├── CHANGELOG.md ├── CMakeLists.txt ├── LICENSE ├── LICENSE-MIT.txt ├── README.md ├── images ├── JP1_final_seg.jpg ├── JP1_final_seg.png ├── JP1_init_seg.jpg ├── JP1_init_seg.png ├── JP1_interim_seg.jpg ├── JP1_interim_seg.png └── forest.png ├── include ├── CMakeLists.txt ├── TreeIso.h ├── TreeIsoHelper.hpp ├── block.hpp ├── ccTreeIsoDlg.h ├── cp_d0_dist.hpp ├── cut_pursuit.hpp ├── cut_pursuit_d0.hpp ├── knncpp.h ├── maxflow.hpp ├── qTreeIso.h └── qTreeIsoCommands.h ├── info.json ├── license.txt ├── qTreeIso.qrc ├── src ├── CMakeLists.txt ├── TreeIso.cpp ├── ccTreeIsoDlg.cpp ├── cp_d0_dist.cpp ├── cut_pursuit.cpp ├── cut_pursuit_d0.cpp ├── maxflow.cpp └── qTreeIso.cpp └── ui ├── CMakeLists.txt └── TreeIsoDlg.ui /CHANGELOG.md: -------------------------------------------------------------------------------- 1 | # Changelog 2 | 3 | All notable changes to this project will be documented in this file. 4 | 5 | The format is based on [this branch](https://github.com/truebelief/cc-treeiso-plugin/tree/revert-7-optimize_treeiso_speed). 6 | 7 | 8 | ## - 2023-05-13 9 | ### Cleanup 10 | - Adjust the Boost and Eigen library dependency on CMake 11 | 12 | ## - 2023-05-20 13 | ### Cleanup 14 | - Major cleanup to integrate into CloudCompare 15 | 16 | ## - 2023-01-15 17 | ### Added 18 | - Icon added and better progress dialog management 19 | 20 | ## - 2023-06-18 21 | ### Cleanup 22 | - Remove two useless pragma once 23 | - Use standard uint64 type 24 | - Fix case of a file name in CMakeLists.txt 25 | 26 | ## - 2023-06-30 27 | ### Cleanup 28 | - Fixes after a scan of the CloudCompare project by Coverity. 29 | 30 | ## - 2025-02-01 31 | ### Cleanup 32 | - Removed the Boost dependency by adopting a newer cut-pursuit version (boosting speed by 5–15×). 33 | - Fixed an issue with the nearest neighbor search caused by an incorrect 2D array order in knn_cpp. 34 | - Enabled thread count customization for knn-cpp (minor speed boost). 35 | - Optimized array calculations with improved use of STL algorithms. 36 | 37 | ## - 2025-02-10 38 | ### Tested 39 | - Tested on several TLS plots 40 | 41 | ## - 2025-02-11 42 | ### Added 43 | - Improved progress bar increment 44 | - Added rapid detection of ground points based on quantile distribution 45 | - Added warning of ground points before continuing initial segmentation 46 | 47 | ## - 2025-02-16 48 | ### Added 49 | - Avoided crashes by adding error handling once the maximum component limit is reached 50 | - Improved the speed of unique and get_subset functions 51 | ======= 52 | \ 53 | -------------------------------------------------------------------------------- /CMakeLists.txt: -------------------------------------------------------------------------------- 1 | cmake_minimum_required( VERSION 3.10 ) 2 | 3 | option( PLUGIN_STANDARD_QTREEISO "Install qTreeIso plugin" OFF ) 4 | 5 | if( PLUGIN_STANDARD_QTREEISO ) 6 | 7 | project( QTREEISO_PLUGIN ) 8 | 9 | AddPlugin( NAME ${PROJECT_NAME} ) 10 | 11 | find_package(Eigen3 QUIET) 12 | if(NOT Eigen3_FOUND) 13 | set( EIGEN_ROOT_DIR "" CACHE PATH "Eigen root (contains the Eigen directory)" ) 14 | if ( NOT EIGEN_ROOT_DIR ) 15 | message( SEND_ERROR "No Eigen root directory specified (EIGEN_ROOT_DIR)" ) 16 | endif() 17 | include_directories(${EIGEN_ROOT_DIR}) 18 | else() 19 | target_link_libraries(${PROJECT_NAME} Eigen3::Eigen) 20 | endif() 21 | 22 | add_subdirectory( include ) 23 | add_subdirectory( src ) 24 | add_subdirectory( ui ) 25 | 26 | endif() 27 | -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | GNU GENERAL PUBLIC LICENSE 2 | Version 3, 29 June 2007 3 | 4 | Copyright (C) 2007 Free Software Foundation, Inc. 5 | Everyone is permitted to copy and distribute verbatim copies 6 | of this license document, but changing it is not allowed. 7 | 8 | Preamble 9 | 10 | The GNU General Public License is a free, copyleft license for 11 | software and other kinds of works. 12 | 13 | The licenses for most software and other practical works are designed 14 | to take away your freedom to share and change the works. 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If not, see . 649 | 650 | Also add information on how to contact you by electronic and paper mail. 651 | 652 | If the program does terminal interaction, make it output a short 653 | notice like this when it starts in an interactive mode: 654 | 655 | Copyright (C) 656 | This program comes with ABSOLUTELY NO WARRANTY; for details type `show w'. 657 | This is free software, and you are welcome to redistribute it 658 | under certain conditions; type `show c' for details. 659 | 660 | The hypothetical commands `show w' and `show c' should show the appropriate 661 | parts of the General Public License. Of course, your program's commands 662 | might be different; for a GUI interface, you would use an "about box". 663 | 664 | You should also get your employer (if you work as a programmer) or school, 665 | if any, to sign a "copyright disclaimer" for the program, if necessary. 666 | For more information on this, and how to apply and follow the GNU GPL, see 667 | . 668 | 669 | The GNU General Public License does not permit incorporating your program 670 | into proprietary programs. If your program is a subroutine library, you 671 | may consider it more useful to permit linking proprietary applications with 672 | the library. If this is what you want to do, use the GNU Lesser General 673 | Public License instead of this License. But first, please read 674 | . 675 | -------------------------------------------------------------------------------- /LICENSE-MIT.txt: -------------------------------------------------------------------------------- 1 | MIT License 2 | 3 | Permission is hereby granted, free of charge, to any person obtaining a copy of 4 | this software and associated documentation files (the "Software"), to deal in 5 | the Software without restriction, including without limitation the rights to 6 | use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies 7 | of the Software, and to permit persons to whom the Software is furnished to do 8 | so, subject to the following conditions: 9 | 10 | The above copyright notice and this permission notice shall be included in all 11 | copies or substantial portions of the Software. 12 | 13 | THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR 14 | IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, 15 | FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE 16 | AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER 17 | LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, 18 | OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN 19 | THE SOFTWARE. 20 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | TreeIso (plugin) 2 | ============ 3 | 4 | Individual-tree isolation (treeiso) from terrestrial laser scanning 5 | ----------------------------------------------------------------- 6 | 7 | Zhouxin Xi, Chris Hopkinson (Department of Geography & Environment, University of Lethbridge, Canada) 8 | 9 | TreeIso can be utilized to separate individual trees from terrestrial laser scanning point clouds, assigning tree IDs as supplementary scalar fields. 10 | 11 | Please cite the following paper if you find this tool helpful: 12 | 13 | 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 14 | 15 | This tool relies on the cut-pursuit algorithm, please also consider citing: 16 | 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. hal link 17 | Raguet, H.; Landrieu, L. Cut-pursuit algorithm for regularizing nonsmooth functionals with graph total variation. In Proceedings of the International Conference on Machine Learning, Stockholm, Sweden, 10–15 July 2018; Volume 80, pp. 4247–4256. 18 | 19 | The cut-pursuit files are licensed under the **MIT** license - see the [LICENSE-MIT](LICENSE-MIT.txt) file for details. 20 | 21 | A Matlab version shared via: 22 | https://github.com/truebelief/artemis_treeiso 23 | 24 | The development of the treeiso plugin was inspired by the CSF plugin originating from: 25 | Zhang W, Qi J, Wan P, Wang H, Xie D, Wang X, Yan G. An Easy-to-Use Airborne LiDAR Data Filtering Method Based on Cloth Simulation. Remote Sensing. 2016; 8(6):501 26 | 27 | 28 | Command line mode 29 | ----------------- 30 | Command line is supported. The plugin consists of three stages of segmentation. In the command line mode, please configure the segmentation parameters for each stage to activate the corresponding stage. If no parameters are provided for a specific stage, the segmentation for that stage will not be executed. 31 | 32 | Available options 33 | ----------------- 34 | 35 | 36 | 37 | 38 | 39 | 40 | 41 | 56 | 57 |
CommandDescription
-TREEISO 42 | Runs the TREEISO plugin 43 |

Optional settings are:

44 |
    45 |
  • -LAMBDA1 [value]: Regularization strength for initial segmentation (default: 1.0)
    • 46 |
  • -K1 [value]: Nearest neighbors to search for initial segmentation(default: 5, minimum>3)
    • 47 |
  • -DECIMATE_RESOLUTION1 [value]: Decimated point resolution (in m) for initial segmentation (default: 0.05)
    • 48 |
  • -LAMBDA2 [value]: Regularization strength for intermediate segmentation (default: 20)
    • 49 |
  • -K2 [value]: Nearest neighbors to search for intermediate segmentation (default: 20)
    • 50 |
  • -MAX_GAP [value]: Maximum point gap (in m) for intermediate segmentation (default: 2.0)
    • 51 |
  • -DECIMATE_RESOLUTION2 [value]: Decimated point resolution (in m) for intermediate segmentation (default: 0.1)
    • 52 |
  • -RHO [value]: Relative height to length ratio (used to detect non-stems for final segmentation) (default: 0.5)
    • 53 |
  • -VERTICAL_OVERLAP_WEIGHT [value]: Vertical overlapping ratio weight for final segmentation (default: 0.5)
    • 54 |
55 |
58 | 59 | ![treeiso_demo](https://user-images.githubusercontent.com/8785889/236364374-5d9f69e0-0877-43b3-9927-f923d65262c1.gif) 60 | 61 | 62 | 63 | 64 | -------------------------------------------------------------------------------- /images/JP1_final_seg.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/truebelief/cc-treeiso-plugin/3556f843a6dcd31ba22c42758fcaff759119a36c/images/JP1_final_seg.jpg -------------------------------------------------------------------------------- /images/JP1_final_seg.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/truebelief/cc-treeiso-plugin/3556f843a6dcd31ba22c42758fcaff759119a36c/images/JP1_final_seg.png -------------------------------------------------------------------------------- /images/JP1_init_seg.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/truebelief/cc-treeiso-plugin/3556f843a6dcd31ba22c42758fcaff759119a36c/images/JP1_init_seg.jpg -------------------------------------------------------------------------------- /images/JP1_init_seg.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/truebelief/cc-treeiso-plugin/3556f843a6dcd31ba22c42758fcaff759119a36c/images/JP1_init_seg.png -------------------------------------------------------------------------------- /images/JP1_interim_seg.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/truebelief/cc-treeiso-plugin/3556f843a6dcd31ba22c42758fcaff759119a36c/images/JP1_interim_seg.jpg -------------------------------------------------------------------------------- /images/JP1_interim_seg.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/truebelief/cc-treeiso-plugin/3556f843a6dcd31ba22c42758fcaff759119a36c/images/JP1_interim_seg.png -------------------------------------------------------------------------------- /images/forest.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/truebelief/cc-treeiso-plugin/3556f843a6dcd31ba22c42758fcaff759119a36c/images/forest.png -------------------------------------------------------------------------------- /include/CMakeLists.txt: -------------------------------------------------------------------------------- 1 | 2 | target_sources( ${PROJECT_NAME} 3 | PRIVATE 4 | ${CMAKE_CURRENT_LIST_DIR}/ccTreeIsoDlg.h 5 | ${CMAKE_CURRENT_LIST_DIR}/block.hpp 6 | ${CMAKE_CURRENT_LIST_DIR}/maxflow.hpp 7 | ${CMAKE_CURRENT_LIST_DIR}/cp_d0_dist.hpp 8 | ${CMAKE_CURRENT_LIST_DIR}/cut_pursuit_d0.hpp 9 | ${CMAKE_CURRENT_LIST_DIR}/cut_pursuit.hpp 10 | ${CMAKE_CURRENT_LIST_DIR}/knncpp.h 11 | ${CMAKE_CURRENT_LIST_DIR}/qTreeIso.h 12 | ${CMAKE_CURRENT_LIST_DIR}/qTreeIsoCommands.h 13 | ${CMAKE_CURRENT_LIST_DIR}/TreeIso.h 14 | ${CMAKE_CURRENT_LIST_DIR}/TreeIsoHelper.hpp 15 | ) 16 | 17 | target_include_directories( ${PROJECT_NAME} 18 | PRIVATE 19 | ${CMAKE_CURRENT_SOURCE_DIR} 20 | ) 21 | -------------------------------------------------------------------------------- /include/TreeIso.h: -------------------------------------------------------------------------------- 1 | #pragma once 2 | 3 | //####################################################################################### 4 | //# # 5 | //# CLOUDCOMPARE PLUGIN: qTreeIso # 6 | //# # 7 | //# This program is free software; you can redistribute it and/or modify # 8 | //# it under the terms of the GNU General Public License as published by # 9 | //# the Free Software Foundation; version 2 or later of the License. # 10 | //# # 11 | //# This program is distributed in the hope that it will be useful, # 12 | //# but WITHOUT ANY WARRANTY; without even the implied warranty of # 13 | //# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # 14 | //# GNU General Public License for more details. # 15 | //# # 16 | //# Please cite the following paper if you find this tool helpful # 17 | //# # 18 | //# Xi, Z.; Hopkinson, C. 3D Graph-Based Individual-Tree Isolation (Treeiso) # 19 | //# from Terrestrial Laser Scanning Point Clouds. Remote Sens. 2022, 14, 6116. # 20 | //# https://doi.org/10.3390/rs14236116 # 21 | //# # 22 | //# Our work relies on the cut-pursuit algorithm, please also consider citing: # 23 | //# Landrieu, L.; Obozinski, G. Cut Pursuit: Fast Algorithms to Learn Piecewise # 24 | //# Constant Functions on General Weighted Graphs. SIAM J. Imaging Sci. # 25 | //# 2017, 10, 1724–1766. # 26 | //# # 27 | //# Copyright © # 28 | //# Artemis Lab, Department of Geography & Environment # 29 | //# University of Lethbridge, Canada # 30 | //# # 31 | //# # 32 | //# Zhouxin Xi and Chris Hopkinson; # 33 | //# truebelief2010@gmail.com; c.hopkinson@uleth.ca # 34 | //# # 35 | //####################################################################################### 36 | 37 | // Matlab and python versions shared via: 38 | // https://github.com/truebelief/artemis_treeiso 39 | 40 | class ccMainAppInterface; 41 | class ccPointCloud; 42 | class QProgressDialog; 43 | 44 | class TreeIso 45 | { 46 | public: 47 | 48 | static bool Init_seg(const unsigned PR_MIN_NN1,const float PR_REG_STRENGTH1,const float PR_DECIMATE_RES1,const unsigned PR_THREADS1,ccMainAppInterface* app, std::function progressCallBack); 49 | static bool Intermediate_seg(const unsigned PR_MIN_NN2, const float PR_REG_STRENGTH2, const float PR_DECIMATE_RES2, const float PR_MAX_GAP, const unsigned PR_THREADS2, ccMainAppInterface* app, std::function progressCallBack); 50 | static bool Final_seg(const unsigned PR_MIN_NN3, const float PR_REL_HEIGHT_LENGTH_RATIO, const float PR_VERTICAL_WEIGHT, ccMainAppInterface* app, std::function progressCallBack); 51 | 52 | static bool Init_seg_pcd(ccPointCloud* pc, const unsigned PR_MIN_NN1, const float PR_REG_STRENGTH1, const float PR_DECIMATE_RES1, const unsigned PR_THREADS1, std::function progressCallback); 53 | static bool Intermediate_seg_pcd(ccPointCloud* pc, const unsigned PR_MIN_NN2, const float PR_REG_STRENGTH2, const float PR_DECIMATE_RES2, const float PR_MAX_GAP, const unsigned PR_THREADS2, std::function progressCallback); 54 | static bool Final_seg_pcd(ccPointCloud* pc, const unsigned PR_MIN_NN3, const float PR_REL_HEIGHT_LENGTH_RATIO, const float PR_VERTICAL_WEIGHT, std::function progressCallback); 55 | }; 56 | -------------------------------------------------------------------------------- /include/TreeIsoHelper.hpp: -------------------------------------------------------------------------------- 1 | #pragma once 2 | 3 | //####################################################################################### 4 | //# # 5 | //# CLOUDCOMPARE PLUGIN: qTreeIso # 6 | //# # 7 | //# This program is free software; you can redistribute it and/or modify # 8 | //# it under the terms of the GNU General Public License as published by # 9 | //# the Free Software Foundation; version 2 or later of the License. # 10 | //# # 11 | //# This program is distributed in the hope that it will be useful, # 12 | //# but WITHOUT ANY WARRANTY; without even the implied warranty of # 13 | //# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # 14 | //# GNU General Public License for more details. # 15 | //# # 16 | //# Please cite the following paper if you find this tool helpful # 17 | //# # 18 | //# Xi, Z.; Hopkinson, C. 3D Graph-Based Individual-Tree Isolation (Treeiso) # 19 | //# from Terrestrial Laser Scanning Point Clouds. Remote Sens. 2022, 14, 6116. # 20 | //# https://doi.org/10.3390/rs14236116 # 21 | //# # 22 | //# Our work relies on the cut-pursuit algorithm, please also consider citing: # 23 | //# Landrieu, L.; Obozinski, G. Cut Pursuit: Fast Algorithms to Learn Piecewise # 24 | //# Constant Functions on General Weighted Graphs. SIAM J. Imaging Sci. # 25 | //# 2017, 10, 1724–1766. # 26 | //# # 27 | //# Copyright © # 28 | //# Artemis Lab, Department of Geography & Environment # 29 | //# University of Lethbridge, Canada # 30 | //# # 31 | //# # 32 | //# Zhouxin Xi and Chris Hopkinson; # 33 | //# truebelief2010@gmail.com; c.hopkinson@uleth.ca # 34 | //# # 35 | //####################################################################################### 36 | 37 | // Matlab and python versions shared via: 38 | // https://github.com/truebelief/artemis_treeiso 39 | 40 | //Local 41 | #include "knncpp.h" 42 | #include "cp_d0_dist.hpp" 43 | 44 | //Eigen 45 | #include 46 | 47 | //STL 48 | #include 49 | #include 50 | #include 51 | #include 52 | 53 | using namespace std; 54 | 55 | 56 | class ccPointCloud; 57 | typedef std::vector Vec3d; 58 | 59 | typedef uint32_t index_t; // For vertex and edge indices 60 | typedef uint16_t comp_t; // For component indices 61 | 62 | 63 | void knn_cpp_build(knncpp::KDTreeMinkowskiX>& kdtree, unsigned n_thread = 0); 64 | void knn_cpp_query(knncpp::KDTreeMinkowskiX>& kdtree, Eigen::MatrixXf& query_points, size_t k, std::vector >& res_idx, std::vector >& res_dists); 65 | float knn_cpp_query_min_d(knncpp::KDTreeMinkowskiX>& kdtree, Eigen::MatrixXf& query_points, size_t k); 66 | void build_knn_graph(const std::vector& points, size_t k, std::vector& first_edge, std::vector& adj_vertices, std::vector& edge_weights, float regStrength1 = 1.0, unsigned n_thread = 8); 67 | void knn_cpp_nearest_neighbors(const std::vector& dataset, size_t k, std::vector>& res_idx, std::vector& res_dists, unsigned n_thread); 68 | 69 | bool detectGroundByQuantile(const std::vector>& points, std::function progressCallBack, float groundThresholdFraction = 0.1f, float ratioThreshold = 0.1f); 70 | 71 | void load_initseg_points(const std::string& filename, std::vector& points, std::vector& in_component); 72 | bool perform_cut_pursuit(const unsigned K, size_t D, const