mirror of
https://github.com/truebelief/cc-treeiso-plugin.git
synced 2026-08-29 16:40:29 +08:00
colorxixi
c928ee794c
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.
400 lines
13 KiB
C++
400 lines
13 KiB
C++
#pragma once
|
||
|
||
//#######################################################################################
|
||
//# #
|
||
//# CLOUDCOMPARE PLUGIN: qTreeIso #
|
||
//# #
|
||
//# This program is free software; you can redistribute it and/or modify #
|
||
//# it under the terms of the GNU General Public License as published by #
|
||
//# the Free Software Foundation; version 2 or later of the License. #
|
||
//# #
|
||
//# This program is distributed in the hope that it will be useful, #
|
||
//# but WITHOUT ANY WARRANTY; without even the implied warranty of #
|
||
//# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the #
|
||
//# GNU General Public License for more details. #
|
||
//# #
|
||
//# Please cite the following paper if you find this tool helpful #
|
||
//# #
|
||
//# Xi, Z.; Hopkinson, C. 3D Graph-Based Individual-Tree Isolation (Treeiso) #
|
||
//# from Terrestrial Laser Scanning Point Clouds. Remote Sens. 2022, 14, 6116. #
|
||
//# https://doi.org/10.3390/rs14236116 #
|
||
//# #
|
||
//# Our work relies on the cut-pursuit algorithm, please also consider citing: #
|
||
//# Landrieu, L.; Obozinski, G. Cut Pursuit: Fast Algorithms to Learn Piecewise #
|
||
//# Constant Functions on General Weighted Graphs. SIAM J. Imaging Sci. #
|
||
//# 2017, 10, 1724–1766. #
|
||
//# #
|
||
//# Copyright © #
|
||
//# Artemis Lab, Department of Geography & Environment #
|
||
//# University of Lethbridge, Canada #
|
||
//# #
|
||
//# #
|
||
//# Zhouxin Xi and Chris Hopkinson; #
|
||
//# truebelief2010@gmail.com; c.hopkinson@uleth.ca #
|
||
//# #
|
||
//#######################################################################################
|
||
|
||
// Matlab and python versions shared via:
|
||
// https://github.com/truebelief/artemis_treeiso
|
||
|
||
//Local
|
||
#include "knncpp.h"
|
||
#include "cp_d0_dist.hpp"
|
||
|
||
//Eigen
|
||
#include <Eigen/Dense>
|
||
|
||
//STL
|
||
#include <vector>
|
||
|
||
class ccPointCloud;
|
||
typedef std::vector<float> Vec3d;
|
||
|
||
typedef uint32_t index_t; // For vertex and edge indices
|
||
typedef uint16_t comp_t; // For component indices
|
||
|
||
|
||
void knn_cpp_build(knncpp::KDTreeMinkowskiX<float, knncpp::EuclideanDistance<float>>& kdtree, unsigned n_thread = 0);
|
||
void knn_cpp_query(knncpp::KDTreeMinkowskiX<float, knncpp::EuclideanDistance<float>>& kdtree, Eigen::MatrixXf& query_points, size_t k, std::vector <std::vector<size_t>>& res_idx, std::vector <std::vector<float>>& res_dists);
|
||
float knn_cpp_query_min_d(knncpp::KDTreeMinkowskiX<float, knncpp::EuclideanDistance<float>>& kdtree, Eigen::MatrixXf& query_points, size_t k);
|
||
void build_knn_graph(const std::vector<Vec3d>& points, size_t k, std::vector<index_t>& first_edge, std::vector<index_t>& adj_vertices, std::vector<float>& edge_weights, float regStrength1 = 1.0, unsigned n_thread = 8);
|
||
void knn_cpp_nearest_neighbors(const std::vector<Vec3d>& dataset, size_t k, std::vector<std::vector<uint32_t>>& res_idx, std::vector<Vec3d>& res_dists, unsigned n_thread);
|
||
|
||
|
||
void load_initseg_points(const std::string& filename, std::vector<Vec3d>& points, std::vector<index_t>& in_component);
