├── .Rbuildignore ├── .gitignore ├── CspStandSegmentation.Rproj ├── DESCRIPTION ├── LICENSE ├── NAMESPACE ├── R ├── 2021-11-12_A1_JF_csp_cost_functions.R ├── 2024-06-10_A1_JF_farthest_point_sampling.R ├── 2025-01-07_A1_JF_merge_las_objects.R ├── 2025-01-10_A1_JF_forest_inventory.R └── RcppExports.R ├── README.md ├── inst ├── extdata │ └── beech.las └── figures │ ├── csp_logo.png │ └── csp_logo.svg ├── man ├── add_geometry.Rd ├── add_las_attributes.Rd ├── add_voxel_coordinates.Rd ├── comparative_shortest_path.Rd ├── csp_cost_segmentation.Rd ├── eigen_decomposition.Rd ├── fast_unlist.Rd ├── fast_unlist_dist.Rd ├── fds.Rd ├── find_base_coordinates_geom.Rd ├── find_base_coordinates_raster.Rd ├── forest_inventory.Rd ├── forest_inventory_simple.Rd ├── las_merge.Rd ├── p_dist.Rd ├── p_mat_dist.Rd ├── plot_inventory.Rd ├── point_center_angle.Rd ├── point_circle_distance.Rd ├── ransac_circle_fit.Rd ├── suppress_cat.Rd └── voxelize_points_mean_attributes.Rd ├── src ├── 2021-11-12_A1_JF_csp_cost_cpp_functions.cpp ├── Makevars.win └── RcppExports.cpp └── tests ├── testthat.R └── testthat ├── test-add_geometry.R ├── test-add_las_attributes.R ├── test-add_voxel_coordinates.R ├── test-csp_cost_segmentation.R └── test-voxelize_points_mean_attributes.R /.Rbuildignore: -------------------------------------------------------------------------------- 1 | ^.*\.Rproj$ 2 | ^\.Rproj\.user$ 3 | -------------------------------------------------------------------------------- /.gitignore: -------------------------------------------------------------------------------- 1 | .Rproj.user 2 | .Rhistory 3 | .RData 4 | .Ruserdata 5 | src/*.o 6 | src/*.so 7 | src/*.dll 8 | -------------------------------------------------------------------------------- /CspStandSegmentation.Rproj: -------------------------------------------------------------------------------- 1 | Version: 1.0 2 | 3 | RestoreWorkspace: Default 4 | SaveWorkspace: Default 5 | AlwaysSaveHistory: Default 6 | 7 | EnableCodeIndexing: Yes 8 | UseSpacesForTab: Yes 9 | NumSpacesForTab: 2 10 | Encoding: UTF-8 11 | 12 | RnwWeave: Sweave 13 | LaTeX: pdfLaTeX 14 | 15 | AutoAppendNewline: Yes 16 | StripTrailingWhitespace: Yes 17 | 18 | BuildType: Package 19 | PackageUseDevtools: Yes 20 | PackageInstallArgs: --no-multiarch --with-keep.source 21 | -------------------------------------------------------------------------------- /DESCRIPTION: -------------------------------------------------------------------------------- 1 | Package: CspStandSegmentation 2 | Type: Package 3 | Title: Comparative Shortest Path Stand Segmentation 4 | Version: 0.1.2 5 | Authors@R: c(person("Julian", "Frey", , "julian.frey@wwd.uni-freiburg.de", role = c("aut", "cre"), comment = c(ORCID = "0000-0001-7895-702X")),person("Zoe", "Schindler", , "zoe.schindler@wwd.uni-freiburg.de", role = c("ctb"), comment = c(ORCID = "0000-0003-2972-1920"))) 6 | Description: This package provides a function to segment trees from a stand scanned with terrestrial laser scanning. It strongly builds upon the work of the lidR and the TreeLS packages. 7 | License: GPL-3 + file LICENSE 8 | Encoding: UTF-8 9 | LazyData: true 10 | Imports: Rcpp (>= 1.0.5), lidR, dbscan, igraph, foreach, parallel, doParallel,magrittr, data.table, sf, terra, RCSF, conicfit 11 | LinkingTo: Rcpp, RcppArmadillo, lidR, BH 12 | RoxygenNote: 7.3.2 13 | Suggests: 14 | testthat (>= 3.0.0) 15 | Config/testthat/edition: 3 16 | -------------------------------------------------------------------------------- /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 | -------------------------------------------------------------------------------- /NAMESPACE: -------------------------------------------------------------------------------- 1 | exportPattern("^[[:alpha:]]+") 2 | importFrom(Rcpp, evalCpp) 3 | importFrom(magrittr, "%>%") 4 | import(lidR) 5 | import(data.table) 6 | import(dbscan) 7 | import(igraph) 8 | import(foreach) 9 | importFrom("stats", "aggregate", "median", "quantile") 10 | export(csp_cost_segmentation) 11 | useDynLib(CspStandSegmentation, .registration = TRUE) 12 | -------------------------------------------------------------------------------- /R/2021-11-12_A1_JF_csp_cost_functions.R: -------------------------------------------------------------------------------- 1 | # load packages 2 | invisible(lapply(c('lidR','dbscan', 'igraph', 'foreach'), require, character.only = TRUE)) 3 | 4 | # ------------------------------------------------------------------------------ 5 | 6 | # thx zoe https://github.com/zoeschindler/masterarbeit/blob/main/03_raster_calculation_functions.R 7 | 8 | #' Add geometric features to a LAS object 9 | #' 10 | #' The function calls a fast cpp multi-core function to calculate eigenvalues 11 | #' for the points in a point cloud based on the k nearest neighbors. Afterwards 12 | #' it adds geometric features like Curvature, Linearity, Planarity, Sphericity, 13 | #' Anisotrophy and Verticlity to the points itself. 14 | #' 15 | #' Details of the metrics can be found in: \ Hackel, T., Wegner, J.D. & 16 | #' Schindler, K. (2016) Contour Detection in Unstructured 3D Point Clouds. In 17 | #' 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). 18 | #' Presented at the 2016 IEEE Conference on Computer Vision and Pattern 19 | #' Recognition (CVPR), IEEE, Las Vegas, NV, USA, pp. 1610–1618. 20 | #' 21 | #' @param las A LAS object (see lidR::LAS) 22 | #' @param k the k nearest neighbors to use for the eigenvalue calculation 23 | #' @param n_cores The number of CPU cores to use 24 | #' @return The function returns a single LAS object with the geometric features 25 | #' attached to it in the LAS@data section. 26 | #' @author Julian Frey 27 | #' @examples 28 | #' 29 | #' LASfile <- system.file("extdata", "MixedConifer.laz", package="lidR") 30 | #' las <- lidR::readLAS(LASfile, select = "xyz", filter = "-inside 481250 3812980 481300 3813030") 31 | #' 32 | #' las <- add_geometry(las, k = 5, n_cores = parallel::detectCores()-1) 33 | #' summary(las@data) 34 | #' 35 | #' 36 | #' @export add_geometry 37 | add_geometry <- function(las, k = 10L, n_cores = 1) { 38 | # check if inputs of the right type 39 | if (!lidR::is(las,"LAS")) { 40 | stop('las has to be a LAS object.') 41 | } 42 | if(!(as.integer(k) == k & length(k) == 1 & k > 0 )) { 43 | stop('k has to be one positive integer.') 44 | } 45 | # necessary for raster_geometry 46 | # returns geometric features based on eigenvalues 47 | eigen <- eigen_decomposition(las, k, n_cores) # k neighbours, n cores 48 | las <- las |> 49 | add_lasattribute(eigen[,3] / (eigen[,1] + eigen[,2] + eigen[, 3]), 'Curvature', 'curvature') |> 50 | add_lasattribute((eigen[,1] - eigen[,2]) / eigen[,1], 'Linearity', 'linearity') |> 51 | add_lasattribute((eigen[,2] - eigen[,3]) / eigen[,1], 'Planarity', 'planarity') |> 52 | add_lasattribute(eigen[,3] / eigen[,1], 'Sphericity', 'sphericity') |> 53 | add_lasattribute((eigen[,1] - eigen[,3]) / eigen[,1], 'Anisotropy', 'anisotropy') |> 54 | add_lasattribute(1 - abs(eigen[,4]) ,'Verticality','verticality') 55 | return(las) 56 | } 57 | 58 | # ------------------------------------------------------------------------------ 59 | 60 | #' helper function to voxelize a las element 61 | #' 62 | #' Calculate voxel mean values for all numeric attributes in the las@data table 63 | #' including the XYZ-coordinates. 64 | #' 65 | #' @param las a lidR::LAS element 66 | #' @param res voxel resolution in meter 67 | #' @return a las element with XYZ-coordinates as the voxel center and 68 | #' X_gr,Y_gr,Z_gr as the center of gravity (mean point coordinates) as well as 69 | #' all other numeric columns voxel mean values with their original name. 70 | #' @author Julian Frey 71 | #' @seealso \code{\link{voxelize_points}} 72 | #' @examples 73 | #' 74 | #' # read example data 75 | #' file = system.file("extdata", "beech.las", package="CspStandSegmentation") 76 | #' las = lidR::readTLSLAS(file) 77 | #' las |> voxelize_points_mean_attributes(1) |> lidR::plot(color = 'X_gr') 78 | #' 79 | #' @export voxelize_points_mean_attributes 80 | voxelize_points_mean_attributes <- function(las, res) { 81 | # check if inputs of the right type 82 | if (!lidR::is(las,"LAS")) { 83 | stop('las has to be a LAS object.') 84 | } 85 | if(!(is.numeric(res) & length(res) < 3 & res > 0 )) { 86 | stop('res has to be numeric and positive.') 87 | } 88 | 89 | # Checking resolution input validity 90 | if (length(res) == 1L) { 91 | res <- c(res, res) 92 | } else if (length(res) > 2L) { 93 | stop('Wrong resolution provided.') 94 | } 95 | 96 | # create voxel coordinates 97 | by <- lidR:::group_grid_3d(las@data$X, las@data$Y, las@data$Z, res, c(0, 0, 0.5*res[2])) 98 | 99 | # add mean attributes 100 | voxels <- las@data[,lapply(.SD, mean), by = by] 101 | if (length(names(las@data)) > 3) { 102 | data.table::setnames(voxels, c('X', 'Y', 'Z', 'X_gr', 'Y_gr', 'Z_gr', names(las@data)[4:length(names(las@data))])) 103 | } else { 104 | data.table::setnames(voxels, c('X', 'Y', 'Z', 'X_gr', 'Y_gr', 'Z_gr')) 105 | } 106 | 107 | # convert voxels to LAS object 108 | output <- LAS(voxels, header = las@header, crs = st_crs(las), check = FALSE, index = las@index) 109 | return(output) 110 | } 111 | 112 | # ------------------------------------------------------------------------------ 113 | 114 | #' Add voxel coordinates to a las file 115 | #' 116 | #' Adds the collums x_vox, y_vox and z_vox in the given ressolution to the las 117 | #' element. This is convenient if information has been derived in voxel space 118 | #' and these should be attached to the original points. 119 | #' 120 | #' Voxel coordinates derived with this function are identical to those derived 121 | #' by lidR::voxelize. 122 | #' 123 | #' @param las an element of lidR::LAS class 124 | #' @param res voxel ressolution in [m] 125 | #' @return las file with additional voxel coordinates 126 | #' @author Julian Frey 127 | #' @examples 128 | #' 129 | #' file = system.file("extdata", "beech.las", package="CspStandSegmentation") 130 | #' las = lidR::readTLSLAS(file) 131 | #' 132 | #' las <- add_voxel_coordinates(las,res = 1) 133 | #' 134 | #' lidR::plot(las, color = 'z_vox') 135 | #' 136 | #' @export add_voxel_coordinates 137 | add_voxel_coordinates <- function(las, res) { 138 | # check if inputs of the right type 139 | if (!lidR::is(las,"LAS")) { 140 | stop('las has to be a LAS object.') 141 | } 142 | if(!(is.numeric(res) & length(res) < 3 & res > 0 )) { 143 | stop('res has to be numeric and positive.') 144 | } 145 | 146 | # create voxel coordinates 147 | vox <- lidR:::group_grid_3d(las@data$X, las@data$Y, las@data$Z, c(res, res), c(0, 0, 0.5*res)) 148 | 149 | # add voxel coordinates to LAS 150 | las <- las |> 151 | add_lasattribute(vox[[1]], 'x_vox', 'x_vox') |> 152 | add_lasattribute(vox[[2]], 'y_vox', 'y_vox') |> 153 | add_lasattribute(vox[[3]], 'z_vox', 'z_vox') 154 | return(las) 155 | } 156 | 157 | # ------------------------------------------------------------------------------ 158 | 159 | #' Add all las_attributes from las@data to the header of a las element 160 | #' 161 | #' The helper function adds all headings from las@data which are not part of 162 | #' lidR:::LASATTRIBUTES to the las header using lidR::add_lasattribute. Only 163 | #' attributes that are included in the header got saved when using 164 | #' lidR::writeLAS, this is a convenient way to add them. 165 | #' 166 | #' @param las an element of lidR::LAS class 167 | #' @return the las file with updated header 168 | #' @author Julian Frey 169 | #' @examples 170 | #' 171 | #' file <- system.file("extdata", "beech.las", package="CspStandSegmentation") 172 | #' las <- lidR::readTLSLAS(file) 173 | #' 174 | #' las@data$noise <- runif(nrow(las@data)) 175 | #' las@data$noiseZ <- las@data$var1 * las@data$Z 176 | #' 177 | #' las <- add_las_attributes(las) 178 | #' 179 | #' @export add_las_attributes 180 | add_las_attributes <- function(las) { 181 | # check if inputs of the right type 182 | if (!lidR::is(las,"LAS")) { 183 | stop('las has to be a LAS object.') 184 | } 185 | 186 | # Add attributes from data.table permanently to attributes 187 | names <- names(las@data) 188 | names <- names[!(names %in% lidR:::LASATTRIBUTES)] 189 | for (name in names) { 190 | if (!with(las@data, is.numeric(get(name)))) { 191 | next 192 | } 193 | las <- las |> 194 | add_lasattribute(name = name, desc = name) 195 | } 196 | return(las) 197 | } 198 | 199 | # ------------------------------------------------------------------------------ 200 | 201 | # V_w, L_W, S_w are the weights for 1-verticality, sphericity, linearity 202 | 203 | #' helper function for csp_cost_segemntation 204 | #' 205 | #' The function performs a Dijkstra algorithm on a 3D voxel file to assign 206 | #' every voxel to the closest seed point using the igraph package. 207 | #' 208 | #' @param vox a LAS S4 element with XYZ voxel coordinates in the @data slot. 209 | #' @param adjacency_df a data.frame with voxel ids (row numbers) in the first 210 | #' column and a neighboring voxel ID in the second column and the weight 211 | #' (distance) in the third column. Might be generated using the dbscan::frNN 212 | #' function (which requires reshaping the data). 213 | #' @param seeds seed points for tree positions. 