float regStrength, const std::vector& pc_vec, std::vector& edge_weights, std::vector& Eu, std::vector& Ev, std::vector& in_component, const unsigned threads, std::function progressCallback); 73 | 74 | 75 | template 76 | size_t arg_min_col(const std::vector& arr) { 77 | return std::distance(arr.begin(), std::min_element(arr.begin(), arr.end())); 78 | } 79 | 80 | template 81 | size_t arg_max_col(const std::vector& arr) { 82 | return std::distance(arr.begin(), std::max_element(arr.begin(), arr.end())); 83 | } 84 | 85 | template 86 | void min_col(const std::vector>& arr, std::vector& min_vals) { 87 | if (arr.empty()) { 88 | min_vals.clear(); 89 | return; 90 | } 91 | 92 | min_vals = arr[0]; 93 | for (const auto& row : arr) { 94 | std::transform(min_vals.begin(), min_vals.end(), row.begin(), 95 | min_vals.begin(), [](const T& a, const T& b) { return std::min(a, b); }); 96 | } 97 | } 98 | 99 | template 100 | T min_col(const std::vector& arr) { 101 | return arr.empty() ? std::numeric_limits::quiet_NaN() 102 | : *std::min_element(arr.begin(), arr.end()); 103 | } 104 | 105 | template 106 | T mean_col(const std::vector& arr) { 107 | if (arr.empty()) return std::numeric_limits::quiet_NaN(); 108 | return static_cast(std::accumulate(arr.begin(), arr.end(), 0.0) / arr.size()); 109 | } 110 | 111 | template 112 | T median_col(std::vector& arr) { 113 | if (arr.empty()) return std::numeric_limits::quiet_NaN(); 114 | 115 | const size_t n = arr.size(); 116 | const size_t mid = n / 2; 117 | std::nth_element(arr.begin(), arr.begin() + mid, arr.end()); 118 | 119 | if (n % 2 == 0) { 120 | const T right = arr[mid]; 121 | std::nth_element(arr.begin(), arr.begin() + mid - 1, arr.end()); 122 | return (arr[mid - 1] + right) / 2; 123 | } 124 | return arr[mid]; 125 | } 126 | 127 | template 128 | T mode_col(const std::vector& arr) { 129 | if (arr.empty()) return std::numeric_limits::quiet_NaN(); 130 | 131 | std::unordered_map freq; 132 | for (const auto& val : arr) ++freq[val]; 133 | return std::max_element(freq.begin(), freq.end(), 134 | [](const auto& a, const auto& b) { return a.second < b.second; })->first; 135 | } 136 | 137 | template 138 | void max_col(const std::vector>& arr, std::vector& max_vals) { 139 | if (arr.empty()) { 140 | max_vals.clear(); 141 | return; 142 | } 143 | 144 | max_vals = arr[0]; 145 | for (const auto& row : arr) { 146 | std::transform(max_vals.begin(), max_vals.end(), row.begin(), 147 | max_vals.begin(), [](const T& a, const T& b) { return std::max(a, b); }); 148 | } 149 | } 150 | 151 | template 152 | void mean_col(const std::vector>& arr, std::vector& mean_vals) { 153 | if (arr.empty()) { 154 | mean_vals.clear(); 155 | return; 156 | } 157 | 158 | const size_t cols = arr[0].size(); 159 | mean_vals.resize(cols); 160 | std::fill(mean_vals.begin(), mean_vals.end(), T{}); 161 | 162 | for (const auto& row : arr) { 163 | std::transform(mean_vals.begin(), mean_vals.end(), row.begin(), 164 | mean_vals.begin(), std::plus()); 165 | } 166 | 167 | const T size = static_cast(arr.size()); 168 | std::transform(mean_vals.begin(), mean_vals.end(), mean_vals.begin(), 169 | [size](T val) { return val / size; }); 170 | } 171 | 172 | template 173 | void decimate_vec(const std::vector>& arr, T res, std::vector>& vec_dec) { 174 | if (arr.empty() || res <= T{}) { 175 | vec_dec.clear(); 176 | return; 177 | } 178 | 179 | std::vector arr_min; 180 | min_col(arr, arr_min); 181 | 182 | vec_dec.resize(arr.size(), std::vector(arr[0].size())); 183 | for (size_t i = 0; i < arr.size(); ++i) { 184 | std::transform(arr[i].begin(), arr[i].end(), arr_min.begin(), 185 | vec_dec[i].begin(), 186 | [res](T val, T min) { return std::floor((val - min) / res) + T{ 1 }; }); 187 | } 188 | } 189 | 190 | 191 | template 192 | void sort_indexes_by_row(const std::vector>& v, std::vector& idx, 193 | std::vector>& v_sorted) { 194 | if (v.empty()) { 195 | idx.clear(); 196 | v_sorted.clear(); 197 | return; 198 | } 199 | 200 | const size_t rows = v.size(); 201 | const size_t cols = v[0].size(); 202 | 203 | idx.resize(rows); 204 | std::iota(idx.begin(), idx.end(), 0); 205 | 206 | std::stable_sort(idx.begin(), idx.end(), [&v](size_t i1, size_t i2) { 207 | return std::lexicographical_compare(v[i1].begin(), v[i1].end(), 208 | v[i2].begin(), v[i2].end()); 209 | }); 210 | 211 | v_sorted.resize(rows); 212 | for (size_t i = 0; i < rows; ++i) { 213 | v_sorted[i] = v[idx[i]]; 214 | } 215 | } 216 | 217 | template 218 | void sort_indexes(const std::vector& v, std::vector& idx, 219 | std::vector& v_sorted) { 220 | if (v.empty()) { 221 | idx.clear(); 222 | v_sorted.clear(); 223 | return; 224 | } 225 | 226 | idx.resize(v.size()); 227 | std::iota(idx.begin(), idx.end(), 0); 228 | 229 | std::stable_sort(idx.begin(), idx.end(), 230 | [&v](IndexType i1, IndexType i2) { return v[i1] < v[i2]; }); 231 | 232 | v_sorted.resize(v.size()); 233 | std::transform(idx.begin(), idx.end(), v_sorted.begin(), 234 | [&v](IndexType i) { return v[i]; }); 235 | } 236 | 237 | 238 | template 239 | struct VectorHash { 240 | std::size_t operator()(const std::vector& vec) const { 241 | std::size_t seed = vec.size(); 242 | for (const auto& elem : vec) { 243 | seed ^= std::hash{}(elem)+0x9e3779b97f4a7c15ULL + (seed << 6) + (seed >> 2); 244 | } 245 | return seed; 246 | } 247 | }; 248 | template 249 | void unique_index_by_rows(const std::vector>& arr, 250 | std::vector& ia, 251 | std::vector& ic) { 252 | 253 | ia.clear(); 254 | ic.resize(arr.size()); 255 | // Map each unique row to its unique group index. 256 | std::unordered_map, size_t, VectorHash> row_to_group; 257 | 258 | size_t unique_counter = 0; 259 | for (size_t i = 0; i < arr.size(); ++i) { 260 | auto it = row_to_group.find(arr[i]); 261 | if (it == row_to_group.end()) { 262 | // First occurrence of this unique row. 263 | row_to_group[arr[i]] = unique_counter; 264 | ia.push_back(i); 265 | ic[i] = unique_counter; 266 | ++unique_counter; 267 | } 268 | else { 269 | ic[i] = it->second; 270 | } 271 | } 272 | } 273 | 274 | template 275 | void to_translated_vector(const ccPointCloud* pc, std::vector>& y) { 276 | if (!pc || pc->size() == 0) { 277 | y.clear(); 278 | return; 279 | } 280 | 281 | const unsigned pointCount = pc->size(); 282 | y.resize(pointCount, std::vector(3)); 283 | 284 | std::vector y_mean(3, 0); 285 | for (unsigned i = 0; i < pointCount; ++i) { 286 | const CCVector3* pv = pc->getPoint(i); 287 | y[i] = { static_cast(pv->x), static_cast(pv->y), static_cast(pv->z) }; 288 | std::transform(y_mean.begin(), y_mean.end(), y[i].begin(), y_mean.begin(), std::plus()); 289 | } 290 | 291 | std::transform(y_mean.begin(), y_mean.end(), y_mean.begin(), 292 | [pointCount](T val) { return val / pointCount; }); 293 | 294 | for (auto& point : y) { 295 | std::transform(point.begin(), point.end(), y_mean.begin(), point.begin(), std::minus()); 296 | } 297 | } 298 | 299 | 300 | template 301 | void unique_group(const std::vector& arr, std::vector>& u_group, 302 | std::vector& arr_unq, std::vector& ui) { 303 | if (arr.empty()) { 304 | arr_unq.clear(); 305 | ui.clear(); 306 | u_group.clear(); 307 | return; 308 | } 309 | 310 | std::vector arr_sorted_idx; 311 | std::vector arr_sorted; 312 | sort_indexes(arr, arr_sorted_idx, arr_sorted); 313 | 314 | arr_unq.clear(); 315 | ui.clear(); 316 | u_group.clear(); 317 | 318 | ui.push_back(arr_sorted_idx[0]); 319 | std::vector current_group = { arr_sorted_idx[0] }; 320 | 321 | for (size_t i = 1; i < arr.size(); ++i) { 322 | if (arr_sorted[i] != arr_sorted[i - 1]) { 323 | ui.push_back(arr_sorted_idx[i]); 324 | arr_unq.push_back(arr_sorted[i - 1]); 325 | u_group.push_back(std::move(current_group)); 326 | current_group = { arr_sorted_idx[i] }; 327 | } 328 | else { 329 | current_group.push_back(arr_sorted_idx[i]); 330 | } 331 | } 332 | 333 | arr_unq.push_back(arr_sorted.back()); 334 | u_group.push_back(std::move(current_group)); 335 | } 336 | 337 | // Overloaded versions with fewer return parameters 338 | template 339 | void unique_group(const std::vector& arr, std::vector>& u_group, 340 | std::vector& arr_unq) { 341 | std::vector ui; 342 | unique_group(arr, u_group, arr_unq, ui); 343 | } 344 | 345 | template 346 | void unique_group(const std::vector& arr, std::vector>& u_group) { 347 | std::vector arr_unq, ui; 348 | unique_group(arr, u_group, arr_unq, ui); 349 | } 350 | 351 | template 352 | void get_subset(const std::vector& arr,const std::vector& indices,std::vector& arr_sub) { 353 | arr_sub.resize(indices.size()); 354 | for (size_t i = 0; i < indices.size(); ++i) { 355 | arr_sub[i] = arr[indices[i]]; 356 | } 357 | } 358 | 359 | template 360 | void get_subset(const std::vector>& arr, const std::vector& indices, Eigen::MatrixXf& arr_sub) 361 | { 362 | arr_sub.setZero(); 363 | 364 | if (arr.empty()) 365 | { 366 | assert(false); 367 | return; 368 | } 369 | 370 | arr_sub.resize(arr[0].size(), indices.size()); 371 | 372 | for (size_t i = 0; i < indices.size(); ++i) 373 | { 374 | for (size_t j = 0; j < arr[0].size(); ++j) 375 | { 376 | arr_sub(j, i) = arr[indices[i]][j]; 377 | } 378 | } 379 | } 380 | 381 | template 382 | void get_subset(const std::vector>& arr, const std::vector& indices, std::vector>& arr_sub) 383 | { 384 | arr_sub.clear(); 385 | 386 | if (arr.empty() || indices.empty()) 387 | { 388 | return; 389 | } 390 | 391 | arr_sub.resize(indices.size()); 392 | for (size_t i = 0; i < indices.size(); ++i) 393 | { 394 | arr_sub[i] = arr[indices[i]]; 395 | } 396 | } 397 | 398 | template 399 | bool get_subset(ccPointCloud* pcd, std::vector& indices, std::vector>& arr_sub) 400 | { 401 | arr_sub.clear(); 402 | arr_sub.resize(indices.size(), std::vector(3)); 403 | for (size_t i = 0; i < indices.size(); ++i) 404 | { 405 | const CCVector3* vec = pcd->getPoint(indices[i]); 406 | arr_sub[i][0] = vec->x; 407 | arr_sub[i][1] = vec->y; 408 | arr_sub[i][2] = vec->z; 409 | } 410 | return true; 411 | } 412 | -------------------------------------------------------------------------------- /include/block.hpp: -------------------------------------------------------------------------------- 1 | /* block.hpp */ 2 | /* Vladimir Kolmogorov vnk@ist.ac.at */ 3 | /* Version slightly modified by Hugo Raguet 2016 (different error handling: no 4 | * error function, message handed to standard error) */ 5 | /* 6 | Template classes Block and DBlock 7 | Implement adding and deleting items of the same type in blocks. 8 | 9 | If there there are many items then using Block or DBlock 10 | is more efficient than using 'new' and 'delete' both in terms 11 | of memory and time since 12 | (1) On some systems there is some minimum amount of memory 13 | that 'new' can allocate (e.g., 64), so if items are 14 | small that a lot of memory is wasted. 15 | (2) 'new' and 'delete' are designed for items of varying size. 16 | If all items has the same size, then an algorithm for 17 | adding and deleting can be made more efficient. 18 | (3) All Block and DBlock functions are inline, so there are 19 | no extra function calls. 20 | 21 | Differences between Block and DBlock: 22 | (1) DBlock allows both adding and deleting items, 23 | whereas Block allows only adding items. 24 | (2) Block has an additional operation of scanning 25 | items added so far (in the order in which they were added). 26 | (3) Block allows to allocate several consecutive 27 | items at a time, whereas DBlock can add only a single item. 28 | 29 | Note that no constructors or destructors are called for items. 30 | 31 | Example usage for items of type 'MyType': 32 | 33 | /////////////////////////////////////////////////// 34 | #include "block.h" 35 | #define BLOCK_SIZE 1024 36 | typedef struct { int a, b; } MyType; 37 | MyType *ptr, *array[10000]; 38 | 39 | ... 40 | 41 | Block *block = new Block(BLOCK_SIZE); 42 | 43 | // adding items 44 | for (int i=0; i New(); 47 | ptr -> a = ptr -> b = rand(); 48 | } 49 | 50 | // reading items 51 | for (ptr=block->ScanFirst(); ptr; ptr=block->ScanNext()) 52 | { 53 | printf("%d %d\n", ptr->a, ptr->b); 54 | } 55 | 56 | delete block; 57 | 58 | ... 59 | 60 | DBlock *dblock = new DBlock(BLOCK_SIZE); 61 | 62 | // adding items 63 | for (int i=0; i New(); 66 | } 67 | 68 | // deleting items 69 | for (int i=0; i Delete(array[i]); 72 | } 73 | 74 | // adding items 75 | for (int i=0; i New(); 78 | } 79 | 80 | delete dblock; 81 | 82 | /////////////////////////////////////////////////// 83 | 84 | Note that DBlock deletes items by marking them as 85 | empty (i.e., by adding them to the list of free items), 86 | so that this memory could be used for subsequently 87 | added items. Thus, at each moment the memory allocated 88 | is determined by the maximum number of items allocated 89 | simultaneously at earlier moments. All memory is 90 | deallocated only when the destructor is called. 91 | */ 92 | 93 | #pragma once 94 | #include 95 | 96 | /***********************************************************************/ 97 | /***********************************************************************/ 98 | /***********************************************************************/ 99 | 100 | template class Block 101 | { 102 | public: 103 | /* Constructor. Arguments are the block size */ 104 | Block(int size) { first = last = nullptr; block_size = size; } 105 | 106 | /* Destructor. Deallocates all items added so far */ 107 | ~Block() { while (first) { block *next = first -> next; delete[] ((char*)first); first = next; } } 108 | 109 | /* Allocates 'num' consecutive items; returns pointer 110 | to the first item. 'num' cannot be greater than the 111 | block size since items must fit in one block */ 112 | Type *New(int num = 1) 113 | { 114 | Type *t; 115 | 116 | if (!last || last->current + num > last->last) 117 | { 118 | if (last && last->next) last = last -> next; 119 | else 120 | { 121 | block *next = (block *) new char [sizeof(block) + (block_size-1)*sizeof(Type)]; 122 | if (!next) { 123 | std::cerr << "Block: not enough memory." << std::endl; 124 | exit(EXIT_FAILURE); 125 | } 126 | if (last) last -> next = next; 127 | else first = next; 128 | last = next; 129 | last -> current = & ( last -> data[0] ); 130 | last -> last = last -> current + block_size; 131 | last -> next = nullptr; 132 | } 133 | } 134 | 135 | t = last -> current; 136 | last -> current += num; 137 | return t; 138 | } 139 | 140 | /* Returns the first item (or nullptr, if no items were added) */ 141 | Type *ScanFirst() 142 | { 143 | for (scan_current_block=first; scan_current_block; scan_current_block = scan_current_block->next) 144 | { 145 | scan_current_data = & ( scan_current_block -> data[0] ); 146 | if (scan_current_data < scan_current_block -> current) return scan_current_data ++; 147 | } 148 | return nullptr; 149 | } 150 | 151 | /* Returns the next item (or nullptr, if all items have been read) 152 | Can be called only if previous ScanFirst() or ScanNext() 153 | call returned not nullptr. */ 154 | Type *ScanNext() 155 | { 156 | while (scan_current_data >= scan_current_block -> current) 157 | { 158 | scan_current_block = scan_current_block -> next; 159 | if (!scan_current_block) return nullptr; 160 | scan_current_data = & ( scan_current_block -> data[0] ); 161 | } 162 | return scan_current_data ++; 163 | } 164 | 165 | struct iterator; // for overlapping scans 166 | Type *ScanFirst(iterator& i) 167 | { 168 | for (i.scan_current_block=first; i.scan_current_block; i.scan_current_block = i.scan_current_block->next) 169 | { 170 | i.scan_current_data = & ( i.scan_current_block -> data[0] ); 171 | if (i.scan_current_data < i.scan_current_block -> current) return i.scan_current_data ++; 172 | } 173 | return nullptr; 174 | } 175 | Type *ScanNext(iterator& i) 176 | { 177 | while (i.scan_current_data >= i.scan_current_block -> current) 178 | { 179 | i.scan_current_block = i.scan_current_block -> next; 180 | if (!i.scan_current_block) return nullptr; 181 | i.scan_current_data = & ( i.scan_current_block -> data[0] ); 182 | } 183 | return i.scan_current_data ++; 184 | } 185 | 186 | /* Marks all elements as empty */ 187 | void Reset() 188 | { 189 | block *b; 190 | if (!first) return; 191 | for (b=first; ; b=b->next) 192 | { 193 | b -> current = & ( b -> data[0] ); 194 | if (b == last) break; 195 | } 196 | last = first; 197 | } 198 | 199 | /***********************************************************************/ 200 | 201 | private: 202 | 203 | typedef struct block_st 204 | { 205 | Type *current, *last; 206 | struct block_st *next; 207 | Type data[1]; 208 | } block; 209 | 210 | int block_size; 211 | block *first; 212 | block *last; 213 | public: 214 | struct iterator 215 | { 216 | block *scan_current_block; 217 | Type *scan_current_data; 218 | }; 219 | private: 220 | block *scan_current_block; 221 | Type *scan_current_data; 222 | }; 223 | 224 | /***********************************************************************/ 225 | /***********************************************************************/ 226 | /***********************************************************************/ 227 | 228 | template class DBlock 229 | { 230 | public: 231 | /* Constructor. Arguments are the block size and 232 | (optionally) the pointer to the function which 233 | will be called if allocation failed; the message 234 | passed to this function is "Not enough memory!" */ 235 | DBlock(int size){ first = nullptr; first_free = nullptr; block_size = size; } 236 | 237 | /* Destructor. Deallocates all items added so far */ 238 | ~DBlock() { while (first) { block *next = first -> next; delete[] ((char*)first); first = next; } } 239 | 240 | /* Allocates one item */ 241 | Type *New() 242 | { 243 | block_item *item; 244 | 245 | if (!first_free) 246 | { 247 | block *next = first; 248 | first = (block *) new char [sizeof(block) + (block_size-1)*sizeof(block_item)]; 249 | if (!first) { 250 | std::cerr << "Block: not enough memory." << std::endl; 251 | exit(EXIT_FAILURE); 252 | } 253 | first_free = & (first -> data[0] ); 254 | for (item=first_free; item next_free = item + 1; 256 | item -> next_free = nullptr; 257 | first -> next = next; 258 | } 259 | 260 | item = first_free; 261 | first_free = item -> next_free; 262 | return (Type *) item; 263 | } 264 | 265 | /* Deletes an item allocated previously */ 266 | void Delete(Type *t) 267 | { 268 | ((block_item *) t) -> next_free = first_free; 269 | first_free = (block_item *) t; 270 | } 271 | 272 | /***********************************************************************/ 273 | 274 | private: 275 | 276 | typedef union block_item_st 277 | { 278 | Type t; 279 | block_item_st *next_free; 280 | } block_item; 281 | 282 | typedef struct block_st 283 | { 284 | struct block_st *next; 285 | block_item data[1]; 286 | } block; 287 | 288 | int block_size; 289 | block *first; 290 | block_item *first_free; 291 | }; 292 | -------------------------------------------------------------------------------- /include/ccTreeIsoDlg.h: -------------------------------------------------------------------------------- 1 | #pragma once 2 | 3 | //####################################################################################### 4 | //# # 5 | //# CLOUDCOMPARE PLUGIN: qTreeIso # 6 | //# # 7 | //# This program is free software; you can redistribute it and/or modify # 8 | //# it under the terms of the GNU General Public License as published by # 9 | //# the Free Software Foundation; version 2 or later of the License. # 10 | //# # 11 | //# This program is distributed in the hope that it will be useful, # 12 | //# but WITHOUT ANY WARRANTY; without even the implied warranty of # 13 | //# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # 14 | //# GNU General Public License for more details. # 15 | //# # 16 | //# Please cite the following paper if you find this tool helpful # 17 | //# # 18 | //# Xi, Z.; Hopkinson, C. 3D Graph-Based Individual-Tree Isolation (Treeiso) # 19 | //# from Terrestrial Laser Scanning Point Clouds. Remote Sens. 2022, 14, 6116. # 20 | //# https://doi.org/10.3390/rs14236116 # 21 | //# # 22 | //# Our work relies on the cut-pursuit algorithm, please also consider citing: # 23 | //# Landrieu, L.; Obozinski, G. Cut Pursuit: Fast Algorithms to Learn Piecewise # 24 | //# Constant Functions on General Weighted Graphs. SIAM J. Imaging Sci. # 25 | //# 2017, 10, 1724–1766. # 26 | //# # 27 | //# Copyright © # 28 | //# Artemis Lab, Department of Geography & Environment # 29 | //# University of Lethbridge, Canada # 30 | //# # 31 | //# # 32 | //# Zhouxin Xi and Chris Hopkinson; # 33 | //# truebelief2010@gmail.com; c.hopkinson@uleth.ca # 34 | //# # 35 | //####################################################################################### 36 | 37 | // Matlab and python versions shared via: 38 | // https://github.com/truebelief/artemis_treeiso 39 | 40 | #ifndef CC_TREEISO_DLG_HEADER 41 | #define CC_TREEISO_DLG_HEADER 42 | 43 | #include "ui_TreeIsoDlg.h" 44 | 45 | //! Dialog for qTreeIso plugin 46 | class ccTreeIsoDlg : public QDialog, public Ui::TreeIsoDialog 47 | { 48 | Q_OBJECT 49 | 50 | public: 51 | //! Default constructor 52 | explicit ccTreeIsoDlg(QWidget* parent = nullptr); 53 | protected: 54 | }; 55 | 56 | #endif 57 | -------------------------------------------------------------------------------- /include/cp_d0_dist.hpp: -------------------------------------------------------------------------------- 1 | /*============================================================================= 2 | * Derived class for cut-pursuit algorithm with d0 (weighted contour length) 3 | * penalization, with a loss akin to a distance: 4 | * 5 | * minimize functional over a graph G = (V, E) 6 | * 7 | * F(x) = sum_v loss(y_v, x_v) + ||x||_d0 8 | * 9 | * where for each vertex, y_v and x_v are D-dimensional vectors, the loss is 10 | * a mix of the sum of square differences and a Kullback-Leibler divergence 11 | * (equivalent to cross-entropy in this formulation); see the 'loss' attribute, 12 | * and ||x||_d0 = sum_{uv in E : xu != xv} w_d0_uv , 13 | * 14 | * using greedy cut-pursuit approach with splitting initialized with k-means++. 