/**
* Обучение перцептрона
* @param x - входной вектор
* @param y - правильный выходной вектор
*/
void Perceptron::Teach(QVector<double> x, QVector<int> y, int speed) {
int delta;
QVector<int> t = Recognize(x);
while (false == (t == y)) {
// подстройка весов каждого нейрона
for (int i = 0; i < _neurons.size(); i++) {
delta = y[i] - t[i];
_neurons[i] -> ChangeWeights(speed, delta, x);
}
t = Recognize(x);
}
}
BOOL MzBarDecoder::DecodeFromFile(PCTSTR FileName,BYTE* pcode, DWORD* pnsize, DECODEPARA_ptr pPara){
BOOL bRet = false;
if(FileName == NULL || pcode == NULL) return bRet;
DECODEPARA_t param;
if(pPara == NULL) {
param.dwThrehold = 128;
param.dwScanRegion.left = 0; param.dwScanRegion.right = m_dib.GetImageWidth();
param.dwScanRegion.top = 0; param.dwScanRegion.bottom = m_dib.GetImageHeight();
}else{
param.dwThrehold = pPara->dwThrehold;
param.dwScanRegion = pPara->dwScanRegion;
if(param.dwScanRegion.right == 0){
param.dwScanRegion.right = m_dib.GetImageWidth();
}
if(param.dwScanRegion.bottom == 0){
param.dwScanRegion.bottom = m_dib.GetImageHeight();
}
}
if(LoadImage(FileName)){
GrayImage(¶m.dwScanRegion);
BinaryImage(param.dwThrehold);
if(PreProcess()){
bRet = Recognize(pcode,pnsize);
}
}
return bRet;
}
int VoiceWriteRecognition::ProcessMsg(int msg)
{
switch (msg)
{
case EH_RECORD:
return Record();
break;
case EH_SAVE:
return Save();
break;
case EH_STOP:
return Stop();
break;
case EH_PLAY:
return Play();
break;
case EH_RECOGNIZEBTN:
return Recognize();
break;
default:
return 0;
}
}
int Perceptron::GetClass(QVector<double> x) {
QVector<int> reaction = Recognize(x);
for (int i = 0; i < reaction.size(); i++) {
if (reaction[i] == 1)
return (i + 1);
}
return 0;
}
// Low-level function to recognize the current global image to a string.
char* TessBaseAPI::RecognizeToString() {
BLOCK_LIST block_list;
FindLines(&block_list);
// Now run the main recognition.
PAGE_RES* page_res = Recognize(&block_list, NULL);
return TesseractToText(page_res);
}
TInt CCmdRemark::ProcessL( const TDesC& aCommand )
{
// Complete the test machine - will then get the next cmd
Machine()->CompleteRequest();
if (!Recognize(aCommand))
return Error(KErrArgument, TFR_KFmtErrBadCmd, &Keyphrase());
// Do nothing.
return KErrNone;
}
TInt CCmdListAll::ProcessL(const TDesC& aCommand)
{
// Complete the test machine - will then get the next cmd
Machine()->CompleteRequest();
if (!Recognize(aCommand))
return Error(KErrArgument, TFR_KFmtErrBadCmd, &Keyphrase());
// List commands out
Family()->ListAll(Console());
return KErrNone;
}
void RunParserTest(std::vector<Token>& tokens)
{
try
{
RemoveWhitespaceAndComments(tokens);
Recognize(tokens);
printf("Parsing Successful\n");
}
catch (ParsingException&)
{
printf("Parsing Failed\n");
}
}
char* TessBaseAPI::TesseractRectUNLV(const unsigned char* imagedata,
int bytes_per_pixel,
int bytes_per_line,
int left, int top,
int width, int height) {
if (width < kMinRectSize || height < kMinRectSize)
return NULL; // Nothing worth doing.
// Copy/Threshold the image to the tesseract global page_image.
CopyImageToTesseract(imagedata, bytes_per_pixel, bytes_per_line,
left, top, width, height);
BLOCK_LIST block_list;
FindLines(&block_list);
// Now run the main recognition.