214 | #' @param v_w,l_w,s_w weights for verticality, linearity spericity see 215 | #' \code{\link{csp_cost_segmentation}} 216 | #' @param N_cores Number of CPU cores for multi-threading 217 | #' @param Voxel_size Edge length used to create the voxels. This is only 218 | #' important to gain comparable distance weights on different voxel sizes. 219 | #' Should be greater than 0. 220 | #' @return voxels with the TreeID in the data slot 221 | #' @author Julian Frey 222 | #' @seealso \code{\link{csp_cost_segmentation}} 223 | #' 224 | #' @export comparative_shortest_path 225 | comparative_shortest_path <- function(vox = vox, adjacency_df = adjacency_df, seeds, v_w = 0, l_w = 0, s_w = 0, N_cores = parallel::detectCores() - 1, Voxel_size) { 226 | 227 | # update weights 228 | adjacency_df$weight <- with(vox@data[adjacency_df$adjacency_list], adjacency_df$weight^2 + ((1 - Verticality) * v_w + Sphericity * s_w + Linearity * l_w) * Voxel_size) 229 | adjacency_df$weight[adjacency_df$weight < 0] <- 0.001 * Voxel_size # catch negative weights 230 | 231 | # set distances to seeds 0 232 | adjacency_df$weight[adjacency_df$adjacency_list_id %in% seeds$SeedID] <- 0 233 | 234 | #----------------------- 235 | # compute dijkstra matrix for each seed (trunk) 236 | # and weigh matrix by DBH^2/3 (Tao et al 2015.) 237 | #----------------------- 238 | 239 | # build graph 240 | vox_graph <- adjacency_df |> 241 | igraph::graph_from_data_frame(directed = F) |> 242 | igraph::simplify() 243 | 244 | # calculate a distance (weight) graph per seed using Dijkstra 245 | doParallel::registerDoParallel(cores = N_cores) 246 | dists_list <- foreach::foreach( 247 | t = 1:nrow(seeds), 248 | .noexport = c('las', 'map', 'vox', 'tree_seeds', 'ground', 'dtm', 'adjacency_df', 'inv'), 249 | .errorhandling = c('pass')) %dopar% { 250 | return(igraph::distances(vox_graph, as.character(seeds$SeedID[t]), algorithm = 'dijkstra')) 251 | } 252 | doParallel::stopImplicitCluster() 253 | 254 | unreachable <- which(sapply(dists_list,function(x) is.character(x[[1]]))) 255 | if(length(unreachable) > 0) { 256 | warning('Not all base positions could be reached by the graph. Try a lower resolution or a different approach to find tree base positions. Error messages for the unreachable TreeIDs:') 257 | warning(paste0(unreachable , paste(":",paste(dists_list[unreachable]), collapse = " "), "\n"), call. = F) 258 | seeds <- seeds[-unreachable,] 259 | dists_list <- dists_list[-unreachable] 260 | } 261 | 262 | # Combine to matrix 263 | dist_matrix <- simplify2array(dists_list)[1,,] 264 | 265 | # get seed with minimum distance 266 | min_matrix <- apply(dist_matrix, 1, which.min) 267 | min_dist_matrix <- suppressWarnings(apply(dist_matrix, 1, min, na.rm = T)) 268 | min_matrix <- data.table::data.table(PointID = as.integer(igraph::V(vox_graph)$name), TreeID = seeds$TreeID[as.integer(min_matrix)], dist = min_dist_matrix) 269 | min_matrix$TreeID[min_dist_matrix == Inf] <- 0 # set SeedIDs 0 for voxels which any seed can't reach 270 | 271 | # assign voxels to seeds (minimum cost/distance to trunk) 272 | vox <- vox |> 273 | remove_lasattribute('TreeID') |> 274 | add_attribute(as.integer(rownames(vox@data)), 'PointID') 275 | vox@data <- merge(vox@data, min_matrix, by = 'PointID') 276 | return(vox) 277 | } 278 | 279 | # ------------------------------------------------------------------------------ 280 | 281 | # This is the main function 282 | # It requires a las point cloud of a forest patch and 283 | # a forest inventory as it can be calculated by CspStandSegmentation::find_base_coordinates_raster 284 | # Preferable geometric features should be computed for the point cloud prior 285 | # to the use of this function using 'add_geometry()'. 286 | 287 | #' Comparative Shortest Path with cost weighting tree segmentation 288 | #' 289 | #' Segments single trees from forest point clouds based on tree positions 290 | #' (xyz-coordinates) provided in the map argument. 291 | #' 292 | #' The whole point cloud is voxelized in the given resolution and the center of 293 | #' gravity for the points inside is calculated as voxel coordinate. A graph is 294 | #' build, which connects the voxel coordinates based on a db-scan algorithm. The 295 | #' distances between the voxel coordinates is weighted based on geometric 296 | #' features computed for the points in the voxels. Distances along planar 297 | #' and/or vertical faces like stems are weighted shorter than distances through 298 | #' voxels with high sphericity like leaves and clusters of twigs. This 299 | #' avoids small individuals spreading into the upper canopy. 300 | #' For every voxel center, the weighted distance in the network is calculated to 301 | #' all tree locations from the map argument. The TreeID of the map argument 302 | #' with the shortest distance is assigned to the voxel. All points in the point 303 | #' cloud receive the TreeID from their parent voxel. 304 | #' 305 | #' @param las A lidR LAS S4 object. 306 | #' @param map A data.frame, including the columns 307 | #' X, Y, Z, TreeID, with X and Y depicting the location of the trees. Can be generated using CspStandSegmentation::find_base_coordinates_raster 308 | #' @param Voxel_size The voxel size (3D resolution) for the routing graph to 309 | #' determine the nearest map location for every point in the point cloud. 310 | #' @param V_w verticality weight. Since trunks are vertical structures, routing 311 | #' through voxels with high verticality can be rated 'cheaper.' should be a 312 | #' number between 0 and 1 with 0 meaning no benefit for more vertical 313 | #' structures. 314 | #' @param L_w Linearity weight. Similar to V_w but for linearity, higher 315 | #' values indicate a malus for linear shapes (usually branches). 316 | #' @param S_w Spericity weight. Similar to V_w but for sphericity, higher 317 | #' values indicate a malus for spherical shapes (usually small branches and 318 | #' leaves). 319 | #' @param N_cores number of CPU cores used for parallel routing using the 320 | #' foreach package. 321 | #' @return Returns a copy of the las point cloud with an additional field for 322 | #' the TreeID. 323 | #' @author Julian Frey 324 | #' @seealso \code{\link{comparative_shortest_path}} 325 | #' 326 | #' @examples 327 | #' 328 | #' # read example data 329 | #' file = system.file("extdata", "beech.las", package="CspStandSegmentation") 330 | #' las = lidR::readTLSLAS(file) 331 | #' 332 | #' # Find tree positions as starting points for segmentation 333 | #' map <- CspStandSegmentation::find_base_coordinates_raster(las) 334 | #' # segment trees 335 | #' segmented <- las |> 336 | #' CspStandSegmentation::add_geometry() |> 337 | #' CspStandSegmentation::csp_cost_segmentation(map, 1) 338 | #' 339 | #' lidR::plot(segmented, color = "TreeID") 340 | #' 341 | #' @export csp_cost_segmentation 342 | csp_cost_segmentation <- function(las, map, Voxel_size = 0.3, V_w = 0, L_w = 0, S_w = 0, N_cores = 1) { 343 | 344 | # if map is a LAS object, extract tree positions 345 | if (lidR::is(map,"LAS")) { 346 | # check if TreeID available 347 | if (!('TreeID' %in% names(map@data))) { 348 | stop('TreeID has to be a column in the map data.frame.') 349 | } 350 | inv <- map@data[map@data$TreePosition,] 351 | if (nrow(inv) == 0) { 352 | inv <- aggregate(map@data[map@data$Z > 1 & map@data$Z < 1.5,], by = list(map@data$TreeID[map@data$Z > 1 & map@data$Z < 1.5]), median) 353 | } 354 | } 355 | # check if inputs of the right type 356 | if (!lidR::is(las,"LAS")) { 357 | stop('las has to be a LAS object.') 358 | } 359 | if (!(is.data.frame(map) & all(c('X', 'Y', 'Z', 'TreeID') %in% names(map)))) { 360 | stop('map has to be a data.frame with collumn names X,Y,Z,TreeID.') 361 | } 362 | if(!all(is.numeric(c(Voxel_size, V_w, L_w, S_w, N_cores)))) { 363 | stop('Voxel_size, V_w, L_w, S_w and N_cores have to be numeric.') 364 | } 365 | 366 | if (Voxel_size <= 0) { 367 | stop('Voxel_size has to be greater than 0.') 368 | } 369 | if(lidR::is.empty(las)) { 370 | stop('No points in the point cloud.') 371 | } 372 | if(nrow(map) == 0) { 373 | stop('No tree positions in the map.') 374 | } 375 | 376 | # check if TreeID already exists 377 | if ('TreeID' %in% names(las@data)) { 378 | warning("'las' already contains TreeIDs', which will be overwritten.") 379 | las <- las |> 380 | lidR::remove_lasattribute('TreeID') 381 | } 382 | # Check if geometric features exist in las and compute dummies if not 383 | if(!all(c('Sphericity', 'Linearity', 'Verticality') %in% names(las@data))) { 384 | warning("no geometric features in las. V_w, L_w and/or S_w weights will be ignored.Use 'las <- las |> add_gemetry()' prior to calling this function.") 385 | las <- las |> 386 | lidR::add_lasattribute(0, 'Sphericity', 'Sphericity') |> 387 | lidR::add_lasattribute(0, 'Linearity', 'Linearity') |> 388 | lidR::add_lasattribute(0, 'Verticality', 'Verticality') 389 | } 390 | 391 | # Voxelize las with mean attributes 392 | vox <- voxelize_points_mean_attributes(las, res = Voxel_size) 393 | 394 | inv <- map 395 | 396 | # Add seeds 397 | vox <- vox |> 398 | lidR::add_lasattribute(0, 'TreeID', 'TreeID') 399 | vox@data <- vox@data[,c('X', 'Y', 'Z', 'X_gr', 'Y_gr', 'Z_gr', 'Sphericity', 'Linearity', 'Verticality', 'TreeID')] 400 | 401 | # Lift the starting points if map doesn't have Z values 402 | if (sum(inv$Z) == 0) { 403 | inv$Z <- 0.5 404 | } 405 | if(las@header$`Min Z` > max(inv$Z)) {stop('The minimum Z value of the point cloud is higher than the tree positions. Is the inventory without Z values and the point clound not normalized?')} 406 | 407 | inv <- inv |> 408 | cbind(X_gr = inv$X) |> 409 | cbind(Y_gr = inv$Y) |> 410 | cbind(Z_gr = inv$Z) |> 411 | cbind(Sphericity = 0) |> 412 | cbind(Linearity = 0) |> 413 | cbind(Verticality = 0) 414 | vox@data <- rbind(vox@data, inv[,c('X', 'Y', 'Z', 'X_gr', 'Y_gr', 'Z_gr', 'Sphericity', 'Linearity', 'Verticality', 'TreeID')]) 415 | 416 | # Seed positions 417 | seed_range <- (nrow(vox@data) - nrow(inv) + 1):nrow(vox@data) 418 | tree_seeds <- data.frame(SeedID = seed_range, TreeID = vox@data$TreeID[seed_range]) 419 | rm(seed_range) 420 | 421 | # Use dbscan to calculate a matrix of neighboring points 422 | neighborhood_list <- dbscan::frNN(vox@data[,c('X_gr', 'Y_gr', 'Z_gr')], Voxel_size * 2, bucketSize = 22) 423 | # dbscan::frNN(vox@data[,c('X_gr', 'Y_gr', 'Z_gr')], Voxel_size * 2, bucketSize = 22) # voxel size * 1.42 (sqrt(1^2 + 1^2)) 1.73 424 | 425 | # The result has to be disentangled we get the adjacent voxel IDs first 426 | adjacency_list <- unlist(neighborhood_list$id) 427 | 428 | # Then we grab the origin voxel using cpp 429 | adjacency_list_id <- fast_unlist(neighborhood_list$id, length(adjacency_list)) + 1 # +1 because of cpp counting 430 | 431 | # We do the same with the distances 432 | dists_vec <- fast_unlist_dist(neighborhood_list$dist, length(adjacency_list)) 433 | 434 | # Compile to a data frame 435 | adjacency_df <- data.frame(adjacency_list_id,adjacency_list, weight = dists_vec) #, TreeID = vox@data$TreeID[adjacency_list_id] 436 | rm(adjacency_list, adjacency_list_id, dists_vec, neighborhood_list) 437 | 438 | # Calculate CSP, including the weights 439 | vox2 <- comparative_shortest_path(vox = vox, adjacency_df = adjacency_df, v_w = V_w, l_w = L_w, s_w = S_w, Voxel_size = Voxel_size, N_cores = N_cores, seeds = tree_seeds) 440 | 441 | las <- las |> 442 | add_voxel_coordinates(Voxel_size) 443 | las@data <- merge(las@data, vox2@data[,c('X', 'Y', 'Z', 'TreeID')], by.x = c('x_vox', 'y_vox', 'z_vox'), by.y = c('X', 'Y', 'Z')) 444 | las <- add_las_attributes(las) 445 | return(las) 446 | } 447 | 448 | # ------------------------------------------------------------------------------ 449 | 450 | # Function to calculate tree start points based on a raster density approach 451 | #' 452 | #' Find stem base position using a density raster approach 453 | #' @param las an element of lidR::LAS class 454 | #' @param zmin lower search boundary 455 | #' @param zmax upper search boundary 456 | #' @param q quantile of raster density to assign a tree region 457 | #' @param eps search radius to merge base points 458 | #' @param res raster resolution 459 | #' @return data.frame with X, Y, Z and TreeID for stem base positions 460 | #' @author Julian Frey 461 | #' @examples 462 | #' # read example data 463 | #' file = system.file("extdata", "beech.las", package="CspStandSegmentation") 464 | #' tls = lidR::readTLSLAS(file) 465 | #' 466 | #' # Find tree positions 467 | #' map <- CspStandSegmentation::find_base_coordinates_raster(tls) 468 | #' @export find_base_coordinates_raster 469 | find_base_coordinates_raster <- function(las, res = 0.1, zmin = 0.5, zmax = 2, q = 0.975, eps = 0.2){ 470 | # check if inputs of the right type 471 | if (!lidR::is(las,"LAS")) { 472 | stop('las has to be a LAS object.') 473 | } 474 | if(!all(is.numeric(c(zmin, zmax, res, q, eps)))) { 475 | stop('zmin, zmax, res, q and eps have to be numeric.') 476 | } 477 | normalized <- T 478 | if (!