CByteImage
HOGFeatureExtractor::render(const Feature& f) const
{
CShape cellShape = _oriMarkers[0].Shape();
CFloatImage hogImgF(CShape(cellShape.width * f.Shape().width, cellShape.height * f.Shape().height, 1));
hogImgF.ClearPixels();
float minBinValue, maxBinValue;
f.getRangeOfValues(minBinValue, maxBinValue);
// For every cell in the HOG
for(int hi = 0; hi < f.Shape().height; hi++) {
for(int hj = 0; hj < f.Shape().width; hj++) {
// Now _oriMarkers, multiplying contribution by bin level
for(int hc = 0; hc < _nAngularBins; hc++) {
float v = f.Pixel(hj, hi, hc) / maxBinValue;
for(int ci = 0; ci < cellShape.height; ci++) {
float* cellIt = (float*) _oriMarkers[hc].PixelAddress(0, ci, 0);
float* hogIt = (float*) hogImgF.PixelAddress(hj * cellShape.height, hi * cellShape.height + ci, 0);
for(int cj = 0; cj < cellShape.width; cj++, hogIt++, cellIt++) {
(*hogIt) += v * (*cellIt);
}
}
}
}
}
CByteImage hogImg;
TypeConvert(hogImgF, hogImg);
return hogImg;
}
void
SupportVectorMachine::train(const std::vector<float>& labels, const FeatureSet& fset, double C)
{
if(labels.size() != fset.size()) throw std::runtime_error("Database size is different from feature set size!");
_fVecShape = fset[0].Shape();
// Figure out size and number of feature vectors
int nVecs = labels.size();
CShape shape = fset[0].Shape();
int dim = shape.width * shape.height * shape.nBands;
// Parameters for SVM
svm_parameter parameter;
parameter.svm_type = C_SVC;
parameter.kernel_type = LINEAR;
parameter.degree = 0;
parameter.gamma = 0;
parameter.coef0 = 0;
parameter.nu = 0.5;
parameter.cache_size = 100; // In MB
parameter.C = C;
parameter.eps = 1e-3;
parameter.p = 0.1;
parameter.shrinking = 1;
parameter.probability = 0;
parameter.nr_weight = 0; // ?
parameter.weight_label = NULL;
parameter.weight = NULL;
//cross_validation = 0;
// Allocate memory
svm_problem problem;
problem.l = nVecs;
problem.y = new double[nVecs];
problem.x = new svm_node*[nVecs];
if(_data) delete [] _data;
/******** BEGIN TODO ********/
// Copy the data used for training the SVM into the libsvm data structures "problem".
// Put the feature vectors in _data and labels in problem.y. Also, problem.x[k]
// should point to the address in _data where the k-th feature vector starts (i.e.,
// problem.x[k] = &_data[starting index of k-th feature])
//
// Hint:
// * Don't forget to set _data[].index to the corresponding dimension in
// the original feature vector. You also need to set _data[].index to -1
// right after the last element of each feature vector
// Vector containing all feature vectors. svm_node is a struct with
// two fields, index and value. Index entry indicates position
// in feature vector while value is the value in the original feature vector,
// each feature vector of size k takes up k+1 svm_node's in _data
// the last one being simply to indicate that the feature has ended by setting the index
// entry to -1
_data = new svm_node[nVecs * (dim + 1)];
int j = 0;
for(int i=0; i<nVecs; i++){
Feature feat = fset.at(i);
int index=0;
problem.x[i] = &_data[j];
problem.y[i] = labels.at(i);
for (int y=0; y<feat.Shape().height; y++){
for (int x=0; x<feat.Shape().width; x++){
for(int b=0; b<feat.Shape().nBands; b++){
_data[j].index = index;
_data[j].value = feat.Pixel(x,y,b);
j++;
index++;
}
}
}
_data[j++].index = -1;
}
//printf("TODO: SupportVectorMachine.cpp:87\n"); exit(EXIT_FAILURE);
/******** END TODO ********/
// Train the model
if(_model != NULL) svm_free_and_destroy_model(&_model);
_model = svm_train(&problem, ¶meter);
// Cleanup
delete [] problem.y;
delete [] problem.x;
}
