FeatureHelper.h

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/*
 * OpenVINS: An Open Platform for Visual-Inertial Research
 * Copyright (C) 2018-2023 Patrick Geneva
 * Copyright (C) 2018-2023 Guoquan Huang
 * Copyright (C) 2018-2023 OpenVINS Contributors
 * Copyright (C) 2018-2019 Kevin Eckenhoff
 *
 * This program is free software: you can redistribute it and/or modify
 * it under the terms of the GNU General Public License as published by
 * the Free Software Foundation, either version 3 of the License, or
 * (at your option) any later version.
 *
 * This program is distributed in the hope that it will be useful,
 * but WITHOUT ANY WARRANTY; without even the implied warranty of
 * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
 * GNU General Public License for more details.
 *
 * You should have received a copy of the GNU General Public License
 * along with this program.  If not, see <https://www.gnu.org/licenses/>.
 */
 
#ifndef OV_CORE_FEATURE_HELPER_H
#define OV_CORE_FEATURE_HELPER_H
 
#include <Eigen/Eigen>
#include <memory>
#include <mutex>
#include <vector>
 
#include "Feature.h"
#include "FeatureDatabase.h"
#include "utils/print.h"
 
namespace ov_core {
 
class FeatureHelper {
 
public:
  static void compute_disparity(std::shared_ptr<ov_core::FeatureDatabase> db, double time0, double time1, double &disp_mean,
                                double &disp_var, int &total_feats) {
 
    // Get features seen from the first image
    std::vector<std::shared_ptr<Feature>> feats0 = db->features_containing(time0, false, true);
 
    // Compute the disparity
    std::vector<double> disparities;
    for (auto &feat : feats0) {
 
      // Get the two uvs for both times
      for (auto &campairs : feat->timestamps) {
 
        // First find the two timestamps
        size_t camid = campairs.first;
        auto it0 = std::find(feat->timestamps.at(camid).begin(), feat->timestamps.at(camid).end(), time0);
        auto it1 = std::find(feat->timestamps.at(camid).begin(), feat->timestamps.at(camid).end(), time1);
        if (it0 == feat->timestamps.at(camid).end() || it1 == feat->timestamps.at(camid).end())
          continue;
        auto idx0 = std::distance(feat->timestamps.at(camid).begin(), it0);
        auto idx1 = std::distance(feat->timestamps.at(camid).begin(), it1);
 
        // Now lets calculate the disparity
        Eigen::Vector2f uv0 = feat->uvs.at(camid).at(idx0).block(0, 0, 2, 1);
        Eigen::Vector2f uv1 = feat->uvs.at(camid).at(idx1).block(0, 0, 2, 1);
        disparities.push_back((uv1 - uv0).norm());
      }
    }
 
    // If no disparities, just return
    if (disparities.size() < 2) {
      disp_mean = -1;
      disp_var = -1;
      total_feats = 0;
    }
 
    // Compute mean and standard deviation in respect to it
    disp_mean = 0;
    for (double disp_i : disparities) {
      disp_mean += disp_i;
    }
    disp_mean /= (double)disparities.size();
    disp_var = 0;
    for (double &disp_i : disparities) {
      disp_var += std::pow(disp_i - disp_mean, 2);
    }
    disp_var = std::sqrt(disp_var / (double)(disparities.size() - 1));
    total_feats = (int)disparities.size();
  }
 
  static void compute_disparity(std::shared_ptr<ov_core::FeatureDatabase> db, double &disp_mean, double &disp_var, int &total_feats,
                                double newest_time = -1, double oldest_time = -1) {
 
    // Compute the disparity
    std::vector<double> disparities;
    for (auto &feat : db->get_internal_data()) {
      for (auto &campairs : feat.second->timestamps) {
 
        // Skip if only one observation
        if (campairs.second.size() < 2)
          continue;
 
        // Now lets calculate the disparity (assumes time array is monotonic)
        size_t camid = campairs.first;
        bool found0 = false;
        bool found1 = false;
        Eigen::Vector2f uv0 = Eigen::Vector2f::Zero();
        Eigen::Vector2f uv1 = Eigen::Vector2f::Zero();
        for (size_t idx = 0; idx < feat.second->timestamps.at(camid).size(); idx++) {
          double time = feat.second->timestamps.at(camid).at(idx);
          if ((oldest_time == -1 || time > oldest_time) && !found0) {
            uv0 = feat.second->uvs.at(camid).at(idx).block(0, 0, 2, 1);
            found0 = true;
            continue;
          }
          if ((newest_time == -1 || time < newest_time) && found0) {
            uv1 = feat.second->uvs.at(camid).at(idx).block(0, 0, 2, 1);
            found1 = true;
            continue;
          }
        }
 
        // If we found both an old and a new time, then we are good!
        if (!found0 || !found1)
          continue;
        disparities.push_back((uv1 - uv0).norm());
      }
    }
 
    // If no disparities, just return
    if (disparities.size() < 2) {
      disp_mean = -1;
      disp_var = -1;
      total_feats = 0;
    }
 
    // Compute mean and standard deviation in respect to it
    disp_mean = 0;
    for (double disp_i : disparities) {
      disp_mean += disp_i;
    }
    disp_mean /= (double)disparities.size();
    disp_var = 0;
    for (double &disp_i : disparities) {
      disp_var += std::pow(disp_i - disp_mean, 2);
    }
    disp_var = std::sqrt(disp_var / (double)(disparities.size() - 1));
    total_feats = (int)disparities.size();
  }
 
private:
  // Cannot construct this class
  FeatureHelper() {}
};
 
} // namespace ov_core
 
#endif /* OV_CORE_FEATURE_HELPER_H */