('Zref' %in% names(las@data))) { 479 | normalized <- F 480 | las <- las |> 481 | lidR::classify_ground(lidR::csf(class_threshold = 0.05, cloth_resolution = 0.05), last_returns = F) 482 | dtm <- lidR::rasterize_terrain(las, 0.5, lidR::tin()) 483 | las <- las |> lidR::normalize_height(lidR::tin(), dtm = dtm) 484 | } 485 | slice <- las |> lidR::filter_poi(Z > zmin & Z < zmax) 486 | density <- lidR::pixel_metrics(slice, length(Z), res = res) 487 | height <- lidR::pixel_metrics(slice, mean(Z), res = res) 488 | q_dens <- quantile(terra::values(density), probs = q, na.rm = T) 489 | seed_rast <- terra::as.points(density > q_dens) 490 | seed_rast <- as.data.frame(terra::subset(seed_rast, seed_rast$V1 == 1), geom = "XY") 491 | seed_rast <- cbind(seed_rast, data.frame(cluster = dbscan::dbscan(seed_rast[,c("x", "y")], eps = eps, minPts = 1)$cluster)) 492 | seed_rast <- aggregate(seed_rast, by = list(seed_rast$cluster), mean)[, 3:5] 493 | if(normalized){ 494 | z_vals <- terra::extract(height, seed_rast[,1:2]) 495 | if(any(is.na(z_vals))) z_vals[is.na(z_vals)] <- mean(c(zmin, zmax)) # catch NA's 496 | } else { 497 | z_vals <- terra::extract(dtm, seed_rast[,1:2])[,2] 498 | if(any(is.na(z_vals))) z_vals[is.na(z_vals)] <- mean(terra::values(dtm)) # catch NA's 499 | } 500 | 501 | seed_rast <- cbind(seed_rast, z_vals)[,c(1,2,4,3)] 502 | colnames(seed_rast) <- c('X','Y','Z','TreeID') 503 | return(seed_rast) 504 | } 505 | 506 | # ------------------------------------------------------------------------------ 507 | 508 | # own function to calculate tree start points 509 | 510 | #' Find stem base position using a geometric feature filtering and clustering 511 | #' approach 512 | #' @param las an element of lidR::LAS class 513 | #' @param zmin lower search boundary 514 | #' @param zmax upper search boundary 515 | #' @param res cluster search radius 516 | #' @param min_verticality minimum verticality >0 & <1 for a point to be 517 | #' considered a stem point 518 | #' @param min_planarity minimum planarity >0 & <1 for a point to be considered 519 | #' a stem point 520 | #' @param min_cluster_size minimum number of points in the cluster to be considered 521 | #' a tree, if NULL median cluster size is chosen 522 | #' @return data.frame with X, Y, Z and TreeID for stem base positions 523 | #' @author Julian Frey 524 | #' @examples 525 | #' # read example data 526 | #' file = system.file("extdata", "beech.las", package="CspStandSegmentation") 527 | #' tls = lidR::readTLSLAS(file) 528 | #' 529 | #' # Find tree positions 530 | #' map <- CspStandSegmentation::find_base_coordinates_geom(tls) 531 | #' @export find_base_coordinates_geom 532 | find_base_coordinates_geom <- function(las, zmin = 0.5, zmax = 2, res = 0.5, min_verticality = 0.9, min_planarity = 0.5, min_cluster_size = NULL) { 533 | # check if inputs of the right type 534 | if (!lidR::is(las,"LAS")) { 535 | stop('las has to be a LAS object.') 536 | } 537 | if(!all(is.numeric(c(zmin, zmax, res, min_verticality, min_planarity)))) { 538 | stop('zmin, zmax, res, min_verticality and min_planarity have to be numeric.') 539 | } 540 | Zref <- T # flag if a normalized point cloud was given 541 | if (!('Zref' %in% names(las@data))) { 542 | las <- las |> 543 | lidR::classify_ground(lidR::csf(class_threshold = 0.05, cloth_resolution = 0.05), last_returns = F) 544 | dtm <- lidR::rasterize_terrain(las, 0.5, lidR::tin()) 545 | las <- las |> lidR::normalize_height(lidR::tin(), dtm = dtm) 546 | Zref <- F 547 | } 548 | 549 | slice <- las |> 550 | filter_poi(Classification != 2 & Z > zmin & Z < zmax) 551 | if (lidR::is.empty(slice)) { 552 | stop('No points found in the specified zmin/xmax range.') 553 | } 554 | 555 | if(!Zref) { 556 | slice <- slice |> 557 | lidR::unnormalize_height() 558 | } 559 | 560 | slice <- slice |> 561 | add_geometry() |> 562 | filter_poi(Planarity > min_planarity & Verticality > min_verticality) 563 | if (lidR::is.empty(slice)) { 564 | stop('No points found in the specified planarity/verticality range. Try lower parameters (> 0 & < 1)') 565 | } 566 | 567 | if (is.null(min_cluster_size)) { 568 | cluster <- slice@data[,1:3] |> 569 | dbscan::dbscan(res, 1) 570 | slice@data$Cluster <- cluster$cluster 571 | slice <- slice |> 572 | filter_poi(Cluster %in% unique(cluster$cluster)[table(cluster$cluster) > median(table(cluster$cluster))]) 573 | } else { 574 | cluster <- slice@data[,1:3] |> 575 | dbscan::dbscan(res, 1) 576 | slice@data$Cluster <- cluster$cluster 577 | slice <- slice |> 578 | filter_poi(Cluster %in% unique(cluster$cluster)[table(cluster$cluster) > min_cluster_size]) 579 | } 580 | map <- aggregate(slice@data[,1:2], by = list(slice@data$Cluster), mean) 581 | Z <- aggregate(slice@data[,3], by = list(slice@data$Cluster), min) 582 | 583 | map <- data.frame(map[,2:3], Z = Z[,2], TreeID = 1:nrow(map)) 584 | } 585 | -------------------------------------------------------------------------------- /R/2024-06-10_A1_JF_farthest_point_sampling.R: -------------------------------------------------------------------------------- 1 | #' Point distance function 2 | #' 3 | #' calculates euclidean distances for n dimensions 4 | #' 5 | #' @param p1 point 1 6 | #' @param p2 point 2 7 | #' 8 | #' @return the distance between the two points 9 | #' @export p_dist 10 | #' 11 | #' @examples 12 | #' p_dist(c(0,0), c(3,4)) 13 | p_dist <- function(p1, p2){ 14 | if(length(p1) != length(p2)){ 15 | stop("p1 and p2 must have the same length") 16 | } 17 | sqrt(sum((p1 - p2)^2)) 18 | } 19 | 20 | #' Point distance function 21 | #' 22 | #' calculates euclidean distances for n dimensions between a matrix of points and a single point 23 | #' 24 | #' @param mat matrix with points as rows 25 | #' @param p point to calculate distances 26 | #' 27 | #' @return the distances between every row of mat and p 28 | #' @export p_mat_dist 29 | #' 30 | #' @examples 31 | #' p_dist(as.matrix(cbind(runf(100),runf(100)), c(3,4)) 32 | p_mat_dist <- function(mat, p){ 33 | mat2 <- mat 34 | for(c in 1:ncol(mat)){ 35 | mat2[,c] <- (mat[,c] - p[c])^2 36 | } 37 | return(sqrt(.rowSums(mat2, nrow(mat2), ncol(mat2)))) 38 | } 39 | 40 | #' Farthest Distance Sampling (Farthest Point Sampling) 41 | #' 42 | #' This function selects n points from a matrix of points such that the minimum distance between any two points is maximized. 43 | #' This version is memory efficient and can handle large matrices. 44 | #' 45 | #' @param mat a matrix of points with one row for each point and one column for each dimension, can also be a las object then only XYZ will be used 46 | #' @param n the number of points to select, or if <1 the proportion of points to select 47 | #' @param ret the type of output to return. Options are "idx" (default) to return the indices of the selected points, "mat" to return the selected points. 48 | #' @param scale logical. If TRUE, the dimensions are scaled to have a mean of 0 and a standard deviation of 1 before calculating distances. 49 | #' 50 | #' @return a vector of indices or a matrix of points 51 | #' @export fds 52 | #' 53 | #' @examples 54 | #' mat <- matrix(rnorm(1000), ncol = 10) 55 | #' sample <- fds(mat, 50, ret = "mat") 56 | #' \dontrun{ 57 | #' plot(mat, col = "black", pch = 19) 58 | #' points(sample, col = "red", pch = 19) 59 | #' } 60 | fds <- function(mat, n, ret = "idx", scale = F){ 61 | # check the inputs 62 | was_las <- FALSE 63 | if(!is.matrix(mat)){ 64 | if(class(mat) == "LAS"){ 65 | was_las <- TRUE 66 | las <- mat 67 | mat <- as.matrix(las@data[,c("X", "Y", "Z")]) 68 | } else { 69 | stop("mat must be a matrix or a LAS object") 70 | } 71 | } 72 | 73 | if(ret != "idx" & ret != "mat"){ 74 | stop("ret must be 'idx' or 'mat'") 75 | } 76 | if(n >= nrow(mat)){ 77 | warning("n is greater or equal than the number of points in mat. Returning all points.") 78 | if(ret == "mat"){ 79 | if(was_las){ 80 | return(las) 81 | } 82 | return(mat) 83 | } 84 | return(1:nrow(mat)) 85 | } 86 | if(n == 1){ 87 | if(was_las){ 88 | return(las[sample(1:nrow(mat), 1),]) 89 | } 90 | if(ret == "mat"){ 91 | return(mat[sample(1:nrow(mat), 1),]) 92 | } else { 93 | return(sample(1:nrow(mat), 1)) 94 | } 95 | } 96 | if(n < 0){ 97 | stop("n must be greater than 0.") 98 | } 99 | if(n < 1){ 100 | n <- round(nrow(mat) * n) 101 | } 102 | 103 | # scale dimensions if requested 104 | if(scale){ 105 | for(c in 1:ncol(mat)){ 106 | mat[,c] <- scale(mat[,c]) 107 | } 108 | } 109 | 110 | # select the first point randomly 111 | idx <- which.max(mat[,1]) 112 | 113 | # calculate a vector of distances from the first point 114 | dists <- p_mat_dist(mat, mat[idx,]) 115 | 116 | # select all further points in a loop 117 | for(i in 2:n){ 118 | # select the next point 119 | idx <- c(idx, which.max(dists)) 120 | # calculate the distances from the new point 121 | dists2 <- p_mat_dist(mat, mat[idx[i],]) 122 | dists <- pmin(dists, dists2) 123 | } 124 | 125 | # return the selected points 126 | if(ret == "mat"){ 127 | if(was_las){ 128 | las@data <- las@data[idx,] 129 | return(las) 130 | } else { 131 | return(mat[idx,]) 132 | } 133 | } else { 134 | return(idx) 135 | } 136 | } 137 | -------------------------------------------------------------------------------- /R/2025-01-07_A1_JF_merge_las_objects.R: -------------------------------------------------------------------------------- 1 | #' Makes one las object from multiple las objects 2 | #' 3 | #' This function merges multiple las objects into one las object. The function checks if all inputs are las objects and if they have the same CRS. The function will also add a column oci with the original cloud index to each las object. The function will then rbind all data by the minimum set of columns. If the fill argument is set to False, columns which do not exist in all las objects will be removed. If the fill argument is set to True, missing columns will be filled with NA. 4 | #' 5 | #' 6 | #' @param ... any number of las objects 7 | #' @param oci add a column with the original cloud index 8 | #' @param fill fill missing columns with NA if it is set to False collumns which do not exist in all las objects will be removed 9 | #' 10 | #' @returns A single las object 11 | #' 12 | #' @examples 13 | #' las1 <- lidR::LAS(data.frame(X = runif(100), Y = runif(100), Z = runif(100))) 14 | #' las2 <- lidR::LAS(data.frame(X = runif(100) + 2, Y = runif(100), Z = runif(100))) 15 | #' las3 <- lidR::LAS(data.frame(X = runif(100) + 4, Y = runif(100), Z = runif(100))) 16 | #' las_merge(las1, las2, las3) 17 | #' # las_merge(las1, las2, las3) |> lidR::plot(color = "oci") 18 | las_merge <- function(..., oci = TRUE, fill = FALSE){ 19 | is_las <- function(x) lidR::is(x, "LAS") 20 | 21 | # check if only las objects are passed 22 | if(!all(sapply(list(...), is_las))) stop("All inputs must be LAS objects") 23 | 24 | # check if the crs is the same 25 | crs <- sapply(list(...), function(x) lidR::crs(x)) 26 | projargs <- sapply(crs, function(x) x@projargs) 27 | if(any(is.na(projargs))) warning("Some inputs do not have a CRS") 28 | if(!all(sapply(crs, terra::same.crs,y = crs[[1]])) & !all(is.na(projargs))) stop("All inputs must have the same CRS") 29 | 30 | # put all objects in a list 31 | las_list <- list(...) 32 | # add original cloud index to each object 33 | if(oci){ 34 | for(i in 1:length(las_list)){ 35 | las_list[[i]] <- las_list[[i]] |> lidR::add_lasattribute(i, "oci", "original cloud index") 36 | } 37 | } 38 | 39 | # rbind all data by the minimum set of columns 40 | las_1 <- las_list[[1]] 41 | if(fill){ 42 | las_1@data <- data.table::rbindlist(lapply(las_list, function(x) x@data), fill = T) 43 | } else { 44 | # build a minimum set of columns 45 | cols <- lapply(las_list, function(x) colnames(x@data)) 46 | # find the intersection of columns 47 | cols <- Reduce(intersect, cols) 48 | las_1@data <- do.call(rbind, lapply(las_list, function(x) x@data[,..cols])) 49 | } 50 | 51 | # quantize the data 52 | las_1 <- las_1 |> lidR::las_quantize() |> lidR::las_update() 53 | 54 | # remove extra byte arguments from header which do not exist any longer 55 | exbytes_header <- names(las_1@header@VLR$Extra_Bytes$`Extra Bytes Description`) 56 | exbytes_data <- names(las_1@data) 57 | exbytes <- setdiff(exbytes_header, exbytes_data) 58 | if(length(exbytes) > 0){ 59 | for(i in exbytes){ 60 | las_1@header@VLR$Extra_Bytes$`Extra Bytes Description`[[i]] <- NULL 61 | } 62 | } 63 | 64 | return(las_1) 65 | } 66 | -------------------------------------------------------------------------------- /R/2025-01-10_A1_JF_forest_inventory.R: -------------------------------------------------------------------------------- 1 | ########### 2 | # functions 3 | ########### 4 | 5 | #' Helper function to compute distances from a point to the circle 6 | #' @param point numeric vector of length 2 c(X,Y) 7 | #' @param circle numeric vector of length 3 c(center_X, center_Y, radius) 8 | #' @return numeric distance from the point to the circle 9 | point_circle_distance <- function(point, circle) { 10 | return(abs(sqrt(sum((point - circle[1:2])^2)) - circle[3])) 11 | } 12 | 13 | #' Returns the angle between the center of the circle and a point in degrees 14 | #' @param point numeric vector of length 2 c(X,Y) 15 | #' @param circle numeric vector of length 3 c(center_X, center_Y, radius) 16 | #' @return numeric angle in degrees 17 | point_center_angle <- function(point, circle){ 18 | ang <- atan2(point[2] - circle[2], point[1] - circle[1]) 19 | return(ang * 180 / pi) 20 | } 21 | 22 | #' Suppress only the cat() output 23 | #' @param f function to be called 24 | #' @return the return value of the function 25 | suppress_cat <- function(f, ...) { 26 | null_device <- if (.Platform$OS.type == "windows") "nul" else "/dev/null" 27 | con <- file(null_device, "w") # Open connection to null device 28 | sink(con) # Redirect output to null device 29 | on.exit({ # Ensure cleanup 30 | sink() 31 | close(con) 32 | }) 33 | f(...) # Call the function and capture its return value 34 | } 35 | 36 | #' RANSAC circle fitting algorithm specially adapted for tree DBH estimation 37 | #' 38 | #' This function fits a circle to a set of points using the RANSAC algorithm it maximizes the points that are in the circle and the number of filled 36 degree angle segments 39 | #' Therefore, this function searches for the most complete circle with the highest number of points represented. 