15 | * 16 | * Parallel implementation with OpenMP API. 17 | * 18 | * L. Landrieu and G. Obozinski, Cut Pursuit: fast algorithms to learn 19 | * piecewise constant functions on general weighted graphs, SIAM Journal on 20 | * Imaging Science, 10(4):1724-1766, 2017 21 | * 22 | * L. Landrieu et al., A structured regularization framework for spatially 23 | * smoothing semantic labelings of 3D point clouds, ISPRS Journal of 24 | * Photogrammetry and Remote Sensing, 132:102-118, 2017 25 | * 26 | * Hugo Raguet 2019, 2022, 2023 27 | *===========================================================================*/ 28 | #pragma once 29 | #include 30 | #include "cut_pursuit_d0.hpp" 31 | 32 | /* real_t is the real numeric type, used for the base field and for the 33 | * objective functional computation; 34 | * index_t must be able to represent the number of vertices and of (undirected) 35 | * edges in the main graph; 36 | * comp_t must be able to represent the number of constant connected components 37 | * in the reduced graph */ 38 | template 39 | class Cp_d0_dist : public Cp_d0 40 | { 41 | public: 42 | /** constructor, destructor **/ 43 | 44 | /* only creates BK graph structure and assign Y, D */ 45 | Cp_d0_dist(index_t V, index_t E, const index_t* first_edge, 46 | const index_t* adj_vertices, const real_t* Y, size_t D = 1); 47 | 48 | /* the destructor does not free pointers which are supposed to be provided 49 | * by the user (forward-star graph structure given at construction, 50 | * monitoring arrays, observation arrays); IT DOES FREE THE REST 51 | * (components assignment and reduced problem elements, etc.), but this can 52 | * be prevented by getting the corresponding pointer member and setting it 53 | * to null beforehand */ 54 | ~Cp_d0_dist(); 55 | 56 | /** methods for manipulating parameters **/ 57 | 58 | /* parameters of d0 penalization (w_d0_uv) can be set using base class Cp 59 | * method set_edge_weights() */ 60 | 61 | /* specific loss */ 62 | real_t quadratic_loss() const { return D; } 63 | 64 | /* Y is changed only if the corresponding argument is not null */ 65 | void set_loss(real_t loss, const real_t* Y = nullptr, 66 | const real_t* vert_weights = nullptr, 67 | const real_t* coor_weights = nullptr); 68 | 69 | /* overload for changing only loss weights */ 70 | void set_loss(const real_t* vert_weights = nullptr, 71 | const real_t* coor_weights = nullptr) 72 | { set_loss(loss, nullptr, vert_weights, coor_weights); } 73 | 74 | /* overload base method for higher init and iter num */ 75 | void set_split_param(index_t max_split_size, comp_t K = 2, 76 | int split_iter_num = 1, real_t split_damp_ratio = 1.0, 77 | int split_values_init_num = 3, int split_values_iter_num = 3); 78 | 79 | void set_min_comp_weight(real_t min_comp_weight = 1.0); 80 | 81 | private: 82 | /** separable loss term: weighted square l2 or smoothed KL **/ 83 | const real_t* Y; // observations, D-by-V array, column major format 84 | 85 | /* D (or public method quadratic_loss()) for quadratic 86 | * f(x) = 1/2 ||y - x||_{l2,W}^2 , 87 | * where W is a diagonal metric (separable product along ℝ^V and ℝ^D), 88 | * that is ||y - x||_{l2,W}^2 = sum_{v in V} w_v ||x_v - y_v||_{l2,M}^2 89 | * = sum_{v in V} w_v sum_d m_d (x_vd - y_vd)^2. 90 | * 91 | * 0 < loss < 1 for smoothed Kullback-Leibler divergence (equivalent to 92 | * cross-entropy) on the probability simplex 93 | * f(x) = sum_v w_v KLs_m(x_v, y_v), 94 | * with KLs(y_v, x_v) = KL(s u + (1 - s) y_v , s u + (1 - s) x_v), where 95 | * KL is the regular Kullback-Leibler divergence, 96 | * u is the uniform discrete distribution over {1,...,D}, and 97 | * s = loss is the smoothing parameter 98 | * it yields 99 | * KLs(y_v, x_v) = - H(s u + (1 - s) y_v) 100 | * - sum_d (s/D + (1 - s) y_{v,d}) log(s/D + (1 - s) x_{v,d}) , 101 | * where H_m is the entropy, that is H(s u + (1 - s) y_v) 102 | * = - sum_d (s/D + (1 - s) y_{v,d}) log(s/D + (1 - s) y_{v,d}) ; 103 | * note that the choosen order of the arguments in the Kullback-Leibler 104 | * does not favor the entropy of x (H(s u + (1 - s) y_v) is a constant), 105 | * hence this loss is actually equivalent to cross-entropy; 106 | * 107 | * 1 <= loss < D for both: quadratic on coordinates from 1 to loss, and 108 | * Kullback-Leibler divergence on coordinates from loss + 1 to D; 109 | * 110 | * the weights w_v are set in vert_weights and m_d are set in coor_weights; 111 | * set corresponding pointer to null for no weight; note that coordinate 112 | * weights makes no sense for Kullback-Leibler divergence alone, but should 113 | * be used for weighting quadratic and KL when mixing both, in which case 114 | * coor_weights should be of length loss + 1 */ 115 | real_t loss; 116 | const real_t *vert_weights, *coor_weights; 117 | 118 | /* minimum weight allowed for a component */ 119 | real_t min_comp_weight; 120 | 121 | /* compute the functional f at a single vertex */ 122 | /* NOTA: not actually a metric, in spite of its name */ 123 | real_t distance(const real_t* Xv, const real_t* Yv) const; 124 | real_t fv(index_t v, const real_t* Xv) const override; 125 | /* override for storing values (used for iterate evolution) */ 126 | real_t compute_f() const override; 127 | real_t fXY; // dist(X, Y), reinitialized when freeing rX 128 | real_t fYY; // dist(Y, Y), reinitialized when modifying the loss 129 | 130 | /** reduced problem **/ 131 | real_t* comp_weights; 132 | 133 | /* allocate and compute reduced values; 134 | * do nothing if the array of reduced values is not null */ 135 | void solve_reduced_problem() override; 136 | 137 | /** greedy splitting **/ 138 | 139 | /* override for setting observation Yv */ 140 | using typename Cp::Split_info; 141 | void set_split_value(Split_info& split_info, comp_t k, index_t v) const 142 | override; 143 | /* override for average of observations Y */ 144 | void update_split_info(Split_info& split_info) const override; 145 | 146 | /** merging components **/ 147 | 148 | /* compute merge information of the given reduced edge; 149 | * populate member arrays merge_gains and merge_values; allocate value with 150 | * malloc; negative gain values might still get accepted, inacceptable 151 | * merge candidate must be deleted */ 152 | void compute_merge_candidate(index_t re) override; 153 | 154 | /* override for transfering component weights to root component */ 155 | comp_t accept_merge_candidate(index_t re) override; 156 | 157 | /* rough estimate of the number of operations for computing merge info of a 158 | * reduced edge; useful for estimating the number of parallel threads */ 159 | size_t merge_info_complexity() const override; 160 | 161 | index_t merge() override; // override for freeing comp_weights 162 | 163 | /** monitoring evolution **/ 164 | 165 | /* iterate evolution in terms of distance relative to distance to Y */ 166 | real_t compute_evolution() const override; 167 | 168 | /** type resolution for base template class members 169 | * https://isocpp.org/wiki/faq/templates#nondependent-name-lookup-members 170 | **/ 171 | using Cp_d0::delete_merge_candidate; 172 | using Cp_d0::merge_gains; 173 | using Cp_d0::merge_values; 174 | using Cp::set_split_param; 175 | using Cp::saturated_vert; 176 | using Cp::last_comp_assign; 177 | using Cp::eps; 178 | using Cp::D; 179 | using Cp::V; 180 | using Cp::rV; 181 | using Cp::rE; 182 | using Cp::rX; 183 | using Cp::last_rX; 184 | using Cp::monitor_evolution; 185 | using Cp::comp_assign; 186 | using Cp::label_assign; 187 | using Cp::comp_list; 188 | using Cp::first_vertex; 189 | using Cp::reduced_edges_u; 190 | using Cp::reduced_edges_v; 191 | using Cp::reduced_edge_weights; 192 | using Cp::is_saturated; 193 | using Cp::malloc_check; 194 | using Cp::real_inf; 195 | }; 196 | -------------------------------------------------------------------------------- /include/cut_pursuit.hpp: -------------------------------------------------------------------------------- 1 | /*============================================================================= 2 | * Base class for cut-pursuit algorithm 3 | * 4 | * L. Landrieu and G. Obozinski, Cut Pursuit: Fast Algorithms to Learn 5 | * Piecewise Constant Functions on General Weighted Graphs, SIAM Journal on 6 | * Imaging Sciences, 2017, 10, 1724-1766 7 | * 8 | * Hugo Raguet 2018, 2020, 2022 9 | * 10 | * This program is free software: you can redistribute it and/or modify 11 | * it under the terms of the GNU General Public License as published by 12 | * the Free Software Foundation, either version 3 of the License, or 13 | * (at your option) any later version. 14 | * 15 | * This program is distributed in the hope that it will be useful, 16 | * but WITHOUT ANY WARRANTY; without even the implied warranty of 17 | * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the 18 | * GNU General Public License for more details. 19 | * 20 | * You should have received a copy of the GNU General Public License 21 | * along with this program. If not, see . 22 | *===========================================================================*/ 23 | #pragma once 24 | #include // for uintmax_t, requires C++11 25 | #include // for size_t, malloc, exit 26 | #include 27 | #include 28 | #include 29 | #include 30 | #include "maxflow.hpp" 31 | 32 | /* real_t is the real numeric type, used for objective functional computation 33 | * and thus for edge weights and flow graph capacities; 34 | * index_t is an integer type able to hold the number of vertices and of edges 35 | * in the main graph; 36 | * comp_t is an integer type able to hold the maximum number of constant 37 | * connected components in the reduced graph; 38 | * value_t is the type associated to the space to which the values belong, it 39 | * is usually real_t, and if multidimensional, this must be specified in the 40 | * parameter D (e.g. for R^3, specify value_t = real_t and D = 3) */ 41 | template 43 | class Cp 44 | { 45 | public: 46 | /** constructor, destructor **/ 47 | 48 | Cp(index_t V, index_t E, const index_t* first_edge, 49 | const index_t* adj_vertices, size_t D = 1); 50 | 51 | /* the destructor does not free pointers which are supposed to be provided 52 | * by the user (forward-star graph structure given at construction, 53 | * monitoring arrays, etc.); IT DOES FREE THE REST (components assignment 54 | * and reduced problem elements, etc.), but this can be prevented by 55 | * getting the corresponding pointer member and setting it to null 56 | * beforehand */ 57 | virtual ~Cp(); 58 | 59 | /** methods for manipulating parameters **/ 60 | 61 | void reset_edges(); // bind all edges 62 | 63 | /* if 'edge_weights' is null, homogeneously equal to 'homo_edge_weight' */ 64 | void set_edge_weights(const real_t* edge_weights = nullptr, 65 | real_t homo_edge_weight = 1.0); 66 | 67 | void set_monitoring_arrays(real_t* objective_values = nullptr, 68 | double* elapsed_time = nullptr, real_t* iterate_evolution = nullptr); 69 | 70 | /* if rV is zero or unity, comp_assign will be automatically initialized; 71 | * if rV is zero, arbitrary components will be assigned at initialization, 72 | * in an attempt to optimize parallelization along components; 73 | * if rV is greater than one, comp_assign must be given and initialized; 74 | * comp_assign is free()'d by destructor, unless set to null beforehand */ 75 | void set_components(comp_t rV = 0, comp_t* comp_assign = nullptr); 76 | 77 | void set_cp_param(real_t dif_tol, int it_max, int verbose, real_t eps); 78 | /* overload for default eps parameter */ 79 | void set_cp_param(real_t dif_tol = 0.0, int it_max = 10, 80 | int verbose = 1000) 81 | { 82 | set_cp_param(dif_tol, it_max, verbose, 83 | std::numeric_limits::epsilon()); 84 | } 85 | 86 | /* tune split parameters; set max_split_size to V for no max */ 87 | void set_split_param(index_t max_split_size, comp_t K = 2, 88 | int split_iter_num = 1, real_t split_damp_ratio = 1.0, 89 | int split_values_init_num = 1, int split_values_iter_num = 1); 90 | 91 | //void set_parallel_param(int max_num_threads, 92 | // bool balance_parallel_split = true); 93 | ///* overload for default max_num_threads parameter */ 94 | //void set_parallel_param(bool balance_parallel_split) 95 | //{ 96 | // set_parallel_param(omp_get_max_threads(), balance_parallel_split); 97 | //} 98 | 99 | /* the 'get' methods takes pointers to pointers as arguments; a null means 100 | * that the user is not interested by the corresponding pointer; NOTA: 101 | * 1) if not explicitely set by the user, memory pointed by these members 102 | * is allocated using malloc(), and thus should be deleted with free() 103 | * 2) they are free()'d by destructor, unless set to null beforehand */ 104 | 105 | comp_t get_components(const comp_t** comp_assign = nullptr, 106 | const index_t** first_vertex = nullptr, 107 | const index_t** comp_list = nullptr) const; 108 | 109 | /* return the number of reduced edges */ 110 | index_t get_reduced_graph(const comp_t** reduced_edges = nullptr, 111 | const real_t** reduced_edge_weights = nullptr); 112 | 113 | /* retrieve the reduced iterate (values of the components); 114 | * WARNING: reduced values are free()'d by destructor */ 115 | const value_t* get_reduced_values() const; 116 | 117 | /* set the reduced iterate (values of the components); 118 | * WARNING: if not set to null before deletion of the main cp object, 119 | * this will be deleted by free() so the given pointer must have been 120 | * allocated with malloc() and the likes */ 121 | void set_reduced_values(value_t* rX); 122 | 123 | /* solve the main problem */ 124 | int cut_pursuit(bool init, std::function progressCallback); 125 | 126 | protected: 127 | /** main graph **/ 128 | 129 | const index_t V, E; // number of vertices, of edges 130 | 131 | /** forward-star graph representation **/ 132 | /* - edges are numeroted so that all edges originating from a same vertex 133 | * are consecutive; 134 | * - for each vertex, 'first_edge' indicates the first edge starting 135 | * from the vertex (or, if there are none, starting from the next vertex); 136 | * array of length V + 1, the first value is always zero and the last 137 | * value is always the total number of edges E 138 | * - for each edge, 'adj_vertices' indicates its ending vertex */ 139 | const index_t *first_edge, *adj_vertices; 140 | 141 | const real_t *edge_weights; // array of length E, weights of edges 142 | real_t homo_edge_weight; // homogeneous weights, set edge_weights to null 143 | 144 | /* dimension of the data; total size signal is V*D */ 145 | const size_t D; 146 | 147 | /** reduced graph **/ 148 | 149 | /* last_* are used to identify saturated components and to compute 150 | * iterate evolution */ 151 | comp_t rV, last_rV; // number of components (reduced vertices) 152 | value_t *rX, *last_rX; // reduced iterate (values of the components) 153 | index_t rE; // number of reduced edges 154 | /* assignment of each vertex to a component */ 155 | comp_t* comp_assign, *last_comp_assign; 156 | /* list the vertices of each components: 157 | * - vertices are gathered in 'comp_list' so that all vertices belonging 158 | * to a same components are consecutive 159 | * - for each component, 'first_vertex' indicates the index of its first 160 | * vertex in 'comp_list' */ 161 | index_t *comp_list, *first_vertex; 162 | /* reverse mapping of comp list: index of a given vertex within its 163 | * components (useful for working within components in parallel) */ 164 | index_t *index_in_comp; 165 | /* components saturation */ 166 | bool* is_saturated; 167 | comp_t saturated_comp; // number of saturated components 168 | index_t saturated_vert; // number of vertices within saturated components 169 | 170 | /* reduced connectivity 171 | * reduced edges represented with edges list (array of size twice the 172 | * number of reduced edges, consecutive indices are linked components) 173 | * guarantees: 174 | * 1) starting component identifiers are smaller than ending components 175 | * 2) increasing order of starting and ending components identifiers 176 | * (this eases some routines, like conversion to forward-star) 177 | * 3) each edge appears only once 178 | * 4) isolated components (not linked to any other component) are linked 179 | * to themselves with epsilon reduced weight */ 180 | comp_t* reduced_edges; 181 | 182 | /* easy accessors for reduced_edges */ 183 | const comp_t& reduced_edges_u(index_t re) const 184 | { return reduced_edges[((size_t) 2)*re]; } 185 | const