在Opencv下的BP神經(jīng)網(wǎng)絡(luò)的搭建

1、調(diào)用BP進(jìn)行圖像分類只需：imgO =cvLoadlmage ("O99.pbm" ,0); BPCIassifierbp_cly ; / 初始化對象 number=bp_cly . classify (img0 );#i nclude <ml.h>CvANN_MLP BP/ope ncv中的 BP/構(gòu)造函數(shù)BPClassifier : BPClassifier ()/i nitial/spri ntf(file_path , "./OCR/");sprintf (file_path , "C:Usersy450Desktoprec

2、ognizeWOCRW");train _samples = 80;classes = 10;size =40;trainData = cvCreateMat (train_samples * classes , size * size , CV_32FC1);trainClasses = cvCreateMat (train_samples * classes , 10,CV_32FC1);neuralLayers =cvCreateMat (3,1,CV_32SC1);sampleWts =cvCreateMat (train_samples * classes ,1, CV_3

3、2FC1);for ( int i =0; i <train_samples* classes ; i +)cvSet1D (sampleWts , i , cvScalar (1);cvSet1D (neuralLayers,0,cvScalar(size* size);cvSet1D (neuralLayers,1,cvScalar(5);cvSet1D (neuralLayers,2,cvScalar(10);/10個輸出/Get data (get images and process it)getData ();train ();/BP.load("bp.xml&qu

4、ot;); /或者調(diào)用訓(xùn)練好的數(shù)據(jù)，把getDate()和train()/注釋掉printf ("n"); printf ("|tClasst|tPrecisio nt|tAccuracyt|n");printf ("n");/ 取得樣本數(shù)據(jù) void BPClassifier : getData () IplImage * src_image ; IplImageprs_image ;CvMatrow , data ; char file 255;int i , j ;for (i =0; i <classes ; i +)f

5、or ( j = 0; j <train_samples ; j +)/Load file if ( j <10)sprintf ( file , "%s%d/%d0%d.pbm" , file_path , i , i , j ); elsesprintf (file , "%s%d/%d%d.pbm" , file_path , i , i , j); src_image = cvLoadImage ( file ,0); if (! src_image )printf ( "Error: Cant load image %sn

6、", file );/exit(-1);/process file prs_image = preprocessing ( src_image , size , size );/Set class label cvGetRow ( trainClasses , & row, i * train_samples + j ); cvSet (& row, cvScalarAll (0);cvSet2D (& row ,0, i , cvRealScalar (1); /Set datacvGetRow ( trainData , & row, i * tr

7、ain_samples + j );IplImage * img = cvCreateImage ( cvSize ( size , size ), IPL_DEPTH_32F , 1 );/convert 8 bits image to 32 float imagecvConvertScale (& prs_image , img, 0.0039215, 0);cvGetSubRect ( img, & data , cvRect (0,0, size , size );CvMatrow_header , * row1 ; /convert data matrix sizex

8、size to vecor row1 = cvReshape ( & data , & row_header , 0, 1 ); cvCopy ( row1, & row, NULL);/ 訓(xùn)練void BPClassifier : train ()BP. create ( neuralLayers );printf ( " 訓(xùn)練中 n" );BP. train ( trainData , trainClasses , sampleWts , 0,CvANN_MLP_TrainParams ( cvTermCriteria ( CV_TERMCRIT

9、_ITER+CV_TERMCRIT _EPS,300,0.01), CvANN_MLP_TrainParams : BACKPROP,0.01);printf ( " 訓(xùn)練結(jié)束 n" );BP. save ( "bp.xml" );/ 分類float BPClassifier : classify ( IplImage * img )IplImageprs_image ;CvMatdata ;CvMat * nearest =cvCreateMat (1,10, CV_32FC1);cvSet ( nearest , cvScalarAll (0);fl

10、oat result ;/process fileprs_image = preprocessing ( img, size , size );/Set dataIplImage * img32 = cvCreateImage ( cvSize ( size , size ), IPL_DEPTH_32F , 1 );cvConvertScale (& prs_image , img32 , 0.0039215, 0); cvGetSubRect ( img32 , & data , cvRect (0,0, size , size );CvMatrow_header , * row1;row1 = cvReshape ( & data , & row_header , 0, 1 ); cvSet ( nearest , cvScalarAll (0); BP. predi