https://github.com/cran/spatstat
Tip revision: faf8864bb7a1236c2b27fd63c8abb76be20e9386 authored by Adrian Baddeley on 19 October 2006, 22:36:34 UTC
version 1.10-1
version 1.10-1
Tip revision: faf8864
images.R
#
# images.R
#
# $Revision: 1.22 $ $Date: 2006/10/18 04:18:58 $
#
# The class "im" of raster images
#
# Temporary code until we sort out the class structure
#
# im() object creator
#
# is.im() tests class membership
#
# rasterx.im(), rastery.im()
# raster X and Y coordinates
#
# nearest.pixel()
# lookup.im()
# facilities for looking up pixel values
#
################################################################
######## basic support for class "im"
################################################################
#
# creator
im <- function(mat, xcol=seq(ncol(mat)), yrow=seq(nrow(mat)),
lev=levels(mat), units=NULL) {
typ <- typeof(mat)
if(typ == "double")
typ <- "real"
if(is.matrix(mat)) {
nr <- nrow(mat)
nc <- ncol(mat)
if(length(xcol) != nc)
stop("Length of xcol does not match ncol(mat)")
if(length(yrow) != nr)
stop("Length of yrow does not match nrow(mat)")
} else {
if(missing(xcol) || missing(yrow))
stop(paste(sQuote("mat"),
"is not a matrix and I can't guess its dimensions"))
stopifnot(length(mat) == length(xcol) * length(yrow))
nc <- length(xcol)
nr <- length(yrow)
}
# deal with factor case
if(!is.null(lev)) {
# convert to integer codes
mat <- as.integer(factor(mat, levels=lev))
typ <- "factor"
}
# finally coerce 'mat' to a matrix
if(!is.matrix(mat))
mat <- matrix(mat, nrow=nr, ncol=nc)
xstep <- diff(xcol)[1]
ystep <- diff(yrow)[1]
xrange <- range(xcol) + c(-1,1) * xstep/2
yrange <- range(yrow) + c(-1,1) * ystep/2
units <- as.units(units)
out <- list(v = mat,
dim = c(nr, nc),
xrange = xrange,
yrange = yrange,
xstep = xstep,
ystep = ystep,
xcol = xcol,
yrow = yrow,
lev = lev,
type = typ,
units = units)
class(out) <- "im"
attr(out, "levels") <- lev
return(out)
}
is.im <- function(x) {
inherits(x,"im")
}
"levels<-.im" <- function(x, value) {
if(x$type != "factor")
stop("image is not factor-valued")
attr(x, "levels") <- x$lev <- value
x
}
################################################################
######## methods for class "im"
################################################################
shift.im <- function(X, vec=c(0,0), ...) {
verifyclass(X, "im")
X$xrange <- X$xrange + vec[1]
X$yrange <- X$yrange + vec[2]
X$xcol <- X$xcol + vec[1]
X$yrow <- X$yrow + vec[2]
return(X)
}
"[.im" <- subset.im <-
function(x, i, j, drop=TRUE, ...) {
lev <- x$lev
if(verifyclass(i, "ppp", fatal=FALSE) && missing(j)) {
# 'i' is a point pattern
# Look up the greyscale values for the points of the pattern
values <- lookup.im(x, i$x, i$y, naok=TRUE)
if(drop) return(values[!is.na(values)]) else return(values)
}
if(verifyclass(i, "owin", fatal=FALSE) && missing(j)) {
# 'i' is a window
# if drop = FALSE, just set values outside window to NA
# if drop = TRUE, extract values for all pixels inside window
# as an image (if 'i' is a rectangle)
# or as a vector (otherwise)
xy <- expand.grid(y=x$yrow,x=x$xcol)
inside <- inside.owin(xy$x, xy$y, i)
if(!drop) {
x$v[!inside] <- NA
return(x)
} else if(i$type != "rectangle") {
values <- x$v[inside]
if(!is.null(lev))
values <- factor(values, levels=seq(lev), labels=lev)
return(values)
} else {
disjoint <- function(r, s) { (r[2] < s[1]) || (r[1] > s[2]) }
clip <- function(r, s) { c(max(r[1],s[1]), min(r[2],s[2])) }
inrange <- function(x, r) { (x >= r[1]) & (x <= r[2]) }
if(disjoint(i$xrange, x$xrange) || disjoint(i$yrange, x$yrange))
# empty intersection
return(numeric(0))
xr <- clip(i$xrange, x$xrange)
yr <- clip(i$yrange, x$yrange)
colsub <- inrange(x$xcol, xr)
rowsub <- inrange(x$yrow, yr)
return(im(x$v[rowsub,colsub], x$xcol[colsub], x$yrow[rowsub],
lev=lev, units=x$units))
}
}
stop("The subset operation is undefined for this type of index")
}
################################################################
######## other tools
################################################################
#
# This function is similar to nearest.raster.point except for
# the third argument 'im' and the different idiom for calculating
# row & column - which could be used in nearest.raster.point()
nearest.pixel <- function(x,y,im) {
verifyclass(im, "im")
nr <- im$dim[1]
nc <- im$dim[2]
cc <- round(1 + (x - im$xcol[1])/im$xstep)
rr <- round(1 + (y - im$yrow[1])/im$ystep)
cc <- pmax(1,pmin(cc, nc))
rr <- pmax(1,pmin(rr, nr))
return(list(row=rr, col=cc))