|
||
bool perform_cut_pursuit(const unsigned K, size_t D, const float regStrength, const std::vector<Vec3d>& pc_vec, std::vector<float>& edge_weights, std::vector<index_t>& Eu, std::vector<index_t>& Ev, std::vector<index_t>& in_component, const unsigned threads);
|
||
|
||
template <typename T>
|
||
size_t arg_min_col(const std::vector<T>& arr) {
|
||
return std::distance(arr.begin(), std::min_element(arr.begin(), arr.end()));
|
||
}
|
||
|
||
template <typename T>
|
||
size_t arg_max_col(const std::vector<T>& arr) {
|
||
return std::distance(arr.begin(), std::max_element(arr.begin(), arr.end()));
|
||
}
|
||
|
||
template <typename T>
|
||
void min_col(const std::vector<std::vector<T>>& arr, std::vector<T>& min_vals) {
|
||
if (arr.empty()) {
|
||
min_vals.clear();
|
||
return;
|
||
}
|
||
|
||
min_vals = arr[0];
|
||
for (const auto& row : arr) {
|
||
std::transform(min_vals.begin(), min_vals.end(), row.begin(),
|
||
min_vals.begin(), [](const T& a, const T& b) { return std::min(a, b); });
|
||
}
|
||
}
|
||
|
||
template <typename T>
|
||
T min_col(const std::vector<T>& arr) {
|
||
return arr.empty() ? std::numeric_limits<T>::quiet_NaN()
|
||
: *std::min_element(arr.begin(), arr.end());
|
||
}
|
||
|
||
template <typename T>
|
||
T mean_col(const std::vector<T>& arr) {
|
||
if (arr.empty()) return std::numeric_limits<T>::quiet_NaN();
|
||
return static_cast<T>(std::accumulate(arr.begin(), arr.end(), 0.0) / arr.size());
|
||
}
|
||
|
||
template <typename T>
|
||
T median_col(std::vector<T>& arr) {
|
||
if (arr.empty()) return std::numeric_limits<T>::quiet_NaN();
|
||
|
||
const size_t n = arr.size();
|
||
const size_t mid = n / 2;
|
||
std::nth_element(arr.begin(), arr.begin() + mid, arr.end());
|
||
|
||
if (n % 2 == 0) {
|
||
const T right = arr[mid];
|
||
std::nth_element(arr.begin(), arr.begin() + mid - 1, arr.end());
|
||
return (arr[mid - 1] + right) / 2;
|
||
}
|
||
return arr[mid];
|
||
}
|
||
|
||
template <typename T>
|
||
T mode_col(const std::vector<T>& arr) {
|
||
if (arr.empty()) return std::numeric_limits<T>::quiet_NaN();
|
||
|
||
std::unordered_map<T, size_t> freq;
|
||
for (const auto& val : arr) ++freq[val];
|
||
return std::max_element(freq.begin(), freq.end(),
|
||
[](const auto& a, const auto& b) { return a.second < b.second; })->first;
|
||
}
|
||
|
||
template <typename T>
|
||
void max_col(const std::vector<std::vector<T>>& arr, std::vector<T>& max_vals) {
|
||
if (arr.empty()) {
|
||
max_vals.clear();
|
||
return;
|
||
}
|
||
|
||
max_vals = arr[0];
|
||
for (const auto& row : arr) {
|
||
std::transform(max_vals.begin(), max_vals.end(), row.begin(),
|
||
max_vals.begin(), [](const T& a, const T& b) { return std::max(a, b); });
|
||
}
|
||
}
|
||
|
||
template <typename T>
|
||
void mean_col(const std::vector<std::vector<T>>& arr, std::vector<T>& mean_vals) {
|
||
if (arr.empty()) {
|
||
mean_vals.clear();
|
||
return;
|
||
}
|
||
|
||
const size_t cols = arr[0].size();
|
||
mean_vals.resize(cols);
|
||
std::fill(mean_vals.begin(), mean_vals.end(), T{});
|
||
|
||
for (const auto& row : arr) {
|
||
std::transform(mean_vals.begin(), mean_vals.end(), row.begin(),
|
||
mean_vals.begin(), std::plus<T>());
|
||
}
|
||
|
||
const T size = static_cast<T>(arr.size());
|
||
std::transform(mean_vals.begin(), mean_vals.end(), mean_vals.begin(),
|
||
[size](T val) { return val / size; });
|
||
}
|
||
|
||
