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【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;
}
複製代碼
 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 }
複製代碼

===================================

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

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