rTLS (version 0.2.3)

voxels_counting: Voxels Counting

Description

Creates cube like voxels of different size on a point cloud using the voxels function, and then return a summary_voxels of their features.

Usage

voxels_counting(
  cloud,
  edge_sizes = NULL,
  min_size,
  length_out = 10,
  bootstrap = FALSE,
  R = NULL,
  progress = TRUE,
  parallel = FALSE,
  threads = NULL
)

Arguments

cloud

A data.table with xyz coordinates of the point clouds in the first three columns.

edge_sizes

A positive numeric vector describing the edge length of the different cubes to perform. If NULL, it use edge sizes by default based on the largest range of XYZ and min_size.

min_size

A positive numeric vector of length 1 describing the minimum cube edge length to perform. This is required if edge_sizes = NULL.

length_out

A positive interger of length 1 indicating the number of different edge lengths to use. This is required if edge_sizes = NULL.

bootstrap

Logical. If TRUE, it computes a bootstrap on the H index calculations. FALSE as default.

R

A positive integer of length 1 indicating the number of bootstrap replicates. This need to be used if bootstrap = TRUE.

progress

Logical, if TRUE displays a graphical progress bar. TRUE as default.

parallel

Logical, if TRUE it uses a parallel processing for the voxelization. FALSE as default.

threads

An integer >= 0 describing the number of threads to use. This need to be used if parallel = TRUE.

Value

A data.table with the summary of the voxels created with their features.

See Also

voxels, summary_voxels, plot_voxels

Examples

# NOT RUN {
data(pc_tree)

#Applying voxels counting.
voxels_counting(pc_tree, min_size = 2)

#Voxels counting using bootstrap on the H indexes with 1000 repetitions.
voxels_counting(pc_tree, min_size = 2, bootstrap = TRUE, R = 1000)


# }