template <typename T>
|
||
void decimate_vec(const std::vector<std::vector<T>>& arr, T res, std::vector<std::vector<T>>& vec_dec) {
|
||
if (arr.empty() || res <= T{}) {
|
||
vec_dec.clear();
|
||
return;
|
||
}
|
||
|
||
std::vector<T> arr_min;
|
||
min_col(arr, arr_min);
|
||
|
||
vec_dec.resize(arr.size(), std::vector<T>(arr[0].size()));
|
||
for (size_t i = 0; i < arr.size(); ++i) {
|
||
std::transform(arr[i].begin(), arr[i].end(), arr_min.begin(),
|
||
vec_dec[i].begin(),
|
||
[res](T val, T min) { return std::floor((val - min) / res) + T{ 1 }; });
|
||
}
|
||
}
|
||
|
||
|
||
template <typename T>
|
||
void sort_indexes_by_row(const std::vector<std::vector<T>>& v, std::vector<size_t>& idx,
|
||
std::vector<std::vector<T>>& v_sorted) {
|
||
if (v.empty()) {
|
||
idx.clear();
|
||
v_sorted.clear();
|
||
return;
|
||
}
|
||
|
||
const size_t rows = v.size();
|
||
const size_t cols = v[0].size();
|
||
|
||
idx.resize(rows);
|
||
std::iota(idx.begin(), idx.end(), 0);
|
||
|
||
std::stable_sort(idx.begin(), idx.end(), [&v](size_t i1, size_t i2) {
|
||
return std::lexicographical_compare(v[i1].begin(), v[i1].end(),
|
||
v[i2].begin(), v[i2].end());
|
||
});
|
||
|
||
v_sorted.resize(rows);
|
||
for (size_t i = 0; i < rows; ++i) {
|
||
v_sorted[i] = v[idx[i]];
|
||
}
|
||
}
|
||
|
||
template <typename ValueType, typename IndexType>
|
||
void sort_indexes(const std::vector<ValueType>& v, std::vector<IndexType>& idx,
|
||
std::vector<ValueType>& v_sorted) {
|
||
if (v.empty()) {
|
||
idx.clear();
|
||
v_sorted.clear();
|
||
return;
|
||
}
|
||
|
||
idx.resize(v.size());
|
||
std::iota(idx.begin(), idx.end(), 0);
|
||
|
||
std::stable_sort(idx.begin(), idx.end(),
|
||
[&v](IndexType i1, IndexType i2) { return v[i1] < v[i2]; });
|
||
|
||
v_sorted.resize(v.size());
|
||
std::transform(idx.begin(), idx.end(), v_sorted.begin(),
|
||
[&v](IndexType i) { return v[i]; });
|
||
}
|
||
|
||
template <typename T>
|
||
void unique_index_by_rows(const std::vector<std::vector<T>>& arr,
|
||
std::vector<size_t>& ia, std::vector<size_t>& ic) {
|
||
if (arr.empty()) {
|
||
ia.clear();
|
||
ic.clear();
|
||
return;
|
||
}
|
||
|
||
std::vector<std::vector<T>> arr_sorted;
|
||
std::vector<size_t> sort_idx;
|
||
sort_indexes_by_row(arr, sort_idx, arr_sorted);
|
||
|
||
const size_t rows = arr_sorted.size();
|
||
ic.resize(rows);
|
||
ia.clear();
|
||
ia.push_back(sort_idx[0]);
|
||
ic[sort_idx[0]] = 0;
|
||
|
||
size_t counter = 0;
|
||
for (size_t i = 1; i < rows; ++i) {
|
||
if (!std::equal(arr_sorted[i].begin(), arr_sorted[i].end(),
|
||
arr_sorted[i - 1].begin())) {
|
||
ia.push_back(sort_idx[i]);
|
||
++counter;
|
||
}
|
||
ic[sort_idx[i]] = counter;
|
||
}
|
||
}
|
||
|
||
template <typename T>
|
||
void to_translated_vector(const ccPointCloud* pc, std::vector<std::vector<T>>& y) {
|
||
if (!pc || pc->size() == 0) {
|
||
y.clear();
|
||
return;
|
||
}
|
||
|
||
const size_t pointCount = pc->size();
|
||
y.resize(pointCount, std::vector<T>(3));
|
||
|
||
std::vector<T> y_mean(3, 0);
|
||
for (size_t i = 0; i < pointCount; ++i) {
|
||
const CCVector3* pv = pc->getPoint(i);
|
||
y[i] = { static_cast<T>(pv->x), static_cast<T>(pv->y), static_cast<T>(pv->z) };
|
||
std::transform(y_mean.begin(), y_mean.end(), y[i].begin(), y_mean.begin(), std::plus<T>());
|
||
}
|
||
|
||
std::transform(y_mean.begin(), y_mean.end(), y_mean.begin(),
|
||
