nara provides tools for working with R’s nativeRaster
image format to enable fast double-buffered graphics rendering.
nativeRaster
buffers are fast enough to use for rendering at speed >30 frames-per-second.
This makes them useful for games and other interactive applications.
An example graphics demo is this non-playable version of pacman created and running in R in realtime (see the vignette):
nara:
nativeRaster
and why is it fast?
A nativeRaster
is a built-in datatype in R.
It is an integer matrix where each integer represents the RGBA colour at a single pixel. The 32-bit integer at each location is interpreted within R to be four colour channels (RGBA) represented by 8 bits each.
This way of encoding colour information is closer to the internal representation used by graphics devices, and therefore can be faster to render, save and load (as fewer data conversion steps are needed).
nara is targeted at fast rendering (>30fps), and tries to minimise R function calls and memory allocations.
When updating nativeRaster
objects with this package, all changes are done in place on the current object i.e. a new object is not created.
No anti-aliasing is done by the draw methods in this package.
No interpolation is done - x
and y
values for drawing coordinates are converted to integers.
You can install from GitHub with:
# install.package('remotes')
remotes::install_github('coolbutuseless/nara')
The following is a rendering of a single scene with multiple elements.
The interesting thing about this scene that drawing all the objects into the nativeRaster
image and rendering to screen can take as little as 5 millseconds.
This means that this scene could render at around 200 frames-per-second.
library(grid)
library(nara)
set.seed(1)
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
# Create 'nr' image
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
w <- 10
h <- 8
nr <- nr_new(w * 30, h * 30, fill = 'grey98')
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
# Draw a grid of squares
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
colours <- viridisLite::inferno(w * h)
coords <- expand.grid(y = seq(0, h-1) * 30 + 1, x = seq(0, w-1) * 30 + 1)
nr_rect(nr, x = coords$x, y = coords$y, w = 27, h = 27, colour = NA, fill = colours)
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
# Draw a bunch of dinos
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
nr_blit(nr, dino[[1]], x = sample(300, 15), y = sample(200, 15))
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
# Add an image read from file (with alpha transparency)
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
img <- png::readPNG(system.file("img", "Rlogo.png", package="png"), native = TRUE)
nr_blit(nr, img, 1, 1)
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
# Add a polygon
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
thetas <- seq(pi/6, 2*pi, pi/3)
x <- 50 * cos(thetas) + 240
y <- 50 * sin(thetas) + 180
nr_polygon(nr, x = x, y = y, fill = '#556688c0', col = 'blue')
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
# Add text to the image
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
nr_text(nr, "Hello #RStats", 180, 1, fontsize = 16)
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
# Copy image to the device
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
grid.raster(nr, interpolate = FALSE)
Included with nara are 16 frames of cartoon dinosaur animation from the BubbleBobble video game - see dino
data.
library(grid)
nr <- nr_new(100, 30, 'grey80')
nr_blit(nr, dino[[1]], 2, 1)
grid.raster(nr, interpolate = FALSE)
The reason to use nara is that operations are fast enough that nativeRaster
can be used as an in-memory buffer for a double-bufferred rendering system.
Double-buffered
rendering is where two buffers are used for rendering with one buffer being shown to the user, and the other existing in memory as a place to render.
In this example, the dino
sprite is renderd to a nativeRaster
image. This in-memory buffer is then displayed to the user using grid.raster()
.
By altering the position and animation frame every time the dino is shown, smooth animation is possible.
This simple code runs at well over 100 frames-per-second.
It is unlikely your screen will refresh this fast, but it does indicate that there is plenty of headroom for more complicated computations for each frame.
library(grid)
# Setup a fast graphics device that can render quickly
x11(type = 'cairo', antialias = 'none')
dev.control('inhibit')
# Create the in-memory nativeRaster canvas
nr <- nr_new(100, 30, 'grey80')
# Clear, blit and render => animation!
for (i in -30:110) {
nr_fill(nr, 'grey80') # Clear the nativeRaster
nr_blit(nr, dino[[(i %% 16) + 1]], i, 1) # copy dino to nativeRaster
grid.raster(nr, interpolate = FALSE) # copy nativeRaster to screen
Sys.sleep(0.02) # Stop animation running too fast.
}
You can quickly blit (i.e. copy) a sprite into multiple locations on the nativeraster with nr_blit()
.
In this example 100 random positions and velocities are first created. A character sprite is then blitted to each of these 100 locations.
The positions are updated using the velocities, and the next frame is rendered. In this way multiple sprites are rendered and animated on screen.
library(grid)
# Setup a fast graphics device that can render quickly
x11(type = 'dbcairo', antialias = 'none', width = 8, height = 6)
dev.control('inhibit')
# Number of sprites
N <- 100
# Window size
w <- 400
h <- 300
# location and movement vector of all the sprites
x <- sample(w -25, N, replace = TRUE)
y <- sample(h - 25, N, replace = TRUE)
vx <- sample(seq(-5, 5), N, replace = TRUE)
vy <- sample(seq(-5, 5)[-6], N, replace = TRUE)
# Create an empty nativeraster with a grey background
nr <- nr_new(w, h, 'grey80')
for (frame in 1:1000) {
# Clear the nativeraster and blit in all the dinosaurs
nr_fill(nr, 'grey80')
nr_blit(nr, dino[[(frame) %% 16 + 1]], x, y)
# Draw the nativeraster to screen
dev.hold()
grid.raster(nr, interpolate = FALSE)
dev.flush()
# Update the position and velocity of each dino
# Dinos move at constant velocity and bounce off the sides of the image
x <- x + vx
y <- y + vy
vx <- ifelse(x > (w - 25)| x < 1, -vx, vx)
vy <- ifelse(y > (h - 25)| y < 1, -vy, vy)
# slight pause to slow things down
Sys.sleep(0.03)
}
The coordinate system for nara
nativeRaster objects has its origins at the bottom left corner of the image with coordinates (1,1)
.
raster
vs nativeRaster
A pretty naive benchmark showing that drawing a nativeRaster
raster
raster
nr <- nr_new(800, 600)
ras <- nr_to_raster(nr)
x11(type = 'dbcairo')
dev.control('inhibit')
draw_nr <- function() {
dev.hold()
grid.raster(nr, interpolate = FALSE)
dev.flush()
}
draw_raster <- function() {
dev.hold()
grid.raster(ras, interpolate = FALSE)
dev.flush()
}
bench::mark(
draw_nr(),
draw_raster()
)
expression | itr/sec | mem_alloc |
---|---|---|
draw_nr() | 269.8799 | 1.83MB |
draw_raster() | 46.6820 | 7.32MB |