Hi John
I agree that the Lab representation is the best so far for the goal of
perceptually
similar colors, and the approximate JND of
2.3 on the distance scale in this space is a useful, non-arbitrary
criterion.
FWIW, your demo might better show the hex and color names adjacently,
for direct
comparison.
cols <- c("#010101", "#EEEEEE", "#AA0000", "#00AA00", "#0000AA", "#AAAA00",
"#AA00AA", "#00AAAA")
(nms <- rgb2col(cols))
pie(rep(1, 2*length(cols)), labels=c(rbind(cols, nms)),
col=c(rbind(cols, nms)))
-Michael
On 6/1/2013 11:31 AM, John Fox wrote:
Hi Michael,
Thanks for the Wikipedia tip -- I'd looked there but didn't find this
article. The article explains that the Lab colour space was formulated to
provide uniform perceptual differences between colours, with a JND of
approximately of 2.3. Ken Knoblauch made a similar point. The article goes
on to describe relatively complicated adjustments meant to improve the LAV
distance metric, which are probably overkill for my application.
I've programmed Lab colour matching as follows, using Euclidean distances
and adapting Kevin Wright's modification of my original code. I used
convertColor(), which Martin Maechler pointed out to me.
----------- snip --------------
r2c <- function(){
all.names <- colors()
all.lab <- t(convertColor(t(col2rgb(all.names)), from="sRGB", to="Lab",
scale.in=255))
find.near <- function(x.lab) {
sq.dist <- colSums((all.lab - x.lab)^2)
rbind(all.names[which.min(sq.dist)], min(sq.dist))
}
function(cols.hex, near=2.3){
cols.lab <- t(convertColor(t(col2rgb(cols.hex)), from="sRGB",
to="Lab", scale.in=255))
cols.near <- apply(cols.lab, 2, find.near)
ifelse(cols.near[2, ] < near^2, cols.near[1, ], cols.hex)
}
}
rgb2col <- r2c()
----------- snip --------------
A bit of experimentation suggests that this works better than using (as I
did previously) direct RGB distances, matching more colours to names and
providing (to my eye, with my monitor) perceptually closer matches, though
sometimes with (again to my eye) perceptible differences. Here's an
illustration, adapting one of Kevin's examples:
----------- snip --------------
cols <- c("#010101", "#EEEEEE", "#AA0000", "#00AA00", "#0000AA", "#AAAA00",
"#AA00AA", "#00AAAA")
(nms <- rgb2col(cols))
pie(rep(1, 2*length(cols)), labels=c(cols, nms), col=c(cols, nms))
----------- snip --------------
Thanks again to everyone who responded to my original, naïve, question.
Best,
John
-----Original Message-----
From: Michael Friendly [mailto:frien...@yorku.ca]
Sent: Friday, May 31, 2013 10:24 AM
To: John Fox
Cc: r-help@r-project.org; 'Martin Maechler'
Subject: Re: measuring distances between colours?
Hi John
This has been an interesting discussion.
Though you have a solution for your needs, you might be interested in
this javascript implementation that allows you to visually compare
color
distances in various color spaces
http://stevehanov.ca/blog/index.php?id=116
And, all the theory of color distance is described in
http://en.wikipedia.org/wiki/Color_difference
PS: This is a very handy function. When I last tried
aplpack::bagplot(), it was annoying that the colors could *only*
be specified in hex.
-Michael
On 5/30/2013 5:14 PM, John Fox wrote:
Dear all,
My thanks to everyone who addressed my question. I've incorporated
Eik
Vettorazzi's suggestion for improved conversion of hexadecimal RGB
colours
to decimal numbers, and Martin Maechler's hint to look at
demo(colors). I've
loosened the default definition of "close enough" from the latter,
since the
following seems to work well for my purposes.
r2c <- function(){
hex2dec <- function(hexnums) {
# suggestion of Eik Vettorazzi
sapply(strtoi(hexnums, 16L), function(x) x %/% 256^(2:0) %%
256)
}
findMatch <- function(dec.col) {
sq.dist <- colSums((hsv - dec.col)^2)
rbind(which.min(sq.dist), min(sq.dist))
}
colors <- colors()
hsv <- rgb2hsv(col2rgb(colors))
function(cols, near=0.25){
cols <- sub("^#", "", toupper(cols))
dec.cols <- rgb2hsv(hex2dec(cols))
which.col <- apply(dec.cols, 2, findMatch)
matches <- colors[which.col[1, ]]
unmatched <- which.col[2, ] > near^2
matches[unmatched] <- paste("#", cols[unmatched], sep="")
matches
}
}
rgb2col <- r2c()
For example,
rgb2col(c("010101", "EEEEEE", "AA0000", "00AA00", "0000AA",
"AAAA00",
"AA00AA", "00AAAA"))
[1] "black" "gray93" "darkred" "green4"
[5] "blue4" "darkgoldenrod" "darkmagenta" "cyan4"
rgb2col(c("010101", "090909", "090000", "000900", "000009",
"090900",
"090009", "000909"))
[1] "black" "gray3" "#090000" "#000900" "#000009" "#090900"
[7] "#090009" "#000909"
Thanks again,
John
--
Michael Friendly Email: friendly AT yorku DOT ca
Professor, Psychology Dept. & Chair, Quantitative Methods
York University Voice: 416 736-2100 x66249 Fax: 416 736-5814
4700 Keele Street Web: http://www.datavis.ca
Toronto, ONT M3J 1P3 CANADA
--
Michael Friendly Email: friendly AT yorku DOT ca
Professor, Psychology Dept. & Chair, Quantitative Methods
York University Voice: 416 736-2100 x66249 Fax: 416 736-5814
4700 Keele Street Web: http://www.datavis.ca
Toronto, ONT M3J 1P3 CANADA
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