# Read the selected 'good' image sample file names
sel.pics <- read.csv('sel-pict.csv')
vvm.sel <- vvm$new(has.RGGB.pattern = TRUE)

# 'Digest' the samples computing noise related statistics
vvm.sel$digest(
    file.name.from = sel.pics$pict,
    file.path      = 'ISO100/Selection/crops'
)

# Fit the usual model
vvm.sel$fit.model(model.name = 'weighted', model.family = 'lmrob', weights=1/mean^2)

# Plot the var vs mean data
vvm.sel$plot(with = ~ channel != 'Green Avg',
             tlab = "VVM Selected samples",
             slab = "Nikon D7000 - ISO 100")

# Plot the SNR vs gray scale (log) in dB
imgnoiser.option('plot.point.opacity',0.3)
add.snr.ref.limits(
vvm.sel$plot(model.name = 'weighted',
             x = log10(mean/157.79), y = 20*log10(mean/sqrt(var)),
             tlab = "SNR Selected samples",
             slab = "Nikon D7000 - ISO 100",
             xlab = 'Gray scale (log)', ylab = 'SNR (dB)', print = FALSE,
             with = ~ channel != 'Green Avg') +
            scale_x_continuous(breaks=-1:2, labels=c('0.1%', '1%', '10%', '100%')) +
            scale_y_continuous(breaks=seq(0, 48, 4))
)

# Plot the SNR vs gray scale in dB
add.snr.ref.limits(
vvm.sel$plot(model.name = 'weighted',
             x = (mean/157.79), y = 20*log10(mean/sqrt(var)),
             tlab = "SNR Selected samples",
             slab = "Nikon D7000 - ISO 100",
             xlab = 'Gray scale', ylab = 'SNR (dB)', print = FALSE,
             with = ~ channel != 'Green Avg') +
            scale_x_continuous(breaks = seq(0, 100, 20),
                               labels=c('0%', '20%', '40%', '60%', '80%', '100%')) +
            scale_y_continuous(breaks=seq(0, 48, 4))
)