rgb-noise: Checking the selected good samples
# 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)) | |
) |
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