float
SupportVectorMachine::predict(const Feature &feature) const
{
CShape shape = feature.Shape();
int dim = shape.width * shape.height * shape.nBands;
svm_node *svmNode = new svm_node[dim + 1];
svm_node *svmNodeIter = svmNode;
for(int y = 0, k = 0; y < shape.height; y++) {
float *data = (float *) feature.PixelAddress(0, y, 0);
for (int x = 0; x < shape.width * shape.nBands; x++, data++, k++, svmNodeIter++) {
svmNodeIter->index = k;
svmNodeIter->value = *data;
}
}
svmNodeIter->index = -1;
double decisionValue;
float label = svm_predict_values(_model, svmNode, &decisionValue);
delete [] svmNode;
return decisionValue;
}
CByteImage
FeatureExtractor::render(const Feature& f, bool normalizeFeat) const
{
if(normalizeFeat) {
CShape shape = f.Shape();
Feature fAux(shape);
float fMin, fMax;
f.getRangeOfValues(fMin, fMax);
for(int y = 0; y < shape.height; y++) {
float* fIt = (float*) f.PixelAddress(0,y,0);
float* fAuxIt = (float*) fAux.PixelAddress(0,y,0);
for(int x = 0; x < shape.width * shape.nBands; x++, fAuxIt++, fIt++) {
*fAuxIt = (*fIt) / fMax;
}
}
return this->render(fAux);
} else {
return this->render(f);
}
}