40 | #' 41 | #' @param data numeric matrix with 2 columns (X, Y) representing the point cloud 42 | #' @param n_iterations integer maximum number of iterations 43 | #' @param distance_threshold numeric maximum distance from a point to the circle to be considered an inlier 44 | #' @param min_inliers integer minimum number of inliers to consider the circle as valid 45 | ransac_circle_fit <- function(data, n_iterations = 1000, distance_threshold = 0.01, min_inliers = 3) { 46 | # catch if less than 3 points are given 47 | if (nrow(data) < 3) { 48 | return(list(circle = c(mean(data[,1]), mean(data[,2]), NA), inliers = NA, angle_segs = NA, n_iter = 0)) 49 | } 50 | 51 | # Initialize best circle 52 | best_circle <- NULL 53 | best_inliers <- 0 54 | best_angle_segs <- 0 55 | n_pts_sample <- min(c(5, nrow(data))) 56 | 57 | # calculate point densities in neighbourhood 58 | p_densities <- dbscan::pointdensity(data, eps = 0.05) 59 | 60 | 61 | for (i in 1:n_iterations) { 62 | # Randomly sample 3 points 63 | sample_points <- data[sample(1:nrow(data), n_pts_sample, prob = p_densities + 1), ] |> as.matrix() 64 | 65 | # Compute circle parameters 66 | tryCatch({ 67 | circle <- suppress_cat(conicfit::CircleFitByPratt, sample_points) 68 | 69 | # Count inliers 70 | distances <- apply(data, 1, point_circle_distance, circle = circle) 71 | inliers <- sum(distances < distance_threshold) 72 | angle_segs <- length(unique(floor(apply(data, 1, point_center_angle, circle = circle)/10))) 73 | 74 | # # Update best circle if more inliers are found 75 | # if (inliers > best_inliers && inliers >= min_inliers) { 76 | # best_circle <- list(circle = circle, inliers = inliers) 77 | # best_inliers <- inliers 78 | # } 79 | 80 | # Update best circle if it has more angle segments and/or inliers 81 | if (angle_segs >= best_angle_segs && inliers >= best_inliers) { 82 | best_circle <- list(circle = circle, inliers = inliers, angle_segs = angle_segs, n_iter = i) 83 | best_angle_segs <- angle_segs 84 | best_inliers <- inliers 85 | } 86 | 87 | if((best_angle_segs == nrow(data) | best_angle_segs >= 30) ){ #& inliers == nrow(data) 88 | break 89 | } 90 | 91 | }, 92 | warnings = function(w){}, 93 | error = function(e) {}) |> suppressWarnings() 94 | } 95 | best_circle$n_iter <- i 96 | return(best_circle) 97 | } 98 | 99 | #' Function to perform a forest inventory based on a segmented las object (needs to contain TreeID) 100 | #' 101 | #' This function estimates a taper curve for evry tree and returns the DBH at 1.3m, its position in XY coordinates, the tree height and the trees 2D projection area. 102 | #' 103 | #' @param las lidR las object with the segmented trees 104 | #' @param slice_min the minimum height of a slice for stems to estimate the taper curve 105 | #' @param slice_max the maximum height of a slice for stems to estimate the taper curve 106 | #' @param increment the increment of the slices 107 | #' @param width the width of the slices 108 | #' @param max_dbh the maximum DBH allowed 109 | #' @param n_cores number of cores to use 110 | #' 111 | #' @returns a data.frame with the TreeID, X, Y, DBH, quality_flag, Height and ConvexHullArea 112 | #' 113 | #' @examples 114 | #' # read example data 115 | #' file = system.file("extdata", "beech.las", package="CspStandSegmentation") 116 | #' tls = lidR::readTLSLAS(file) 117 | #' 118 | #' # find tree positions as starting points for segmentation 119 | #' map <- CspStandSegmentation::find_base_coordinates_raster(tls) 120 | #' 121 | #' # segment trees 122 | #' segmented <- tls |> 123 | #' CspStandSegmentation::add_geometry(n_cores = parallel::detectCores()/2) |> 124 | #' CspStandSegmentation::csp_cost_segmentation(map, 1, N_cores = parallel::detectCores()/2) 125 | #' 126 | #' # show results 127 | #' \dontrun{lidR::plot(segmented, color = "TreeID")} 128 | #' 129 | #' # perform inventory 130 | #' inventory <- CspStandSegmentation::forest_inventory(segmented) 131 | forest_inventory <- function (tls, slice_min = 0.3, slice_max = 4, increment = 0.2, width = 0.1, max_dbh = 1, n_cores = max(c(1, parallel::detectCores()/2 - 1))) { 132 | if (!"TreeID" %in% names(tls)) { 133 | stop("The las object does not contain a TreeID attribute") 134 | } 135 | tls <- lidR::filter_poi(tls, !is.na(TreeID) & TreeID > 0) 136 | if ("Zref" %in% names(tls)) { 137 | dbh_slice <- lidR::filter_poi(tls, Z > slice_min & Z < 138 | slice_max) 139 | } else if ("Znorm" %in% names(tls)) { 140 | dbh_slice <- lidR::filter_poi(tls, Znorm > slice_min & 141 | Znorm < slice_max) 142 | } else { 143 | tls_norm <- lidR::normalize_height(lidR::classify_ground(tls, 144 | lidR::csf()), lidR::tin()) 145 | tls <- lidR::add_lasattribute(tls, tls_norm$Z, "Znorm", 146 | "Z normalized") 147 | dbh_slice <- lidR::filter_poi(tls, Znorm > slice_min & 148 | Znorm < slice_max) 149 | } 150 | seq <- seq(slice_min, slice_max, by = increment) 151 | seq <- data.frame(id = 1:length(seq), Zmin = seq - width * 152 | 0.5, Zmax = seq + width * 0.5) 153 | na2true <- function(x) ifelse(is.na(x) | is.infinite(x), 154 | TRUE, x) 155 | points_per_stem <- aggregate(dbh_slice$TreeID, by = list(dbh_slice$TreeID), 156 | FUN = length) 157 | t1 <- Sys.time() 158 | IDs <- points_per_stem$Group.1[points_per_stem$x > 3] 159 | las_list <- list() 160 | for(i in 1:length(IDs)){ 161 | las_list[[i]] <- lidR::filter_poi(tls, TreeID == IDs[i]) 162 | } 163 | print("Fit a DBH value to every tree:") 164 | require(foreach) 165 | cl = parallel::makeCluster(n_cores) 166 | doParallel::registerDoParallel(cl) 167 | dbh_results <- foreach::foreach(tree = las_list, .combine = rbind, .errorhandling = "remove") %dopar% 168 | { 169 | t <- tree@data$TreeID[1] 170 | if (nrow(tree) < 3) { 171 | dbh <- NA 172 | return(data.frame(TreeID = t, X = mean(tree$X), 173 | Y = mean(tree$Y), DBH = dbh, quality_flag = 1)) 174 | warning(paste("to little points in tree", 175 | t)) 176 | } 177 | seq <- seq(slice_min, slice_max, by = increment) 178 | seq <- data.frame(id = 1:length(seq), Zmin = seq - 179 | width * 0.5, Zmax = seq + width * 0.5) 180 | t_seq <- cbind(seq, X = NA, Y = NA, r = NA) 181 | rs <- numeric() 182 | xs <- numeric() 183 | ys <- numeric() 184 | for (s in seq$id) { 185 | slice <- lidR::filter_poi(tree, Znorm > t_seq$Zmin[s] & 186 | Znorm < t_seq$Zmax[s]) 187 | if (nrow(slice) < 3) { 188 | rs <- c(rs, NA) 189 | xs <- c(xs, NA) 190 | ys <- c(ys, NA) 191 | next 192 | } 193 | else if (nrow(slice) < 100) { 194 | planes <- slice 195 | } 196 | else { 197 | slice <- CspStandSegmentation::add_geometry(slice) 198 | planes <- lidR::filter_poi(slice, Planarity > 199 | quantile(Planarity, 0.95) & Verticality > 200 | quantile(Verticality, 0.95)) 201 | q <- 0.95 202 | while (nrow(planes) < 100) { 203 | q <- q - 0.05 204 | planes <- lidR::filter_poi(slice, Planarity > 205 | quantile(Planarity, q) & Verticality > 206 | quantile(Verticality, q)) 207 | if (q < 0.2) { 208 | planes <- slice 209 | break 210 | } 211 | } 212 | } 213 | planes <- slice 214 | circle <- CspStandSegmentation::ransac_circle_fit(planes@data[, c("X", 215 | "Y")], n_iterations = 500) 216 | rs <- c(rs, circle$circle[3]) 217 | xs <- c(xs, circle$circle[1]) 218 | ys <- c(ys, circle$circle[2]) 219 | } 220 | t_seq$r <- rs 221 | t_seq$X <- xs 222 | t_seq$Y <- ys 223 | t_seq$r[abs(t_seq$r - median(t_seq$r, na.rm = TRUE)) > 224 | 2 * sd(t_seq$r, na.rm = TRUE)] <- NA 225 | for (s in 2:nrow(t_seq)) { 226 | if (na2true(t_seq$r[s] > suppressWarnings(min(t_seq$r[1:(s - 227 | 1)], na.rm = TRUE) * 1.3)) | na2true(t_seq$r[s] < 228 | 0.04) | all(is.na(t_seq$r[1:s]))) { 229 | t_seq[s, c("X", "Y", "r")] <- NA 230 | } 231 | } 232 | if ((sum(!is.na(t_seq$r)) <= 3) | (sum(!is.na(t_seq$X)) <= 233 | 3) | (sum(!is.na(t_seq$Y)) <= 3)) { 234 | dbh <- mean(t_seq$r, na.rm = TRUE) * 2 235 | if (is.na(dbh) | dbh > max_dbh | dbh < 0) { 236 | return(data.frame(TreeID = t, X = mean(tree$X), 237 | Y = mean(tree$Y), DBH = NA, quality_flag = 2)) 238 | } 239 | else { 240 | return(data.frame(TreeID = t, X = mean(tree$X), 241 | Y = mean(tree$Y), DBH = dbh, quality_flag = 2)) 242 | } 243 | } 244 | spline_r <- suppressWarnings(with(t_seq[!is.na(t_seq$r), 245 | ], smooth.spline(Zmin, r, df = 3))) 246 | spline_x <- suppressWarnings(with(t_seq[!is.na(t_seq$X), 247 | ], smooth.spline(Zmin, X, df = 20))) 248 | spline_y <- suppressWarnings(with(t_seq[!is.na(t_seq$Y), 249 | ], smooth.spline(Zmin, Y, df = 20))) 250 | r <- as.numeric(predict(spline_r, 1.3)$y) 251 | x <- as.numeric(predict(spline_x, 1.3)$y) 252 | y <- as.numeric(predict(spline_y, 1.3)$y) 253 | dbh <- r * 2 254 | if (is.na(dbh) | dbh > max_dbh | dbh < 0) { 255 | return(data.frame(TreeID = t, X = mean(tree$X), 256 | Y = mean(tree$Y), DBH = NA, quality_flag = 3)) 257 | } 258 | else { 259 | return(data.frame(TreeID = t, X = x, Y = y, DBH = dbh, 260 | quality_flag = 4)) 261 | } 262 | } 263 | parallel::stopCluster(cl) 264 | dbh_slice <- lidR::add_attribute(dbh_slice, dbh_slice@data$Z - 265 | dbh_slice@data$Znorm, "Zdiff") 266 | tree_pos_height <- aggregate(dbh_slice$Zdiff, by = list(dbh_slice$TreeID), 267 | FUN = mean) 268 | names(tree_pos_height) <- c("TreeID", "Z") 269 | heights <- aggregate(tls@data$Z, by = list(tls@data$TreeID), 270 | FUN = function(x) max(x) - min(x)) 271 | names(heights) <- c("TreeID", "Height") 272 | print("Tree heights calculated.") 273 | convhull_area <- function(xy) { 274 | xy <- as.data.frame(xy) 275 | if (nrow(xy) < 3) { 276 | return(NA) 277 | } 278 | ch <- chull(xy) 279 | return(abs(0.5 * sum(xy[ch, 1] * c(tail(xy[ch, 2], -1), 280 | head(xy[ch, 2], 1)) - c(tail(xy[ch, 1], -1), head(xy[ch, 281 | 1], 1)) * xy[ch, 2]))) 282 | } 283 | print("Calculating convex hull areas.") 284 | pb = txtProgressBar(min = 0, max = length(IDs), initial = 0, 285 | style = 3) 286 | i <- 1 287 | cpa <- data.frame(TreeID = numeric(), ConvexHullArea = numeric()) 288 | for (t in IDs) { 289 | tree <- lidR::filter_poi(tls, TreeID == t & Znorm > 0.5) 290 | if (nrow(tree) < 3) { 291 | cpa <- rbind(cpa, data.frame(TreeID = t, ConvexHullArea = NA)) 292 | next 293 | } 294 | cpa <- rbind(cpa, data.frame(TreeID = t, ConvexHullArea = convhull_area(tree@data[, 295 | c("X", "Y")]))) 296 | setTxtProgressBar(pb, i) 297 | i <- i + 1 298 | } 299 | dbh_results <- merge(dbh_results, heights, by = "TreeID") 300 | dbh_results <- merge(dbh_results, cpa, by = "TreeID") 301 | dbh_results <- merge(dbh_results, tree_pos_height, by = "TreeID") 302 | return(dbh_results) 303 | } 304 | 305 | #' Function to perform a forest inventory based on a segmented las object (needs to contain TreeID) 306 | #' This version is a faster but more simplistic approach than forest_inventory() for the DBH estimates 307 | #' 308 | #' @param tls lidR las object with the segmented trees 309 | #' @param slice_min the minimum height of a DBH slice 310 | #' @param slice_max the maximum height of a DBH slice 311 | #' @param max_dbh the maximum DBH allowed 312 | #' @param n_cores number of cores to use 313 | #' 314 | #' @return a data.frame with the TreeID, X, Y, DBH, quality_flag, Height and ConvexHullArea 315 | forest_inventory_simple <- function (tls, slice_min = 1.2, slice_max = 1.4, max_dbh = 1, n_cores = max(c(1, parallel::detectCores()/2 - 1))) 316 | { 317 | if (!"TreeID" %in% names(tls)) { 318 | stop("The las object does not contain a TreeID attribute") 319 | } 320 | tls <- lidR::filter_poi(tls, !is.na(TreeID) & TreeID > 0) 321 | if ("Zref" %in% names(tls)) { 322 | dbh_slice <- lidR::filter_poi(tls, Z > slice_min & Z < 323 | slice_max) 324 | } else if ("Znorm" %in% names(tls)) { 325 | dbh_slice <- lidR::filter_poi(tls, Znorm > slice_min & 326 | Znorm < slice_max) 327 | } else { 328 | tls_norm <- lidR::normalize_height(lidR::classify_ground(tls, 329 | lidR::csf()), lidR::tin()) 330 | tls <- lidR::add_lasattribute(tls, tls_norm$Z, "Znorm", 331 | "Z normalized") 332 | dbh_slice <- lidR::filter_poi(tls, Znorm > slice_min & 333 | Znorm < slice_max) 334 | } 335 | 336 | na2true <- function(x) ifelse(is.na(x) | is.infinite(x), TRUE, x) 337 | 338 | t1 <- Sys.time() 339 | IDs <- unique(dbh_slice$TreeID) 340 | print("Fit a DBH value to every tree:") 341 | las_list <- list() 342 | for(i in 1:length(IDs)) { 343 | las_list[[i]] <- lidR::filter_poi(tls, TreeID == IDs[i]) 344 | } 345 | 346 | require(foreach) 347 | cl = parallel::makeCluster(n_cores) 348 | doParallel::registerDoParallel(cl) 349 | dbh_results <- foreach::foreach(tree = las_list, .combine = rbind, .errorhandling = "remove") %dopar% 350 | { 351 | t <- tree@data$TreeID[1] 352 | if (nrow(tree) < 3) { 353 | dbh <- NA 354 | return(data.frame(TreeID = t, X = mean(tree$X), 355 | Y = mean(tree$Y), DBH = dbh, quality_flag = 1)) 356 | warning(paste("to little points in tree", 357 | t)) 358 | } 359 | 360 | circle <- CspStandSegmentation::ransac_circle_fit(tree@data[, c("X", "Y")], n_iterations = 500) 361 | return(data.frame(TreeID = t, X = circle$circle[1], Y = circle$circle[2], DBH = min(c( circle$circle[3] * 2,2)), quality_flag = 4)) 362 | 363 | 364 | } 365 | parallel::stopCluster(cl) 366 | dbh_slice <- lidR::add_attribute(dbh_slice, dbh_slice@data$Z - 367 | dbh_slice@data$Znorm, "Zdiff") 368 | tree_pos_height <- aggregate(dbh_slice$Zdiff, by = list(dbh_slice$TreeID), 369 | FUN = mean) 370 | names(tree_pos_height) <- c("TreeID", "Z") 371 | heights <- aggregate(tls@data$Z, by = list(as.character(tls@data$TreeID)), 372 | FUN = function(x) max(x) - min(x)) 373 | names(heights) <- c("TreeID", "Height") 374 | heights$TreeID <- as.numeric(heights$TreeID) 375 | print("Tree heights calculated.") 