comp_t& reduced_edges_v(index_t re) const 186 | { return reduced_edges[((size_t) 2)*re + 1]; } 187 | comp_t& reduced_edges_u(index_t re) 188 | { return reduced_edges[((size_t) 2)*re]; } 189 | comp_t& reduced_edges_v(index_t re) 190 | { return reduced_edges[((size_t) 2)*re + 1]; } 191 | 192 | real_t* reduced_edge_weights; 193 | 194 | /** parameters **/ 195 | 196 | real_t dif_tol, eps; // eps gives a characteristic precision 197 | /* with nonzero verbose information on the process will be printed; 198 | * for convex methods, this will be passed on to the reduced problem 199 | * subroutine, controlling the number of subiterations between prints */ 200 | int verbose; 201 | 202 | /** split components with graph cuts **/ 203 | struct Split_info { 204 | comp_t rv; // component to split 205 | comp_t K; // number of alternative values in the component's split 206 | /* first alternative to compete, useful to avoid competing with a value 207 | * already assigned to all vertices, or for single cut with K = 2 */ 208 | comp_t first_k; 209 | value_t* sX; // D-by-K array with alternative values in the split 210 | Split_info(comp_t rv); 211 | ~Split_info(); 212 | }; 213 | comp_t K; // maximum number of alternative values in any component's split 214 | int split_iter_num; // number of partition-and-update iterations 215 | real_t split_damp_ratio; // split damping along iterations 216 | /* number of repetitions in case of stochastic split values computation */ 217 | int split_values_init_num; 218 | int split_values_iter_num; 219 | 220 | virtual index_t split(); 221 | 222 | virtual void split_component(comp_t rv, Maxflow* maxflow); 223 | 224 | /* initialize candidate split values, schedule and assignments; 225 | * base class version implements a kmeans++, with distances replaced by 226 | * split costs, and centroids assignments and updates made purely virtual 227 | * for specializing data and computations */ 228 | virtual Split_info initialize_split_info(comp_t rv); 229 | /* make split value k that would optimaly fit vertex v */ 230 | virtual void set_split_value(Split_info& split_info, comp_t k, index_t v) 231 | const = 0; 232 | /* usually some kind of averaging; must remove alternative values which are 233 | * no longer interesting (e.g. associated to no vertex) */ 234 | virtual void update_split_info(Split_info& split_info) const = 0; 235 | /* rough estimate of the number of operations for initializing the split 236 | * values and all subsequent updates */ 237 | virtual uintmax_t split_values_complexity() const; 238 | /* compute unary cost of split value k at vertex v in component rv; 239 | * can be +infinity, not -infinity */ 240 | virtual real_t vert_split_cost(const Split_info& split_info, index_t v, 241 | comp_t k) const = 0; 242 | /* overload for possibly saving computations for the difference when 243 | * choosing alternative k against alternative l */ 244 | virtual real_t vert_split_cost(const Split_info& split_info, index_t v, 245 | comp_t k, comp_t l) const; 246 | /* compute binary cost of choosing alternatives lu and lv at edge e */ 247 | virtual real_t edge_split_cost(const Split_info& split_info, index_t e, 248 | comp_t lu, comp_t lv) const = 0; 249 | 250 | /* methods for setting and checking edge status */ 251 | bool is_cut(index_t e) const // check if edge e is cut (active) 252 | { return edge_status[e] == CUT; } 253 | bool is_bind(index_t e) const // check if edge e is binding (inactive) 254 | { return edge_status[e] == BIND; } 255 | bool is_separation(index_t e) const // check if edge is a separation 256 | { return edge_status[e] == SEPARATION; } 257 | void cut(index_t e) // flag a cut (active) edge 258 | { edge_status[e] = CUT; } 259 | void bind(index_t e) // flag a binding (inactive) edge 260 | { edge_status[e] = BIND; } 261 | void separate(index_t e) // flag a balancing separation edge 262 | { edge_status[e] = SEPARATION; } 263 | 264 | /* split large components for balancing split, either for parallelism or 265 | * for preventing bad maxflow performance on huge components; 266 | * new components are computed by breadth-first search, restarting when a 267 | * maximum size is reached; 268 | * reorder comp_list and populate first vertex accordingly; 269 | * rV_new is the number of components resulting from such split; 270 | * rV_big is the number of large original components split this way; 271 | * first_vertex_big holds the first vertices of components split this way; 272 | * returns the number of useful parallel threads */ 273 | int balance_split(comp_t& rV_new, comp_t& rV_big, 274 | index_t*& first_vertex_big); 275 | 276 | /* after splitting, separation edges must be removed or activated; 277 | * when called, first_vertex contains additional components due to large 278 | * components being split by balance_split(); 279 | * NOTA: currently, separation edges must be either removed or activated 280 | * at this step; this cannot wait for a future split step, because 281 | * components list of vertex must be kept consecutive for parallel 282 | * treatment of the resulting connected components, and removing parallel 283 | * separation edges in a later step might connect components whose list of 284 | * vertices are not consecutive */ 285 | virtual index_t remove_balance_separations(comp_t rV_new); 286 | 287 | /* revert the above process; 288 | * no change to comp_list, only suppress elements from first_vertex */ 289 | void revert_balance_split(comp_t rV_new, comp_t rV_big, 290 | index_t* first_vertex_big); 291 | 292 | /* rough estimate of the number of operations for split step; 293 | * useful for estimating the number of parallel threads */ 294 | uintmax_t maxflow_complexity() const 295 | { return (uintmax_t) 2*E + V; } // just for a graph cut; heuristic 296 | virtual uintmax_t split_complexity() const; 297 | 298 | /* prefered alternative value for each vertex */ 299 | comp_t*& label_assign = comp_assign; // reuse the same storage 300 | 301 | /** compute reduced values **/ 302 | 303 | virtual void solve_reduced_problem() = 0; 304 | 305 | /** merging components when deemed useful **/ 306 | 307 | /* during the merging step, merged components are stored as chains, 308 | * represented by arrays of length rV 'merge_chains_root', '_next' and 309 | * '_leaf'; merge chain involving component rv follows the scheme 310 | * root[rv] -> ... -> rv -> next[rv] -> ... -> leaf[rv] ; 311 | * NOTA: CHAIN_END is a special values, and: 312 | * - only next[rv] is always up-to-date; 313 | * - root[rv] is always a strictly preceding component in its chain, or 314 | * CHAIN_END if rv is a root; 315 | * - leaf[rv] is up-to-date if rv is a root; 316 | * - rv is the leaf of its chain if, and only if next[rv] == CHAIN_END; 317 | * an additional requirement is that the root of each chain should be the 318 | * component in the chain with lowest index */ 319 | comp_t get_merge_chain_root(comp_t rv) const; 320 | 321 | /* merge the merge chains of the two given roots; 322 | * the root of the resulting chain will be the component in the chains 323 | * with lowest index, which is returned by the function */ 324 | comp_t merge_components(comp_t ru, comp_t rv); 325 | 326 | /* compute the merge chains and return the number of effective merges */ 327 | virtual comp_t compute_merge_chains() = 0; 328 | 329 | /* main routine using the above to perform the merge step; 330 | * NOTA: reduced edges must guarantee 1-4), see member declaration */ 331 | virtual index_t merge(); 332 | 333 | /** monitoring evolution **/ 334 | 335 | /* test if computation of evolution is required */ 336 | virtual bool monitor_evolution() const 337 | { return dif_tol > (real_t) 0.0 || iterate_evolution; } 338 | 339 | /* compute relative iterate evolution */ 340 | virtual real_t compute_evolution() const = 0; 341 | 342 | /* compute objective functional, often on the reduced problem objects */ 343 | virtual real_t compute_objective() const = 0; 344 | 345 | /* allocate memory and fail with error message if not successful */ 346 | static void* malloc_check(size_t size) 347 | { 348 | void *ptr = malloc(size); 349 | if (!ptr){ 350 | std::cerr << "Cut-pursuit: not enough memory." << std::endl; 351 | exit(EXIT_FAILURE); 352 | } 353 | return ptr; 354 | } 355 | 356 | /* simply free if size is zero */ 357 | static void* realloc_check(void* ptr, size_t size) 358 | { 359 | if (!size){ 360 | free(ptr); 361 | return nullptr; 362 | } 363 | ptr = realloc(ptr, size); 364 | if (!ptr){ 365 | std::cerr << "Cut-pursuit: not enough memory." << std::endl; 366 | exit(EXIT_FAILURE); 367 | } 368 | return ptr; 369 | } 370 | 371 | ///** control parallelization **/ 372 | //int max_num_threads; // maximum number of parallel threads 373 | ///* take into account max_num_threads attribute */ 374 | //int compute_num_threads(uintmax_t num_ops, uintmax_t max_threads) const 375 | //{ 376 | // int num_threads = ::compute_num_threads(num_ops, max_threads); 377 | // return num_threads < max_num_threads ? num_threads : max_num_threads; 378 | //} 379 | ///* overload for max_threads defaulting to num_ops */ 380 | //int compute_num_threads(uintmax_t num_ops) const 381 | //{ return compute_num_threads(num_ops, num_ops); } 382 | 383 | /* representing infinite values (has_infinity checked by constructor) */ 384 | static real_t real_inf(){ return std::numeric_limits::infinity(); } 385 | 386 | private: 387 | enum Edge_status : char // requires C++11 to ensure 1 byte 388 | {BIND, CUT, SEPARATION}; 389 | Edge_status* edge_status; // edge activation 390 | 391 | /* parameters */ 392 | int it_max; // maximum number of cut-pursuit iterations 393 | //bool balance_parallel_split; // switch parallel split balancing 394 | index_t max_split_size; // ensure maxflow not working on components too big 395 | 396 | /* monitoring */ 397 | real_t* objective_values; 398 | double* elapsed_time; 399 | real_t* iterate_evolution; 400 | 401 | /* during the merging step, merged components are stored as chains */ 402 | comp_t *merge_chains_root, *merge_chains_next, *merge_chains_leaf; 403 | 404 | double monitor_time(std::chrono::steady_clock::time_point start) const; 405 | 406 | void print_progress(int it, real_t dif, double t) const; 407 | 408 | /* set components assignment and values (and allocate them if needed); 409 | * assumes that no edge of the graph are cut when it is called */ 410 | void initialize(); 411 | 412 | /* initialize with components specified in 'comp_assign' */ 413 | void assign_connected_components(); 414 | 415 | /* initialize with only one component and reduced graph accordingly */ 416 | void single_connected_component(); 417 | 418 | /* compute binding reverse edge forward star graph structure */ 419 | void get_bind_reverse_edges(comp_t rv, index_t*& first_edge_r, 420 | index_t*& adj_vertices_r); 421 | 422 | /* update connected components and count saturated ones */ 423 | bool compute_connected_components(); 424 | 425 | 426 | /* allocate and compute reduced graph structure; 427 | * NOTA: reduced edges must guarantee 1-4), see member declaration */ 428 | void compute_reduced_graph(); 429 | }; 430 | -------------------------------------------------------------------------------- /include/cut_pursuit_d0.hpp: -------------------------------------------------------------------------------- 1 | /*============================================================================= 2 | * Derived class for cut-pursuit algorithm with d0 (weighted contour length) 3 | * penalization, with a separable loss term over a given space: 4 | * 5 | * minimize functional over a graph G = (V, E) 6 | * 7 | * F(x) = f(x) + ||x||_d0 8 | * 9 | * where for each vertex, x_v belongs in a possibly multidimensional space Ω, 10 | * f(x) = sum_{v in V} f_v(x_v) is separable along V with f_v : Ω → ℝ 11 | * and ||x||_d0 = sum_{uv in E : xu != xv} w_d0_uv , 12 | * 13 | * using greedy cut-pursuit approach. 14 | * 15 | * Parallel implementation with OpenMP API. 16 | * 17 | * References: 18 | * 19 | * L. Landrieu and G. Obozinski, Cut Pursuit: fast algorithms to learn 20 | * piecewise constant functions on general weighted graphs, SIAM Journal on 21 | * Imaging Science, 10(4):1724-1766, 2017 22 | * 23 | * L. Landrieu et al., A structured regularization framework for spatially 24 | * smoothing semantic labelings of 3D point clouds, ISPRS Journal of 25 | * Photogrammetry and Remote Sensing, 132:102-118, 2017 26 | * 27 | * Hugo Raguet 2019, 2020 28 | *===========================================================================*/ 29 | #pragma once 30 | #include "cut_pursuit.hpp" 31 | 32 | /* real_t is the real numeric type, used for objective functional computation; 33 | * index_t must be able to represent the number of vertices and of (undirected) 34 | * edges in the main graph; 35 | * comp_t must be able to represent the number of constant connected components 36 | * in the reduced graph; 37 | * value_t is the type associated to the space to which the values belong, it 38 | * is usually real_t, and if multidimensional, this must be specified in the 39 | * parameter D (e.g. for R^3, specify value_t = real_t and D = 3) */ 40 | template 42 | class Cp_d0 : public Cp 43 | { 44 | public: 45 | Cp_d0(index_t V, index_t E, const index_t* first_edge, 46 | const index_t* adj_vertices, size_t D = 1); 47 | 48 | protected: 49 | /* compute the functional f at a single vertex */ 50 | virtual real_t fv(index_t v, const value_t* Xv) const = 0; 51 | 52 | /* compute graph contour length; use reduced edges and reduced weights */ 53 | real_t compute_graph_d0() const; 54 | 55 | /* compute objective functional */ 56 | virtual real_t compute_f() const; 57 | real_t compute_objective() const override; 58 | 59 | /** greedy splitting **/ 60 | 61 | /* compute unary cost of split value k at vertex v in component rv */ 62 | using typename Cp::Split_info; 63 | real_t vert_split_cost(const Split_info& split_info, index_t v, 64 | comp_t k) const override; 65 | /* compute binary cost of choosing alternatives lu and lv at edge e */ 66 | real_t edge_split_cost(const Split_info& split_info, index_t e, 67 | comp_t lu, comp_t lv) const override; 68 | 69 | /** merging components **/ 70 | 71 | /* the strategy is to compute the gain on the functional for the merge of 72 | * each reduced edge, and accept greedily the candidates with greatest 73 | * gain; merge gains and values of neighboring components might be impacted 74 | * by the merge of two components, see implementation for details; override 75 | * the following virtual merge methods for taking additional information 76 | * into account 77 | * NOTA: during the merging step, merged components are stored as chains, 78 | * see header `cut_pursuit.hpp` for details */ 79 | 80 | /* arrays are indexed by reduced edges */ 81 | real_t* merge_gains; // gain on the objective if components are merged 82 | value_t** merge_values; // the value of the components if they are merged 83 | 84 | /* compute merge information of the given reduced edge; 85 | * populate member arrays merge_gains and merge_values; allocate value with 86 | * malloc; negative gain values might still get accepted, inacceptable 87 | * merge candidate must be deleted */ 88 | virtual void compute_merge_candidate(index_t re) = 0; 89 | 90 | /* accept and delete the merge candidate, and return the component root of 91 | * the resulting merge chain; can be overriden to take into account other 92 | * merge effects */ 93 | virtual comp_t accept_merge_candidate(index_t re); 94 | 95 | /* frees the merge value and flag it to null pointer */ 96 | void delete_merge_candidate(index_t re); 97 | 98 | /* rough estimate of the number of operations for computing merge info of a 99 | * reduced edge; useful for estimating the number of parallel threads */ 100 | virtual size_t merge_info_complexity() const = 0; 101 | 102 | 103 | 104 | /** type resolution for base template class members 105 | * https://isocpp.org/wiki/faq/templates#nondependent-name-lookup-members 106 | **/ 107 | using Cp::get_merge_chain_root; 108 | using Cp::K; 109 | using Cp::split_iter_num; 110 | using Cp::split_damp_ratio; 111 | using Cp::split_values_init_num; 112 | using Cp::split_values_iter_num; 113 | using Cp::V; 114 | using Cp::E; 115 | using Cp::D; 116 | using Cp::rV; 117 | using Cp::rE; 118 | using Cp::rX; 119 | using Cp::edge_weights; 120 | using Cp::homo_edge_weight; 121 | using Cp::comp_list; 122 | using Cp::first_vertex; 123 | using Cp::reduced_edges_u; 124 | using Cp::reduced_edges_v; 125 | using Cp::reduced_edge_weights; 126 | using Cp::merge_components; 127 | using Cp::realloc_check; 128 | using Cp::malloc_check; 129 | 130 | private: 131 | /* compute the merge chains and return the number of effective merges */ 132 | comp_t compute_merge_chains() override; 133 | }; 134 | -------------------------------------------------------------------------------- /include/maxflow.hpp: -------------------------------------------------------------------------------- 1 | /* maxflow.hpp */ 2 | /* modified from graph.h by Hugo Raguet 2020, for use with cut-pursuit 3 | * algorithms */ 4 | /* 5 | Copyright Vladimir Kolmogorov and Yuri Boykov 6 | 7 | This file is part of MAXFLOW. 8 | 9 | MAXFLOW is free software: you can redistribute it and/or modify 10 | it under the terms of the GNU General Public License as published by 11 | the Free Software Foundation, either version 3 of the License, or 12 | (at your option) any later version. 13 | 14 | MAXFLOW is distributed in the hope that it will be useful, 15 | but WITHOUT ANY WARRANTY; without even the implied warranty of 16 | MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the 17 | GNU General Public License for more details. 18 | 19 | You should have received a copy of the GNU General Public License 20 | along with MAXFLOW. If not, see . 