在Opencv下的BP神经网络的搭建.docxVIP

调用BP进行图像分类只需：img0=cvLoadImage(099.pbm,0);BPClassifierbp_cly;//初始化对象.number=bp_cly.classify(img0);#includeml.hCvANN_MLP BP;//opencv中的BP//构造函数BPClassifier::BPClassifier(){//initial//sprintf(file_path , ../OCR/);sprintf(file_path , C:\\Users\\y450\\Desktop\\recognize\\OCR\\);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_32FC1);for (inti=0;itrain_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个输出//Get data (get images and process it)getData();train();//BP.load(bp.xml); //或者调用训练好的数据,把getDate()和train()//注释掉printf( ---------------------------------------------------------------\n);printf(|\tClass\t|\tPrecision\t|\tAccuracy\t|\n);printf( ---------------------------------------------------------------\n);}//取得样本数据voidBPClassifier::getData(){IplImage* src_image;IplImageprs_image;CvMatrow,data;charfile[255];inti,j;for(i =0; iclasses; i++){for( j = 0; j train_samples; j++){//Load fileif(j10)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 %s\n, file);//exit(-1);}//process fileprs_image = preprocessing(src_image, size, size);//Set class labelcvGetRow(trainClasses, row, i*train_samples + j);cvSet(row,cvScalarAll(0));cvSet2D(row,0,i, cvRealScalar(1));//Set data cvGetRow(trainData, row, i*train_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 sizexsize to vecorrow1 = cvReshape( data, row_header, 0, 1 );cvCopy