376 | convhull_area <- function(xy) { 377 | xy <- as.data.frame(xy) 378 | if (nrow(xy) < 3) { 379 | return(NA) 380 | } 381 | ch <- chull(xy) 382 | return(abs(0.5 * sum(xy[ch, 1] * c(tail(xy[ch, 2], -1), 383 | head(xy[ch, 2], 1)) - c(tail(xy[ch, 1], -1), head(xy[ch, 384 | 1], 1)) * xy[ch, 2]))) 385 | } 386 | print("Calculating convex hull areas.") 387 | pb = txtProgressBar(min = 0, max = length(IDs), initial = 0, 388 | style = 3) 389 | i <- 1 390 | cpa <- data.frame(TreeID = numeric(), ConvexHullArea = numeric()) 391 | for (t in IDs) { 392 | tree <- lidR::filter_poi(tls, TreeID == t & Znorm > 0.5) 393 | if (nrow(tree) < 3) { 394 | cpa <- rbind(cpa, data.frame(TreeID = t, ConvexHullArea = NA)) 395 | next 396 | } 397 | cpa <- rbind(cpa, data.frame(TreeID = t, ConvexHullArea = convhull_area(tree@data[, 398 | c("X", "Y")]))) 399 | setTxtProgressBar(pb, i) 400 | i <- i + 1 401 | } 402 | dbh_results2 <- merge(dbh_results, heights, by = "TreeID") 403 | dbh_results2 <- merge(dbh_results2, cpa, by = "TreeID") 404 | dbh_results2 <- merge(dbh_results2, tree_pos_height, by = "TreeID") 405 | return(dbh_results2) 406 | } 407 | 408 | 409 | #' Function to plot the inventory results into a lidR 3d plot of the point cloud 410 | #' @param plot lidR 3d plot 411 | #' @param inventory data.frame with the inventory results 412 | #' @param cex numeric size of the labels 413 | #' @param label_col character color of the labels 414 | #' @param col color of the spheres 415 | #' @return the plot with the inventory results 416 | #' @examples 417 | #' \dontrun{ 418 | #' x <- lidR::plot(segmented, color = "TreeID") 419 | #' plot_inventory(x, inventory) 420 | #' } 421 | plot_inventory <- function(plot, inventory,col = NA,cex = 1.5, label_col = "white"){ 422 | if(is.na(col)){ 423 | col <- rainbow(max(inventory$TreeID) - min(inventory$TreeID)) 424 | } 425 | # generate a circle for evry dbh estimation 426 | rgl::spheres3d(inventory$X - plot[1], inventory$Y - plot[2], inventory$Z+1.3, radius = inventory$DBH/2,col = col) 427 | for(i in 1:nrow(inventory)){ 428 | rgl::texts3d(inventory$X[i] - plot[1] + inventory$DBH[i], inventory$Y[i] - plot[2] + inventory$DBH[i], inventory$Z[i] + 1.3, text = inventory$TreeID[i], adj = c(0.5,0.5), col = label_col, cex = cex, family = "mono", font = 2) 429 | rgl::lines3d(c(inventory$X[i] - plot[1], inventory$X[i] - plot[1]), c(inventory$Y[i] - plot[2], inventory$Y[i] - plot[2]), c(inventory$Z[i], inventory$Height[i]), col = ifelse(length(col) >= i, col[i], col), lwd = 2) 430 | } 431 | } 432 | 433 | -------------------------------------------------------------------------------- /R/RcppExports.R: -------------------------------------------------------------------------------- 1 | # Generated by using Rcpp::compileAttributes() -> do not edit by hand 2 | # Generator token: 10BE3573-1514-4C36-9D1C-5A225CD40393 3 | 4 | #' helper function to unlist IDs generated by dbscan::frNN 5 | #' 6 | #' 7 | #' creates a vector of indices from a nested list created by dbscan::frNN 8 | #' 9 | #' @param list a list element created by dbscan::frNN 10 | #' @param l the expected length of the result 11 | #' @author Julian Frey 12 | #' @export fast_unlist 13 | fast_unlist <- function(list, l) { 14 | .Call(`_CspStandSegmentation_fast_unlist`, list, l) 15 | } 16 | 17 | #' helper function to unlist distances computed by dbscan::frNN 18 | #' 19 | #' extracts the distances from a nested list created by dbscan::frNN 20 | #' 21 | #' @param list a list element created by dbscan::frNN 22 | #' @param l the expected length of the result 23 | #' @author Julian Frey 24 | #' @export fast_unlist_dist 25 | fast_unlist_dist <- function(list, l) { 26 | .Call(`_CspStandSegmentation_fast_unlist_dist`, list, l) 27 | } 28 | 29 | #' Fast Eigenvalues decomposition for k nearest neighbors using a C++ function 30 | #' 31 | #' C++ helper function to compute eigenvalues for geometric feature 32 | #' calculation. 33 | #' 34 | #' @param las LAS element 35 | #' @param k k nearest neighbors 36 | #' @param ncpu number of cpu cores to use 37 | #' @return The function returns for every point the 3 eigenvalues and the 38 | #' third element of the third eigenvector. These values are needed to compute 39 | #' planarity, linerity, verticality etc. in the add_geometry function 40 | #' @author Julian Frey 41 | #' @seealso \link{add_geometry} 42 | #' @export eigen_decomposition 43 | eigen_decomposition <- function(las, k, ncpu = 1L) { 44 | .Call(`_CspStandSegmentation_eigen_decomposition`, las, k, ncpu) 45 | } 46 | 47 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | ## CspStandSegmentation is an R-package for the segmentation of single trees from forest point clouds scanned with terrestrial, mobile or unmanned LiDAR systems 2 | 3 | Authors: Julian Frey and Zoe Schindler, University of Freiburg, Chair of Forest Growth and Dendroecology 4 | 5 | 6 | [![DOI](https://zenodo.org/badge/546133784.svg)](https://doi.org/10.5281/zenodo.14204459) 7 | 8 | 9 | 10 | 11 | ## Installation 12 | 13 | If you are working on Windows operating systems, you will need to install Rtools prior to installation: https://cran.r-project.org/bin/windows/Rtools/>. On Mac, Xcode is required. 14 | 15 | ```R 16 | install.packages(c('devtools', 'Rcpp', 'lidR', 'dbscan', 'igraph', 'foreach', 'doParallel','magrittr', 'data.table')) 17 | 18 | devtools::install_github('https://github.com/JulFrey/CspStandSegmentation') 19 | 20 | # Check if it is working 21 | library(CspStandSegmentation) 22 | example("csp_cost_segmentation") 23 | 24 | ``` 25 | 26 | ## Usage 27 | The package is firmly based on the `lidR` package and uses the las file structure. Smaller point clouds can be directly segmented using the ```csp_cost_segmentation``` function. This requires a set of tree positions (map) as starting points, which can be derived using the ```find_base_coordinates_raster``` function, which might require parameter optimization. Theoretically, tree positions might also come from field measurements or manual assignments.: 28 | 29 | ```R 30 | # read example data 31 | file = system.file("extdata", "beech.las", package="CspStandSegmentation") 32 | tls = lidR::readTLSLAS(file) 33 | 34 | # find tree positions as starting points for segmentation 35 | map <- CspStandSegmentation::find_base_coordinates_raster(tls) 36 | 37 | # segment trees 38 | segmented <- tls |> 39 | CspStandSegmentation::add_geometry(n_cores = parallel::detectCores()/2) |> 40 | CspStandSegmentation::csp_cost_segmentation(map, 1, N_cores = parallel::detectCores()/2) 41 | 42 | # show results 43 | lidR::plot(segmented, color = "TreeID") 44 | 45 | # create inventory 46 | inventory <- CspStandSegmentation::forest_inventory(segmented) 47 | head(inventory) 48 | lidR::plot(segmented, color = "TreeID") |> CspStandSegmentation::plot_inventory(inventory) 49 | ``` 50 | 51 | For large areas, the package can be used within the lidR LAScatalogue engine to cope with memory limitations. The following example shows how this can be done. The single tiles of segmented trees are saved in a folder in this example and merged afterwards. 52 | 53 | ```R 54 | # packages 55 | library(lidR) 56 | library(CspStandSegmentation) 57 | 58 | # parameters 59 | las_file <- "your_file.laz" 60 | base_dir <- "~/your_project_folder/" # with trailing / 61 | cores <- parallel::detectCores()/2 # number od cpu cores 62 | res <- 0.3 # voxel resolution for segmentation 63 | chunk_size <- 50 # size of one tile in m excl. buffer 64 | chunk_buffer <- 10 # buffer around tile in m 65 | 66 | # main 67 | 68 | # create dir for segmentation tiles 69 | if(!dir.exists(paste0(base_dir,"segmentation_tiles/"))) { 70 | dir.create(paste0(base_dir,"segmentation_tiles/")) 71 | } 72 | 73 | uls = lidR::readTLSLAScatalog(paste0(base_dir,las_file), select = "XYZ0", chunk_size = chunk_size, chunk_buffer = chunk_buffer) 74 | plot(uls, chunk_pattern = T) 75 | # plot(dtm,add = T) 76 | # sf::as_Spatial(sf::st_as_sf(map, coords = c("X", "Y"))) |> plot(add = T) 77 | 78 | opt_output_files(uls) <- paste0(base_dir,"segmentation_tiles/{ID}") 79 | segmented <- catalog_apply(uls, function(cluster) { 80 | 81 | las <- suppressWarnings(readLAS(cluster)) # read files 82 | if (is.empty(las) ) return(NULL) # stop if empty 83 | print(str(cluster)) 84 | # find tree positions as starting points for segmentation 85 | map <- CspStandSegmentation::find_base_coordinates_raster(las) 86 | 87 | # add the tile ID*100,000 to the TreeID to ensure unique IDs across all tiles 88 | map$TreeID <- map$TreeID + as.numeric(basename(cluster@save)) * 100000 89 | 90 | # only use seed positions within the tile+buffer and save the tile bbox to only return tree pos within the tile (excl. buffer) 91 | inv <- map 92 | invb <- map 93 | # the bbox includes the buffer, the bbbox excludes the buffer 94 | bbox <- cluster@bbox 95 | bbbox <- cluster@bbbox 96 | inv <- inv[inv$X < bbox[1,2] & inv$X > bbox[1,1] & inv$Y < bbox[2,2] & inv$Y > bbox[2,1],] 97 | invb <- invb[invb$X < bbbox[1,2] & invb$X > bbbox[1,1] & invb$Y < bbbox[2,2] & invb$Y > bbbox[2,1],] 98 | if (nrow(inv) == 0) return(NULL) # stop if no tree pos in tile found 99 | if (is.empty(las) ) return(NULL) # stop if empty 100 | 101 | # Assign all points to trees 102 | las <- las |> add_geometry(n_cores = cores) |> csp_cost_segmentation(invb,res, N_cores = cores, V_w = 0.5) 103 | # las <- las |> csp_cost_segmentation(map,res, N_cores = cores, V_w = 0.5) # this is a faster version which does not make use of the geometric feature weights 104 | if (is.empty(las)) return(NULL) 105 | 106 | las <- las |> filter_poi(TreeID %in% c(0,inv$TreeID)) # only return trees within the tile 107 | if (is.empty(las)) return(NULL) # stop if empty 108 | 109 | # remove unneccesary attributes for further processing 110 | las <- las |> remove_lasattribute('x_vox') |> 111 | remove_lasattribute('y_vox') |> 112 | remove_lasattribute('z_vox') |> 113 | remove_lasattribute('buffer') |> 114 | remove_lasattribute('Linearity') |> 115 | remove_lasattribute('Sphericity') |> 116 | remove_lasattribute('Verticality') 117 | 118 | # validate las 119 | las <- las |> las_quantize() |> las_update() 120 | if (is.empty(las)) return(NULL) 121 | return(las) 122 | }, .options = list(automerge = TRUE)) 123 | 124 | # merge segmented trees 125 | segmented <- readTLSLAScatalog(paste0(base_dir,"segmentation_tiles/"), select = "xyz0", chunk_buffer = 0) 126 | opt_merge(segmented) <- TRUE 127 | opt_output_files(segmented) <- paste0("") 128 | segmented <- catalog_apply(segmented, function(cluster) { 129 | las <- suppressWarnings(readLAS(cluster)) # read files 130 | if (is.empty(las) ) return(NULL) # stop if empty 131 | return(las) 132 | }, .options = list(automerge = TRUE)) 133 | 134 | # write results in a single file 135 | writeLAS(segmented, paste0(base_dir,"segmented.las")) 136 | ``` 137 | -------------------------------------------------------------------------------- /inst/extdata/beech.las: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/JulFrey/CspStandSegmentation/362d39342bc91dfdb45f913573bc8a5b07d1d60e/inst/extdata/beech.las -------------------------------------------------------------------------------- /inst/figures/csp_logo.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/JulFrey/CspStandSegmentation/362d39342bc91dfdb45f913573bc8a5b07d1d60e/inst/figures/csp_logo.png -------------------------------------------------------------------------------- /man/add_geometry.Rd: -------------------------------------------------------------------------------- 1 | % Generated by roxygen2: do not edit by hand 2 | % Please edit documentation in R/2021-11-12_A1_JF_csp_cost_functions.R 3 | \name{add_geometry} 4 | \alias{add_geometry} 5 | \title{Add geometric features to a LAS object} 6 | \usage{ 7 | add_geometry(las, k = 10L, n_cores = 1) 8 | } 9 | \arguments{ 10 | \item{las}{A LAS object (see lidR::LAS)} 11 | 12 | \item{k}{the k nearest neighbors to use for the eigenvalue calculation} 13 | 14 | \item{n_cores}{The number of CPU cores to use} 15 | } 16 | \value{ 17 | The function returns a single LAS object with the geometric features 18 | attached to it in the LAS@data section. 19 | } 20 | \description{ 21 | The function calls a fast cpp multi-core function to calculate eigenvalues 22 | for the points in a point cloud based on the k nearest neighbors. Afterwards 23 | it adds geometric features like Curvature, Linearity, Planarity, Sphericity, 24 | Anisotrophy and Verticlity to the points itself. 25 | } 26 | \details{ 27 | Details of the metrics can be found in: \ Hackel, T., Wegner, J.D. & 28 | Schindler, K. (2016) Contour Detection in Unstructured 3D Point Clouds. In 29 | 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). 