rasterpic_img {rasterpic} | R Documentation |
Convert an image to a geo-tagged SpatRaster
Description
Geotags an image based on the coordinates of a given spatial object.
Usage
rasterpic_img(
x,
img,
halign = 0.5,
valign = 0.5,
expand = 0,
crop = FALSE,
mask = FALSE,
inverse = FALSE,
crs = NULL
)
Arguments
x |
R object that may be:
|
img |
An image to be geotagged. It can be a local file or an online
file (e.g.
|
halign , valign |
Horizontal and vertical alignment of
|
expand |
An expansion factor of the bounding box of |
crop |
Logical. Should the raster be cropped to the (expanded) bounding
box of |
mask |
Logical, applicable only if |
inverse |
Logical. It affects only if |
crs |
Character string describing a coordinate reference system.
This parameter would only affect if |
Details
vignette("rasterpic", package = "rasterpic")
explains with examples the
effect of parameters halign
, valign
, expand
, crop
and mask
.
CRS
The function preserves the Coordinate Reference System of x
if applicable.
For optimal results do not use geographic coordinates
(longitude/latitude).
crs
can be in a WKT format, as a "authority:number"
code such as
"EPSG:4326"
, or a PROJ-string format such as "+proj=utm +zone=12"
. It can
be also retrieved with:
See Value and Notes on terra::crs()
.
Value
A SpatRaster
object (see terra::rast()
) where each layer corresponds to
a color channel of img
:
If
img
has at least 3 channels (e.g. layers), the result would have an additional property setting the layers 1 to 3 as the Red, Green and Blue channels.If
img
already has a definition or RGB values (this may be the case fortiff/tif
files) the result would keep that channel definition.
See Also
From sf:
-
vignette("sf1", package = "sf")
to understand how sf organizes R objects.
From terra:
For plotting:
Examples
library(sf)
library(terra)
library(ggplot2)
library(tidyterra)
x_path <- system.file("gpkg/UK.gpkg", package = "rasterpic")
x <- st_read(x_path, quiet = TRUE)
img <- system.file("img/vertical.png", package = "rasterpic")
# Default config
ex1 <- rasterpic_img(x, img)
ex1
autoplot(ex1) +
geom_sf(data = x, fill = NA, color = "white", linewidth = .5)
# Expand
ex2 <- rasterpic_img(x, img, expand = 0.5)
autoplot(ex2) +
geom_sf(data = x, fill = NA, color = "white", linewidth = .5)
# Align
ex3 <- rasterpic_img(x, img, halign = 0)
autoplot(ex3) +
geom_sf(data = x, fill = NA, color = "white", linewidth = .5)
labs(title = "Align")
# Crop
ex4 <- rasterpic_img(x, img, crop = TRUE)
autoplot(ex4) +
geom_sf(data = x, fill = NA, color = "white", linewidth = .5) +
labs(title = "Crop")
# Mask
ex5 <- rasterpic_img(x, img, mask = TRUE)
autoplot(ex5) +
geom_sf(data = x, fill = NA, color = "white", linewidth = .5) +
labs(title = "Mask")
# Mask inverse
ex6 <- rasterpic_img(x, img, mask = TRUE, inverse = TRUE)
autoplot(ex6) +
geom_sf(data = x, fill = NA, color = "white", linewidth = .5) +
labs(title = "Mask Inverse")
# Combine Mask inverse and crop
ex7 <- rasterpic_img(x, img, crop = TRUE, mask = TRUE, inverse = TRUE)
autoplot(ex7) +
geom_sf(data = x, fill = NA, color = "white", linewidth = .5) +
labs(title = "Combine")
# RGB channels ------
plot(ex1)
ex_rgb <- ex1
has.RGB(ex_rgb)
RGB(ex_rgb)
# Modify RGB channels
RGB(ex_rgb) <- c(2, 3, 1)
RGB(ex_rgb)
plot(ex_rgb)
# Remove RGB channels
RGB(ex_rgb) <- NULL
has.RGB(ex_rgb)
RGB(ex_rgb)
# Note the difference with terra::plot
plot(ex_rgb)