window_name = "Capture - Face detection";
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RNG rng(12345);
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/** @function main */
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int main( int argc, const char** argv )
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{
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CvCapture* capture;
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Mat frame;
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//-- 1. Load the cascades
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if( !face_cascade.load( face_cascade_name ) ){ printf("--(!)Error loading\n"); return -1; };
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if( !eyes_cascade.load( eyes_cascade_name ) ){ printf("--(!)Error loading\n"); return -1; };
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//-- 2. Read the video stream
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capture = cvCaptureFromCAM( -1 );
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if( capture )
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{
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while( true )
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{
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frame = cvQueryFrame( capture );
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//-- 3. Apply the classifier to the frame
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if( !frame.empty() )
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{ detectAndDisplay( frame ); }
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else
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{ printf(" --(!) No captured frame -- Break!"); break; }
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int c = waitKey(10);
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if( (char)c == 'c' ) { break; }
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}
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}
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return 0;
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}
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/** @function detectAndDisplay */
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void detectAndDisplay( Mat frame )
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{
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std::vector<Rect> faces;
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Mat frame_gray;
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cvtColor( frame, frame_gray, CV_BGR2GRAY );
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equalizeHist( frame_gray, frame_gray );
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//-- Detect faces
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face_cascade.detectMultiScale( frame_gray, faces, 1.1, 2, 0|CV_HAAR_SCALE_IMAGE, Size(30, 30) );
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for( size_t i = 0; i < faces.size(); i++ )
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{
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Point center( faces[i].x + faces[i].width*0.5, faces[i].y + faces[i].height*0.5 );
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ellipse( frame, center, Size( faces[i].width*0.5, faces[i].height*0.5), 0, 0, 360, Scalar( 255, 0, 255 ), 4, 8, 0 );
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Mat faceROI = frame_gray( faces[i] );
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std::vector<Rect> eyes;
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//-- In each face, detect eyes
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eyes_cascade.detectMultiScale( faceROI, eyes, 1.1, 2, 0 |CV_HAAR_SCALE_IMAGE, Size(30, 30) );
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for( size_t j = 0; j < eyes.size(); j++ )
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{
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Point center( faces[i].x + eyes[j].x + eyes[j].width*0.5, faces[i].y + eyes[j].y + eyes[j].height*0.5 );
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int radius = cvRound( (eyes[j].width + eyes[j].height)*0.25 );
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circle( frame, center, radius, Scalar( 255, 0, 0 ), 4, 8, 0 );
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}
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}
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//-- Show what you got
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imshow( window_name, frame );
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}
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Explanation
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============
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Result
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======
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#. Here is the result of running the code above and using as input the video stream of a build-in webcam:
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.. image:: images/Cascade_Classifier_Tutorial_Result_Haar.jpg
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:align: center
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:height: 300pt
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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*
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#. 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.
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.. image:: images/Cascade_Classifier_Tutorial_Result_LBP.jpg
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:align: center
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:height: 300pt
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