30 | Presented at the 2016 IEEE Conference on Computer Vision and Pattern 31 | Recognition (CVPR), IEEE, Las Vegas, NV, USA, pp. 1610–1618. 32 | } 33 | \examples{ 34 | 35 | LASfile <- system.file("extdata", "MixedConifer.laz", package="lidR") 36 | las <- lidR::readLAS(LASfile, select = "xyz", filter = "-inside 481250 3812980 481300 3813030") 37 | 38 | las <- add_geometry(las, k = 5, n_cores = parallel::detectCores()-1) 39 | summary(las@data) 40 | 41 | 42 | } 43 | \author{ 44 | Julian Frey 45 | } 46 | -------------------------------------------------------------------------------- /man/add_las_attributes.Rd: -------------------------------------------------------------------------------- 1 | % Generated by roxygen2: do not edit by hand 2 | % Please edit documentation in R/2021-11-12_A1_JF_csp_cost_functions.R 3 | \name{add_las_attributes} 4 | \alias{add_las_attributes} 5 | \title{Add all las_attributes from las@data to the header of a las element} 6 | \usage{ 7 | add_las_attributes(las) 8 | } 9 | \arguments{ 10 | \item{las}{an element of lidR::LAS class} 11 | } 12 | \value{ 13 | the las file with updated header 14 | } 15 | \description{ 16 | The helper function adds all headings from las@data which are not part of 17 | lidR:::LASATTRIBUTES to the las header using lidR::add_lasattribute. Only 18 | attributes that are included in the header got saved when using 19 | lidR::writeLAS, this is a convenient way to add them. 20 | } 21 | \examples{ 22 | 23 | file <- system.file("extdata", "beech.las", package="CspStandSegmentation") 24 | las <- lidR::readTLSLAS(file) 25 | 26 | las@data$noise <- runif(nrow(las@data)) 27 | las@data$noiseZ <- las@data$var1 * las@data$Z 28 | 29 | las <- add_las_attributes(las) 30 | 31 | } 32 | \author{ 33 | Julian Frey 34 | } 35 | -------------------------------------------------------------------------------- /man/add_voxel_coordinates.Rd: -------------------------------------------------------------------------------- 1 | % Generated by roxygen2: do not edit by hand 2 | % Please edit documentation in R/2021-11-12_A1_JF_csp_cost_functions.R 3 | \name{add_voxel_coordinates} 4 | \alias{add_voxel_coordinates} 5 | \title{Add voxel coordinates to a las file} 6 | \usage{ 7 | add_voxel_coordinates(las, res) 8 | } 9 | \arguments{ 10 | \item{las}{an element of lidR::LAS class} 11 | 12 | \item{res}{voxel ressolution in [m]} 13 | } 14 | \value{ 15 | las file with additional voxel coordinates 16 | } 17 | \description{ 18 | Adds the collums x_vox, y_vox and z_vox in the given ressolution to the las 19 | element. This is convenient if information has been derived in voxel space 20 | and these should be attached to the original points. 21 | } 22 | \details{ 23 | Voxel coordinates derived with this function are identical to those derived 24 | by lidR::voxelize. 25 | } 26 | \examples{ 27 | 28 | file = system.file("extdata", "beech.las", package="CspStandSegmentation") 29 | las = lidR::readTLSLAS(file) 30 | 31 | las <- add_voxel_coordinates(las,res = 1) 32 | 33 | lidR::plot(las, color = 'z_vox') 34 | 35 | } 36 | \author{ 37 | Julian Frey 38 | } 39 | -------------------------------------------------------------------------------- /man/comparative_shortest_path.Rd: -------------------------------------------------------------------------------- 1 | % Generated by roxygen2: do not edit by hand 2 | % Please edit documentation in R/2021-11-12_A1_JF_csp_cost_functions.R 3 | \name{comparative_shortest_path} 4 | \alias{comparative_shortest_path} 5 | \title{helper function for csp_cost_segemntation} 6 | \usage{ 7 | comparative_shortest_path( 8 | vox = vox, 9 | adjacency_df = adjacency_df, 10 | seeds, 11 | v_w = 0, 12 | l_w = 0, 13 | s_w = 0, 14 | N_cores = parallel::detectCores() - 1, 15 | Voxel_size 16 | ) 17 | } 18 | \arguments{ 19 | \item{vox}{a LAS S4 element with XYZ voxel coordinates in the @data slot.} 20 | 21 | \item{adjacency_df}{a data.frame with voxel ids (row numbers) in the first 22 | column and a neighboring voxel ID in the second column and the weight 23 | (distance) in the third column. Might be generated using the dbscan::frNN 24 | function (which requires reshaping the data).} 25 | 26 | \item{seeds}{seed points for tree positions.} 27 | 28 | \item{v_w, l_w, s_w}{weights for verticality, linearity spericity see 29 | \code{\link{csp_cost_segmentation}}} 30 | 31 | \item{N_cores}{Number of CPU cores for multi-threading} 32 | 33 | \item{Voxel_size}{Edge length used to create the voxels. This is only 34 | important to gain comparable distance weights on different voxel sizes. 35 | Should be greater than 0.} 36 | } 37 | \value{ 38 | voxels with the TreeID in the data slot 39 | } 40 | \description{ 41 | The function performs a Dijkstra algorithm on a 3D voxel file to assign 42 | every voxel to the closest seed point using the igraph package. 43 | } 44 | \seealso{ 45 | \code{\link{csp_cost_segmentation}} 46 | } 47 | \author{ 48 | Julian Frey 49 | } 50 | -------------------------------------------------------------------------------- /man/csp_cost_segmentation.Rd: -------------------------------------------------------------------------------- 1 | % Generated by roxygen2: do not edit by hand 2 | % Please edit documentation in R/2021-11-12_A1_JF_csp_cost_functions.R 3 | \name{csp_cost_segmentation} 4 | \alias{csp_cost_segmentation} 5 | \title{Comparative Shortest Path with cost weighting tree segmentation} 6 | \usage{ 7 | csp_cost_segmentation( 8 | las, 9 | map, 10 | Voxel_size = 0.3, 11 | V_w = 0, 12 | L_w = 0, 13 | S_w = 0, 14 | N_cores = 1 15 | ) 16 | } 17 | \arguments{ 18 | \item{las}{A lidR LAS S4 object.} 19 | 20 | \item{map}{A data.frame, including the columns 21 | X, Y, Z, TreeID, with X and Y depicting the location of the trees. Can be generated using CspStandSegmentation::find_base_coordinates_raster} 22 | 23 | \item{Voxel_size}{The voxel size (3D resolution) for the routing graph to 24 | determine the nearest map location for every point in the point cloud.} 25 | 26 | \item{V_w}{verticality weight. Since trunks are vertical structures, routing 27 | through voxels with high verticality can be rated 'cheaper.' should be a 28 | number between 0 and 1 with 0 meaning no benefit for more vertical 29 | structures.} 30 | 31 | \item{L_w}{Linearity weight. Similar to V_w but for linearity, higher 32 | values indicate a malus for linear shapes (usually branches).} 33 | 34 | \item{S_w}{Spericity weight. Similar to V_w but for sphericity, higher 35 | values indicate a malus for spherical shapes (usually small branches and 36 | leaves).} 37 | 38 | \item{N_cores}{number of CPU cores used for parallel routing using the 39 | foreach package.} 40 | } 41 | \value{ 42 | Returns a copy of the las point cloud with an additional field for 43 | the TreeID. 44 | } 45 | \description{ 46 | Segments single trees from forest point clouds based on tree positions 47 | (xyz-coordinates) provided in the map argument. 48 | } 49 | \details{ 50 | The whole point cloud is voxelized in the given resolution and the center of 51 | gravity for the points inside is calculated as voxel coordinate. A graph is 52 | build, which connects the voxel coordinates based on a db-scan algorithm. The 53 | distances between the voxel coordinates is weighted based on geometric 54 | features computed for the points in the voxels. Distances along planar 55 | and/or vertical faces like stems are weighted shorter than distances through 56 | voxels with high sphericity like leaves and clusters of twigs. This 57 | avoids small individuals spreading into the upper canopy. 58 | For every voxel center, the weighted distance in the network is calculated to 59 | all tree locations from the map argument. The TreeID of the map argument 60 | with the shortest distance is assigned to the voxel. All points in the point 61 | cloud receive the TreeID from their parent voxel. 62 | } 63 | \examples{ 64 | 65 | # read example data 66 | file = system.file("extdata", "beech.las", package="CspStandSegmentation") 67 | las = lidR::readTLSLAS(file) 68 | 69 | # Find tree positions as starting points for segmentation 70 | map <- CspStandSegmentation::find_base_coordinates_raster(las) 71 | # segment trees 72 | segmented <- las |> 73 | CspStandSegmentation::add_geometry() |> 74 | CspStandSegmentation::csp_cost_segmentation(map, 1) 75 | 76 | lidR::plot(segmented, color = "TreeID") 77 | 78 | } 79 | \seealso{ 80 | \code{\link{comparative_shortest_path}} 81 | } 82 | \author{ 83 | Julian Frey 84 | } 85 | -------------------------------------------------------------------------------- /man/eigen_decomposition.Rd: -------------------------------------------------------------------------------- 1 | % Generated by roxygen2: do not edit by hand 2 | % Please edit documentation in R/RcppExports.R 3 | \name{eigen_decomposition} 4 | \alias{eigen_decomposition} 5 | \title{Fast Eigenvalues decomposition for k nearest neighbors using a C++ function} 6 | \usage{ 7 | eigen_decomposition(las, k, ncpu = 1L) 8 | } 9 | \arguments{ 10 | \item{las}{LAS element} 11 | 12 | \item{k}{k nearest neighbors} 13 | 14 | \item{ncpu}{number of cpu cores to use} 15 | } 16 | \value{ 17 | The function returns for every point the 3 eigenvalues and the 18 | third element of the third eigenvector. These values are needed to compute 19 | planarity, linerity, verticality etc. in the add_geometry function 20 | } 21 | \description{ 22 | C++ helper function to compute eigenvalues for geometric feature 23 | calculation. 24 | } 25 | \seealso{ 26 | \link{add_geometry} 27 | } 28 | \author{ 29 | Julian Frey 30 | } 31 | -------------------------------------------------------------------------------- /man/fast_unlist.Rd: -------------------------------------------------------------------------------- 1 | % Generated by roxygen2: do not edit by hand 2 | % Please edit documentation in R/RcppExports.R 3 | \name{fast_unlist} 4 | \alias{fast_unlist} 5 | \title{helper function to unlist IDs generated by dbscan::frNN} 6 | \usage{ 7 | fast_unlist(list, l) 8 | } 9 | \arguments{ 10 | \item{list}{a list element created by dbscan::frNN} 11 | 12 | \item{l}{the expected length of the result 13 | @author Julian Frey 14 | @export fast_unlist} 15 | } 16 | \description{ 17 | creates a vector of indices from a nested list created by dbscan::frNN 18 | } 19 | -------------------------------------------------------------------------------- /man/fast_unlist_dist.Rd: -------------------------------------------------------------------------------- 1 | % Generated by roxygen2: do not edit by hand 2 | % Please edit documentation in R/RcppExports.R 3 | \name{fast_unlist_dist} 4 | \alias{fast_unlist_dist} 5 | \title{helper function to unlist distances computed by dbscan::frNN} 6 | \usage{ 7 | fast_unlist_dist(list, l) 8 | } 9 | \arguments{ 10 | \item{list}{a list element created by dbscan::frNN} 11 | 12 | \item{l}{the expected length of the result} 13 | } 14 | \description{ 15 | extracts the distances from a nested list created by dbscan::frNN 16 | } 17 | \author{ 18 | Julian Frey 19 | } 20 | -------------------------------------------------------------------------------- /man/fds.Rd: -------------------------------------------------------------------------------- 1 | % Generated by roxygen2: do not edit by hand 2 | % Please edit documentation in R/2024-06-10_A1_JF_farthest_point_sampling.R 3 | \name{fds} 4 | \alias{fds} 5 | \title{Farthest Distance Sampling (Farthest Point Sampling)} 6 | \usage{ 7 | fds(mat, n, ret = "idx", scale = F) 8 | } 9 | \arguments{ 10 | \item{mat}{a matrix of points with one row for each point and one column for each dimension, can also be a las object then only XYZ will be used} 11 | 12 | \item{n}{the number of points to select, or if <1 the proportion of points to select} 13 | 14 | \item{ret}{the type of output to return. Options are "idx" (default) to return the indices of the selected points, "mat" to return the selected points.} 15 | 16 | \item{scale}{logical. If TRUE, the dimensions are scaled to have a mean of 0 and a standard deviation of 1 before calculating distances.} 17 | } 18 | \value{ 19 | a vector of indices or a matrix of points 20 | } 21 | \description{ 22 | This function selects n points from a matrix of points such that the minimum distance between any two points is maximized. 23 | This version is memory efficient and can handle large matrices. 