( 由於收到附件大小的限制，此篇的圖片無法上傳，請大家見諒，可登陸原網站查看：http://dasl.mem.drexel.edu/~noahKuntz/openCVTut11.html)
Author: Noah Kuntz (2009)
Contact: [email protected]
Keywords: OpenCV, computer vision, machine learning, haar classifier, face detection
This tutorial assumes the reader:
(1) Has a basic knowledge of Visual C++
(2) Has some familiarity with computer vision concepts
(3) Has read the previous tutorials in this series
The rest of the tutorial is presented as follows:
Important Note!
More information on the topics of these tutorials can be found in this book:Learning OpenCV: Computer Vision with the OpenCV Library
Step 1: Face Detection with the Haar Classifier(人臉檢測和Haar分類器)
Face Detection（人臉檢測）
Machine learning is a powerful set of algorithms that can be used to improve computer vision algorithms by allowing the program to build on the knowledge of many example cases（機器學習是一套功能強大的可以用來提高計算機視覺算法的算法，該算法允許該程序在許多例子案件知識的基礎上建立）. For face detection, this example
uses a Haar classifier, which is a statistically boosted classifier supported by training on many sample images of faces（對於人臉檢測，這個例子使用了haar分類器，這是一個統計上提高了的分類器，它支持許多人臉圖像樣本）. Much more detail of machine learning theory can be found in the book（更詳細的機器學習理論可以在這本書中找到）.
This code uses a CvHaarClassifierCascade* matrix that should be copied from your OpenCV directory to your working directory (see the code comments), and loaded withcvLoad（此代碼使用一個CvHaarClassifierCascade *矩陣，應該從你的OpenCV的目錄複製到你的工作目錄（見代碼註釋），並用cvLoad加載）.
ThencvHaarDetectObjects takes the input image, the loaded cascade, the empty memory storage and a few options and performs the face detection（然後cvHaarDetectObjects接受輸入圖像，加載的級聯，空的記憶存儲和一些選項並執行人臉檢測）. The important option here is the minimum size defined
incvSize（這裏最重要的選項是用cvSize定義的最小規模）.Base this on the size of the faces in your image, in pixels（在你的圖像中，立足於臉的大小，以像素爲單位）. Then once the detected faces are stored in a sequence, a loop can be used to go through them and draw aCvRect object around
each one（那麼一旦被檢測的臉被存儲在一個序列中，一個循環可用於通過他們和繪製一個CvRect對象包括周圍的每人一個）. Here is the code（以下是代碼）:
int _tmain(int argc, _TCHAR* argv[])
{
IplImage* img;
img = cvLoadImage( "dasl_hubo.jpg" );
CvMemStorage* storage = cvCreateMemStorage(0);
// Note that you must copy C:\Program Files\OpenCV\data\haarcascades\haarcascade_frontalface_alt2.xml
// to your working directory
CvHaarClassifierCascade* cascade = (CvHaarClassifierCascade*)cvLoad( "haarcascade_frontalface_alt2.xml" );
double scale = 1.3;
static CvScalar colors[] = { {{0,0,255}}, {{0,128,255}}, {{0,255,255}},
{{0,255,0}}, {{255,128,0}}, {{255,255,0}}, {{255,0,0}}, {{255,0,255}} };
// Detect objects
cvClearMemStorage( storage );
CvSeq* objects = cvHaarDetectObjects( img, cascade, storage, 1.1, 4, 0, cvSize( 40, 50 ));
CvRect* r;
// Loop through objects and draw boxes
for( int i = 0; i < (objects ? objects->total : 0 ); i++ ){
r = ( CvRect* )cvGetSeqElem( objects, i );
cvRectangle( img, cvPoint( r->x, r->y ), cvPoint( r->x + r->width, r->y + r->height ),
colors[i%8]);
}
cvNamedWindow( "Output" );
cvShowImage( "Output", img );
cvWaitKey();
cvReleaseImage( &img );
return 0;
}
Final Words（結束語）
This tutorial's objective was to show how to use a basic machine learning classifier to detect faces（本教程的目標是展示如何使用基本的機器學習分類器來檢測人臉）.
Click here to email me.
Click here to return to my Tutorials page.
