void Part::toVector(vectorMat& vec) {
// if root, allocate space for all of the filters
if (isRoot()) vec.resize((ndescendants_+1) * nmixtures_);
// add my filters to the vector, then my children's, etc
int os = pos_ * nmixtures_;
for (unsigned int n = 0; n < nmixtures_; ++n) vec[os+n] = filters_[n];
for (unsigned int c = 0; c < children_.size(); ++c) children_[c].toVector(vec);
}
void HOGFeatures<T>::pyramid(const Mat& im, vectorMat& pyrafeatures) {
// calculate the scaling factor
Size_<float> imsize = im.size();
nscales_ = 1 + floor(log(min(imsize.height, imsize.width)/(5.0f*(float)binsize_))/log(sfactor_));
vectorMat pyraimages;
pyraimages.resize(nscales_);
pyrafeatures.clear();
pyrafeatures.resize(nscales_);
pyraimages.resize(nscales_);
scales_.clear();
scales_.resize(nscales_);
// perform the non-power of two scaling
// TODO: is this the most intuitive way to represent scaling?
#ifdef _OPENMP
#pragma omp parallel for
#endif
for (unsigned int i = 0; i < interval_; ++i) {
Mat scaled;
resize(im, scaled, imsize * (1.0f/pow(sfactor_,(int)i)));
pyraimages[i] = scaled;
scales_[i] = pow(sfactor_,(int)i)*binsize_;
// perform subsequent power of two scaling
for (unsigned int j = i+interval_; j < nscales_; j+=interval_) {
Mat scaled2;
pyrDown(scaled, scaled2);
pyraimages[j] = scaled2;
scales_[j] = 2 * scales_[j-interval_];
scaled2.copyTo(scaled);
}
}
// perform the actual feature computation, in parallel if possible
#ifdef _OPENMP
#pragma omp parallel for
#endif
for (unsigned int n = 0; n < nscales_; ++n) {
Mat feature;
Mat padded;
switch (im.depth()) {
case CV_32F: features<float>(pyraimages[n], feature); break;
case CV_64F: features<double>(pyraimages[n], feature); break;
case CV_8U: features<uint8_t>(pyraimages[n], feature); break;
case CV_16U: features<uint16_t>(pyraimages[n], feature); break;
default: CV_Error(cv::Error::StsUnsupportedFormat, "Unsupported image type"); break;
}
//copyMakeBorder(feature, padded, 3, 3, 3*flen_, 3*flen_, BORDER_CONSTANT, 0);
//boundaryOcclusionFeature(padded, flen_, 3);
pyrafeatures[n] = feature;
}
}
