【原】訓練自己的haar-like特征分類器並識別物體(3)
在前兩篇文章中,我介紹了《訓練自己的haar-like特征分類器並識別物體》的前三個步驟:
1.準備訓練樣本圖片,包括正例及反例樣本
2.生成樣本描述文件
3.訓練樣本
4.目標識別
==============
本文將著重說明最後一個階段——目標識別,也即利用前麵訓練出來的分類器文件(.xml文件)對圖片中的物體進行識別,並在圖中框出在該物體。由於邏輯比較簡單,這裏直接上代碼:
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int _tmain( int argc,
_TCHAR* argv[])
{
char *cascade_name
= CASCADE_HEAD_MY; //上文最終生成的xml文件命名為"CASCADE_HEAD_MY.xml"
cascade
= (CvHaarClassifierCascade*)cvLoad( cascade_name, 0, 0, 0 ); //加載xml文件
if (
!cascade )
{
fprintf (
stderr, "ERROR:
Could not load classifier cascade\n" );
system ( "pause" );
return -1;
}
storage
= cvCreateMemStorage(0);
cvNamedWindow(
"face" ,
1 );
const char *
filename = "(12).bmp" ;
IplImage*
image = cvLoadImage( filename, 1 );
if (
image )
{
detect_and_draw(
image ); //函數見下方
cvWaitKey(0);
cvReleaseImage(
&image );
}
cvDestroyWindow( "result" );
return 0;
}
|

1 void detect_and_draw(IplImage* img ) 2 { 3 double scale=1.2; 4 static CvScalar colors[] = { 5 {{0,0,255}},{{0,128,255}},{{0,255,255}},{{0,255,0}}, 6 {{255,128,0}},{{255,255,0}},{{255,0,0}},{{255,0,255}} 7 };//Just some pretty colors to draw with 8 9 //Image Preparation 10 // 11 IplImage* gray = cvCreateImage(cvSize(img->width,img->height),8,1); 12 IplImage* small_img=cvCreateImage(cvSize(cvRound(img->width/scale),cvRound(img->height/scale)),8,1); 13 cvCvtColor(img,gray, CV_BGR2GRAY); 14 cvResize(gray, small_img, CV_INTER_LINEAR); 15 16 cvEqualizeHist(small_img,small_img); //直方圖均衡 17 18 //Detect objects if any 19 // 20 cvClearMemStorage(storage); 21 double t = (double)cvGetTickCount(); 22 CvSeq* objects = cvHaarDetectObjects(small_img, 23 cascade, 24 storage, 25 1.1, 26 2, 27 0/*CV_HAAR_DO_CANNY_PRUNING*/, 28 cvSize(30,30)); 29 30 t = (double)cvGetTickCount() - t; 31 printf( "detection time = %gms\n", t/((double)cvGetTickFrequency()*1000.) ); 32 33 //Loop through found objects and draw boxes around them 34 for(int i=0;i<(objects? objects->total:0);++i) 35 { 36 CvRect* r=(CvRect*)cvGetSeqElem(objects,i); 37 cvRectangle(img, cvPoint(r->x*scale,r->y*scale), cvPoint((r->x+r->width)*scale,(r->y+r->height)*scale), colors[i%8]); 38 } 39 for( int i = 0; i < (objects? objects->total : 0); i++ ) 40 { 41 CvRect* r = (CvRect*)cvGetSeqElem( objects, i ); 42 CvPoint center; 43 int radius; 44 center.x = cvRound((r->x + r->width*0.5)*scale); 45 center.y = cvRound((r->y + r->height*0.5)*scale); 46 radius = cvRound((r->width + r->height)*0.25*scale); 47 cvCircle( img, center, radius, colors[i%8], 3, 8, 0 ); 48 } 49 50 cvShowImage( "result", img ); 51 cvReleaseImage(&gray); 52 cvReleaseImage(&small_img); 53 }
===================================
其實上麵的代碼可以運用於大部分模式識別問題,無論是自己生成的xml文件還是opencv自帶的xml文件。在opencv的工程目錄opencv\data文件夾下有大量的xml文件,這些都是opencv開源項目中的程序員們自己訓練出來的。然而,效果一般不會合你預期,所以才有了本係列文章。天下沒有免費的午餐,想要獲得更高的查準率與查全率,不付出點努力是不行的!
【原】訓練自己的haar-like特征分類器並識別物體(3)
最後更新:2017-04-03 05:40:08