21 | =============================================================================*/ 22 | 23 | #pragma once 24 | #include "block.hpp" 25 | 26 | /* index_t is an integer type able to hold the number of nodes and of edges; 27 | * flow_t is a numeric type for the flow (capacities) */ 28 | template class Maxflow 29 | { 30 | public: 31 | Maxflow(index_t node_num, index_t edge_num); 32 | 33 | ~Maxflow(); 34 | 35 | void add_edge(index_t i, index_t j); 36 | 37 | flow_t& terminal_capacity(index_t i); 38 | 39 | void set_edge_capacities(index_t e, flow_t cap, flow_t rev_cap); 40 | 41 | /* retrieve (signed) flow passing through a given edge 42 | second parameter is the initial capacity */ 43 | flow_t get_edge_flow(index_t e, flow_t cap); 44 | 45 | void maxflow(); 46 | 47 | bool is_sink(index_t i, bool default_side = false); 48 | 49 | private: 50 | struct node; 51 | struct arc; 52 | 53 | // internal variables and functions 54 | 55 | struct node 56 | { 57 | arc *first; // first outcoming arc 58 | arc *parent; // node's parent 59 | node *next; // pointer to the next active node 60 | index_t TS; // timestamp showing when DIST was computed 61 | index_t DIST; // distance to the terminal 62 | bool is_sink : 1; // indicate source or sink tree, if parent not null 63 | /* positive if connected to source, negative if connected to sink */ 64 | flow_t term_res_cap; 65 | }; 66 | 67 | struct arc 68 | { 69 | node* head; // node the arc points to 70 | arc* next; // next arc with the same originating node 71 | arc* sister; // reverse arc 72 | flow_t res_cap; // residual capacity 73 | }; 74 | 75 | struct nodeptr 76 | { 77 | node *ptr; 78 | nodeptr *next; 79 | }; 80 | static const int NODEPTR_BLOCK_SIZE = 128; 81 | 82 | node *nodes, *node_last; 83 | arc *arcs, *arc_last; 84 | 85 | /* special constants for parent arcs */ 86 | arc reserved_terminal_arc; // the parent is an arc to terminal 87 | arc* const terminal; 88 | arc reserved_orphan_arc; // no parent 89 | arc* const orphan; 90 | 91 | DBlock *nodeptr_block; 92 | 93 | node *queue_first[2], *queue_last[2]; // list of active nodes 94 | nodeptr *orphan_first, *orphan_last; // list of pointers to orphans 95 | index_t TIME; // monotonically increasing global counter 96 | 97 | // functions for processing active list 98 | void set_active(node *i); 99 | node *next_active(); 100 | 101 | // functions for processing orphans list 102 | void set_orphan_front(node* i); // add to the beginning of the list 103 | void set_orphan_rear(node* i); // add to the end of the list 104 | 105 | void maxflow_init(); // called if reuse_trees == false 106 | void augment(arc *middle_arc); 107 | void process_source_orphan(node *i); 108 | void process_sink_orphan(node *i); 109 | }; 110 | 111 | #define TPL template 112 | #define MXFL Maxflow 113 | 114 | TPL inline void MXFL::add_edge(index_t _i, index_t _j) 115 | { 116 | arc *a = arc_last++; 117 | arc *a_rev = arc_last++; 118 | 119 | node* i = nodes + _i; 120 | node* j = nodes + _j; 121 | 122 | a->sister = a_rev; 123 | a_rev->sister = a; 124 | a->next = i->first; 125 | i->first = a; 126 | a_rev->next = j->first; 127 | j->first = a_rev; 128 | a->head = j; 129 | a_rev->head = i; 130 | } 131 | 132 | TPL inline flow_t& MXFL::terminal_capacity(index_t i) 133 | { 134 | return nodes[i].term_res_cap; 135 | } 136 | 137 | TPL inline void MXFL::set_edge_capacities(index_t e, flow_t cap, 138 | flow_t rev_cap) 139 | { 140 | arc* a = arcs + (size_t) 2*e; 141 | a->res_cap = cap; 142 | (a + 1)->res_cap = rev_cap; 143 | } 144 | 145 | TPL inline flow_t MXFL::get_edge_flow(index_t e, flow_t cap) 146 | { 147 | arc* a = arcs + (size_t) 2*e; 148 | return cap - a->res_cap; 149 | } 150 | 151 | TPL inline bool MXFL::is_sink(index_t i, bool default_side) 152 | { 153 | return nodes[i].parent ? nodes[i].is_sink : default_side; 154 | } 155 | 156 | #undef TPL 157 | #undef MXFL 158 | -------------------------------------------------------------------------------- /include/qTreeIso.h: -------------------------------------------------------------------------------- 1 | #pragma once 2 | 3 | //####################################################################################### 4 | //# # 5 | //# CLOUDCOMPARE PLUGIN: qTreeIso # 6 | //# # 7 | //# This program is free software; you can redistribute it and/or modify # 8 | //# it under the terms of the GNU General Public License as published by # 9 | //# the Free Software Foundation; version 2 or later of the License. # 10 | //# # 11 | //# This program is distributed in the hope that it will be useful, # 12 | //# but WITHOUT ANY WARRANTY; without even the implied warranty of # 13 | //# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # 14 | //# GNU General Public License for more details. # 15 | //# # 16 | //# Please cite the following paper if you find this tool helpful # 17 | //# # 18 | //# Xi, Z.; Hopkinson, C. 3D Graph-Based Individual-Tree Isolation (Treeiso) # 19 | //# from Terrestrial Laser Scanning Point Clouds. Remote Sens. 2022, 14, 6116. # 20 | //# https://doi.org/10.3390/rs14236116 # 21 | //# # 22 | //# Our work relies on the cut-pursuit algorithm, please also consider citing: # 23 | //# Landrieu, L.; Obozinski, G. Cut Pursuit: Fast Algorithms to Learn Piecewise # 24 | //# Constant Functions on General Weighted Graphs. SIAM J. Imaging Sci. # 25 | //# 2017, 10, 1724–1766. # 26 | //# # 27 | //# Copyright © # 28 | //# Artemis Lab, Department of Geography & Environment # 29 | //# University of Lethbridge, Canada # 30 | //# # 31 | //# # 32 | //# Zhouxin Xi and Chris Hopkinson; # 33 | //# truebelief2010@gmail.com; c.hopkinson@uleth.ca # 34 | //# # 35 | //####################################################################################### 36 | 37 | // Matlab and python versions shared via: 38 | // https://github.com/truebelief/artemis_treeiso 39 | 40 | #include "ccStdPluginInterface.h" 41 | 42 | class ccTreeIsoDlg; 43 | 44 | //! TreeIso plugin 45 | class qTreeIso : public QObject, public ccStdPluginInterface 46 | { 47 | Q_OBJECT 48 | Q_INTERFACES( ccPluginInterface ccStdPluginInterface ) 49 | 50 | Q_PLUGIN_METADATA( IID "cccorp.cloudcompare.plugin.qTreeISO" FILE "../info.json" ) 51 | 52 | public: 53 | 54 | //! Default constructor 55 | explicit qTreeIso(QObject* parent = nullptr); 56 | //! Destructor 57 | virtual ~qTreeIso() = default; 58 | 59 | //inherited from ccStdPluginInterface 60 | void onNewSelection(const ccHObject::Container& selectedEntities) override; 61 | QList getActions() override; 62 | void registerCommands(ccCommandLineInterface* cmd) override; 63 | 64 | //! Parameters 65 | struct Parameters 66 | { 67 | float reg_strength1 = 1.0f; //lambda1 68 | int min_nn1 = 5; //K1:key parameter 69 | float decimate_res1 = 0.05f; 70 | int threads1 = 1; 71 | 72 | int reg_strength2 = 20; //lambda2:key parameter 73 | int min_nn2 = 20; //K2:key parameter 74 | float decimate_res2 = 0.1f; 75 | float max_gap = 2.0f; 76 | int threads2 = 1; 77 | 78 | float rel_height_length_ratio = 0.5f; //rho 79 | float vertical_weight = 0.5; //w:key parameter 80 | }; 81 | 82 | protected: 83 | 84 | //! Slot called when associated ation is triggered 85 | void doAction(); 86 | 87 | void init_segs(const Parameters& parameters, QWidget* parent = nullptr); 88 | void intermediate_segs(const Parameters& parameters, QWidget* parent = nullptr); 89 | void final_segs(const Parameters& parameters, QWidget* parent = nullptr); 90 | 91 | protected: // members 92 | 93 | //! Associated action 94 | QAction* m_action; 95 | }; 96 | -------------------------------------------------------------------------------- /include/qTreeIsoCommands.h: -------------------------------------------------------------------------------- 1 | #pragma once 2 | 3 | //####################################################################################### 4 | //# # 5 | //# CLOUDCOMPARE PLUGIN: qTreeIso # 6 | //# # 7 | //# This program is free software; you can redistribute it and/or modify # 8 | //# it under the terms of the GNU General Public License as published by # 9 | //# the Free Software Foundation; version 2 or later of the License. # 10 | //# # 11 | //# This program is distributed in the hope that it will be useful, # 12 | //# but WITHOUT ANY WARRANTY; without even the implied warranty of # 13 | //# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # 14 | //# GNU General Public License for more details. # 15 | //# # 16 | //# Please cite the following paper if you find this tool helpful # 17 | //# # 18 | //# Xi, Z.; Hopkinson, C. 3D Graph-Based Individual-Tree Isolation (Treeiso) # 19 | //# from Terrestrial Laser Scanning Point Clouds. Remote Sens. 2022, 14, 6116. # 20 | //# https://doi.org/10.3390/rs14236116 # 21 | //# # 22 | //# Our work relies on the cut-pursuit algorithm, please also consider citing: # 23 | //# Landrieu, L.; Obozinski, G. Cut Pursuit: Fast Algorithms to Learn Piecewise # 24 | //# Constant Functions on General Weighted Graphs. SIAM J. Imaging Sci. # 25 | //# 2017, 10, 1724–1766. # 26 | //# # 27 | //# Copyright © # 28 | //# Artemis Lab, Department of Geography & Environment # 29 | //# University of Lethbridge, Canada # 30 | //# # 31 | //# # 32 | //# Zhouxin Xi and Chris Hopkinson; # 33 | //# truebelief2010@gmail.com; c.hopkinson@uleth.ca # 34 | //# # 35 | //####################################################################################### 36 | 37 | // Matlab and python versions shared via: 38 | // https://github.com/truebelief/artemis_treeiso 39 | 40 | //CloudCompare 41 | #include 42 | 43 | //Local 44 | #include "ccTreeIsoDlg.h" 45 | #include "qTreeIso.h" 46 | #include "TreeIso.h" 47 | 48 | static const char COMMAND_TREEISO[] = "TREEISO"; 49 | 50 | static const char COMMAND_LAMBDA1[] = "LAMBDA1"; 51 | static const char COMMAND_K1[] = "K1"; 52 | static const char COMMAND_DECIMATE_RESOLUTION1[] = "DECIMATE_RESOLUTION1"; 53 | 54 | static const char COMMAND_LAMBDA2[] = "LAMBDA2"; 55 | static const char COMMAND_K2[] = "K2"; 56 | static const char COMMAND_MAX_GAP[] = "MAX_GAP"; 57 | static const char COMMAND_DECIMATE_RESOLUTION2[] = "DECIMATE_RESOLUTION2"; 58 | 59 | static const char COMMAND_RHO[] = "RHO"; 60 | static const char COMMAND_VERTICAL_OVERLAP_WEIGHT[] = "VERTICAL_OVERLAP_WEIGHT"; 61 | 62 | //! qTreeIso command line processor 63 | struct CommandTreeIso : public ccCommandLineInterface::Command 64 | { 65 | CommandTreeIso() : ccCommandLineInterface::Command("TREEISO", COMMAND_TREEISO) {} 66 | 67 | bool process(ccCommandLineInterface& cmd) override 68 | { 69 | cmd.print("[TreeIso]"); 70 | 71 | if (cmd.clouds().empty()) 72 | { 73 | cmd.error("No cloud loaded"); 74 | return false; 75 | } 76 | 77 | //initial parameters 78 | qTreeIso::Parameters parameters; 79 | 80 | bool try_init_seg = false; 81 | bool try_intermediate_seg = false; 82 | bool try_final_seg = false; 83 | 84 | while (!cmd.arguments().empty()) 85 | { 86 | const QString& ARGUMENT = cmd.arguments().front(); 87 | if ((ccCommandLineInterface::IsCommand(ARGUMENT, COMMAND_LAMBDA1))) 88 | { 89 | try_init_seg = true; 90 | cmd.arguments().pop_front(); 91 | bool convert = false; 92 | 93 | parameters.reg_strength1 = cmd.arguments().takeFirst().toFloat(&convert); 94 | if ((!convert) & (parameters.reg_strength1 <= 0.0)) 95 | { 96 | return cmd.error(QObject::tr("Invalid parameter: value after \"-%1\"").arg(COMMAND_LAMBDA1)); 97 | } 98 | cmd.print(QString("lambda1 (Regularization strength for initial segmentation) set: %1").arg(parameters.reg_strength1)); 99 | } 100 | else if (ccCommandLineInterface::IsCommand(ARGUMENT, COMMAND_K1)) 101 | { 102 | try_init_seg = true; 103 | cmd.arguments().pop_front(); 104 | bool convert = false; 105 | 106 | parameters.min_nn1 = cmd.arguments().takeFirst().toInt(&convert); 107 | if ((!convert) & (parameters.min_nn1 < 3)) 108 | { 109 | return cmd.error(QObject::tr("Invalid parameter: value after \"-%1\"").arg(COMMAND_K1)); 110 | } 111 | cmd.print(QString("K1 (Nearest neighbors to search for initial segmentation) set: %1").arg(parameters.min_nn1)); 112 | } 113 | else if (ccCommandLineInterface::IsCommand(ARGUMENT, COMMAND_DECIMATE_RESOLUTION1)) 114 | { 115 | try_init_seg = true; 116 | cmd.arguments().pop_front(); 117 | bool convert = false; 118 | 119 | parameters.decimate_res1 = cmd.arguments().takeFirst().toFloat(&convert); 120 | if ((!convert) & (parameters.decimate_res1 < 0.001)) 121 | { 122 | return cmd.error(QObject::tr("Invalid parameter: value after \"-%1\"").arg(COMMAND_DECIMATE_RESOLUTION1)); 123 | } 124 | cmd.print(QString("Decimated resolution (in m) for initial segmentation set: %1").arg(parameters.decimate_res1)); 125 | } 126 | else if (ccCommandLineInterface::IsCommand(ARGUMENT, COMMAND_LAMBDA2)) 127 | { 128 | try_intermediate_seg = true; 129 | cmd.arguments().pop_front(); 130 | bool convert = false; 131 | 132 | parameters.reg_strength2 = cmd.arguments().takeFirst().toFloat(&convert); 133 | if ((!convert) & (parameters.reg_strength2 <= 0.0)) 134 | { 135 | return cmd.error(QObject::tr("Invalid parameter: value after \"-%1\"").arg(COMMAND_LAMBDA2)); 136 | } 137 | cmd.print(QString("lambda2 (Regularization strength for intermediate segmentation) set: %1").arg(parameters.reg_strength2)); 138 | } 139 | else if (ccCommandLineInterface::IsCommand(ARGUMENT, COMMAND_K2)) 140 | { 141 | try_intermediate_seg = true; 142 | cmd.arguments().pop_front(); 143 | bool convert = false; 144 | 145 | parameters.min_nn2 = cmd.arguments().takeFirst().toInt(&convert); 146 | if ((!convert) & (parameters.min_nn2 < 3)) 147 | { 148 | return cmd.error(QObject::tr("Invalid parameter: value after \"-%1\"").arg(COMMAND_K2)); 149 | } 150 | cmd.print(QString("K2 (Nearest neighbors to search for intermediate segmentation) set: %1").arg(parameters.min_nn2)); 151 | } 152 | else if (ccCommandLineInterface::IsCommand(ARGUMENT, COMMAND_MAX_GAP)) 153 | { 154 | try_intermediate_seg = true; 155 | cmd.arguments().pop_front(); 156 | bool convert = false; 157 | 158 | parameters.max_gap = cmd.arguments().takeFirst().toFloat(&convert); 159 | if ((!convert) & (parameters.max_gap <= 0.0001)) 160 | { 161 | return cmd.error(QObject::tr("Invalid parameter: value after \"-%1\"").arg(COMMAND_MAX_GAP)); 162 | } 163 | cmd.print(QString("Maximum point gap (in m) for intermediate segmentation set: %1").arg(parameters.max_gap)); 164 | } 165 | else if (ccCommandLineInterface::IsCommand(ARGUMENT, COMMAND_DECIMATE_RESOLUTION2)) 166 | { 167 | try_intermediate_seg = true; 168 | cmd.arguments().pop_front(); 169 | bool convert = false; 170 | 171 | parameters.decimate_res2 = cmd.arguments().takeFirst().toFloat(&convert); 172 | if ((!convert) & (parameters.decimate_res2 < 0.001)) 173 | { 174 | return cmd.error(QObject::tr("Invalid parameter: value after \"-%1\"").arg(COMMAND_DECIMATE_RESOLUTION2)); 175 | } 176 | cmd.print(QString("Decimated resolution (in m) for intermediate segmentation set: %1").arg(parameters.decimate_res2)); 177 | } 178 | else if (ccCommandLineInterface::IsCommand(ARGUMENT, COMMAND_RHO)) 179 | { 180 | try_final_seg = true; 181 | cmd.arguments().pop_front(); 182 | bool convert = false; 183 | 184 | parameters.rel_height_length_ratio = cmd.arguments().takeFirst().toFloat(&convert); 185 | if ((!convert) & (parameters.rel_height_length_ratio < 0.001)) 186 | { 187 | return cmd.error(QObject::tr("Invalid parameter: value after \"-%1\"").arg(COMMAND_RHO)); 188 | } 189 | cmd.print(QString("Relative height to length ratio (used to detect non-stems for final segmentation) set: %1").arg(parameters.rel_height_length_ratio)); 190 | 191 | } 192 | else if (ccCommandLineInterface::IsCommand(ARGUMENT, COMMAND_VERTICAL_OVERLAP_WEIGHT)) 193 | { 194 | try_final_seg = true; 195 | cmd.arguments().pop_front(); 196 | bool convert = false; 197 | 198 | parameters.vertical_weight = cmd.arguments().takeFirst().toFloat(&convert); 199 | if ((!convert) & (parameters.vertical_weight < 0.001)) 200 | { 201 | return cmd.error(QObject::tr("Invalid parameter: value after \"-%1\"").arg(COMMAND_VERTICAL_OVERLAP_WEIGHT)); 202 | } 203 | cmd.print(QString("Vertical overlapping ratio weight for final segmentation set: %1").arg(parameters.vertical_weight)); 204 | } 205 | else 206 | { 207 | cmd.print("Parameters All Set"); 208 | break; 209 | } 210 | } 211 | 212 | for (CLCloudDesc& desc : cmd.clouds()) 213 | { 214 | //Convert CC point cloud to treeIso type 215 | unsigned count = desc.pc->size(); 216 | if (count == 0) 217 | { 218 | cmd.print(QString("Cloud %1 is empty").arg(desc.pc->getName())); 219 | continue; 220 | } 221 | 222 | if (try_init_seg) 223 | { 224 | if (!TreeIso::Init_seg_pcd(desc.pc, parameters.min_nn1, parameters.reg_strength1, parameters.decimate_res1, parameters.threads1, [](int) {/*No implementation for progress bar*/})) 225 | { 226 | return cmd.error("Failed to finish initial segmentation due to unknown reasons."); 227 | } 228 | } 229 | if (try_intermediate_seg) 230 | { 231 | if (!TreeIso::Intermediate_seg_pcd(desc.pc, parameters.min_nn2, parameters.reg_strength2, parameters.decimate_res2, parameters.max_gap, parameters.threads2, [](int) {/*No implementation for progress bar*/})) 232 | { 233 | return cmd.error("Failed to finish intermediate segmentation due to unknown reasons."); 234 | } 235 | } 236 | if (try_final_seg) 237 | { 238 | if (!TreeIso::Final_seg_pcd(desc.pc, parameters.min_nn2, parameters.rel_height_length_ratio, parameters.vertical_weight, [](int) {/*No implementation for progress bar*/})) 239 | { 240 | return cmd.error("Failed to finish final segmentation due to unknown reasons."); 241 | } 242 | } 243 | } 244 | 245 | return true; 246 | } 247 | }; 248 | 249 | -------------------------------------------------------------------------------- /info.json: -------------------------------------------------------------------------------- 1 | { 2 | "type" : "Standard", 3 | "name" : "TreeIso", 4 | "icon" : ":/CC/plugin/qTreeIso/images/forest.png", 5 | "description": "A 3D graph-based individual-tree isolator (treeiso) from Terrestrial Laser Scanning point clouds", 6 | "authors" : [ 7 | { 8 | "name" : "Zhouxin Xi" 9 | }, 10 | { 11 | "name" : "Chris Hopkinson" 12 | } 13 | ], 14 | "maintainers" : [ 15 | { 16 | "name" : "Zhouxin Xi" 17 | } 18 | ], 19 | "references": [ 20 | { 21 | "text": "3D Graph-Based Individual-Tree Isolation (Treeiso) from Terrestrial Laser Scanning Point Clouds.", 22 | "url": "https://www.mdpi.com/2072-4292/14/23/6116" 23 | }, 24 | { 25 | "text": "Artemis Lab, Department of Geography & Environment, University of Lethbridge", 26 | "url": "https://github.com/truebelief/artemis_treeiso" 27 | } 28 | ] 29 | } 30 | -------------------------------------------------------------------------------- /license.txt: -------------------------------------------------------------------------------- 1 | //####################################################################################### 2 | //# # 3 | //# CLOUDCOMPARE PLUGIN: qTreeIso # 4 | //# # 5 | //# This program is free software; you can redistribute it and/or modify # 6 | //# it under the terms of the GNU General Public License as published by # 7 | //# the Free Software Foundation; version 2 or later of the License. # 8 | //# # 9 | //# This program is distributed in the hope that it will be useful, # 10 | //# but WITHOUT ANY WARRANTY; without even the implied warranty of # 11 | //# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # 12 | //# GNU General Public License for more details. # 13 | //# # 14 | //# Please cite the following paper if you find this tool helpful # 15 | //# # 16 | //# Xi, Z.; Hopkinson, C. 3D Graph-Based Individual-Tree Isolation (Treeiso) # 17 | //# from Terrestrial Laser Scanning Point Clouds. Remote Sens. 2022, 14, 6116. # 18 | //# https://doi.org/10.3390/rs14236116 # 19 | //# # 20 | //# Our work relies on the cut-pursuit algorithm, please also consider citing: # 21 | //# Landrieu, L.; Obozinski, G. Cut Pursuit: Fast Algorithms to Learn Piecewise # 22 | //# Constant Functions on General Weighted Graphs. SIAM J. Imaging Sci. # 23 | //# 2017, 10, 1724–1766. # 24 | //# # 25 | //# Copyright © # 26 | //# Artemis Lab, Department of Geography & Environment # 27 | //# University of Lethbridge, Canada # 28 | //# # 29 | //# # 30 | //# Zhouxin Xi and Chris Hopkinson; # 31 | //# truebelief2010@gmail.com; c.hopkinson@uleth.ca # 32 | //# # 33 | //####################################################################################### 34 | 35 | // A Matlab version shared via: 36 | // https://github.com/truebelief/artemis_treeiso 37 | 38 | // The development of the treeiso plugin was inspired by the CSF plugin originating from: 39 | // Zhang W, Qi J, Wan P, Wang H, Xie D, Wang X, Yan G. An Easy-to-Use Airborne LiDAR Data Filtering Method Based on Cloth Simulation. Remote Sensing. 