rgb-noise: Scale function from linear sRGB to Lightroom sRGB

gistfile1.r

# Get the LR tifs var data
tif.var.zero <- vvm.tif$var.df
# Rename 'mean' and 'var' columns to avoid later name colision in merging
names(tif.var.zero)[3:4] <- paste0('tif.', names(tif.var.zero)[3:4])
# Remove sample file name extension to make possible the merge by picture file name
tif.var.zero$pict <- substr(tif.var.zero$pict, 1, 8)

# Compute the linear sRGB values
vvm.rgb.lin <- convert.to.rgb(target.space = 'sRGB', use.camera.tc = FALSE, target.tc = 'linear')
# Get the sRGB linear wide var data
lin.rgb.var <- vvm.rgb.lin$var.df
# Remove sample file name extension to make possible the merge by picture file name
lin.rgb.var$pict <- substr(lin.rgb.var$pict, 1, 8)

# Merge linear sRGB and LR tiff var data for each sample
lin.rgb.and.tif <- merge(lin.rgb.var, tif.var.zero, c('pict', 'channel'))

# Plot LR values as function of our sRGB linear values
ggplot(lin.rgb.and.tif, aes(x = mean/255, y = tif.mean/255, 
                            group = channel, colour = channel)) +
      ggtitle('Scale Applied to Linear sRGB\nBy Lightroom') +
      xlab('Linear sRGB') + ylab('Lightroom sRGB') +
      scale_x_continuous(breaks = seq(0, 1, 0.25), 
                                   labels=c('0%', '25%', '50%', '75%', '100%')) +
      scale_y_continuous(breaks = seq(0, 1, 0.25), 
                                   labels=c('0%', '25%', '50%', '75%', '100%')) +
      geom_point(size = 1.5)