TreeIsoHelper.h

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// ----------------------------------------------------------------------------
// -                        CloudViewer: www.cloudViewer.org                  -
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// Copyright (c) 2018-2024 www.cloudViewer.org
// SPDX-License-Identifier: MIT
// ----------------------------------------------------------------------------
 
#pragma once
 
// #######################################################################################
// # # #                              ACLOUDVIEWER PLUGIN: qTreeIso # # # # 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; version 2 or later of the License.             # #
// # #        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. # # # #        Please cite the
// following paper if you find this tool helpful                # # # # Xi, Z.;
// Hopkinson, C. 3D Graph-Based Individual-Tree Isolation (Treeiso)     # # from
// Terrestrial Laser Scanning Point Clouds. Remote Sens. 2022, 14, 6116.   # #
// https://doi.org/10.3390/rs14236116 # # # #          Our work relies on
// the cut-pursuit algorithm, please also consider citing:   # # Landrieu, L.;
// Obozinski, G. Cut Pursuit: Fast Algorithms to Learn Piecewise  # # Constant
// Functions on General Weighted Graphs. SIAM J. Imaging Sci.          # # 2017,
// 10, 1724–1766. # # # #                                     Copyright © # #
// Artemis Lab, Department of Geography & Environment                 # #
// University of Lethbridge, Canada                         # # # # # # Zhouxin
// Xi and Chris Hopkinson;                           # #
// truebelief2010@gmail.com; c.hopkinson@uleth.ca                   # # #
// #######################################################################################
 
// Local
#include "API.h"
#include "knncpp.h"
 
// Eigen
#include <Eigen/Dense>
 
// STL
#include <vector>
 
class ccPointCloud;
 
bool perform_cut_pursuit(const uint32_t K,
                         const float regStrength,
                         const std::vector<std::vector<float>>& pc_vec,
                         std::vector<float>& edgeWeight,
                         std::vector<uint32_t>& Eu,
                         std::vector<uint32_t>& Ev,
                         std::vector<uint32_t>& in_component,
                         std::vector<std::vector<uint32_t>>& components);
void perform_cut_pursuit2d(const uint32_t K,
                           const float regStrength,
                           const std::vector<std::vector<float>>& pc_vec,
                           std::vector<float>& edgeWeight,
                           std::vector<uint32_t>& Eu,
                           std::vector<uint32_t>& Ev,
                           std::vector<uint32_t>&);
 
template <typename T>
void toTranslatedVector(const ccPointCloud* pc, std::vector<std::vector<T>>& y);
template <typename T>
size_t arg_min_col(std::vector<T>& arr);
template <typename T>
size_t arg_max_col(std::vector<T>& arr);
template <typename T>
void min_col(std::vector<std::vector<T>>& arr, std::vector<T>&);
template <typename T>
T min_col(std::vector<T>& arr);
template <typename T>
void max_col(std::vector<std::vector<T>>& arr, std::vector<T>&);
template <typename T>
void mean_col(std::vector<std::vector<T>>& arr, std::vector<T>&);
template <typename T>
T mean_col(std::vector<T>& arr);
template <typename T>
T median_col(std::vector<T>& arr);
template <typename T>
T mode_col(std::vector<T>& arr);
template <typename T>
void decimate_vec(std::vector<std::vector<T>>& arr,
                  T res,
                  std::vector<std::vector<T>>& vec_dec);
 
template <typename T>
void unique_group(std::vector<T>& arr,
                  std::vector<std::vector<T>>& u_group,
                  std::vector<T>& arr_unq,
                  std::vector<T>& ui);
template <typename T>
void unique_group(std::vector<T>& idx, std::vector<std::vector<T>>&);
template <typename T>
void unique_group(std::vector<T>& arr,
                  std::vector<std::vector<T>>& u_group,
                  std::vector<T>& arr_unq);
template <typename T>
void unique_index_by_rows(std::vector<std::vector<T>>& arr,
                          std::vector<size_t>& ia,
                          std::vector<size_t>& ic);
template <typename T>
void sort_indexes_by_row(std::vector<std::vector<T>>& v,
                         std::vector<size_t>& idx,
                         std::vector<std::vector<T>>&);
template <typename ValueType, typename IndexType>
void sort_indexes(std::vector<ValueType>& v,
                  std::vector<IndexType>& idx,
                  std::vector<ValueType>&);
 
template <typename T1, typename T2>
void get_subset(const std::vector<std::vector<T1>>& arr,
                const std::vector<T2>& indices,
                std::vector<std::vector<T1>>&);
template <typename T1, typename T2>
void get_subset(const std::vector<std::vector<T1>>& arr,
                const std::vector<T2>& indices,
                Eigen::MatrixXf&);
template <typename T1, typename T2>
bool get_subset(ccPointCloud* pcd,
                std::vector<T2>& indices,
                std::vector<std::vector<T1>>& arr_sub);
template <typename T1, typename T2>
void get_subset(std::vector<T1>& arr,
                std::vector<T2>& indices,
                std::vector<T1>& arr_sub);
 
void knn_cpp_nearest_neighbors(const std::vector<std::vector<float>>& dataset,
                               size_t k,
                               std::vector<std::vector<uint32_t>>& res_idx,
                               std::vector<std::vector<float>>& res_dists,
                               unsigned n_thread);
void knn_cpp_build(
        knncpp::KDTreeMinkowskiX<float, knncpp::EuclideanDistance<float>>&
                kdtree,
        unsigned n_thread = 0);
void knn_cpp_query(
        knncpp::KDTreeMinkowskiX<float, knncpp::EuclideanDistance<float>>&
                kdtree,
        Eigen::MatrixXf& query_points,
        size_t k,
        std::vector<std::vector<size_t>>& res_idx,
        std::vector<std::vector<float>>& res_dists);
float knn_cpp_query_min_d(
        knncpp::KDTreeMinkowskiX<float, knncpp::EuclideanDistance<float>>&
                kdtree,
        Eigen::MatrixXf& query_points,
        size_t k);