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# Compute the SNR.
# Returns "high" values when the model variance is negative to 
# hint the optimizer to get out of there. 
snr <- function(mean, var) {
  negatives <- which(var < 0)
  result <- 20*log10(mean/sqrt(abs(var)))
  result[negatives] <- result[negatives]*8
  result;
}

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library(imgnoiser)
library(dplyr)
library(robustbase)

# Acquire the data
vvm.all <- vvm$new(has.RGGB.pattern = TRUE)
vvm.all$digest(
    file.name.from = '_ODL0387s4',
    file.name.to   = '_ODL1671s6',
    file.name.ext  = '.pgm', 

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# Get the weighted errors `w.mean` and `w.var`
test.error.weighted.var <- function(sel.channel, coeffs) {
  tests.pics <- read.csv('test-pics.csv', stringsAsFactors = FALSE, row.names=NULL)
  tests.pics <- as.vector(tests.pics[,1])
  data.df <- subset(data.frame(vvm.all$var.df), 
                 channel == sel.channel & 
                 pict %in% tests.pics)

  predicted.var <- (data.df$mean*coeffs[3] + coeffs[2])*data.df$mean + coeffs[1]
  error.var <- mean((data.df$var - predicted.var)^2/predicted.var)

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execute.tests <- function(n) {
  # A place-holder for the test results 
  result.df <- data.frame()
  # Initial values for snr model optimization
  param.init <- c(2, 0.4, 5e-06)
  
  # Iterate n times
  for (n in 1:n) {
    split.data()
    # Model each channel per iteration

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avg.loss.of.snr <- function(color.conv.matrix, white.bal.scales) {
  sqr.conv.mtx <- color.conv.matrix^2
  wb.conv.mtx <- t(apply(sqr.conv.mtx, 1, function(x) x*white.bal.scales ))
  mc <- apply(wb.conv.mtx, 1, sum)
  loss <- mean(log10(mc)) - mean(log(white.bal.scales) )
  # Result
  10*loss;
}
# Helper function to build the color matrix
matrix_3x3 <- function(x) matrix(x, nrow=3L, ncol=3L, byrow=TRUE);

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# Read the TIFF files
vvm.tif <- vvm$new(has.RGGB.pattern = TRUE)
vvm.tif$digest.from.rgb(
  file.name.from  = '_ODL0415', 
  file.name.to    = '_ODL1668', 
  file.path       = 'ISO100/Selection/LR-zero'
)
# VVM plot
vvm.tif$plot(tlab = "VVM: sRGB images from Lightroom",
             slab = 'With zero settings',

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# Draw SNR quality reference limits for 18% gray
add.snr.ref.limits <- function(p) {
  p + 
    geom_hline(aes(yintercept=20), colour='red', linetype = 6, alpha = 0.4) +
    geom_hline(aes(yintercept=32), colour='cyan', linetype = 6, alpha = 0.8) +
    geom_hline(aes(yintercept=38), colour='green', linetype = 6, alpha = 0.8)
}

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library(imgnoiser)
library(ggplot2)
# Y coordinate of sRGB primaries. From http://en.wikipedia.org/wiki/SRGB
weights.sRGB <- c(0.2126, 0.7152, 0.0722)
# D65 white point in XYZ. From ASTM E308-01 in http://www.brucelindbloom.com/
D65.XYZ <- c(0.95047,  1.00000, 1.08883)
# With "D65 color matrix" in the camera color data, we can convert 
# the D65 XYZ white point to the camera raw color space:
D65.raw <- nikon.d7000.ISO100.colmap$cam.matrices.2$color.matrix %*% D65.XYZ
#>           [,1]

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vvm.rgb <- convert.to.rgb(target.space = 'sRGB', use.camera.tc = FALSE, target.tc = 'linear')
x11()
vvm.rgb$plot(tlab = "VVM Selected samples in linear sRGB",
             slab = 'Without camera tone curve')

vvm.rgb$plot(x = log2(mean), y = 20*log10(mean/sqrt(var)),
             tlab = "SNR Selected samples in linear sRGB",
             slab = 'Without camera tone curve',
             xlab = "Signal (stops)",  xlim=c(-1.5,8),
             ylab = "SNR (dB)")

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# 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