mirror of
https://github.com/opencv/opencv.git
synced 2024-12-02 16:00:17 +08:00
be63ce723f
updated links in cheatsheet renamed directory for Mat tutorial changed links from willow docs to opencv.itseez.com, from Trac to current Redmine
135 lines
4.2 KiB
ReStructuredText
135 lines
4.2 KiB
ReStructuredText
.. _cascade_classifier:
|
|
|
|
Cascade Classifier
|
|
*******************
|
|
|
|
Goal
|
|
=====
|
|
|
|
In this tutorial you will learn how to:
|
|
|
|
.. container:: enumeratevisibleitemswithsquare
|
|
|
|
* Use the :cascade_classifier:`CascadeClassifier <>` class to detect objects in a video stream. Particularly, we will use the functions:
|
|
|
|
* :cascade_classifier_load:`load <>` to load a .xml classifier file. It can be either a Haar or a LBP classifer
|
|
* :cascade_classifier_detect_multiscale:`detectMultiScale <>` to perform the detection.
|
|
|
|
|
|
Theory
|
|
======
|
|
|
|
Code
|
|
====
|
|
|
|
This tutorial code's is shown lines below. You can also download it from `here <http://code.opencv.org/svn/opencv/trunk/opencv/samples/cpp/tutorial_code/objectDetection/objectDetection.cpp>`_ . The second version (using LBP for face detection) can be `found here <http://code.opencv.org/svn/opencv/trunk/opencv/samples/cpp/tutorial_code/objectDetection/objectDetection2.cpp>`_
|
|
|
|
.. code-block:: cpp
|
|
|
|
#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 cascades
|
|
if( !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 stream
|
|
capture = cvCaptureFromCAM( -1 );
|
|
if( capture )
|
|
{
|
|
while( true )
|
|
{
|
|
frame = cvQueryFrame( capture );
|
|
|
|
//-- 3. Apply the classifier to the frame
|
|
if( !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 faces
|
|
face_cascade.detectMultiScale( frame_gray, faces, 1.1, 2, 0|CV_HAAR_SCALE_IMAGE, Size(30, 30) );
|
|
|
|
for( int 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 eyes
|
|
eyes_cascade.detectMultiScale( faceROI, eyes, 1.1, 2, 0 |CV_HAAR_SCALE_IMAGE, Size(30, 30) );
|
|
|
|
for( int 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 got
|
|
imshow( window_name, frame );
|
|
}
|
|
|
|
Explanation
|
|
============
|
|
|
|
Result
|
|
======
|
|
|
|
#. Here is the result of running the code above and using as input the video stream of a build-in webcam:
|
|
|
|
.. image:: images/Cascade_Classifier_Tutorial_Result_Haar.jpg
|
|
:align: center
|
|
:height: 300pt
|
|
|
|
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.
|
|
|
|
.. image:: images/Cascade_Classifier_Tutorial_Result_LBP.jpg
|
|
:align: center
|
|
:height: 300pt
|
|
|