PAGE_RES* page_res = Recognize(&block_list, NULL);
return TesseractToUNLV(page_res);
}
int main(int argc,char ** argv){
int key;
double *Features;
//code to record video
CvVideoWriter *writer=0;
int isColor=1;
int fps=10;
int frameW=640;
int frameH=480;
writer=cvCreateVideoWriter("out.avi",CV_FOURCC('D','I','V','X'),fps,cvSize(frameW,frameH),isColor);
CvMat *mat=cvCreateMat(8,8,CV_64FC1);
char *filenamesCov = "../../../fourier/fourier/Variances";
char * FileMeans = "../../../fourier/fourier/Means.txt";
int numGestures;
numGestures=11;
int numTraining=80;
int numFeatures=8;
//CvArr** invCovMatrices= (CvArr **)malloc(sizeof(CvArr *)*numGestures);
CvArr** eigenVects= (CvArr **)malloc(sizeof(CvArr *)*numGestures);
CvArr** Means=(CvArr **)malloc(sizeof(CvArr *)*numGestures);
CvArr** eigenVals=(CvArr **)malloc(sizeof(CvArr*)*numGestures);
//end record video
int indx[] = {6,11,12,14,15,22,27,28,33,36,42};
//5, a, b, c,caps,g,l,LC,p,RC,v,
CvCapture* capture = NULL;
cascade = (CvHaarClassifierCascade*)cvLoad( "../NewTrained.xml",
0, 0, 0 );
storage = cvCreateMemStorage(0);
IplImage* img;
IplImage* img1;
IplImage* img2;
IplImage* imb;
if(NULL==(capture = cvCaptureFromCAM(1))){
printf("\nError on cvCaptureFromCAM");
return -1;
}
train(Means,eigenVects,eigenVals,numGestures,19,numFeatures,indx);
fprintf(stderr,"blahblah\n");
//ReadInData(filenamesCov,FileMeans,invCovMatrices,Means,numGestures,numFeatures);
//-----
cvNamedWindow("Capture", CV_WINDOW_AUTOSIZE);
cvNamedWindow("Capture2", CV_WINDOW_AUTOSIZE);
cvNamedWindow("Capture3", CV_WINDOW_AUTOSIZE);
cvNamedWindow("Window",CV_WINDOW_AUTOSIZE);
cvMoveWindow("Capture", 550, 250);
cvMoveWindow("Capture2",850, 50);
cvMoveWindow("Capture3",100,500);
cvMoveWindow("Capture4",500,600);
for(;;){
if(NULL==(img=cvQueryFrame(capture))){
printf("\nError on cvQueryFrame");
break;
}
img1=detect_and_draw(img,1);
img2=binary_threshold_hsl(img1);
cvShowImage("Capture3",img2);
cvShowImage("Capture", img1);
cvShowImage("Capture2",img);
if(HAND==1)
{
Features=computeFDFeatures(img2,8);
fprintf(stderr,"Found a hand. The gesture recognized is : %c\n",(char) Gest[Recognize(Features,eigenVects,eigenVals,Means,numGestures,numFeatures,6)]);
//imb=cvCreateImage("",8,1);
}
cvWriteFrame(writer,img1);
key = cvWaitKey(10);
if(key==0x1b)
break;
}
cvReleaseCapture(&capture);
cvDestroyWindow("Capture");
cvDestroyWindow("Capture2");
cvDestroyWindow("Capture3");
cvReleaseImage( &img );
cvReleaseImage(&img1);
cvWaitKey(0);
}
void test( CvArr** Means,CvArr** eigenVects,CvArr** eigenVals,int numGestures, int numTesting,int numFeatures,int *indx) {
char num[] ="01-01";
char *fo = ".png";
char *filename;
if(TESTING) {
filename = (char *)malloc((strlen(test_path)+1)*sizeof(char *));
filename[0]='\0';
strcat(filename,test_path);
}
else {
filename = (char *)malloc((strlen(train_path)+1)*sizeof(char *));
filename[0]='\0';
strcat(filename,train_path);
}
int stats[numGestures];
int i,j,t;
double *Features;
IplImage* src;
for(i=0;i<numGestures;i++)
stats[i]=0;
for(i=0; i<numGestures; i++) {
for(j=0;j<numTesting;j++) {
if(TESTING)
filename[21] = '\0';
else
filename[22] = '\0';
num[0] = '0'+indx[i]/10 ;
num[1] = '0'+indx[i]%10;
if((j+1)>9) {
num[3] = '0'+(j+1)/10;
num[4] = '0'+(j+1)%10;
num[5] = '\0';
}
else {
num[3] = '0'+j+1;
num[4] = '\0';
}
strcat(filename,num);
strcat(filename,fo);
//fprintf(stderr,"i=%d j=%d %s \n",i,j,filename);
src = cvLoadImage( filename,CV_LOAD_IMAGE_GRAYSCALE );
Features=computeFDFeatures(src,8);
t=Recognize(Features,eigenVects,eigenVals,Means,numGestures,numFeatures,5);
free(Features);
if(t==i)
stats[i]++;
}
}
int sum=0;
for(i=0;i<numGestures;i++) {
sum = sum+stats[i];
}
fprintf(stderr,"Percent Accuracy %f\n",(float)sum/(numGestures*numTesting));
/*for(i=0;i<numGestures;i++) {
fprintf(stderr,"num_incorrect for %d=%d\n",i+1,stats[i]);
}*/
}
// Recognize the member char sample as a word
WordAltList *CubeObject::RecognizePhrase(LangModel *lang_mod) {
return Recognize(lang_mod, false);
}
// Recognize the member char sample as a word
WordAltList *CubeObject::RecognizeWord(LangModel *lang_mod) {
return Recognize(lang_mod, true);
}
