Method Sample

src/colmap/optim/progressive_sampler.cc:68–107

}

void ProgressiveSampler::Sample(std::vector<size_t>* sampled_idxs) {
  t_ += 1;

  sampled_idxs->clear();
  sampled_idxs->reserve(num_samples_);

  // Compute T_n_p_ using recurrent relation in equation 3 (second part).
  if (t_ == T_n_p_ && n_ < total_num_samples_) {
    const double T_n_plus_1 = T_n_ * (n_ + 1.0) / (n_ + 1.0 - num_samples_);
    T_n_p_ += std::ceil(T_n_plus_1 - T_n_);
    T_n_ = T_n_plus_1;
    n_ += 1;
  }

  // Decide how many samples to draw from which part of the data as
  // specified in equation 5.
  size_t num_random_samples = num_samples_;
  size_t max_random_sample_idx = n_ - 1;
  if (T_n_p_ >= t_) {
    num_random_samples -= 1;
    max_random_sample_idx -= 1;
  }

  // Draw semi-random samples as described in algorithm 1.
  for (size_t i = 0; i < num_random_samples; ++i) {
    while (true) {
      const size_t random_idx =
          RandomUniformInteger<uint32_t>(0, max_random_sample_idx);
      if (!VectorContainsValue(*sampled_idxs, random_idx)) {
        sampled_idxs->push_back(random_idx);
        break;
      }
    }
  }

  // In progressive sampling mode, the last element is mandatory.
  if (T_n_p_ >= t_) {
    sampled_idxs->push_back(n_);
  }
}

}  // namespace colmap