}
# This function is a generalisation of inside.owin()
# to images other than binary-valued images.
lookup.im <- function(Z, x, y, naok=FALSE) {
verifyclass(Z, "im")
if(length(x) != length(y))
stop("x and y must be numeric vectors of equal length")
value <- rep(NA, length(x))
# test whether inside bounding rectangle
xr <- Z$xrange
yr <- Z$yrange
eps <- sqrt(.Machine$double.eps)
frameok <- (x >= xr[1] - eps) & (x <= xr[2] + eps) &
(y >= yr[1] - eps) & (y <= yr[2] + eps)
if(!any(frameok)) # all points OUTSIDE range - no further work needed
return(value) # all zero
# consider only those points which are inside the frame
xf <- x[frameok]
yf <- y[frameok]
# map locations to raster (row,col) coordinates
loc <- nearest.pixel(xf,yf,Z)
# look up image values
vf <- Z$v[cbind(loc$row, loc$col)]
# insert into 'ok' vector
value[frameok] <- vf
if(!naok && any(is.na(value)))
warning("Internal error: NA's generated")
# return factor, if it's a factor valued image
if(!is.null(lev <- Z$lev))
value <- factor(value, levels=seq(lev), labels=lev)
return(value)
}
rasterx.im <- function(x) {
verifyclass(x, "im")
v <- x$v
xx <- x$xcol
matrix(xx[col(v)], ncol=ncol(v), nrow=nrow(v))
}
rastery.im <- function(x) {
verifyclass(x, "im")
v <- x$v
yy <- x$yrow
matrix(yy[row(v)], ncol=ncol(v), nrow=nrow(v))
}
##############
# methods for other functions
as.matrix.im <- function(x) {
return(x$v)
}
mean.im <- function(x, ...) {
verifyclass(x, "im")
return(mean.default(as.matrix(x), na.rm=TRUE, ...))
}
hist.im <- function(x, ..., probability=FALSE) {
verifyclass(x, "im")
xname <- paste(deparse(substitute(x), 500), collapse="\n")
main <- paste("Histogram of", xname)
# default plot arguments
# extract pixel values
values <- as.vector(as.matrix(x))
if(x$type == "factor") {
values <- factor(values)
levels(values) <- x$lev
}
# barplot or histogram
if(x$type %in% c("logical", "factor")) {
tab <- table(values)
if(probability) {
tab <- tab/sum(tab)
ylab <- "Probability"
} else
ylab <- "Number of pixels"
arglist <-
out <- do.call("barplot",
resolve.defaults(list(tab),
list(...),
list(xlab=paste("Pixel value"),
ylab=ylab,
main=main)))
} else {
values <- values[!is.na(values)]
ylab <- if(probability) "Probability density" else "Number of pixels"
out <- do.call("hist.default",
resolve.defaults(list(values),
list(probability=probability),
list(...),
list(xlab=paste("Pixel value"),
ylab=ylab,
main=main)))
}
return(invisible(out))
}
cut.im <- function(x, ...) {
verifyclass(x, "im")
vcut <- cut(as.numeric(as.matrix(x)), ...)
lev <- if(is.factor(vcut)) levels(vcut) else NULL
return(im(vcut, xcol=x$xcol, yrow=x$yrow, lev=lev, units=units(x)))
}
quantile.im <- function(x, ...) {
verifyclass(x, "im")
q <- do.call("quantile",
resolve.defaults(list(as.numeric(as.matrix(x))),
list(...),
list(na.rm=TRUE)))
return(q)
}