mirror of
https://github.com/truebelief/cc-treeiso-plugin.git
synced 2026-08-29 08:34:29 +08:00
ZXi
0e18ee5e2b
* Major upgrade of dependencies and running time 1. Removed the Boost dependency by adopting a newer cut-pursuit version from the authors (resulting in a 5–15× speed improvement). 2. Fixed an issue with the nearest neighbor search caused by an incorrect 2D array order input to the knn_cpp query function. 3. Enabled customization of the number of threads for knn-cpp, yielding a slight speed improvement. 4. Optimized array calculations through enhanced use of STL algorithms. * Change log added * Smoothened the progress bar and added ground detectioned - Improved progress bar increment - Added rapid detection of ground points based on quantile distribution - Added warning of ground points before continuing initial segmentation * Crash fix - Avoided crashes by adding error handling once the maximum component limit is reached - Improved the speed of unique and get_subset functions
56 lines
4.3 KiB
C++
56 lines
4.3 KiB
C++
#pragma once
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//#######################################################################################
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//# #
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//# CLOUDCOMPARE PLUGIN: qTreeIso #
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//# #
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//# This program is free software; you can redistribute it and/or modify #
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//# it under the terms of the GNU General Public License as published by #
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//# the Free Software Foundation; version 2 or later of the License. #
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//# #
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//# This program is distributed in the hope that it will be useful, #
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//# but WITHOUT ANY WARRANTY; without even the implied warranty of #
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//# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the #
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//# GNU General Public License for more details. #
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//# #
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//# Please cite the following paper if you find this tool helpful #
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//# #
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//# Xi, Z.; Hopkinson, C. 3D Graph-Based Individual-Tree Isolation (Treeiso) #
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//# from Terrestrial Laser Scanning Point Clouds. Remote Sens. 2022, 14, 6116. #
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//# https://doi.org/10.3390/rs14236116 #
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//# #
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//# Our work relies on the cut-pursuit algorithm, please also consider citing: #
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//# Landrieu, L.; Obozinski, G. Cut Pursuit: Fast Algorithms to Learn Piecewise #
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//# Constant Functions on General Weighted Graphs. SIAM J. Imaging Sci. #
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//# 2017, 10, 1724–1766. #
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//# #
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//# Copyright © #
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//# Artemis Lab, Department of Geography & Environment #
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//# University of Lethbridge, Canada #
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//# #
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//# #
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//# Zhouxin Xi and Chris Hopkinson; #
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//# truebelief2010@gmail.com; c.hopkinson@uleth.ca #
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//# #
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//#######################################################################################
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// Matlab and python versions shared via:
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// https://github.com/truebelief/artemis_treeiso
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class ccMainAppInterface;
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class ccPointCloud;
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class QProgressDialog;
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class TreeIso
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{
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public:
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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<void(int)> progressCallBack);
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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<void(int)> progressCallBack);
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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<void(int)> progressCallBack);
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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<void(int)> progressCallback);
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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<void(int)> progressCallback);
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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<void(int)> progressCallback);
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};
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