2016; 8(6):501 -------------------------------------------------------------------------------- /qTreeIso.qrc: -------------------------------------------------------------------------------- 1 | 2 | 3 | images/forest.png 4 | info.json 5 | images/JP1_final_seg.png 6 | images/JP1_init_seg.png 7 | images/JP1_interim_seg.png 8 | 9 | 10 | -------------------------------------------------------------------------------- /src/CMakeLists.txt: -------------------------------------------------------------------------------- 1 | 2 | target_sources( ${PROJECT_NAME} 3 | PRIVATE 4 | ${CMAKE_CURRENT_LIST_DIR}/ccTreeIsoDlg.cpp 5 | ${CMAKE_CURRENT_LIST_DIR}/cp_d0_dist.cpp 6 | ${CMAKE_CURRENT_LIST_DIR}/cut_pursuit.cpp 7 | ${CMAKE_CURRENT_LIST_DIR}/cut_pursuit_d0.cpp 8 | ${CMAKE_CURRENT_LIST_DIR}/maxflow.cpp 9 | ${CMAKE_CURRENT_LIST_DIR}/TreeIso.cpp 10 | ${CMAKE_CURRENT_LIST_DIR}/qTreeIso.cpp 11 | ) 12 | -------------------------------------------------------------------------------- /src/ccTreeIsoDlg.cpp: -------------------------------------------------------------------------------- 1 | //####################################################################################### 2 | //# # 3 | //# CLOUDCOMPARE PLUGIN: qTreeIso # 4 | //# # 5 | //# This program is free software; you can redistribute it and/or modify # 6 | //# it under the terms of the GNU General Public License as published by # 7 | //# the Free Software Foundation; version 2 or later of the License. # 8 | //# # 9 | //# This program is distributed in the hope that it will be useful, # 10 | //# but WITHOUT ANY WARRANTY; without even the implied warranty of # 11 | //# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # 12 | //# GNU General Public License for more details. # 13 | //# # 14 | //# Please cite the following paper if you find this tool helpful # 15 | //# # 16 | //# Xi, Z.; Hopkinson, C. 3D Graph-Based Individual-Tree Isolation (Treeiso) # 17 | //# from Terrestrial Laser Scanning Point Clouds. Remote Sens. 2022, 14, 6116. # 18 | //# https://doi.org/10.3390/rs14236116 # 19 | //# # 20 | //# Our work relies on the cut-pursuit algorithm, please also consider citing: # 21 | //# Landrieu, L.; Obozinski, G. Cut Pursuit: Fast Algorithms to Learn Piecewise # 22 | //# Constant Functions on General Weighted Graphs. SIAM J. Imaging Sci. # 23 | //# 2017, 10, 1724–1766. # 24 | //# # 25 | //# Copyright © # 26 | //# Artemis Lab, Department of Geography & Environment # 27 | //# University of Lethbridge, Canada # 28 | //# # 29 | //# # 30 | //# Zhouxin Xi and Chris Hopkinson; # 31 | //# truebelief2010@gmail.com; c.hopkinson@uleth.ca # 32 | //# # 33 | //####################################################################################### 34 | 35 | 36 | #pragma once 37 | // A Matlab version shared via: 38 | // https://github.com/truebelief/artemis_treeiso 39 | 40 | #include "ccTreeIsoDlg.h" 41 | 42 | #include 43 | #include 44 | 45 | 46 | ccTreeIsoDlg::ccTreeIsoDlg(QWidget* parent) : QDialog(parent), Ui::TreeIsoDialog() 47 | { 48 | setupUi(this); 49 | setWindowFlags(Qt::Tool/*Qt::Dialog | Qt::WindowStaysOnTopHint*/); 50 | } 51 | 52 | 53 | 54 | 55 | -------------------------------------------------------------------------------- /src/cp_d0_dist.cpp: -------------------------------------------------------------------------------- 1 | /*============================================================================= 2 | * Hugo Raguet 2018, 2022, 2023 3 | *===========================================================================*/ 4 | #include "cp_d0_dist.hpp" 5 | 6 | #define VERT_WEIGHTS_(v) (vert_weights ? vert_weights[(v)] : (real_t) 1.0) 7 | #define COOR_WEIGHTS_(d) (coor_weights ? coor_weights[(d)] : (real_t) 1.0) 8 | 9 | #define TPL template 10 | #define CP_D0_DIST Cp_d0_dist 11 | 12 | using namespace std; 13 | 14 | TPL CP_D0_DIST::Cp_d0_dist(index_t V, index_t E, const index_t* first_edge, 15 | const index_t* adj_vertices, const real_t* Y, size_t D) 16 | : Cp_d0(V, E, first_edge, adj_vertices, D), Y(Y) 17 | { 18 | vert_weights = coor_weights = nullptr; 19 | comp_weights = nullptr; 20 | 21 | loss = quadratic_loss(); 22 | fYY = 0.0; 23 | fXY = real_inf(); 24 | 25 | min_comp_weight = 0.0; 26 | } 27 | 28 | TPL CP_D0_DIST::~Cp_d0_dist(){ free(comp_weights); } 29 | 30 | TPL real_t CP_D0_DIST::distance(const real_t* Yv, const real_t* Xv) const 31 | { 32 | real_t dist = 0.0; 33 | size_t Q = loss; // number of coordinates for quadratic part 34 | if (Q != 0){ /* quadratic part */ 35 | for (size_t d = 0; d < Q; d++){ 36 | dist += COOR_WEIGHTS_(d)*(Yv[d] - Xv[d])*(Yv[d] - Xv[d]); 37 | } 38 | } 39 | if (Q != D){ /* smoothed Kullback-Leibler; 40 | just compute cross-entropy here */ 41 | real_t distKL = 0.0; 42 | const real_t s = loss < 1.0 ? loss : eps; 43 | const real_t c = 1.0 - s; 44 | const real_t u = s/(D - Q); 45 | for (size_t d = Q; d < D; d++){ 46 | distKL -= (u + c*Yv[d])*log(u + c*Xv[d]); 47 | } 48 | dist += COOR_WEIGHTS_(Q)*distKL; 49 | } 50 | return dist; 51 | } 52 | 53 | TPL void CP_D0_DIST::set_loss(real_t loss, const real_t* Y, 54 | const real_t* vert_weights, const real_t* coor_weights) 55 | { 56 | if (loss < 0.0 || (loss > 1.0 && ((size_t) loss) != loss) || loss > D){ 57 | cerr << "Cut-pursuit d0 distance: loss parameter should be positive," 58 | "either in (0,1) or an integer that do not exceed the dimension " 59 | "(" << loss << " given)." << endl; 60 | exit(EXIT_FAILURE); 61 | } 62 | if (loss == 0.0){ loss = eps; } // avoid singularities 63 | this->loss = loss; 64 | if (Y){ this->Y = Y; } 65 | this->vert_weights = vert_weights; 66 | if (0.0 < loss && loss < 1.0 && coor_weights){ 67 | cerr << "Cut-pursuit d0 distance: no sense in weighting coordinates of" 68 | " the probability space in Kullback-Leibler divergence." << endl; 69 | exit(EXIT_FAILURE); 70 | } 71 | this->coor_weights = coor_weights; 72 | if (loss == quadratic_loss()){ fYY = 0.0; return; } 73 | /* recompute the constant dist(Y, Y) for Kullback-Leibler */ 74 | const size_t Q = loss; // number of coordinates for quadratic part 75 | const real_t s = loss < 1.0 ? loss : eps; 76 | const real_t c = 1.0 - s; 77 | const real_t u = s/(D - Q); 78 | real_t fYY_par = 0.0; // auxiliary variable for parallel region 79 | for (index_t v = 0; v < V; v++){ 80 | const real_t* Yv = Y + D*v; 81 | real_t H_Yv = 0.0; 82 | for (size_t d = Q; d < D; d++){ 83 | H_Yv -= (u + c*Yv[d])*log(u + c*Yv[d]); 84 | } 85 | fYY_par += VERT_WEIGHTS_(v)*H_Yv; 86 | } 87 | fYY = fYY_par; 88 | } 89 | 90 | TPL void CP_D0_DIST::set_split_param(index_t max_split_size, comp_t K, 91 | int split_iter_num, real_t split_damp_ratio, int split_values_init_num, 92 | int split_values_iter_num) 93 | { 94 | Cp::set_split_param(max_split_size, K, 95 | split_iter_num, split_damp_ratio, split_values_init_num, 96 | split_values_iter_num); 97 | } 98 | 99 | TPL void CP_D0_DIST::set_min_comp_weight(real_t min_comp_weight) 100 | { 101 | if (min_comp_weight < 0.0){ 102 | cerr << "Cut-pursuit d0 distance: min component weight parameter " 103 | "should be positive (" << min_comp_weight << " given)." << endl; 104 | exit(EXIT_FAILURE); 105 | } 106 | this->min_comp_weight = min_comp_weight; 107 | } 108 | 109 | TPL real_t CP_D0_DIST::fv(index_t v, const real_t* Xv) const 110 | { return VERT_WEIGHTS_(v)*distance(Y + D*v, Xv); } 111 | 112 | TPL real_t CP_D0_DIST::compute_f() const 113 | { 114 | return fXY == real_inf() ? 115 | Cp_d0::compute_f() - fYY : fXY - fYY; 116 | } 117 | 118 | TPL void CP_D0_DIST::solve_reduced_problem() 119 | { 120 | free(comp_weights); 121 | comp_weights = (real_t*) malloc_check(sizeof(real_t)*rV); 122 | 123 | for (comp_t rv = 0; rv < rV; rv++){ 124 | real_t* rXv = rX + D*rv; 125 | comp_weights[rv] = 0.0; 126 | for (size_t d = 0; d < D; d++){ rXv[d] = 0.0; } 127 | for (index_t i = first_vertex[rv]; i < first_vertex[rv + 1]; i++){ 128 | index_t v = comp_list[i]; 129 | comp_weights[rv] += VERT_WEIGHTS_(v); 130 | const real_t* Yv = Y + D*v; 131 | for (size_t d = 0; d < D; d++){ rXv[d] += VERT_WEIGHTS_(v)*Yv[d]; } 132 | } 133 | if (comp_weights[rv] <= 0.0){ 134 | cerr << "Cut-pursuit d0 distance: nonpositive total component " 135 | "weight; something went wrong." << endl; 136 | exit(EXIT_FAILURE); 137 | } 138 | for (size_t d = 0; d < D; d++){ rXv[d] /= comp_weights[rv]; } 139 | } 140 | } 141 | 142 | TPL void CP_D0_DIST::set_split_value(Split_info& split_info, comp_t k, 143 | index_t v) const 144 | { 145 | const real_t* Yv = Y + D*v; 146 | real_t* sXk = split_info.sX + D*k; 147 | for (size_t d = 0; d < D; d++){ sXk[d] = Yv[d]; } 148 | } 149 | 150 | TPL void CP_D0_DIST::update_split_info(Split_info& split_info) const 151 | { 152 | comp_t rv = split_info.rv; 153 | real_t* sX = split_info.sX; 154 | real_t* total_weights = (real_t*) 155 | malloc_check(sizeof(real_t)*split_info.K); 156 | for (comp_t k = 0; k < split_info.K; k++){ 157 | total_weights[k] = 0.0; 158 | real_t* sXk = sX + D*k; 159 | for (size_t d = 0; d < D; d++){ sXk[d] = 0.0; } 160 | } 161 | for (index_t i = first_vertex[rv]; i < first_vertex[rv + 1]; i++){ 162 | index_t v = comp_list[i]; 163 | comp_t k = label_assign[v]; 164 | total_weights[k] += VERT_WEIGHTS_(v); 165 | const real_t* Yv = Y + D*v; 166 | real_t* sXk = sX + D*k; 167 | for (size_t d = 0; d < D; d++){ sXk[d] += VERT_WEIGHTS_(v)*Yv[d]; } 168 | } 169 | comp_t kk = 0; // actual number of alternatives kept 170 | for (comp_t k = 0; k < split_info.K; k++){ 171 | const real_t* sXk = sX + D*k; 172 | real_t* sXkk = sX + D*kk; 173 | if (total_weights[k]){ 174 | for (size_t d = 0; d < D; d++){ 175 | sXkk[d] = sXk[d]/total_weights[k]; 176 | } 177 | kk++; 178 | } // else no vertex assigned to k, discard this alternative 179 | } 180 | split_info.K = kk; 181 | free(total_weights); 182 | } 183 | 184 | TPL void CP_D0_DIST::compute_merge_candidate(index_t re) 185 | { 186 | comp_t ru = reduced_edges_u(re); 187 | comp_t rv = reduced_edges_v(re); 188 | real_t edge_weight = reduced_edge_weights[re]; 189 | 190 | real_t* rXu = rX + D*ru; 191 | real_t* rXv = rX + D*rv; 192 | real_t wru = comp_weights[ru]/(comp_weights[ru] + comp_weights[rv]); 193 | real_t wrv = comp_weights[rv]/(comp_weights[ru] + comp_weights[rv]); 194 | 195 | real_t gain = edge_weight; 196 | size_t Q = loss; // number of coordinates for quadratic part 197 | 198 | if (Q != 0){ 199 | /* quadratic gain */ 200 | real_t gainQ = 0.0; 201 | for (size_t d = 0; d < Q; d++){ 202 | gainQ -= COOR_WEIGHTS_(d)*(rXu[d] - rXv[d])*(rXu[d] - rXv[d]); 203 | } 204 | gain += comp_weights[ru]*wrv*gainQ; 205 | } 206 | 207 | if (gain > 0.0 || comp_weights[ru] < min_comp_weight 208 | || comp_weights[rv] < min_comp_weight){ 209 | if (!merge_values[re]){ 210 | merge_values[re] = (real_t*) malloc_check(sizeof(real_t)*D); 211 | } 212 | real_t* value = merge_values[re]; 213 | for (size_t d = 0; d < D; d++){ value[d] = wru*rXu[d] + wrv*rXv[d]; } 214 | 215 | if (Q != D){ 216 | /* smoothed Kullback-Leibler gain */ 217 | real_t gainKLu = 0.0, gainKLv = 0.0; 218 | const real_t s = loss < 1.0 ? loss : eps; 219 | const real_t c = 1.0 - s; 220 | const real_t u = s/(D - Q); 221 | for (size_t d = Q; d < D; d++){ 222 | real_t u_value_d = u + c*value[d]; 223 | real_t u_rXu_d = u + c*rXu[d]; 224 | real_t u_rXv_d = u + c*rXv[d]; 225 | gainKLu -= (u_rXu_d)*log(u_rXu_d/u_value_d); 226 | gainKLv -= (u_rXv_d)*log(u_rXv_d/u_value_d); 227 | } 228 | gain += COOR_WEIGHTS_(Q)* 229 | (comp_weights[ru]*gainKLu + comp_weights[rv]*gainKLv); 230 | } 231 | } 232 | 233 | merge_gains[re] = gain; 234 | if (gain <= 0.0 && comp_weights[ru] >= min_comp_weight 235 | && comp_weights[rv] >= min_comp_weight){ 236 | delete_merge_candidate(re); 237 | } 238 | } 239 | 240 | TPL size_t CP_D0_DIST::merge_info_complexity() const 241 | { return 2*D; } 242 | 243 | TPL comp_t CP_D0_DIST::accept_merge_candidate(index_t re) 244 | { 245 | comp_t ro = Cp_d0::accept_merge_candidate(re); 246 | comp_t ru = reduced_edges_u(re); 247 | comp_t rv = reduced_edges_v(re); 248 | if (ro != ru){ rv = ru; ru = ro; } 249 | comp_weights[ru] += comp_weights[rv]; 250 | return ru; 251 | } 252 | 253 | TPL index_t CP_D0_DIST::merge() 254 | { 255 | index_t deactivation = Cp_d0::merge(); 256 | free(comp_weights); comp_weights = nullptr; 257 | /* fXY can be updated now to avoid computing it twice later */ 258 | if (monitor_evolution()){ 259 | fXY = Cp_d0::compute_f(); 260 | } 261 | return deactivation; 262 | } 263 | 264 | TPL real_t CP_D0_DIST::compute_evolution() const 265 | { 266 | real_t dif = 0.0; 267 | for (comp_t rv = 0; rv < rV; rv++){ 268 | if (is_saturated[rv]){ continue; } 269 | const real_t* rXv = rX + D*rv; 270 | real_t distXX = 0.0; 271 | if (loss != quadratic_loss()){ 272 | const size_t Q = loss; // number of coordinates for quadratic part 273 | const real_t s = loss < 1.0 ? loss : eps; 274 | const real_t c = 1.0 - s; 275 | const real_t u = s/(D - Q); 276 | for (size_t d = Q; d < D; d++){ 277 | distXX -= (u + c*rXv[d])*log(u + c*rXv[d]); 278 | } 279 | distXX *= COOR_WEIGHTS_(Q); 280 | } 281 | for (index_t i = first_vertex[rv]; i < first_vertex[rv + 1]; i++){ 282 | index_t v = comp_list[i]; 283 | const real_t* lrXv = last_rX + D*last_comp_assign[v]; 284 | dif += VERT_WEIGHTS_(v)*(distance(rXv, lrXv) - distXX); 285 | } 286 | } 287 | real_t amp = compute_f(); 288 | return amp > eps ? dif/amp : dif/eps; 289 | } 290 | 291 | template class Cp_d0_dist; 292 | template class Cp_d0_dist; 293 | template class Cp_d0_dist; 294 | template class Cp_d0_dist; 295 | -------------------------------------------------------------------------------- /src/cut_pursuit_d0.cpp: -------------------------------------------------------------------------------- 1 | /*============================================================================= 2 | * Hugo Raguet 2019 3 | *===========================================================================*/ 4 | #include "cut_pursuit_d0.hpp" 5 | #include 6 | #include 7 | 8 | #define EDGE_WEIGHTS_(e) (edge_weights ? edge_weights[(e)] : homo_edge_weight) 9 | 10 | #define TPL template 12 | #define CP_D0 Cp_d0 13 | 14 | using namespace std; 15 | 16 | TPL CP_D0::Cp_d0(index_t V, index_t E, const index_t* first_edge, 17 | const index_t* adj_vertices, size_t D) 18 | : Cp(V, E, first_edge, adj_vertices, D) 19 | { 20 | K = 2; 21 | split_iter_num = 2; 22 | split_damp_ratio = 1.0; 23 | split_values_init_num = 3; 24 | split_values_iter_num = 3; 25 | merge_gains = nullptr; 26 | merge_values = nullptr; 27 | } 28 | 29 | TPL real_t CP_D0::compute_graph_d0() const 30 | { 31 | real_t weighted_contour_length = 0.0; 32 | 33 | for (index_t re = 0; re < rE; re++){ 34 | weighted_contour_length += reduced_edge_weights[re]; 35 | } 36 | return weighted_contour_length; 37 | } 38 | 39 | TPL real_t CP_D0::compute_f() const 40 | { 41 | real_t f = 0.0; 42 | for (comp_t rv = 0; rv < rV; rv++){ 43 | real_t* rXv = rX + D*rv; 44 | for (index_t i = first_vertex[rv]; i < first_vertex[rv + 1]; i++){ 45 | f += fv(comp_list[i], rXv); 46 | } 47 | } 48 | return f; 49 | } 50 | 51 | TPL real_t CP_D0::compute_objective() const 52 | { return compute_f() + compute_graph_d0(); } // f(x) + ||x||_d0 53 | 54 | TPL real_t CP_D0::vert_split_cost(const Split_info& split_info, index_t v, 55 | comp_t k) const 56 | { return fv(v, split_info.sX + D*k); } 57 | 58 | /* compute binary cost of choosing alternatives lu and lv at edge e */ 59 | TPL real_t CP_D0::edge_split_cost(const Split_info& split_info, index_t e, 60 | comp_t lu, comp_t lv) const 61 | { return lu == lv ? 0.0 : EDGE_WEIGHTS_(e); } 62 | 63 | TPL comp_t CP_D0::accept_merge_candidate(index_t re) 64 | { 65 | comp_t ru = reduced_edges_u(re); 66 | comp_t rv = reduced_edges_v(re); 67 | ru = merge_components(ru, rv); 68 | value_t* rXu = rX + D*ru; 69 | for (size_t d = 0; d < D; d++){ rXu[d] = merge_values[re][d]; } 70 | delete_merge_candidate(re); 71 | return ru; 72 | } 73 | 74 | TPL void CP_D0::delete_merge_candidate(index_t re) 75 | { free(merge_values[re]); merge_values[re] = nullptr; } 76 | 77 | TPL comp_t CP_D0::compute_merge_chains() 78 | { 79 | comp_t merge_count = 0; 80 | 81 | /* compute merge candidates in parallel */ 82 | merge_gains = (real_t*) malloc_check(sizeof(real_t)*rE); 83 | merge_values = (value_t**) malloc_check(sizeof(value_t*)*rE); 84 | for (index_t re = 0; re < rE; re++){ merge_values[re] = nullptr; } 85 | index_t num_pos_candidates = 0, num_neg_candidates = 0; 86 | for (index_t re = 0; re < rE; re++){ 87 | comp_t ru = reduced_edges_u(re); 88 | comp_t rv = reduced_edges_v(re); 89 | if (ru == rv){ continue; } 90 | compute_merge_candidate(re); 91 | if (merge_values[re]){ 92 | if (merge_gains[re] > 0.0){ num_pos_candidates++; } 93 | else{ num_neg_candidates++; } 94 | } 95 | } 96 | 97 | if (!