This function converts a three-band SpatialGridDataFrame into a single band of colour indices and a colour look-up table using RGB2PCT. vec2RGB uses given breaks and colours (like image) to make a three column matrix of red, green, and blue values for a numeric vector.

SGDF2PCT(x, ncolors = 256, adjust.bands=TRUE)
vec2RGB(vec, breaks, col)

## Arguments

x a three-band SpatialGridDataFrame object a number of colours between 2 and 256 default TRUE; if FALSE the three bands must lie each between 0 and 255, but will not be streched within those bounds a numeric vector a set of breakpoints for the colours: must give one more breakpoint than colour a list of colors

## Value

The value returned is a list:

idx

a vector of colour indices in the same spatial order as the input object

ct

a vector of RGB colours

## References

https://gdal.org/

Roger Bivand

## Examples

logo <- system.file("pictures/Rlogo.jpg", package="rgdal")[1]
#> /tmp/Rtmpu1yWGf/temp_libpathfa5c0298539de/rgdal/pictures/Rlogo.jpg has GDAL driver JPEG
#> and has 175 rows and 200 columns#> Warning: GeoTransform values not availablecols <- SGDF2PCT(SGlogo)
SGlogo$idx <- cols$idx
image(SGlogo, "idx", col=cols$ct) SGlogo <- readGDAL(logo) #> /tmp/Rtmpu1yWGf/temp_libpathfa5c0298539de/rgdal/pictures/Rlogo.jpg has GDAL driver JPEG #> and has 175 rows and 200 columns#> Warning: GeoTransform values not availablecols <- SGDF2PCT(SGlogo, ncolors=64) SGlogo$idx <- cols$idx image(SGlogo, "idx", col=cols$ct)
#> /tmp/Rtmpu1yWGf/temp_libpathfa5c0298539de/rgdal/pictures/Rlogo.jpg has GDAL driver JPEG
#> and has 175 rows and 200 columns#> Warning: GeoTransform values not availablecols <- SGDF2PCT(SGlogo, ncolors=8)
SGlogo$idx <- cols$idx
image(SGlogo, "idx", col=cols$ct) data(meuse.grid) coordinates(meuse.grid) <- c("x", "y") gridded(meuse.grid) <- TRUE fullgrid(meuse.grid) <- TRUE summary(meuse.grid$dist)
#>    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's
#>   0.000   0.119   0.272   0.297   0.440   0.993    5009 opar <- par(no.readonly=TRUE)
par(mfrow=c(1,2), mar=c(1,1,1,1)+0.1)
image(meuse.grid, "dist", breaks=seq(0,1,1/10), col=bpy.colors(10))
RGB <- vec2RGB(meuse.grid$dist, breaks=seq(0,1,1/10), col=bpy.colors(10)) summary(RGB) #> red green blue #> Min. : 0.00 Min. : 0.00 Min. : 36.0 #> 1st Qu.: 0.00 1st Qu.: 0.00 1st Qu.: 87.0 #> Median : 0.00 Median : 0.00 Median :189.0 #> Mean : 75.31 Mean : 22.98 Mean :171.1 #> 3rd Qu.:159.00 3rd Qu.: 15.00 3rd Qu.:255.0 #> Max. :255.00 Max. :255.00 Max. :255.0 #> NA's :5009 NA's :5009 NA's :5009 meuse.grid$red <- RGB[,1]
meuse.grid$green <- RGB[,2] meuse.grid$blue <- RGB[,3]
cols <- SGDF2PCT(meuse.grid[c("red", "green", "blue")], ncolors=10,
is.na(cols$idx) <- is.na(meuse.grid$dist)
meuse.grid$idx <- cols$idx
image(meuse.grid, "idx", col=cols$ct) par(opar) # Note: only one wrongly classified pixel after NA handling/dropping # The functions are not written to be reversible sort(table(findInterval(meuse.grid$dist, seq(0,1,1/10), all.inside=TRUE)))
#>  82  99 118 237 417 431 507 525 687