// Copyright (c) 2022, ETH Zurich and UNC Chapel Hill.
// All rights reserved.
//
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// ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDERS OR CONTRIBUTORS BE
// LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
// CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
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// Author: Johannes L. Schoenberger (jsch-at-demuc-dot-de)

#ifndef COLMAP_SRC_FEATURE_UTILS_H_
#define COLMAP_SRC_FEATURE_UTILS_H_

#include "feature/types.h"

namespace colmap {

// Convert feature keypoints to vector of points.
std::vector<Eigen::Vector2d> FeatureKeypointsToPointsVector(
    const FeatureKeypoints& keypoints);

// L2-normalize feature descriptor, where each row represents one feature.
Eigen::MatrixXf L2NormalizeFeatureDescriptors(
    const Eigen::MatrixXf& descriptors);

// L1-Root-normalize feature descriptors, where each row represents one feature.
// See "Three things everyone should know to improve object retrieval",
// Relja Arandjelovic and Andrew Zisserman, CVPR 2012.
Eigen::MatrixXf L1RootNormalizeFeatureDescriptors(
    const Eigen::MatrixXf& descriptors);

// Convert normalized floating point feature descriptor to unsigned byte
// representation by linear scaling from range [0, 0.5] to [0, 255]. Truncation
// to a maximum value of 0.5 is used to avoid precision loss and follows the
// common practice of representing SIFT vectors.
FeatureDescriptors FeatureDescriptorsToUnsignedByte(
    const Eigen::MatrixXf& descriptors);

// Extract the descriptors corresponding to the largest-scale features.
void ExtractTopScaleFeatures(FeatureKeypoints* keypoints,
                             FeatureDescriptors* descriptors,
                             const size_t num_features);

}  // namespace colmap

#endif  // COLMAP_SRC_FEATURE_UTILS_H_