image_density.R

setwd("WORKINGDIRECTORY")

library(png)     ## split .png into component matrices
library(RSAGA)   ## grid.to.xyz, load before dplyr
library(dplyr)   ## group_by(), summarise(), %>% 
library(ggplot2) ## plotting
library(ggExtra) ## ggMarginal

## load the .png file
img <- png::readPNG("file10a566a2b4dc3.png")

## convert each channel into a data.frame
imgdf1 <- RSAGA::grid.to.xyz(img[,,1]) %>% mutate(col="red")   # red channel
imgdf2 <- RSAGA::grid.to.xyz(img[,,2]) %>% mutate(col="green") # green channel
imgdf3 <- RSAGA::grid.to.xyz(img[,,3]) %>% mutate(col="blue")  # blue channel

## collect these together to average
imgdf <- bind_rows(imgdf1, imgdf2, imgdf3)

## shift the coordinates to the correct values, and subtract the pixel values from 1 (white=0)
imgdf$x <- imgdf$x + 1
imgdf$y <- imgdf$y + 1
imgdf$z <- 1 - imgdf$z

## average the three channels (median) at each pixel
imgdf %<>% group_by(x, y) %>% summarise(z=median(z))

## plot the pixels, removing white pixels (z==0)
p <- ggplot(imgdf %>% filter(z!=0), aes(x=x, y=y)) + geom_point(size=0.1, pch=19) + 
  theme(panel.background=element_blank(), 
        line=element_blank(), 
        axis.text=element_blank(),
        axis.title=element_blank(),
        plot.margin=unit(c(0,0,0,0), "lines"), 
        complete=TRUE)

## add a marginal histogram of each pixel
ggExtra::ggMarginal(p, type="histogram", binwidth=1, col="darkturquoise")