24 | } 25 | \examples{ 26 | mat <- matrix(rnorm(1000), ncol = 10) 27 | sample <- fds(mat, 50, ret = "mat") 28 | \dontrun{ 29 | plot(mat, col = "black", pch = 19) 30 | points(sample, col = "red", pch = 19) 31 | } 32 | } 33 | -------------------------------------------------------------------------------- /man/find_base_coordinates_geom.Rd: -------------------------------------------------------------------------------- 1 | % Generated by roxygen2: do not edit by hand 2 | % Please edit documentation in R/2021-11-12_A1_JF_csp_cost_functions.R 3 | \name{find_base_coordinates_geom} 4 | \alias{find_base_coordinates_geom} 5 | \title{Find stem base position using a geometric feature filtering and clustering 6 | approach} 7 | \usage{ 8 | find_base_coordinates_geom( 9 | las, 10 | zmin = 0.5, 11 | zmax = 2, 12 | res = 0.5, 13 | min_verticality = 0.9, 14 | min_planarity = 0.5, 15 | min_cluster_size = NULL 16 | ) 17 | } 18 | \arguments{ 19 | \item{las}{an element of lidR::LAS class} 20 | 21 | \item{zmin}{lower search boundary} 22 | 23 | \item{zmax}{upper search boundary} 24 | 25 | \item{res}{cluster search radius} 26 | 27 | \item{min_verticality}{minimum verticality >0 & <1 for a point to be 28 | considered a stem point} 29 | 30 | \item{min_planarity}{minimum planarity >0 & <1 for a point to be considered 31 | a stem point} 32 | 33 | \item{min_cluster_size}{minimum number of points in the cluster to be considered 34 | a tree, if NULL median cluster size is chosen} 35 | } 36 | \value{ 37 | data.frame with X, Y, Z and TreeID for stem base positions 38 | } 39 | \description{ 40 | Find stem base position using a geometric feature filtering and clustering 41 | approach 42 | } 43 | \examples{ 44 | # read example data 45 | file = system.file("extdata", "beech.las", package="CspStandSegmentation") 46 | tls = lidR::readTLSLAS(file) 47 | 48 | # Find tree positions 49 | map <- CspStandSegmentation::find_base_coordinates_geom(tls) 50 | } 51 | \author{ 52 | Julian Frey 53 | } 54 | -------------------------------------------------------------------------------- /man/find_base_coordinates_raster.Rd: -------------------------------------------------------------------------------- 1 | % Generated by roxygen2: do not edit by hand 2 | % Please edit documentation in R/2021-11-12_A1_JF_csp_cost_functions.R 3 | \name{find_base_coordinates_raster} 4 | \alias{find_base_coordinates_raster} 5 | \title{Find stem base position using a density raster approach} 6 | \usage{ 7 | find_base_coordinates_raster( 8 | las, 9 | res = 0.1, 10 | zmin = 0.5, 11 | zmax = 2, 12 | q = 0.975, 13 | eps = 0.2 14 | ) 15 | } 16 | \arguments{ 17 | \item{las}{an element of lidR::LAS class} 18 | 19 | \item{res}{raster resolution} 20 | 21 | \item{zmin}{lower search boundary} 22 | 23 | \item{zmax}{upper search boundary} 24 | 25 | \item{q}{quantile of raster density to assign a tree region} 26 | 27 | \item{eps}{search radius to merge base points} 28 | } 29 | \value{ 30 | data.frame with X, Y, Z and TreeID for stem base positions 31 | } 32 | \description{ 33 | Find stem base position using a density raster approach 34 | } 35 | \examples{ 36 | # read example data 37 | file = system.file("extdata", "beech.las", package="CspStandSegmentation") 38 | tls = lidR::readTLSLAS(file) 39 | 40 | # Find tree positions 41 | map <- CspStandSegmentation::find_base_coordinates_raster(tls) 42 | } 43 | \author{ 44 | Julian Frey 45 | } 46 | -------------------------------------------------------------------------------- /man/forest_inventory.Rd: -------------------------------------------------------------------------------- 1 | % Generated by roxygen2: do not edit by hand 2 | % Please edit documentation in R/2025-01-10_A1_JF_forest_inventory.R 3 | \name{forest_inventory} 4 | \alias{forest_inventory} 5 | \title{Function to perform a forest inventory based on a segmented las object (needs to contain TreeID)} 6 | \usage{ 7 | forest_inventory( 8 | tls, 9 | slice_min = 0.3, 10 | slice_max = 4, 11 | increment = 0.2, 12 | width = 0.1, 13 | max_dbh = 1, 14 | n_cores = max(c(1, parallel::detectCores()/2 - 1)) 15 | ) 16 | } 17 | \arguments{ 18 | \item{slice_min}{the minimum height of a slice for stems to estimate the taper curve} 19 | 20 | \item{slice_max}{the maximum height of a slice for stems to estimate the taper curve} 21 | 22 | \item{increment}{the increment of the slices} 23 | 24 | \item{width}{the width of the slices} 25 | 26 | \item{max_dbh}{the maximum DBH allowed} 27 | 28 | \item{n_cores}{number of cores to use} 29 | 30 | \item{las}{lidR las object with the segmented trees} 31 | } 32 | \value{ 33 | a data.frame with the TreeID, X, Y, DBH, quality_flag, Height and ConvexHullArea 34 | } 35 | \description{ 36 | This function estimates a taper curve for evry tree and returns the DBH at 1.3m, its position in XY coordinates, the tree height and the trees 2D projection area. 37 | } 38 | \examples{ 39 | # read example data 40 | file = system.file("extdata", "beech.las", package="CspStandSegmentation") 41 | tls = lidR::readTLSLAS(file) 42 | 43 | # find tree positions as starting points for segmentation 44 | map <- CspStandSegmentation::find_base_coordinates_raster(tls) 45 | 46 | # segment trees 47 | segmented <- tls |> 48 | CspStandSegmentation::add_geometry(n_cores = parallel::detectCores()/2) |> 49 | CspStandSegmentation::csp_cost_segmentation(map, 1, N_cores = parallel::detectCores()/2) 50 | 51 | # show results 52 | \dontrun{lidR::plot(segmented, color = "TreeID")} 53 | 54 | # perform inventory 55 | inventory <- CspStandSegmentation::forest_inventory(segmented) 56 | } 57 | -------------------------------------------------------------------------------- /man/forest_inventory_simple.Rd: -------------------------------------------------------------------------------- 1 | % Generated by roxygen2: do not edit by hand 2 | % Please edit documentation in R/2025-01-10_A1_JF_forest_inventory.R 3 | \name{forest_inventory_simple} 4 | \alias{forest_inventory_simple} 5 | \title{Function to perform a forest inventory based on a segmented las object (needs to contain TreeID) 6 | This version is a faster but more simplistic approach than forest_inventory() for the DBH estimates} 7 | \usage{ 8 | forest_inventory_simple( 9 | tls, 10 | slice_min = 1.2, 11 | slice_max = 1.4, 12 | max_dbh = 1, 13 | n_cores = max(c(1, parallel::detectCores()/2 - 1)) 14 | ) 15 | } 16 | \arguments{ 17 | \item{tls}{lidR las object with the segmented trees} 18 | 19 | \item{slice_min}{the minimum height of a DBH slice} 20 | 21 | \item{slice_max}{the maximum height of a DBH slice} 22 | 23 | \item{max_dbh}{the maximum DBH allowed} 24 | 25 | \item{n_cores}{number of cores to use} 26 | } 27 | \value{ 28 | a data.frame with the TreeID, X, Y, DBH, quality_flag, Height and ConvexHullArea 29 | } 30 | \description{ 31 | Function to perform a forest inventory based on a segmented las object (needs to contain TreeID) 32 | This version is a faster but more simplistic approach than forest_inventory() for the DBH estimates 33 | } 34 | -------------------------------------------------------------------------------- /man/las_merge.Rd: -------------------------------------------------------------------------------- 1 | % Generated by roxygen2: do not edit by hand 2 | % Please edit documentation in R/2025-01-07_A1_JF_merge_las_objects.R 3 | \name{las_merge} 4 | \alias{las_merge} 5 | \title{Makes one las object from multiple las objects} 6 | \usage{ 7 | las_merge(..., oci = TRUE, fill = FALSE) 8 | } 9 | \arguments{ 10 | \item{...}{any number of las objects} 11 | 12 | \item{oci}{add a column with the original cloud index} 13 | 14 | \item{fill}{fill missing columns with NA if it is set to False collumns which do not exist in all las objects will be removed} 15 | } 16 | \value{ 17 | A single las object 18 | } 19 | \description{ 20 | This function merges multiple las objects into one las object. The function checks if all inputs are las objects and if they have the same CRS. The function will also add a column oci with the original cloud index to each las object. The function will then rbind all data by the minimum set of columns. If the fill argument is set to False, columns which do not exist in all las objects will be removed. If the fill argument is set to True, missing columns will be filled with NA. 21 | } 22 | \examples{ 23 | las1 <- lidR::LAS(data.frame(X = runif(100), Y = runif(100), Z = runif(100))) 24 | las2 <- lidR::LAS(data.frame(X = runif(100) + 2, Y = runif(100), Z = runif(100))) 25 | las3 <- lidR::LAS(data.frame(X = runif(100) + 4, Y = runif(100), Z = runif(100))) 26 | las_merge(las1, las2, las3) 27 | # las_merge(las1, las2, las3) |> lidR::plot(color = "oci") 28 | } 29 | -------------------------------------------------------------------------------- /man/p_dist.Rd: -------------------------------------------------------------------------------- 1 | % Generated by roxygen2: do not edit by hand 2 | % Please edit documentation in R/2024-06-10_A1_JF_farthest_point_sampling.R 3 | \name{p_dist} 4 | \alias{p_dist} 5 | \title{Point distance function} 6 | \usage{ 7 | p_dist(p1, p2) 8 | } 9 | \arguments{ 10 | \item{p1}{point 1} 11 | 12 | \item{p2}{point 2} 13 | } 14 | \value{ 15 | the distance between the two points 16 | } 17 | \description{ 18 | calculates euclidean distances for n dimensions 19 | } 20 | \examples{ 21 | p_dist(c(0,0), c(3,4)) 22 | } 23 | -------------------------------------------------------------------------------- /man/p_mat_dist.Rd: -------------------------------------------------------------------------------- 1 | % Generated by roxygen2: do not edit by hand 2 | % Please edit documentation in R/2024-06-10_A1_JF_farthest_point_sampling.R 3 | \name{p_mat_dist} 4 | \alias{p_mat_dist} 5 | \title{Point distance function} 6 | \usage{ 7 | p_mat_dist(mat, p) 8 | } 9 | \arguments{ 10 | \item{mat}{matrix with points as rows} 11 | 12 | \item{p}{point to calculate distances} 13 | } 14 | \value{ 15 | the distances between every row of mat and p 16 | } 17 | \description{ 18 | calculates euclidean distances for n dimensions between a matrix of points and a single point 19 | } 20 | \examples{ 21 | p_dist(as.matrix(cbind(runf(100),runf(100)), c(3,4)) 22 | } 23 | -------------------------------------------------------------------------------- /man/plot_inventory.Rd: -------------------------------------------------------------------------------- 1 | % Generated by roxygen2: do not edit by hand 2 | % Please edit documentation in R/2025-01-10_A1_JF_forest_inventory.R 3 | \name{plot_inventory} 4 | \alias{plot_inventory} 5 | \title{Function to plot the inventory results into a lidR 3d plot of the point cloud} 6 | \usage{ 7 | plot_inventory(plot, inventory, col = NA, cex = 1.5, label_col = "white") 8 | } 9 | \arguments{ 10 | \item{plot}{lidR 3d plot} 11 | 12 | \item{inventory}{data.frame with the inventory results} 13 | 14 | \item{col}{color of the spheres} 15 | 16 | \item{cex}{numeric size of the labels} 17 | 18 | \item{label_col}{character color of the labels} 19 | } 20 | \value{ 21 | the plot with the inventory results 22 | } 23 | \description{ 24 | Function to plot the inventory results into a lidR 3d plot of the point cloud 25 | } 26 | \examples{ 27 | \dontrun{ 28 | x <- lidR::plot(segmented, color = "TreeID") 29 | plot_inventory(x, inventory) 30 | } 31 | } 32 | -------------------------------------------------------------------------------- /man/point_center_angle.Rd: -------------------------------------------------------------------------------- 1 | % Generated by roxygen2: do not edit by hand 2 | % Please edit documentation in R/2025-01-10_A1_JF_forest_inventory.R 3 | \name{point_center_angle} 4 | \alias{point_center_angle} 5 | \title{Returns the angle between the center of the circle and a point in degrees} 6 | \usage{ 7 | point_center_angle(point, circle) 8 | } 9 | \arguments{ 10 | \item{point}{numeric vector of length 2 c(X,Y)} 11 | 12 | \item{circle}{numeric vector of length 3 c(center_X, center_Y, radius)} 13 | } 14 | \value{ 15 | numeric angle in degrees 16 | } 17 | \description{ 18 | Returns the angle between the center of the circle and a point in degrees 19 | } 20 | -------------------------------------------------------------------------------- /man/point_circle_distance.Rd: -------------------------------------------------------------------------------- 1 | % Generated by roxygen2: do not edit by hand 2 | % Please edit documentation in R/2025-01-10_A1_JF_forest_inventory.R 3 | \name{point_circle_distance} 4 | \alias{point_circle_distance} 5 | \title{Helper function to compute distances from a point to the circle} 6 | \usage{ 7 | point_circle_distance(point, circle) 8 | } 9 | \arguments{ 10 | \item{point}{numeric vector of length 2 c(X,Y)} 11 | 12 | \item{circle}{numeric vector of length 3 c(center_X, center_Y, radius)} 13 | } 14 | \value{ 15 | numeric distance from the point to the circle 16 | } 17 | \description{ 18 | Helper function to compute distances from a point to the circle 19 | } 20 | -------------------------------------------------------------------------------- /man/ransac_circle_fit.Rd: -------------------------------------------------------------------------------- 1 | % Generated by roxygen2: do not edit by hand 2 | % Please edit documentation in R/2025-01-10_A1_JF_forest_inventory.R 3 | \name{ransac_circle_fit} 4 | \alias{ransac_circle_fit} 5 | \title{RANSAC circle fitting algorithm specially adapted for tree DBH estimation} 6 | \usage{ 7 | ransac_circle_fit( 8 | data, 9 | n_iterations = 1000, 10 | distance_threshold = 0.01, 11 | min_inliers = 3 12 | ) 13 | } 14 | \arguments{ 15 | \item{data}{numeric matrix with 2 columns (X, Y) representing the point cloud} 16 | 17 | \item{n_iterations}{integer maximum number of iterations} 18 | 19 | \item{distance_threshold}{numeric maximum distance from a point to the circle to be considered an inlier} 20 | 21 | \item{min_inliers}{integer minimum number of inliers to consider the circle as valid} 22 | } 23 | \description{ 24 | This function fits a circle to a set of points using the RANSAC algorithm it maximizes the points that are in the circle and the number of filled 36 degree angle segments 25 | Therefore, this function searches for the most complete circle with the highest number of points represented. 