void Mixture::convolve(const HOGPyramid & pyramid, vector<HOGPyramid::Matrix> & scores,
vector<Indices> & argmaxes,
vector<vector<vector<Model::Positions> > > * positions) const
{
if(empty() || pyramid.empty()) {
scores.clear();
argmaxes.clear();
if(positions)
positions->clear();
return;
}
const int nbModels = models_.size();
const int nbLevels = pyramid.levels().size();
// Convolve with all the models
vector<vector<HOGPyramid::Matrix> > tmp(nbModels);
convolve(pyramid, tmp, positions);
// In case of error
if(tmp.empty()) {
scores.clear();
argmaxes.clear();
if(positions)
positions->clear();
return;
}
// Resize the scores and argmaxes
scores.resize(nbLevels);
argmaxes.resize(nbLevels);
int i;
#pragma omp parallel for private(i)
for(i = 0; i < nbLevels; ++i) {
scores[i].resize(pyramid.levels()[i].rows() - maxSize().first + 1,
pyramid.levels()[i].cols() - maxSize().second + 1);
argmaxes[i].resize(scores[i].rows(), scores[i].cols());
for(int y = 0; y < scores[i].rows(); ++y) {
for(int x = 0; x < scores[i].cols(); ++x) {
int argmax = 0;
for(int j = 1; j < nbModels; ++j)
if(tmp[j][i](y, x) > tmp[argmax][i](y, x))
argmax = j;
scores[i](y, x) = tmp[argmax][i](y, x);
argmaxes[i](y, x) = argmax;
}
}
}
}
void Mixture::convolve(const HOGPyramid & pyramid,
vector<vector<HOGPyramid::Matrix> > & scores,
vector<vector<vector<Model::Positions> > > * positions) const
{
if(empty() || pyramid.empty()) {
scores.clear();
if(positions)
positions->clear();
}
const int nbModels = models_.size();
scores.resize(nbModels);
if(positions)
positions->resize(nbModels);
// Transform the filters if needed
#pragma omp critical
if(filterCache_.empty())
cacheFilters();
while(!cached_);
// Create a patchwork
const Patchwork patchwork(pyramid);
// Convolve the patchwork with the filters
vector<vector<HOGPyramid::Matrix> > convolutions(filterCache_.size());
patchwork.convolve(filterCache_, convolutions);
// In case of error
if(convolutions.empty()) {
scores.clear();
if(positions)
positions->clear();
return;
}
// Save the offsets of each model in the filter list
vector<int> offsets(nbModels);
for(int i = 0, j = 0; i < nbModels; ++i) {
offsets[i] = j;
j += models_[i].parts_.size();
}
// For each model
int i;
#pragma omp parallel for private(i)
for(i = 0; i < nbModels; ++i) {
vector<vector<HOGPyramid::Matrix> > tmp(models_[i].parts_.size());
for(size_t j = 0; j < tmp.size(); ++j)
tmp[j].swap(convolutions[offsets[i] + j]);
models_[i].convolve(pyramid, tmp, scores[i], positions ? & (*positions)[i] : 0);
}
}
