mirror of
https://github.com/truebelief/cc-treeiso-plugin.git
synced 2026-08-30 17:10:29 +08:00
Daniel Girardeau-Montaut
79 lines
6.3 KiB
C++
79 lines
6.3 KiB
C++
#pragma once
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//#######################################################################################
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//# #
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//# CLOUDCOMPARE PLUGIN: qTreeIso #
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//# #
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//# This program is free software; you can redistribute it and/or modify #
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//# it under the terms of the GNU General Public License as published by #
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//# the Free Software Foundation; version 2 or later of the License. #
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//# #
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//# This program is distributed in the hope that it will be useful, #
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//# but WITHOUT ANY WARRANTY; without even the implied warranty of #
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//# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the #
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//# GNU General Public License for more details. #
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//# #
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//# Please cite the following paper if you find this tool helpful #
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//# #
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//# Xi, Z.; Hopkinson, C. 3D Graph-Based Individual-Tree Isolation (Treeiso) #
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//# from Terrestrial Laser Scanning Point Clouds. Remote Sens. 2022, 14, 6116. #
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//# https://doi.org/10.3390/rs14236116 #
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//# #
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//# Our work relies on the cut-pursuit algorithm, please also consider citing: #
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//# Landrieu, L.; Obozinski, G. Cut Pursuit: Fast Algorithms to Learn Piecewise #
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//# Constant Functions on General Weighted Graphs. SIAM J. Imaging Sci. #
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//# 2017, 10, 1724–1766. #
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//# #
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//# Copyright © #
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//# Artemis Lab, Department of Geography & Environment #
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//# University of Lethbridge, Canada #
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//# #
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//# #
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//# Zhouxin Xi and Chris Hopkinson; #
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//# truebelief2010@gmail.com; c.hopkinson@uleth.ca #
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//# #
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//#######################################################################################
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//Local
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#include "API.h"
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#include "knncpp.h"
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//Eigen
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#include <Eigen/Dense>
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//STL
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#include <vector>
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class ccPointCloud;
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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);
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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>&);
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template <typename T> void toTranslatedVector(const ccPointCloud* pc, std::vector<std::vector<T>>& y);
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template <typename T> size_t arg_min_col(std::vector<T>& arr);
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template <typename T> size_t arg_max_col(std::vector<T>& arr);
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template <typename T> void min_col(std::vector<std::vector<T>>& arr, std::vector<T>&);
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template <typename T> T min_col(std::vector<T>& arr);
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template <typename T> void max_col(std::vector<std::vector<T>>& arr, std::vector<T>&);
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template <typename T> void mean_col(std::vector<std::vector<T>>& arr, std::vector<T>&);
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template <typename T> T mean_col(std::vector<T>& arr);
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template <typename T> T median_col(std::vector<T>& arr);
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template <typename T> void decimate_vec(std::vector<std::vector<T>>& arr, T res, std::vector<std::vector<T>>& vec_dec);
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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);
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template <typename T> void unique_group(std::vector<T>& idx, std::vector<std::vector<T>>&);
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template <typename T> void unique_group(std::vector<T>& arr, std::vector<std::vector<T>>& u_group, std::vector<T>& arr_unq);
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template <typename T>void unique_index_by_rows(std::vector<std::vector<T>>& arr, std::vector<size_t>& ia, std::vector<size_t>& ic);
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template <typename T> void sort_indexes_by_row(std::vector<std::vector<T>>& v, std::vector<size_t>& idx, std::vector<std::vector<T>>&);
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template <typename ValueType, typename IndexType> void sort_indexes(std::vector<ValueType>& v, std::vector<IndexType>& idx, std::vector<ValueType>&);
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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>>&);
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template <typename T1, typename T2> void get_subset(const std::vector<std::vector<T1>>& arr, const std::vector<T2>& indices, Eigen::MatrixXf&);
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template <typename T1, typename T2> bool get_subset(ccPointCloud* pcd, std::vector<T2>& indices, std::vector<std::vector<T1>>& arr_sub);
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template <typename T1, typename T2> void get_subset(std::vector<T1>& arr, std::vector<T2>& indices, std::vector<T1>& arr_sub);
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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);
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void knn_cpp_build(knncpp::KDTreeMinkowskiX<float, knncpp::EuclideanDistance<float>>& kdtree, unsigned n_thread = 0);
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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);
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float knn_cpp_query_min_d(knncpp::KDTreeMinkowskiX<float, knncpp::EuclideanDistance<float>>& kdtree, Eigen::MatrixXf& query_points, size_t k);
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