"im"
representing a two-dimensional pixel image.im(mat, xcol=seq_len(ncol(mat)), yrow=seq_len(nrow(mat)),
xrange=NULL, yrange=NULL,
unitname=NULL)
xcol
, yrow
are present.)as.matrix.im
or [.im
."im"
representing
a two-dimensional pixel image. See im.object
for details of this class. The matrix mat
contains the mat[i,j]
gives the pixel value at the location (xcol[j],yrow[i])
.
That is, the row index of the matrix mat
corresponds
to increasing y coordinate, while the column index of mat
corresponds to increasing x coordinate.
Thus yrow
has one entry for each row of mat
and xcol
has one entry for each column of mat
.
Under the usual convention in R, a correct
display of the image would be obtained by transposing the matrix, e.g.
image.default(xcol, yrow, t(mat))
, if you wanted to do it by hand.
The entries of mat
may be numeric (real or integer), complex,
logical, character, or factor values.
If mat
is not a matrix, it will be converted into
a matrix with nrow(mat) = length(yrow)
and
ncol(mat) = length(xcol)
.
To make a factor-valued image, note that
Rhas a quirky way of handling matrices with
factor-valued entries. The command matrix
cannot be used
directly, because it destroys factor information.
To make a factor-valued image, do one of the following:
factor
containing the pixel values,
saymat <- factor(.....)
,
and then assign matrix dimensions to it bydim(mat) <- c(nr, nc)
wherenr, nc
are the numbers of rows and columns. The
resulting objectmat
is both a factor and a vector.mat
as a one-dimensional factor
and specify the argumentsxcol
andyrow
to determine the dimensions of the image.cut.im
oreval.im
to make factor-valued
images from other images).im.object
. To convert other kinds of data to a pixel image (for example,
functions or windows), use as.im
.
im.object
,
as.im
,
as.matrix.im
,
[.im
,
eval.im
vec <- rnorm(1200)
mat <- matrix(vec, nrow=30, ncol=40)
whitenoise <- im(mat)
whitenoise <- im(mat, xrange=c(0,1), yrange=c(0,1))
whitenoise <- im(mat, xcol=seq(0,1,length=40), yrow=seq(0,1,length=30))
whitenoise <- im(vec, xcol=seq(0,1,length=40), yrow=seq(0,1,length=30))
plot(whitenoise)
# Factor-valued images:
f <- factor(letters[1:12])
dim(f) <- c(3,4)
Z <- im(f)
# Factor image from other image:
cutwhite <- cut(whitenoise, 3)
plot(cutwhite)
# Factor image from raw data
cutmat <- cut(mat, 3)
dim(cutmat) <- c(30,40)
cutwhite <- im(cutmat)
plot(cutwhite)
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