[pointCount](T val) { return val / pointCount; });
|
||
|
||
for (auto& point : y) {
|
||
std::transform(point.begin(), point.end(), y_mean.begin(), point.begin(), std::minus<T>());
|
||
}
|
||
}
|
||
|
||
|
||
template <typename T>
|
||
void unique_group(const std::vector<T>& arr, std::vector<std::vector<T>>& u_group,
|
||
std::vector<T>& arr_unq, std::vector<T>& ui) {
|
||
if (arr.empty()) {
|
||
arr_unq.clear();
|
||
ui.clear();
|
||
u_group.clear();
|
||
return;
|
||
}
|
||
|
||
std::vector<T> arr_sorted_idx;
|
||
std::vector<T> arr_sorted;
|
||
sort_indexes(arr, arr_sorted_idx, arr_sorted);
|
||
|
||
arr_unq.clear();
|
||
ui.clear();
|
||
u_group.clear();
|
||
|
||
ui.push_back(arr_sorted_idx[0]);
|
||
std::vector<T> current_group = { arr_sorted_idx[0] };
|
||
|
||
for (size_t i = 1; i < arr.size(); ++i) {
|
||
if (arr_sorted[i] != arr_sorted[i - 1]) {
|
||
ui.push_back(arr_sorted_idx[i]);
|
||
arr_unq.push_back(arr_sorted[i - 1]);
|
||
u_group.push_back(std::move(current_group));
|
||
current_group = { arr_sorted_idx[i] };
|
||
}
|
||
else {
|
||
current_group.push_back(arr_sorted_idx[i]);
|
||
}
|
||
}
|
||
|
||
arr_unq.push_back(arr_sorted.back());
|
||
u_group.push_back(std::move(current_group));
|
||
}
|
||
|
||
// Overloaded versions with fewer return parameters
|
||
template <typename T>
|
||
void unique_group(const std::vector<T>& arr, std::vector<std::vector<T>>& u_group,
|
||
std::vector<T>& arr_unq) {
|
||
std::vector<T> ui;
|
||
unique_group(arr, u_group, arr_unq, ui);
|
||
}
|
||
|
||
template <typename T>
|
||
void unique_group(const std::vector<T>& arr, std::vector<std::vector<T>>& u_group) {
|
||
std::vector<T> arr_unq, ui;
|
||
unique_group(arr, u_group, arr_unq, ui);
|
||
}
|
||
|
||
|
||
template <typename T1, typename T2>
|
||
void get_subset(std::vector<T1>& arr, std::vector<T2>& indices, std::vector<T1>& arr_sub)
|
||
{
|
||
arr_sub.clear();
|
||
for (const auto& idx : indices)
|
||
{
|
||
arr_sub.push_back(arr[idx]);
|
||
}
|
||
}
|
||
|
||
template <typename T1, typename T2>
|
||
void get_subset(const std::vector<std::vector<T1>>& arr, const std::vector<T2>& indices, Eigen::MatrixXf& arr_sub)
|
||
{
|
||
arr_sub.setZero();
|
||
|
||
if (arr.empty())
|
||
{
|
||
assert(false);
|
||
return;
|
||
}
|
||
|
||
arr_sub.resize(arr[0].size(), indices.size());
|
||
|
||
for (size_t i = 0; i < indices.size(); ++i)
|
||
{
|
||
for (size_t j = 0; j < arr[0].size(); ++j)
|
||
{
|
||
arr_sub(j, i) = arr[indices[i]][j];
|
||
}
|
||
}
|
||
}
|
||
|
||
template <typename T1, typename T2>
|
||
void get_subset(const std::vector<std::vector<T1>>& arr, const std::vector<T2>& indices, std::vector<std::vector<T1>>& arr_sub)
|
||
{
|
||
arr_sub.clear();
|
||
|
||
if (arr.empty() || indices.empty())
|
||
{
|
||
return;
|
||
}
|
||
|
||
arr_sub.resize(indices.size());
|
||
for (size_t i = 0; i < indices.size(); ++i)
|
||
{
|
||
arr_sub[i] = arr[indices[i]];
|
||
}
|
||
}
|
||
|
||
template <typename T1, typename T2>
|
||
bool get_subset(ccPointCloud* pcd, std::vector<T2>& indices, std::vector<std::vector<T1>>& arr_sub)
|
||
{
|
||
arr_sub.clear();
|
||
arr_sub.resize(indices.size(), std::vector<T1>(3));
|
||
for (size_t i = 0; i < indices.size(); ++i)
|
||
{
|
||
const CCVector3* vec = pcd->getPoint(indices[i]);
|
||
arr_sub[i][0] = vec->x;
|
||
arr_sub[i][1] = vec->y;
|
||
arr_sub[i][2] = vec->z;
|
||
}
|
||
return true;
|
||
}
|