(num_pos_candidates || num_neg_candidates)){ 98 | free(merge_gains); free(merge_values); 99 | return 0; 100 | } 101 | 102 | /* local read-only access to merge_gains; useful for lambdas below, 103 | * since one cannot directly capture member variables */ 104 | const real_t* _merge_gains = merge_gains; 105 | 106 | if (num_pos_candidates){ 107 | /** merge candidates with positive gains; 108 | ** these are important enough to be merged in decreasing gain order, and 109 | ** to update surrounding merge candidates after each merge: 110 | ** 1) maintain candidates in a priority order on the gain 111 | ** 2) maintain access to all reduced edges involving a given vertex, and 112 | ** to their potential corresponding candidate in the priority order; 113 | ** because of 2), the best choice for 1) is a binary search tree **/ 114 | 115 | /* 1) binary search tree on the gain */ 116 | auto compare_candidates = [_merge_gains] (index_t mc1, index_t mc2) -> bool 117 | { return _merge_gains[mc1] > _merge_gains[mc2] || 118 | /* ensure unique identification of merge candidates */ 119 | (_merge_gains[mc1] == _merge_gains[mc2] && mc1 < mc2); }; 120 | set 121 | candidates_queue(compare_candidates); 122 | for (index_t re = 0; re < rE; re++){ 123 | if (merge_values[re] && merge_gains[re] > 0.0){ 124 | candidates_queue.insert(re); 125 | } 126 | } 127 | 128 | /* 2) linked list structure for updating reduced graph while merging */ 129 | /* - given a component, we need access to the list of merge candidates 130 | * whose corresponding reduced edge involves the considered component; 131 | * - to that purpose, we maintain for each component a linked list of such 132 | * merge candidates; we call "merge candidate cell" the data structure with 133 | * the merge candidate identifier and the access to the next cell in such a 134 | * linked list; 135 | * - each active merge candidates is thus referenced in two such cells: one 136 | * within both lists of starting and ending components of the corresponding 137 | * reduced edge; 138 | * - one can thus compact information mapping unequivocally each merge 139 | * candidate mc to merge candidate cells identifiers 2*mc and 2*mc + 1; 140 | * conversely, the merge candidate of a cell mcc is mcc/2 141 | * - the link list structure can thus be maintained with the following 142 | * tables: 143 | * first_candidate_cell[ru] is the index of the first merge candidate 144 | * cell of the list of adjacent candidates for component ru 145 | * next_candidate_cell[mcc] is the index of the merge candidate cell 146 | * that comes after mcc within the list containing it 147 | */ 148 | typedef size_t Cell_id; 149 | #define EMPTY_CELL (std::numeric_limits::max()) 150 | Cell_id* first_candidate_cell = (Cell_id*) 151 | malloc_check(sizeof(Cell_id*)*rV); 152 | Cell_id* next_candidate_cell = (Cell_id*) 153 | malloc_check(sizeof(Cell_id*)*2*rE); 154 | for (comp_t rv = 0; rv < rV; rv++){ 155 | first_candidate_cell[rv] = EMPTY_CELL; 156 | } 157 | for (Cell_id mcc = 0; mcc < ((Cell_id) 2)*rE; mcc++){ 158 | next_candidate_cell[mcc] = EMPTY_CELL; 159 | } 160 | #define GET_REDUCED_EDGE(mcc) (*mcc/2) 161 | #define FIRST_CELL(mcc, rv) (mcc = &first_candidate_cell[rv]) 162 | #define NEXT_CELL(mcc) (mcc = &next_candidate_cell[*mcc]) 163 | #define DELETE_CELL(mcc) (*mcc = next_candidate_cell[*mcc]) 164 | #define IS_EMPTY(mcc) (*mcc == EMPTY_CELL) 165 | 166 | /* construct the linked list structure; 167 | * last_candidate_cell[ru] is the index of the last merge candidate cell 168 | * of the list of adjacent candidates for component ru; 169 | * useful only for constructing the list in linear time */ 170 | Cell_id* last_candidate_cell = (Cell_id*) 171 | malloc_check(sizeof(index_t*)*rV); 172 | for (comp_t rv = 0; rv < rV; rv++){ last_candidate_cell[rv] = EMPTY_CELL; } 173 | for (index_t re = 0; re < rE; re++){ 174 | comp_t ru = reduced_edges_u(re); 175 | comp_t rv = reduced_edges_v(re); 176 | if (ru == rv){ continue; } 177 | #define INSERT_CELL(rv, mcc) \ 178 | if (last_candidate_cell[rv] == EMPTY_CELL){ \ 179 | first_candidate_cell[rv] = mcc; \ 180 | last_candidate_cell[rv] = mcc; \ 181 | }else{ \ 182 | next_candidate_cell[last_candidate_cell[rv]] = mcc; \ 183 | last_candidate_cell[rv] = mcc; \ 184 | } 185 | Cell_id mcc_ru = ((Cell_id) 2)*re, mcc_rv = ((Cell_id) 2)*re + 1; 186 | INSERT_CELL(ru, mcc_ru); INSERT_CELL(rv, mcc_rv); 187 | } 188 | free(last_candidate_cell); 189 | 190 | /* iterative merge following the above order */ 191 | while (!candidates_queue.empty()){ 192 | typename set::iterator candidate = candidates_queue.begin(); 193 | index_t re = *candidate; 194 | comp_t ru = reduced_edges_u(re); 195 | comp_t rv = reduced_edges_v(re); 196 | 197 | /** accept the merge and remove from the queue **/ 198 | comp_t ro = accept_merge_candidate(re); // merge ru and rv 199 | if (ro != ru){ rv = ru; ru = ro; } // makes sure ru is the root 200 | candidates_queue.erase(candidate); 201 | merge_count++; 202 | 203 | /** update reduced graph structure and adjacent merge candidates **/ 204 | Cell_id *mcc_ru, *mcc_rv; 205 | 206 | /* first pass on the list of rv: cleanup deleted candidates, remove 207 | * current merging candidate, update vertices by replacing rv by ru */ 208 | FIRST_CELL(mcc_rv, rv); 209 | while (!IS_EMPTY(mcc_rv)){ 210 | index_t re_rv = GET_REDUCED_EDGE(mcc_rv); 211 | if (!reduced_edge_weights[re_rv]){ DELETE_CELL(mcc_rv); continue; } 212 | comp_t end_re_rv; 213 | if (reduced_edges_u(re_rv) == rv){ 214 | reduced_edges_u(re_rv) = ru; 215 | end_re_rv = reduced_edges_v(re_rv); 216 | }else{ 217 | reduced_edges_v(re_rv) = ru; 218 | end_re_rv = reduced_edges_u(re_rv); 219 | } 220 | if (end_re_rv == ru){ DELETE_CELL(mcc_rv); continue; } 221 | NEXT_CELL(mcc_rv); 222 | } 223 | 224 | /* cleanup deleted candidates and delete current merging candidate from 225 | * ru list, and search candidates adjacent to both ru and rv with same 226 | * end vertex; 227 | * NOTA: bilinear time cost in orders of merging components cannot be 228 | * avoided; in particular, ordering lists by end vertex identifiers 229 | * would require reordering of all adjacent candidates of rv, bilinear 230 | * in order of rv and sum of orders of its adjacent candidates 231 | * NOTA: might be done in parallel along ru list, but current merging 232 | * candidate must be removed before, and might not be worth it */ 233 | FIRST_CELL(mcc_ru, ru); 234 | while (!IS_EMPTY(mcc_ru)){ 235 | index_t re_ru = GET_REDUCED_EDGE(mcc_ru); 236 | if (!reduced_edge_weights[re_ru]){ DELETE_CELL(mcc_ru); continue; } 237 | comp_t end_re_ru = reduced_edges_u(re_ru) == ru ? 238 | reduced_edges_v(re_ru) : reduced_edges_u(re_ru); 239 | if (end_re_ru == ru){ DELETE_CELL(mcc_ru); continue; } 240 | for (FIRST_CELL(mcc_rv, rv); !IS_EMPTY(mcc_rv); NEXT_CELL(mcc_rv)){ 241 | index_t re_rv = GET_REDUCED_EDGE(mcc_rv); 242 | comp_t end_re_rv = reduced_edges_u(re_rv) == ru ? 243 | reduced_edges_v(re_rv) : reduced_edges_u(re_rv); 244 | if (end_re_ru == end_re_rv){ 245 | reduced_edge_weights[re_ru] += reduced_edge_weights[re_rv]; 246 | reduced_edge_weights[re_rv] = 0.0; // sum must be constant 247 | if (merge_gains[re_rv] > 0.0){ /* remove from queue */ 248 | candidate = candidates_queue.find(re_rv); 249 | candidate = candidates_queue.erase(candidate); 250 | merge_gains[re_rv] = 0.0; 251 | } 252 | delete_merge_candidate(re_rv); 253 | DELETE_CELL(mcc_rv); 254 | /* NOTA: sister candidate cell for re_rv still exists in 255 | * the list of adjacent candidates of end_re_rv; but this 256 | * situation is flagged with zero reduced edge weight */ 257 | break; 258 | } 259 | } 260 | NEXT_CELL(mcc_ru); 261 | } 262 | 263 | /* at that point, mcc_ru is the last (empty) cell of the ru list; 264 | * concatenate adjacent candidate list of rv after the one of ru */ 265 | *mcc_ru = first_candidate_cell[rv]; 266 | 267 | /* update all adjacent candidates */ 268 | for (FIRST_CELL(mcc_ru, ru); !IS_EMPTY(mcc_ru); NEXT_CELL(mcc_ru)){ 269 | index_t re = GET_REDUCED_EDGE(mcc_ru); 270 | if (merge_gains[re] > 0.0){ /* already in the queue */ 271 | candidate = candidates_queue.find(re); 272 | candidate = candidates_queue.erase(candidate); 273 | }else{ 274 | candidate = candidates_queue.end(); 275 | } 276 | compute_merge_candidate(re); 277 | if (merge_gains[re] > 0.0){ 278 | candidates_queue.insert(candidate, re); 279 | } 280 | } 281 | } // end while candidates queue not empty 282 | 283 | free(first_candidate_cell); free(next_candidate_cell); 284 | } // end if num_pos_candidates 285 | 286 | if (num_neg_candidates){ 287 | /** merge candidates with negative gains; 288 | ** these are less important, no update of adajacent candidates; 289 | ** only sort once and merge in that order **/ 290 | index_t bufsize = num_neg_candidates; 291 | index_t* neg_candidates = (index_t*) malloc_check(sizeof(index_t)*bufsize); 292 | num_neg_candidates = 0; // recounting 293 | for (index_t re = 0; re < rE; re++){ 294 | if (merge_values[re]){ 295 | if (num_neg_candidates == bufsize){ 296 | bufsize += bufsize/2 + 1; 297 | neg_candidates = (index_t*) realloc_check(neg_candidates, 298 | sizeof(index_t)*bufsize); 299 | } 300 | neg_candidates[num_neg_candidates++] = re; 301 | } 302 | } 303 | sort(neg_candidates, neg_candidates + num_neg_candidates, 304 | [_merge_gains] (index_t re1, index_t re2) -> bool 305 | { return _merge_gains[re1] > _merge_gains[re2]; }); 306 | for (index_t mc = 0; mc < num_neg_candidates; mc++){ 307 | index_t re = neg_candidates[mc]; 308 | /* ensure candidate info is up-to-date */ 309 | comp_t ru = get_merge_chain_root(reduced_edges_u(re)); 310 | comp_t rv = get_merge_chain_root(reduced_edges_v(re)); 311 | if (ru == rv){ 312 | delete_merge_candidate(re); 313 | }else{ 314 | reduced_edges_u(re) = ru; 315 | reduced_edges_v(re) = rv; 316 | compute_merge_candidate(re); 317 | if (merge_values[re]){ 318 | accept_merge_candidate(re); 319 | merge_count++; 320 | } 321 | } 322 | } 323 | 324 | free(neg_candidates); 325 | } // end if num_neg_candidates 326 | 327 | free(merge_gains); free(merge_values); 328 | return merge_count; 329 | } 330 | 331 | template class Cp_d0; 332 | template class Cp_d0; 333 | template class Cp_d0; 334 | template class Cp_d0; 335 | -------------------------------------------------------------------------------- /src/maxflow.cpp: -------------------------------------------------------------------------------- 1 | /* maxflow.cpp */ 2 | 3 | #include 4 | #include 5 | #include // for instantiation 6 | #include "maxflow.hpp" 7 | 8 | /* special constants for parent arcs */ 9 | #define TERMINAL terminal 10 | #define ORPHAN orphan 11 | /* infinite distance to the terminal */ 12 | #define INFINITE_D (std::numeric_limits::max()) 13 | 14 | #define TPL template 15 | #define MXFL Maxflow 16 | 17 | using namespace std; 18 | 19 | TPL MXFL::Maxflow(index_t node_num, index_t edge_num) 20 | : terminal(&reserved_terminal_arc), orphan(&reserved_orphan_arc), 21 | nodeptr_block(nullptr) 22 | { 23 | nodes = (node*) malloc(sizeof(node)*node_num); 24 | arcs = (arc*) malloc(sizeof(arc)*2*edge_num); 25 | if (!nodes || !arcs) { 26 | cerr << "Maxflow: not enough memory." << endl; 27 | exit(EXIT_FAILURE); 28 | } 29 | 30 | node_last = nodes + node_num; 31 | arc_last = arcs; // arcs not created yet 32 | 33 | for (node* i = nodes; i < node_last; i++){ i->first = nullptr; } 34 | } 35 | 36 | TPL MXFL::~Maxflow() 37 | { 38 | if (nodeptr_block){ 39 | delete nodeptr_block; 40 | nodeptr_block = nullptr; 41 | } 42 | free(nodes); 43 | free(arcs); 44 | } 45 | 46 | /***********************************************************************/ 47 | 48 | /* 49 | Functions for processing active list. 50 | i->next points to the next node in the list 51 | (or to i, if i is the last node in the list). 52 | If i->next is nullptr iff i is not in the list. 53 | 54 | There are two queues. Active nodes are added 55 | to the end of the second queue and read from 56 | the front of the first queue. If the first queue 57 | is empty, it is replaced by the second queue 58 | (and the second queue becomes empty). 59 | */ 60 | 61 | 62 | TPL inline void MXFL::set_active(node *i) 63 | { 64 | if (!i->next){ 65 | /* it's not in the list yet */ 66 | if (queue_last[1]) queue_last[1]->next = i; 67 | else queue_first[1] = i; 68 | queue_last[1] = i; 69 | i->next = i; 70 | } 71 | } 72 | 73 | /* 74 | Returns the next active node. 75 | If it is connected to the sink, it stays in the list, (???) 76 | otherwise it is removed from the list 77 | */ 78 | TPL inline typename MXFL::node* MXFL::next_active() 79 | { 80 | node *i; 81 | 82 | while (true){ 83 | if (!(i = queue_first[0])){ 84 | queue_first[0] = i = queue_first[1]; 85 | queue_last[0] = queue_last[1]; 86 | queue_first[1] = nullptr; 87 | queue_last[1] = nullptr; 88 | if (!i) return nullptr; 89 | } 90 | 91 | /* remove it from the active list */ 92 | if (i->next == i) queue_first[0] = queue_last[0] = nullptr; 93 | else queue_first[0] = i->next; 94 | i->next = nullptr; 95 | 96 | /* a node in the list is active iff it has a parent */ 97 | if (i->parent) return i; 98 | } 99 | } 100 | 101 | /***********************************************************************/ 102 | 103 | TPL inline void MXFL::set_orphan_front(node *i) 104 | { 105 | nodeptr *np; 106 | i->parent = ORPHAN; 107 | np = nodeptr_block->New(); 108 | np->ptr = i; 109 | np->next = orphan_first; 110 | orphan_first = np; 111 | } 112 | 113 | TPL inline void MXFL::set_orphan_rear(node *i) 114 | { 115 | nodeptr *np; 116 | i->parent = ORPHAN; 117 | np = nodeptr_block->New(); 118 | np->ptr = i; 119 | if (orphan_last) orphan_last->next = np; 120 | else orphan_first = np; 121 | orphan_last = np; 122 | np->next = nullptr; 123 | } 124 | 125 | /***********************************************************************/ 126 | 127 | TPL void MXFL::maxflow_init() 128 | { 129 | node *i; 130 | 131 | queue_first[0] = queue_last[0] = nullptr; 132 | queue_first[1] = queue_last[1] = nullptr; 133 | orphan_first = nullptr; 134 | 135 | TIME = 0; 136 | 137 | for (i = nodes; i < node_last; i++){ 138 | i->next = nullptr; 139 | i->TS = TIME; 140 | if (i->term_res_cap > 0){ 141 | /* i is connected to the source */ 142 | i->is_sink = false; 143 | i->parent = TERMINAL; 144 | set_active(i); 145 | i->DIST = 1; 146 | }else if (i->term_res_cap < 0){ 147 | /* i is connected to the sink */ 148 | i->is_sink = true; 149 | i->parent = TERMINAL; 150 | set_active(i); 151 | i->DIST = 1; 152 | }else{ 153 | i->parent = nullptr; 154 | } 155 | } 156 | } 157 | 158 | TPL void MXFL::augment(arc *middle_arc) 159 | { 160 | node *i; 161 | arc *a; 162 | flow_t bottleneck; 163 | 164 | 165 | /* 1. Finding bottleneck capacity */ 166 | /* 1a - the source tree */ 167 | bottleneck = middle_arc->res_cap; 168 | for (i = middle_arc->sister->head; ; i = a->head) 169 | { 170 | a = i->parent; 171 | if (a == TERMINAL) break; 172 | if (bottleneck > a->sister->res_cap) bottleneck = a->sister->res_cap; 173 | } 174 | if (bottleneck > i->term_res_cap) bottleneck = i->term_res_cap; 175 | /* 1b - the sink tree */ 176 | for (i = middle_arc->head; ; i = a->head) 177 | { 178 | a = i->parent; 179 | if (a == TERMINAL) break; 180 | if (bottleneck > a->res_cap) bottleneck = a->res_cap; 181 | } 182 | if (bottleneck > - i->term_res_cap) bottleneck = - i->term_res_cap; 183 | 184 | 185 | /* 2. Augmenting */ 186 | /* 2a - the source tree */ 187 | middle_arc->sister->res_cap += bottleneck; 188 | middle_arc->res_cap -= bottleneck; 189 | for (i = middle_arc->sister->head; ; i = a->head) 190 | { 191 | a = i->parent; 192 | if (a == TERMINAL) break; 193 | a->res_cap += bottleneck; 194 | a->sister->res_cap -= bottleneck; 195 | if (!a->sister->res_cap){ set_orphan_front(i); } 196 | } 197 | i->term_res_cap -= bottleneck; 198 | if (!i->term_res_cap){ set_orphan_front(i); } 199 | /* 2b - the sink tree */ 200 | for (i = middle_arc->head; ; i = a->head) 201 | { 202 | a = i->parent; 203 | if (a == TERMINAL) break; 204 | a->sister->res_cap += bottleneck; 205 | a->res_cap -= bottleneck; 206 | if (!a->res_cap){ set_orphan_front(i); } 207 | } 208 | i->term_res_cap += bottleneck; 209 | if (!i->term_res_cap){ set_orphan_front(i); } 210 | } 211 | 212 | /***********************************************************************/ 213 | 214 | TPL void MXFL::process_source_orphan(node *i) 215 | { 216 | node *j; 217 | arc *a0, *a0_min = nullptr, *a; 218 | index_t d, d_min = INFINITE_D; 219 | 220 | /* trying to find a new parent */ 221 | for (a0 = i->first; a0; a0 = a0->next) 222 | if (a0->sister->res_cap){ 223 | j = a0->head; 224 | if (!j->is_sink && (a = j->parent)){ 225 | /* checking the origin of j */ 226 | d = 0; 227 | while (true){ 228 | if (j->TS == TIME){ 229 | d += j->DIST; 230 | break; 231 | } 232 | a = j->parent; 233 | d++; 234 | if (a == TERMINAL){ 235 | j->TS = TIME; 236 | j->DIST = 1; 237 | break; 238 | } 239 | if (a == ORPHAN){ 240 | d = INFINITE_D; 241 | break; 242 | } 243 | j = a->head; 244 | } 245 | if (d < INFINITE_D){ /* j originates from the source - done */ 246 | if (d < d_min){ 247 | a0_min = a0; 248 | d_min = d; 249 | } 250 | /* set marks along the path */ 251 | for (j = a0->head; j->TS != TIME; j = j->parent->head){ 252 | j->TS = TIME; 253 | j->DIST = d--; 254 | } 255 | } 256 | } 257 | } 258 | 259 | if ((i->parent = a0_min)){ 260 | i->TS = TIME; 261 | i->DIST = d_min + 1; 262 | }else{ 263 | /* process neighbors */ 264 | for (a0 = i->first; a0; a0 = a0->next){ 265 | j = a0->head; 266 | if (!j->is_sink && (a = j->parent)){ 267 | if (a0->sister->res_cap){ set_active(j); } 268 | if (a != TERMINAL && a != ORPHAN && a->head == i){ 269 | set_orphan_rear(j); 270 | } 271 | } 272 | } 273 | } 274 | } 275 | 276 | TPL void MXFL::process_sink_orphan(node *i) 277 | { 278 | node *j; 279 | arc *a0, *a0_min = nullptr, *a; 280 | index_t d, d_min = INFINITE_D; 281 | 282 | /* trying to find a new parent */ 283 | for (a0 = i->first; a0; a0 = a0->next) 284 | if (a0->res_cap){ 285 | j = a0->head; 286 | if (j->is_sink && (a = j->parent)){ 287 | /* checking the origin of j */ 288 | d = 0; 289 | while (true){ 290 | if (j->TS == TIME){ 291 | d += j->DIST; 292 | break; 293 | } 294 | a = j->parent; 295 | d++; 296 | if (a == TERMINAL){ 297 | j->TS = TIME; 298 | j->DIST = 1; 299 | break; 300 | } 301 | if (a == ORPHAN){ 302 | d = INFINITE_D; 303 | break; 304 | } 305 | j = a->head; 306 | } 307 | if (d < INFINITE_D){ /* j originates from the sink - done */ 308 | if (d < d_min){ 309 | a0_min = a0; 310 | d_min = d; 311 | } 312 | /* set marks along the path */ 313 | for (j = a0->head; j->TS != TIME; j = j->parent->head){ 314 | j->TS = TIME; 315 | j->DIST = d--; 316 | } 317 | } 318 | } 319 | } 320 | 321 | if ((i->parent = a0_min)){ 322 | i->TS = TIME; 323 | i->DIST = d_min + 1; 324 | }else{ 325 | /* process neighbors */ 326 | for (a0 = i->first; a0; a0 = a0->next){ 327 | j = a0->head; 328 | if (j->is_sink && (a = j->parent)){ 329 | if (a0->res_cap) set_active(j); 330 | if (a != TERMINAL && a != ORPHAN && a->head == i){ 331 | set_orphan_rear(j); 332 | } 333 | } 334 | } 335 | } 336 | } 337 | 338 | /***********************************************************************/ 339 | 340 | TPL void MXFL::maxflow() 341 | { 342 | node *i, *j, *current_node = nullptr; 343 | arc *a; 344 | nodeptr *np, *np_next; 345 | 346 | if (!nodeptr_block){ 347 | nodeptr_block = new DBlock(NODEPTR_BLOCK_SIZE); 348 | } 349 | 350 | maxflow_init(); 351 | 352 | while (true){ 353 | 354 | if ((i = current_node)){ 355 | i->next = nullptr; /* remove active flag */ 356 | if (!i->parent) i = nullptr; 357 | } 358 | 359 | if (!i){ if (!