26 | } 27 | -------------------------------------------------------------------------------- /man/suppress_cat.Rd: -------------------------------------------------------------------------------- 1 | % Generated by roxygen2: do not edit by hand 2 | % Please edit documentation in R/2025-01-10_A1_JF_forest_inventory.R 3 | \name{suppress_cat} 4 | \alias{suppress_cat} 5 | \title{Suppress only the cat() output} 6 | \usage{ 7 | suppress_cat(f, ...) 8 | } 9 | \arguments{ 10 | \item{f}{function to be called} 11 | } 12 | \value{ 13 | the return value of the function 14 | } 15 | \description{ 16 | Suppress only the cat() output 17 | } 18 | -------------------------------------------------------------------------------- /man/voxelize_points_mean_attributes.Rd: -------------------------------------------------------------------------------- 1 | % Generated by roxygen2: do not edit by hand 2 | % Please edit documentation in R/2021-11-12_A1_JF_csp_cost_functions.R 3 | \name{voxelize_points_mean_attributes} 4 | \alias{voxelize_points_mean_attributes} 5 | \title{helper function to voxelize a las element} 6 | \usage{ 7 | voxelize_points_mean_attributes(las, res) 8 | } 9 | \arguments{ 10 | \item{las}{a lidR::LAS element} 11 | 12 | \item{res}{voxel resolution in meter} 13 | } 14 | \value{ 15 | a las element with XYZ-coordinates as the voxel center and 16 | X_gr,Y_gr,Z_gr as the center of gravity (mean point coordinates) as well as 17 | all other numeric columns voxel mean values with their original name. 18 | } 19 | \description{ 20 | Calculate voxel mean values for all numeric attributes in the las@data table 21 | including the XYZ-coordinates. 22 | } 23 | \examples{ 24 | 25 | # read example data 26 | file = system.file("extdata", "beech.las", package="CspStandSegmentation") 27 | las = lidR::readTLSLAS(file) 28 | las |> voxelize_points_mean_attributes(1) |> lidR::plot(color = 'X_gr') 29 | 30 | } 31 | \seealso{ 32 | \code{\link{voxelize_points}} 33 | } 34 | \author{ 35 | Julian Frey 36 | } 37 | -------------------------------------------------------------------------------- /src/2021-11-12_A1_JF_csp_cost_cpp_functions.cpp: -------------------------------------------------------------------------------- 1 | // [[Rcpp::depends(lidR)]] 2 | // [[Rcpp::depends(RcppArmadillo)]] 3 | // [[Rcpp::plugins(openmp)]] 4 | 5 | #include 6 | #include 7 | #include 8 | 9 | using namespace Rcpp; 10 | using namespace lidR; 11 | 12 | 13 | 14 | //' helper function to unlist IDs generated by dbscan::frNN 15 | //' 16 | //' 17 | //' creates a vector of indices from a nested list created by dbscan::frNN 18 | //' 19 | //' @param list a list element created by dbscan::frNN 20 | //' @param l the expected length of the result 21 | //' @author Julian Frey 22 | //' @export fast_unlist 23 | // [[Rcpp::export]] 24 | IntegerVector fast_unlist(List list, int l){ 25 | 26 | // define results 27 | IntegerVector res(l); 28 | 29 | int k = 0; 30 | for (int i = 0; i < list.length(); ++i) { 31 | for (int j = 0; j < as(list[i]).length(); j++){ 32 | res[k] = i; 33 | k++; 34 | } 35 | } 36 | 37 | // return indices 38 | return res; 39 | } 40 | 41 | //' helper function to unlist distances computed by dbscan::frNN 42 | //' 43 | //' extracts the distances from a nested list created by dbscan::frNN 44 | //' 45 | //' @param list a list element created by dbscan::frNN 46 | //' @param l the expected length of the result 47 | //' @author Julian Frey 48 | //' @export fast_unlist_dist 49 | // [[Rcpp::export]] 50 | NumericVector fast_unlist_dist(List list, int l){ 51 | 52 | // define results 53 | NumericVector res(l); 54 | 55 | int k = 0; 56 | for (int i = 0; i < list.length(); ++i) { 57 | NumericVector dists = as(list[i]); 58 | for (int j = 0; j < dists.length(); j++){ 59 | res[k] = dists[j]; 60 | k++; 61 | } 62 | } 63 | 64 | // return indices 65 | return res; 66 | } 67 | 68 | 69 | //' Fast Eigenvalues decomposition for k nearest neighbors using a C++ function 70 | //' 71 | //' C++ helper function to compute eigenvalues for geometric feature 72 | //' calculation. 73 | //' 74 | //' @param las LAS element 75 | //' @param k k nearest neighbors 76 | //' @param ncpu number of cpu cores to use 77 | //' @return The function returns for every point the 3 eigenvalues and the 78 | //' third element of the third eigenvector. These values are needed to compute 79 | //' planarity, linerity, verticality etc. in the add_geometry function 80 | //' @author Julian Frey 81 | //' @seealso \link{add_geometry} 82 | //' @export eigen_decomposition 83 | // [[Rcpp::export]] 84 | NumericMatrix eigen_decomposition(S4 las, int k, int ncpu = 1) 85 | { 86 | DataFrame data = as(las.slot("data")); 87 | NumericVector X = data["X"]; 88 | NumericVector Y = data["Y"]; 89 | NumericVector Z = data["Z"]; 90 | unsigned int npoints = X.size(); 91 | 92 | NumericMatrix out(npoints, 4); 93 | 94 | SpatialIndex index(las); 95 | 96 | #pragma omp parallel for num_threads(ncpu) 97 | for (unsigned int i = 0 ; i < npoints ; i++) 98 | { 99 | arma::mat A(k,3); 100 | arma::mat coeff; // Principle component matrix 101 | arma::mat score; 102 | arma::vec latent; // Eigenvalues in descending order 103 | 104 | PointXYZ p(X[i], Y[i], Z[i]); 105 | 106 | std::vector pts; 107 | index.knn(p, k, pts); 108 | 109 | for (unsigned int j = 0 ; j < pts.size() ; j++) 110 | { 111 | A(j,0) = pts[j].x; 112 | A(j,1) = pts[j].y; 113 | A(j,2) = pts[j].z; 114 | } 115 | 116 | arma::princomp(coeff, score, latent, A); 117 | 118 | #pragma omp critical 119 | { 120 | out(i, 0) = latent[0]; 121 | out(i, 1) = latent[1]; 122 | out(i, 2) = latent[2]; 123 | out(i, 3) = coeff[8]; 124 | } 125 | } 126 | 127 | return out; 128 | } 129 | -------------------------------------------------------------------------------- /src/Makevars.win: -------------------------------------------------------------------------------- 1 | PKG_CXXFLAGS = $(SHLIB_OPENMP_CXXFLAGS) -I../inst/include/ -DRCPP_NO_MODULES 2 | PKG_LIBS = $(LAPACK_LIBS) $(BLAS_LIBS) $(FLIBS) $(SHLIB_OPENMP_CXXFLAGS) 3 | CXX_STD = CXX14 4 | -------------------------------------------------------------------------------- /src/RcppExports.cpp: -------------------------------------------------------------------------------- 1 | // Generated by using Rcpp::compileAttributes() -> do not edit by hand 2 | // Generator token: 10BE3573-1514-4C36-9D1C-5A225CD40393 3 | 4 | #include 5 | #include 6 | 7 | using namespace Rcpp; 8 | 9 | #ifdef RCPP_USE_GLOBAL_ROSTREAM 10 | Rcpp::Rostream& Rcpp::Rcout = Rcpp::Rcpp_cout_get(); 11 | Rcpp::Rostream& Rcpp::Rcerr = Rcpp::Rcpp_cerr_get(); 12 | #endif 13 | 14 | // fast_unlist 15 | IntegerVector fast_unlist(List list, int l); 16 | RcppExport SEXP _CspStandSegmentation_fast_unlist(SEXP listSEXP, SEXP lSEXP) { 17 | BEGIN_RCPP 18 | Rcpp::RObject rcpp_result_gen; 19 | Rcpp::RNGScope rcpp_rngScope_gen; 20 | Rcpp::traits::input_parameter< List >::type list(listSEXP); 21 | Rcpp::traits::input_parameter< int >::type l(lSEXP); 22 | rcpp_result_gen = Rcpp::wrap(fast_unlist(list, l)); 23 | return rcpp_result_gen; 24 | END_RCPP 25 | } 26 | // fast_unlist_dist 27 | NumericVector fast_unlist_dist(List list, int l); 28 | RcppExport SEXP _CspStandSegmentation_fast_unlist_dist(SEXP listSEXP, SEXP lSEXP) { 29 | BEGIN_RCPP 30 | Rcpp::RObject rcpp_result_gen; 31 | Rcpp::RNGScope rcpp_rngScope_gen; 32 | Rcpp::traits::input_parameter< List >::type list(listSEXP); 33 | Rcpp::traits::input_parameter< int >::type l(lSEXP); 34 | rcpp_result_gen = Rcpp::wrap(fast_unlist_dist(list, l)); 35 | return rcpp_result_gen; 36 | END_RCPP 37 | } 38 | // eigen_decomposition 39 | NumericMatrix eigen_decomposition(S4 las, int k, int ncpu); 40 | RcppExport SEXP _CspStandSegmentation_eigen_decomposition(SEXP lasSEXP, SEXP kSEXP, SEXP ncpuSEXP) { 41 | BEGIN_RCPP 42 | Rcpp::RObject rcpp_result_gen; 43 | Rcpp::RNGScope rcpp_rngScope_gen; 44 | Rcpp::traits::input_parameter< S4 >::type las(lasSEXP); 45 | Rcpp::traits::input_parameter< int >::type k(kSEXP); 46 | Rcpp::traits::input_parameter< int >::type ncpu(ncpuSEXP); 47 | rcpp_result_gen = Rcpp::wrap(eigen_decomposition(las, k, ncpu)); 48 | return rcpp_result_gen; 49 | END_RCPP 50 | } 51 | 52 | static const R_CallMethodDef CallEntries[] = { 53 | {"_CspStandSegmentation_fast_unlist", (DL_FUNC) &_CspStandSegmentation_fast_unlist, 2}, 54 | {"_CspStandSegmentation_fast_unlist_dist", (DL_FUNC) &_CspStandSegmentation_fast_unlist_dist, 2}, 55 | {"_CspStandSegmentation_eigen_decomposition", (DL_FUNC) &_CspStandSegmentation_eigen_decomposition, 3}, 56 | {NULL, NULL, 0} 57 | }; 58 | 59 | RcppExport void R_init_CspStandSegmentation(DllInfo *dll) { 60 | R_registerRoutines(dll, NULL, CallEntries, NULL, NULL); 61 | R_useDynamicSymbols(dll, FALSE); 62 | } 63 | -------------------------------------------------------------------------------- /tests/testthat.R: -------------------------------------------------------------------------------- 1 | # This file is part of the standard setup for testthat. 2 | # It is recommended that you do not modify it. 3 | # 4 | # Where should you do additional test configuration? 5 | # Learn more about the roles of various files in: 6 | # * https://r-pkgs.org/tests.html 7 | # * https://testthat.r-lib.org/reference/test_package.html#special-files 8 | 9 | library(testthat) 10 | library(CspStandSegmentation) 11 | 12 | test_check("CspStandSegmentation") 13 | -------------------------------------------------------------------------------- /tests/testthat/test-add_geometry.R: -------------------------------------------------------------------------------- 1 | las <- suppressMessages(suppressWarnings(lidR::LAS(data.frame(X=runif(10),Y=runif(10),Z=runif(10))))) 2 | 3 | testthat::test_that("errors", { 4 | testthat::expect_error( 5 | add_geometry(1,1), 6 | "las has to be a LAS object." 7 | ) 8 | 9 | testthat::expect_error( 10 | add_geometry(las, -1), 11 | "k has to be one positive integer." 12 | ) 13 | }) 14 | 15 | testthat::test_that("output type", { 16 | testthat::expect_s4_class( 17 | add_geometry(las, 1), 18 | "LAS" 19 | ) 20 | }) 21 | -------------------------------------------------------------------------------- /tests/testthat/test-add_las_attributes.R: -------------------------------------------------------------------------------- 1 | las <- las <- suppressMessages(suppressWarnings(lidR::LAS(data.frame(X=runif(10),Y=runif(10),Z=runif(10))))) 2 | las@data$test <- 1 3 | 4 | testthat::test_that("errors", { 5 | testthat::expect_error( 6 | add_las_attributes(1), 7 | "las has to be a LAS object." 8 | ) 9 | }) 10 | 11 | testthat::test_that("output type", { 12 | testthat::expect_s4_class( 13 | add_las_attributes(las), 14 | "LAS" 15 | ) 16 | testthat::expect_equal( 17 | length(add_las_attributes(las)@header@VLR), 18 | 1 19 | ) 20 | }) 21 | -------------------------------------------------------------------------------- /tests/testthat/test-add_voxel_coordinates.R: -------------------------------------------------------------------------------- 1 | las <- suppressMessages(suppressWarnings(lidR::LAS(data.frame(X=runif(10),Y=runif(10),Z=runif(10))))) 2 | 3 | testthat::test_that("errors", { 4 | testthat::expect_error( 5 | add_voxel_coordinates(1,1), 6 | "las has to be a LAS object." 7 | ) 8 | 9 | testthat::expect_error( 10 | add_voxel_coordinates(las, -1), 11 | "res has to be numeric and positive." 12 | ) 13 | }) 14 | 15 | testthat::test_that("output type", { 16 | testthat::expect_s4_class( 17 | add_voxel_coordinates(las, 1), 18 | "LAS" 19 | ) 20 | testthat::expect_equal( 21 | ncol(add_voxel_coordinates(las, 1)@data), 22 | 6 23 | ) 24 | }) 25 | -------------------------------------------------------------------------------- /tests/testthat/test-csp_cost_segmentation.R: -------------------------------------------------------------------------------- 1 | las <- suppressMessages(suppressWarnings(lidR::LAS(data.frame(X=runif(10),Y=runif(10),Z=runif(10))))) 2 | map <- data.frame(X=0:1,Y=0:1,Z=0:1,TreeID = 1:2) 3 | 4 | testthat::test_that("errors", { 5 | testthat::expect_error( 6 | csp_cost_segmentation(1,1), 7 | "las has to be a LAS object." 8 | ) 9 | 10 | testthat::expect_error( 11 | csp_cost_segmentation(las, data.frame(X=1,Y=1,Z=1,Tree = 1)), 12 | "map has to be a data.frame with collumn names X,Y,Z,TreeID." 13 | ) 14 | testthat::expect_error( 15 | csp_cost_segmentation(las, map, "a"), 16 | "Voxel_size, V_w, L_w, S_w and N_cores have to be numeric." 17 | ) 18 | }) 19 | -------------------------------------------------------------------------------- /tests/testthat/test-voxelize_points_mean_attributes.R: -------------------------------------------------------------------------------- 1 | las <- suppressMessages(suppressWarnings(lidR::LAS(data.frame(X=runif(10),Y=runif(10),Z=runif(10))))) 2 | 3 | testthat::test_that("errors", { 4 | testthat::expect_error( 5 | voxelize_points_mean_attributes(1,1), 6 | "las has to be a LAS object." 7 | ) 8 | 9 | testthat::expect_error( 10 | voxelize_points_mean_attributes(las, -1), 11 | "res has to be numeric and positive." 12 | ) 13 | }) 14 | 15 | testthat::test_that("output type", { 16 | testthat::expect_s4_class( 17 | voxelize_points_mean_attributes(las, 1), 18 | "LAS" 19 | ) 20 | }) 21 | --------------------------------------------------------------------------------