The anglr package aims to provide direct access to generic 3D tools.
… geospatial is cool … 3D is cool … putting them together is challenging …
The anglr package provides a full suite of mesh-creation and 3D plotting
functions, extending the rgl
Convert geospatial objects to 3D with
as.mesh3d()- works for matrix, raster, sf-polygons, sp-polygons, silicate-triangles
Plot geospatial objects with
plot3d()- works for matrix, raster, sf (any type), sp, trip, silicate
Merge raster and vector with
as.mesh3d- elevate polygons or lines with a raster dem
Drape imagery onto raster or polygon surfaces with
Copy Z values from a raster, polygon/line field value, or constant onto mesh vertices
Create any mesh above and reproject to any projection, including geocentric
wire3d()from the rgl package to plot any
mesh3dobject created. (
plot3d()also calls the converters for non-mesh types, so we can quickly get a 3D scene from any spatial data)
Why anglr, isn’t this all done?
The rgl package has long supported powerful interactive 3D visualizations for points, lines, and surfaces. Surfaces can be of any form, triangles or quad primitives wrapped around a bunny, a globe, or platonic shapes such as cubes and octahedrons. There is support for textures, which is essentially imagery draped onto a surface at native resolution - there is no loss of image quality at all.
But, rgl is hard to use, and there are very few helpers for converting
existing formats into the right form. There are some, for example
as.mesh3d() methods for commonly used triangulation packages. It also
was harder to use in the past, but recently has been improved a great
deal for taking generic data and converting it.
There has been little support in rgl for geo-spatial data structures, such as spatial rasters, polygons and lines that are common in GIS workflows. The anglr package aims to fill this gap, and gives total control over conversion of spatial and other data into mesh forms - even if visualization is not the end goal.
There are packages that convert matrices and imagery to compelling
animated 3D scenes, but these are targetted at presentation rather than
generality and development. The scenes are targetted towards
presentation, and adding general extra data to a given scene requires a
lot of workarounds. There are also only few well-known packages that use
the rgl mesh3d format (Rvcg, …). Many examples use
which is limited to the model that provides for an input matrix and
overlaid colours or textures. These cannot be wrapped arbitrarily around
curves in any 3D coordinate system.
Install from CRAN with
To install the development version use
## install.packages("remotes") ## if required remotes::install_github("hypertidy/anglr")
Feel free to contact me via the Issues if you have problems, or these notes don’t make sense.
On Linux you will need at least the following installed.
libproj-dev \ libgl1-mesa-dev \ libglu1-mesa-dev
Let me know if you have problems, or are interested in this work. See the issues tab to make suggestions or report bugs.
These examples are a bit out of date, anglr visualization works best interactively and with your own data, but these demo pages give a flavour for what is possible.
Demo 01 - merge vector and raster data
An example of merging vector and raster, with a continuous interpretation applied to the polygon mesh and underlying elevation.
## a global DEM data("gebco", package = "anglr") library(sf) ## North Carolina, the sf boilerplate polygon layer nc <- read_sf(system.file("shape/nc.shp", package="sf", mustWork = TRUE)) library(raster) library(anglr) library(silicate) p_mesh <- DEL(nc, max_area = 0.002) ## a relief map, composed of triangles grouped by polygon with ## interpolated raster elevation p_mesh <- copy_down(p_mesh, z = gebco) ## plot the scene library(rgl) rgl.clear() ## rerun the cycle from clear to widget in browser contexts plot3d(p_mesh) bg3d("black"); material3d(specular = "black") aspect3d(1, 1, .1)
Follow this link to see the result:
z argument to
copy_down() is a raster, and so the
coordinate of the mesh is updated by extracting values from the raster
using bilinear interpolation.
Demo 02 - copy discrete values to polygon elevation
An example of elevating polygons with constant attribute values, a discrete interpretation that requires separating the (until-now) continuous mesh of polygon features.
z set to a column in the layer or a specific vector of
values this is used as a constant offset for the
z_ value, and the
mesh is separated by feature.
(The simpler TRI model is used here because we only need poor quality triangles for planar geometry. )
## either form works #c_mesh <- copy_down(TRI(nc), z = p_mesh$object$BIR74) c_mesh <- copy_down(TRI(nc), z = "BIR74") open3d() a <- plot3d(c_mesh) bg3d("black"); material3d(specular = "black") aspect3d(1, 1, .2)
Follow this link to see the result:
In the silicate models, complex objects are decomposed to a set of related, linked tables. Object identity is maintained with attribute metadata and this is carried through to colour and other aesthetics.
Plot (3D) methods take those tables and generate the “indexed array”
structures needed for ‘rgl’. (
plot3d will return the rgl-model form).
This gets us part of the way towards having the best of both worlds of
GIS and 3D graphics.
Another example shows this approach applied to a 3D multipatch shapefile, with a few easy steps we can plot a wiremesh or roof/floor polygons from a civic building footprint.
Please note that this project is released with a Contributor Code of Conduct. By participating in this project you agree to abide by its terms.