double EuclidianMetric::getDistance(Descriptor &desc1, Descriptor &desc2)
{
if (desc1.size() != desc2.size())
throw std::logic_error("Error: out of bounds!");
double distance = 0;
for (int i = 0; i < desc1.size(); i++)
distance += sqr(desc1[i] - desc2[i]);
return std::sqrt(distance);
}
/*
* SSD
* @short Computes the squareroot of squared differences
* @param a First descriptor
* @param b second descriptor
* @return value of squareroot of squared differences
*/
double SSD(Descriptor& a, Descriptor& b)
{
double diff = 0;
for (unsigned int i = 0; i < a.size(); ++i) {
float delta = a[i] - b[i];
diff += delta*delta;
}
return diff;
}
// Add descriptors to the index.
void FlannDescriptorKDTree::AddDescriptor(Descriptor& descriptor) {
// Copy the input descriptor into FLANN's Matrix type.
const size_t cols = descriptor.size();
flann::Matrix<double> flann_descriptor(new double[cols], 1, cols);
for (size_t ii = 0; ii < cols; ++ii) {
flann_descriptor[0][ii] = descriptor(ii);
}
// If this is the first point in the index, create the index and exit.
if (index_ == nullptr) {
// Single kd-tree. No approximation.
const int kNumRandomizedKDTrees = 1;
index_.reset(new flann::Index<flann::L2<double> >(
flann_descriptor, flann::KDTreeIndexParams(kNumRandomizedKDTrees)));
index_->buildIndex();
return;
}
// If the index is already created, add the data point to the index. Rebuild
// every time the index doubles in size to occasionally rebalance the kd tree.
const int kRebuildThreshold = 2;
index_->addPoints(flann_descriptor, kRebuildThreshold);
}
