knitr::opts_chunk$set(warning=FALSE, message=FALSE, cache=FALSE, comment=NA, verbose=TRUE, fig.width=7.5, fig.height=5, dev='jpeg',dev.args=list(quality=50)) library(imager) res <- vector("list",3) a <- imfill(300, 300, 3, c(1,0,0)) %>% grayscale() res[[1]] <- unique(a) a <- imfill(300, 300, 3, c(0,1,0)) %>% grayscale() res[[2]] <- unique(a) a <- imfill(300, 300, 3, c(0,0,1)) %>% grayscale() res[[3]] <- unique(a)
We have two options when treating color images with imagerExtra.
The former is straightforward.
One example is shown below.
library(imagerExtra) x <- boats s <- 0.1 R(x) <- BalanceSimplest(R(x), s, s, range=c(0,1)) G(x) <- BalanceSimplest(G(x), s, s, range=c(0,1)) B(x) <- BalanceSimplest(B(x), s, s, range=c(0,1)) layout(matrix(1:2, 1, 2)) plot(boats, main = "Original") plot(x, main = "Independently Processed")
The latter needs three functions: Grayscale, GetHue, RestoreHue.
grayscale function of imager computes as shown below by default.
text1 <- sprintf("Y = %fR + %fG + %fB", res[[1]], res[[2]], res[[3]]) cat(text1)
where Y is grayscale value, R is R value, G is G value, and B is B value.
This equation reflects the way of human visual perception.
This grayscale conversion makes it difficult to restore hue of image.
That's why we need Grayscale function, which just compute average of RGB channels.
How to use these functions is shown below.
g <- Grayscale(boats) hueim <- GetHue(boats) g <- BalanceSimplest(g, s, s, range=c(0,1)) y <- RestoreHue(g, hueim) layout(matrix(1:2, 1, 2)) plot(boats, main = "Original") plot(y, main = "Processed While Preserving Hue")
Which way is better?
It's your call.
You should consider which way is better when treating color images.
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