(i = next_active())){ break; } } 360 | 361 | /* growth */ 362 | if (!i->is_sink){ 363 | /* grow source tree */ 364 | for (a = i->first; a; a = a->next) 365 | if (a->res_cap){ 366 | j = a->head; 367 | if (!j->parent){ 368 | j->is_sink = false; 369 | j->parent = a->sister; 370 | j->TS = i->TS; 371 | j->DIST = i->DIST + 1; 372 | set_active(j); 373 | }else if (j->is_sink){ 374 | break; 375 | }else if (j->TS <= i->TS && j->DIST > i->DIST){ 376 | /* heuristic - trying to make the distance from j to the 377 | source shorter */ 378 | j->parent = a->sister; 379 | j->TS = i->TS; 380 | j->DIST = i->DIST + 1; 381 | } 382 | } 383 | }else{ 384 | /* grow sink tree */ 385 | for (a = i->first; a; a = a->next) 386 | if (a->sister->res_cap){ 387 | j = a->head; 388 | if (!j->parent){ 389 | j->is_sink = true; 390 | j->parent = a->sister; 391 | j->TS = i->TS; 392 | j->DIST = i->DIST + 1; 393 | set_active(j); 394 | }else if (!j->is_sink){ 395 | a = a->sister; break; 396 | }else if (j->TS <= i->TS && j->DIST > i->DIST){ 397 | /* heuristic - trying to make the distance from j to the 398 | sink shorter */ 399 | j->parent = a->sister; 400 | j->TS = i->TS; 401 | j->DIST = i->DIST + 1; 402 | } 403 | } 404 | } 405 | 406 | if (++TIME <= 0){ 407 | /* changed type from long to index_t */ 408 | /* can't we prove this won't overflow? */ 409 | cerr << "Maxflow: timestamp overflow." << endl; 410 | exit(EXIT_FAILURE); 411 | } 412 | 413 | if (a){ /* found a valid path; a is the middle arc */ 414 | i->next = i; // set active flag 415 | current_node = i; 416 | 417 | augment(a); 418 | 419 | /* adoption */ 420 | while ((np = orphan_first)){ 421 | np_next = np->next; 422 | np->next = nullptr; 423 | 424 | while ((np = orphan_first)){ 425 | orphan_first = np->next; 426 | i = np->ptr; 427 | nodeptr_block->Delete(np); 428 | if (!orphan_first) orphan_last = nullptr; 429 | if (i->is_sink) process_sink_orphan(i); 430 | else process_source_orphan(i); 431 | } 432 | 433 | orphan_first = np_next; 434 | } 435 | }else{ 436 | current_node = nullptr; 437 | } 438 | 439 | } // end main loop 440 | 441 | delete nodeptr_block; 442 | nodeptr_block = nullptr; 443 | } 444 | 445 | template class Maxflow; 446 | template class Maxflow; 447 | -------------------------------------------------------------------------------- /src/qTreeIso.cpp: -------------------------------------------------------------------------------- 1 | //####################################################################################### 2 | //# # 3 | //# CLOUDCOMPARE PLUGIN: qTreeIso # 4 | //# # 5 | //# This program is free software; you can redistribute it and/or modify # 6 | //# it under the terms of the GNU General Public License as published by # 7 | //# the Free Software Foundation; version 2 or later of the License. # 8 | //# # 9 | //# This program is distributed in the hope that it will be useful, # 10 | //# but WITHOUT ANY WARRANTY; without even the implied warranty of # 11 | //# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # 12 | //# GNU General Public License for more details. # 13 | //# # 14 | //# Please cite the following paper if you find this tool helpful # 15 | //# # 16 | //# Xi, Z.; Hopkinson, C. 3D Graph-Based Individual-Tree Isolation (Treeiso) # 17 | //# from Terrestrial Laser Scanning Point Clouds. Remote Sens. 2022, 14, 6116. # 18 | //# https://doi.org/10.3390/rs14236116 # 19 | //# # 20 | //# Our work relies on the cut-pursuit algorithm, please also consider citing: # 21 | //# Landrieu, L.; Obozinski, G. Cut Pursuit: Fast Algorithms to Learn Piecewise # 22 | //# Constant Functions on General Weighted Graphs. SIAM J. Imaging Sci. # 23 | //# 2017, 10, 1724–1766. # 24 | //# # 25 | //# Copyright © # 26 | //# Artemis Lab, Department of Geography & Environment # 27 | //# University of Lethbridge, Canada # 28 | //# # 29 | //# # 30 | //# Zhouxin Xi and Chris Hopkinson; # 31 | //# truebelief2010@gmail.com; c.hopkinson@uleth.ca # 32 | //# # 33 | //####################################################################################### 34 | 35 | // Matlab and python versions shared via: 36 | // https://github.com/truebelief/artemis_treeiso 37 | 38 | #include "qTreeIso.h" 39 | 40 | //Qt 41 | #include 42 | #include 43 | #include 44 | #include 45 | #include 46 | #include 47 | 48 | //Local 49 | #include "ccTreeIsoDlg.h" 50 | #include "qTreeIsoCommands.h" 51 | 52 | //System 53 | #include 54 | #include 55 | #include 56 | #include 57 | 58 | //qCC_db 59 | #include 60 | #include 61 | #include 62 | #include 63 | #include 64 | 65 | //TreeIso 66 | #include 67 | 68 | qTreeIso::qTreeIso(QObject* parent) 69 | : QObject(parent) 70 | , ccStdPluginInterface(":/CC/plugin/qTreeIso/info.json") 71 | , m_action(nullptr) 72 | { 73 | } 74 | 75 | void qTreeIso::onNewSelection(const ccHObject::Container& selectedEntities) 76 | { 77 | if (m_action) 78 | { 79 | bool hasCloud = false; 80 | for (ccHObject* entity : selectedEntities) 81 | { 82 | if (entity && entity->isA(CC_TYPES::POINT_CLOUD)) 83 | { 84 | hasCloud = true; 85 | break; 86 | } 87 | } 88 | m_action->setEnabled(hasCloud); 89 | } 90 | } 91 | 92 | QList qTreeIso::getActions() 93 | { 94 | if (!m_action) 95 | { 96 | m_action = new QAction(getName(), this); 97 | m_action->setToolTip(getDescription()); 98 | m_action->setIcon(getIcon()); 99 | 100 | //connect appropriate signal 101 | connect(m_action, &QAction::triggered, this, &qTreeIso::doAction); 102 | } 103 | 104 | return { m_action }; 105 | } 106 | 107 | void qTreeIso::doAction() 108 | { 109 | Parameters parameters; 110 | ccTreeIsoDlg treeisoDlg(m_app ? m_app->getMainWindow() : nullptr); 111 | 112 | connect(treeisoDlg.pushButtonInitSeg, &QPushButton::clicked, [&] 113 | { 114 | parameters.min_nn1 = treeisoDlg.spinBoxK1->value(); 115 | parameters.reg_strength1 = treeisoDlg.doubleSpinBoxLambda1->value();; 116 | parameters.decimate_res1 = treeisoDlg.doubleSpinBoxDecRes1->value(); 117 | parameters.threads1 = treeisoDlg.doubleSpinBoxThreads1->value(); 118 | 119 | init_segs(parameters, &treeisoDlg); 120 | }); 121 | 122 | connect(treeisoDlg.pushButtonInterSeg, &QPushButton::clicked, [&] 123 | { 124 | parameters.min_nn2 = treeisoDlg.spinBoxK2->value(); 125 | parameters.reg_strength2 = treeisoDlg.doubleSpinBoxLambda2->value(); 126 | parameters.decimate_res2 = treeisoDlg.doubleSpinBoxDecRes2->value(); 127 | parameters.max_gap = treeisoDlg.doubleSpinBoxMaxGap->value(); 128 | parameters.threads2 = treeisoDlg.doubleSpinBoxThreads2->value(); 129 | 130 | intermediate_segs(parameters, &treeisoDlg); 131 | 132 | }); 133 | 134 | connect(treeisoDlg.pushButtonReseg, &QPushButton::clicked, [&] 135 | { 136 | parameters.rel_height_length_ratio = treeisoDlg.doubleSpinBoxRelHLRatio->value(); 137 | parameters.vertical_weight = treeisoDlg.doubleSpinBoxVWeight->value(); 138 | 139 | final_segs(parameters, &treeisoDlg); 140 | }); 141 | treeisoDlg.pushButtonInitSeg->setEnabled(true); 142 | 143 | if (treeisoDlg.exec()) 144 | { 145 | if (m_app) 146 | { 147 | m_app->refreshAll(); 148 | } 149 | else 150 | { 151 | // m_app should have already been initialized by CC when plugin is loaded! 152 | assert(false); 153 | } 154 | } 155 | } 156 | 157 | void qTreeIso::init_segs(const Parameters& parameters, QWidget* parent/*=nullptr*/) 158 | { 159 | // display the progress dialog 160 | QProgressDialog* progressDlg = new QProgressDialog(parent); 161 | progressDlg->setWindowTitle("TreeIso Step 1. Initial segmentation"); 162 | progressDlg->setLabelText(tr("Computing....")); 163 | progressDlg->setCancelButton(nullptr); 164 | progressDlg->setRange(0, 100); // infinite progress bar 165 | progressDlg->show(); 166 | 167 | std::function progressCallback = [progressDlg](int percent) { 168 | progressDlg->setValue(percent); 169 | QApplication::processEvents(); // Allow UI updates 170 | }; 171 | 172 | if (!TreeIso::Init_seg(parameters.min_nn1, parameters.reg_strength1, parameters.decimate_res1, parameters.threads1, m_app, progressCallback)) 173 | { 174 | progressCallback(0); 175 | m_app->dispToConsole("Discontinued in initial segmentation.", ccMainAppInterface::ERR_CONSOLE_MESSAGE); 176 | progressDlg->close(); 177 | QApplication::processEvents(); 178 | m_app->updateUI(); 179 | m_app->refreshAll(); 180 | return; 181 | } 182 | 183 | progressDlg->close(); 184 | QApplication::processEvents(); 185 | 186 | m_app->updateUI(); 187 | m_app->refreshAll(); 188 | } 189 | 190 | void qTreeIso::intermediate_segs(const Parameters& parameters, QWidget* parent/*=nullptr*/) 191 | { 192 | // display the progress dialog 193 | QProgressDialog* progressDlg = new QProgressDialog(parent); 194 | progressDlg->setWindowTitle("TreeIso Step 2. Interim segmentation"); 195 | progressDlg->setLabelText(tr("Computing....")); 196 | progressDlg->setCancelButton(nullptr); 197 | progressDlg->setRange(0, 100); // infinite progress bar 198 | progressDlg->show(); 199 | 200 | std::function progressCallback = [progressDlg](int percent) { 201 | progressDlg->setValue(percent); 202 | QApplication::processEvents(); // Allow UI updates 203 | }; 204 | 205 | if (!TreeIso::Intermediate_seg(parameters.min_nn2, parameters.reg_strength2, parameters.decimate_res2, parameters.max_gap, parameters.threads2, m_app, progressCallback)) 206 | { 207 | progressCallback(0); 208 | m_app->dispToConsole("Error in interim segmentation!", ccMainAppInterface::ERR_CONSOLE_MESSAGE); 209 | progressDlg->close(); 210 | QApplication::processEvents(); 211 | m_app->updateUI(); 212 | m_app->refreshAll(); 213 | return; 214 | } 215 | 216 | progressDlg->hide(); 217 | QApplication::processEvents(); 218 | 219 | m_app->updateUI(); 220 | m_app->refreshAll(); 221 | } 222 | 223 | void qTreeIso::final_segs(const Parameters& parameters, QWidget* parent/*=nullptr*/) 224 | { 225 | // display the progress dialog 226 | QProgressDialog* progressDlg = new QProgressDialog(parent); 227 | progressDlg->setWindowTitle("TreeIso Step 3. Final segmentation"); 228 | progressDlg->setLabelText(tr("Computing....")); 229 | progressDlg->setCancelButton(nullptr); 230 | progressDlg->setRange(0, 100); // infinite progress bar 231 | progressDlg->show(); 232 | 233 | std::function progressCallback = [progressDlg](int percent) { 234 | progressDlg->setValue(percent); 235 | QApplication::processEvents(); // Allow UI updates 236 | }; 237 | 238 | if (!TreeIso::Final_seg(parameters.min_nn2, parameters.rel_height_length_ratio, parameters.vertical_weight, m_app, progressCallback)) 239 | { 240 | progressCallback(0); 241 | m_app->dispToConsole("Error in final segmentation!", ccMainAppInterface::ERR_CONSOLE_MESSAGE); 242 | progressDlg->close(); 243 | QApplication::processEvents(); 244 | m_app->updateUI(); 245 | m_app->refreshAll(); 246 | return; 247 | } 248 | 249 | progressDlg->close(); 250 | QApplication::processEvents(); 251 | 252 | m_app->updateUI(); 253 | m_app->refreshAll(); 254 | } 255 | 256 | void qTreeIso::registerCommands(ccCommandLineInterface* cmd) 257 | { 258 | if (!cmd) 259 | { 260 | assert(false); 261 | return; 262 | } 263 | cmd->registerCommand(ccCommandLineInterface::Command::Shared(new CommandTreeIso)); 264 | } 265 | -------------------------------------------------------------------------------- /ui/CMakeLists.txt: -------------------------------------------------------------------------------- 1 | 2 | target_sources( ${PROJECT_NAME} 3 | PRIVATE 4 | ${CMAKE_CURRENT_LIST_DIR}/TreeIsoDlg.ui 5 | ) 6 | -------------------------------------------------------------------------------- /ui/TreeIsoDlg.ui: -------------------------------------------------------------------------------- 1 | 2 | 3 | TreeIsoDialog 4 | 5 | 6 | 7 | 0 8 | 0 9 | 392 10 | 519 11 | 12 | 13 | 14 | Individual-tree isolator (ground LiDAR) 15 | 16 | 17 | 18 | :/CC/plugin/qTreeIso/images/forest.png:/CC/plugin/qTreeIso/images/forest.png 19 | 20 | 21 | 22 | 23 | 24 | true 25 | 26 | 27 | ***Treeiso *plugin (v2)** 28 | 29 | `A graph-based tree point cloud isolator (assuming ground points have already 30 | been removed)` 31 | 32 | `Reference` 33 | 34 | `Xi, Z.; Hopkinson, C. 3D Graph-Based Individual-Tree Isolation (Treeiso) from 35 | Terrestrial Laser Scanning Point Clouds. Remote Sens. 2022, 14, 6116. 36 | https://doi.org/10.3390/rs14236116` 37 | 38 | `Landrieu, Loic, and Guillaume Obozinski. Cut pursuit: Fast algorithms to learn 39 | piecewise constant functions on general weighted graphs. SIAM Journal on 40 | Imaging Sciences. 2017, 10.4, 1724-1766.` 41 | 42 | 43 | 44 | 45 | <!DOCTYPE HTML PUBLIC "-//W3C//DTD HTML 4.0//EN" "http://www.w3.org/TR/REC-html40/strict.dtd"> 46 | <html><head><meta name="qrichtext" content="1" /><meta charset="utf-8" /><style type="text/css"> 47 | p, li { white-space: pre-wrap; } 48 | hr { height: 1px; border-width: 0; } 49 | li.unchecked::marker { content: "\2610"; } 50 | li.checked::marker { content: "\2612"; } 51 | </style></head><body style=" font-family:'Segoe UI'; font-size:9pt; font-weight:400; font-style:normal;"> 52 | <p style=" margin-top:12px; margin-bottom:12px; margin-left:0px; margin-right:0px; -qt-block-indent:0; text-indent:0px;"><span style=" font-weight:700; font-style:italic;">Treeiso </span><span style=" font-weight:700;">plugin (v2)</span></p> 53 | <p style=" margin-top:6px; margin-bottom:6px; margin-left:0px; margin-right:0px; -qt-block-indent:0; text-indent:0px;"><span style=" font-family:'Courier New';">A graph-based tree point cloud isolator (assuming ground points have already been removed)</span></p> 54 | <p style=" margin-top:6px; margin-bottom:6px; margin-left:0px; margin-right:0px; -qt-block-indent:0; text-indent:0px;"><span style=" font-family:'Courier New'; font-weight:700;">Reference</span></p> 55 | <p style=" margin-top:6px; margin-bottom:6px; margin-left:0px; margin-right:0px; -qt-block-indent:0; text-indent:0px;"><span style=" font-family:'Courier New';">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</span></p> 56 | <p style=" margin-top:6px; margin-bottom:6px; margin-left:0px; margin-right:0px; -qt-block-indent:0; text-indent:0px;"><span style=" font-family:'Courier New';">Landrieu, Loic, and Guillaume Obozinski. Cut pursuit: Fast algorithms to learn piecewise constant functions on general weighted graphs. SIAM Journal on Imaging Sciences. 2017, 10.4, 1724-1766.</span></p></body></html> 57 | 58 | 59 | 60 | 61 | 62 | 63 | 0 64 | 65 | 66 | 67 | 68 | 0 69 | 0 70 | 71 | 72 | 73 | false 74 | 75 | 76 | Initial segmentation 77 | 78 | 79 | 80 | 81 | 82 | 83 | 84 | 85 | 86 | <html><head/><body><p><span style=" font-size:10pt; font-weight:600; color:#aa0000;">1. 3D Cut-pursuit algorithm</span></p></body></html> 87 | 88 | 89 | 90 | 91 | 92 | 93 | <html><head/><body><p>This step may take a little while—please be patient. <br> It will create small clusters with natural breaks.</p></body></html> 94 | 95 | 96 | 97 | 98 | 99 | 100 | Qt::Vertical 101 | 102 | 103 | 104 | 20 105 | 40 106 | 107 | 108 | 109 | 110 | 111 | 112 | 113 | <span style=" font-size:10pt; font-weight:600; color:#aa0000;">K: Nearest neighbors to search</span> 114 | 115 | 116 | 117 | 118 | 119 | 120 | 3 121 | 122 | 123 | 40 124 | 125 | 126 | 5 127 | 128 | 129 | 130 | 131 | 132 | 133 | Qt::Vertical 134 | 135 | 136 | 137 | 20 138 | 40 139 | 140 | 141 | 142 | 143 | 144 | 145 | 146 | <html><head/><body><p><span style=" font-size:10pt; font-weight:600; color:#aa0000;">λ: Regularization strength</span></p></body></html> 147 | 148 | 149 | 150 | 151 | 152 | 153 | 0.000000000000000 154 | 155 | 156 | 50.000000000000000 157 | 158 | 159 | 0.050000000000000 160 | 161 | 162 | 1.000000000000000 163 | 164 | 165 | 166 | 167 | 168 | 169 | Qt::Vertical 170 | 171 | 172 | 173 | 20 174 | 40 175 | 176 | 177 | 178 | 179 | 180 | 181 | 182 | <html><head/><body><p><span style=" font-size:10pt; font-weight:600; color:#aa0000;">Decimated resolution (m) to speed up</span></p></body></html> 183 | 184 | 185 | 186 | 187 | 188 | 189 | 3 190 | 191 | 192 | 0.001000000000000 193 | 194 | 195 | 0.010000000000000 196 | 197 | 198 | 0.050000000000000 199 | 200 | 201 | 202 | 203 | 204 | 205 | Qt::Vertical 206 | 207 | 208 | 209 | 20 210 | 40 211 | 212 | 213 | 214 | 215 | 216 | 217 | 218 | <html><head/><body><p><span style=" font-size:10pt; font-weight:600; color:#aa0000;">Number of threads for KNN calculation </span></p></body></html> 219 | 220 | 221 | 222 | 223 | 224 | 225 | 1 226 | 227 | 228 | 229 | 230 | 231 | 232 | Execute 233 | 234 | 235 | 236 | 237 | 238 | 239 | 240 | 241 | true 242 | 243 | 244 | 245 | 246 | 247 | 248 | 249 | 250 | :/CC/plugin/qTreeIso/images/JP1_init_seg.png 251 | 252 | 253 | false 254 | 255 | 256 | Qt::AlignCenter 257 | 258 | 259 | 260 | 261 | 262 | 263 | 264 | 265 | 266 | 267 | 0 268 | 0 269 | 270 | 271 | 272 | false 273 | 274 | 275 | Interim segmentation 276 | 277 | 278 | 279 | 280 | 281 | 282 | 283 | 284 | 285 | <html><head/><body><p><span style=" font-size:10pt; font-weight:600; color:#aa0000;">2. 2D Cut-pursuit algorithm</span></p></body></html> 286 | 287 | 288 | 289 | 290 | 291 | 292 | This step merges previous clusters horizontally. 293 | 294 | 295 | 296 | 297 | 298 | 299 | Qt::Vertical 300 | 301 | 302 | 303 | 20 304 | 40 305 | 306 | 307 | 308 | 309 | 310 | 311 | 312 | <span style=" font-size:10pt; font-weight:600; color:#aa0000;">K: Nearest neighbors to search</span> 313 | 314 | 315 | 316 | 317 | 318 | 319 | 2 320 | 321 | 322 | 10 323 | 324 | 325 | 326 | 327 | 328 | 329 | Qt::Vertical 330 | 331 | 332 | 333 | 20 334 | 40 335 | 336 | 337 | 338 | 339 | 340 | 341 | 342 | <html><head/><body><p><span style=" font-size:10pt; font-weight:600; color:#aa0000;">λ: Regularization strength</span></p></body></html> 343 | 344 | 345 | 346 | 347 | 348 | 349 | 50.000000000000000 350 | 351 | 352 | 0.050000000000000 353 | 354 | 355 | 10.000000000000000 356 | 357 | 358 | 359 | 360 | 361 | 362 | Qt::Vertical 363 | 364 | 365 | 366 | 20 367 | 40 368 | 369 | 370 | 371 | 372 | 373 | 374 | 375 | <html><head/><body><p><span style=" font-size:10pt; font-weight:600; color:#aa0000;">Max gap (m)</span></p></body></html> 376 | 377 | 378 | 379 | 380 | 381 | 382 | 0.010000000000000 383 | 384 | 385 | 0.500000000000000 386 | 387 | 388 | 2.000000000000000 389 | 390 | 391 | 392 | 393 | 394 | 395 | Qt::Vertical 396 | 397 | 398 | 399 | 20 400 | 40 401 | 402 | 403 | 404 | 405 | 406 | 407 | 408 | <html><head/><body><p><span style=" font-size:10pt; font-weight:600; color:#aa0000;">Decimated resolution (m) to speed up</span></p></body></html> 409 | 410 | 411 | 412 | 413 | 414 | 415 | 3 416 | 417 | 418 | 0.001000000000000 419 | 420 | 421 | 0.010000000000000 422 | 423 | 424 | 0.100000000000000 425 | 426 | 427 | 428 | 429 | 430 | 431 | Qt::Vertical 432 | 433 | 434 | 435 | 20 436 | 40 437 | 438 | 439 | 440 | 441 | 442 | 443 | 444 | <html><head/><body><p><span style=" font-size:10pt; font-weight:600; color:#aa0000;">Number of threads for KNN calculation </span></p></body></html> 445 | 446 | 447 | 448 | 449 | 450 | 451 | 1 452 | 453 | 454 | 455 | 456 | 457 | 458 | Execute 459 | 460 | 461 | 462 | 463 | 464 | 465 | 466 | 467 | true 468 | 469 | 470 | 471 | 472 | 473 | 474 | 475 | 476 | :/CC/plugin/qTreeIso/images/JP1_interim_seg.png 477 | 478 | 479 | false 480 | 481 | 482 | Qt::AlignCenter 483 | 484 | 485 | 486 | 487 | 488 | 489 | 490 | 491 | 492 | 493 | 0 494 | 0 495 | 496 | 497 | 498 | false 499 | 500 | 501 | Refined segmentation 502 | 503 | 504 | 505 | 506 | 507 | 508 | 509 | 510 | 511 | <html><head/><body><p><span style=" font-size:10pt; font-weight:600; color:#aa0000;">3. Global refinement</span></p></body></html> 512 | 513 | 514 | 515 | 516 | 517 | 518 | Final merging based on gaps and overlapping ratio. 519 | 520 | 521 | 522 | 523 | 524 | 525 | Qt::Vertical 526 | 527 | 528 | 529 | 20 530 | 40 531 | 532 | 533 | 534 | 535 | 536 | 537 | 538 | <html><head/><body><p><span style=" font-size:10pt; font-weight:600; color:#aa0000;">Vertical weight</span></p></body></html> 539 | 540 | 541 | 542 | 543 | 544 | 545 | 0.010000000000000 546 | 547 | 548 | 0.100000000000000 549 | 550 | 551 | 0.500000000000000 552 | 553 | 554 | 555 | 556 | 557 | 558 | Qt::Vertical 559 | 560 | 561 | 562 | 20 563 | 40 564 | 565 | 566 | 567 | 568 | 569 | 570 | 571 | <html><head/><body><p><span style=" font-size:10pt; font-weight:600; color:#aa0000;">Relative height length ratio</span></p></body></html> 572 | 573 | 574 | 575 | 576 | 577 | 578 | 0.010000000000000 579 | 580 | 581 | 0.100000000000000 582 | 583 | 584 | 0.500000000000000 585 | 586 | 587 | 588 | 589 | 590 | 591 | Qt::Vertical 592 | 593 | 594 | 595 | 20 596 | 40 597 | 598 | 599 | 600 | 601 | 602 | 603 | 604 | Execute 605 | 606 | 607 | 608 | 609 | 610 | 611 | 612 | 613 | true 614 | 615 | 616 | 617 | 618 | 619 | 620 | 621 | 622 | :/CC/plugin/qTreeIso/images/JP1_final_seg.png 623 | 624 | 625 | false 626 | 627 | 628 | Qt::AlignCenter 629 | 630 | 631 | 632 | 633 | 634 | 635 | 636 | 637 | 638 | 639 | 640 | 641 | 642 | Times New Roman 643 | false 644 | true 645 | 646 | 647 | 648 | Qt::Horizontal 649 | 650 | 651 | QDialogButtonBox::Ok 652 | 653 | 654 | 655 | 656 | 657 | 658 | 659 | 660 | 661 | 662 | buttonBox 663 | rejected() 664 | TreeIsoDialog 665 | reject() 666 | 667 | 668 | 316 669 | 260 670 | 671 | 672 | 286 673 | 274 674 | 675 | 676 | 677 | 678 | buttonBox 679 | accepted() 680 | TreeIsoDialog 681 | accept() 682 | 683 | 684 | 248 685 | 254 686 | 687 | 688 | 157 689 | 274 690 | 691 | 692 | 693 | 694 | 695 | --------------------------------------------------------------------------------