rgb-noise: digest as RGB each set

gistfile1.r

# Convert selected raw images to a given destinations space
# without demosaicing
convert.to.rgb <- function(target.space, 
                          use.camera.tc, 
                          target.tc, 
                          cam.coldata = imgnoiser::nikon.d7000.ISO100.colmap,
                          dest.scale = 255
) {
  
  library(imgnoiser)
  # Red the selected samples names and save them as a vector
  sel.pics <- read.csv('sel-pict.csv')
  sel.pics <- as.vector(sel.pics$pict)
  
  # Place holder where all the resulting data will be collected
  vvm.rgb.all <- vvm$new(has.RGGB.pattern = TRUE)
  # For each set 
  for (set.nbr in 1L:6L) {
    set.id <- paste0('s', set.nbr)
    # Select the samples belonging to the set
    pict.set <- sel.pics[grepl(set.id, sel.pics, fixed = TRUE)]
    
    # Digest the raw images in the set
    vvm.set <- vvm$new(has.RGGB.pattern = TRUE)
    vvm.set$digest(
        file.name.from = pict.set,
        file.path      = 'H:/FUN/FOTOS/Noise Study/ipadWhite/ISO100/Selection/crops'
    )
    
    # Get the 45 and 80 percentile of average green mean
    qvals <- quantile(vvm.set$wide.var.df$green.avg.mean, c(0.45, 0.80))
    # Select the samples with average green mean between the 45 and 80 percentile
    raw.means <- subset(vvm.set$wide.var.df, 
                        green.avg.mean > qvals[1] & green.avg.mean < qvals[2], 
                        c(red.mean, green.r.mean, green.b.mean, blue.mean))
    if (nrow(raw.means) == 0) stop("No rows selected for white balance!")
    # Compute the average RGB raw values in the selected samples
    neutral.raw <- with(raw.means, 
                        c(mean(red.mean), mean(c(green.r.mean, green.b.mean)), mean(blue.mean)))
    
    # Create a colmap object and calibrate it with this neutral reference value
    cm.set <- colmap$new(cam.coldata)
    
    if (target.space == 'raw') {
      no.conv.mtx <- diag(1,3,3)
      raw.to.rgb.mtx <- cm.set$set.conv.matrix.from.raw(neutral.raw, no.conv.mtx)
    } else {
    # Get and print the conversion matrix from raw to RGB
      raw.to.rgb.mtx <- cm.set$get.conv.matrix.from.raw(neutral.raw, target.space)
    }
    print(neutral.raw)
    print(raw.to.rgb.mtx)
  
    # Digest the set as rgb, white-balancing each image with the colmap object
    vvm.set.rgb <- vvm$new(has.RGGB.pattern = TRUE)
#     debug( vvm.set.rgb$digest.as.rgb)
    vvm.set.rgb$digest.as.rgb(
        file.name.from = pict.set,
        file.path      = 'H:/FUN/FOTOS/Noise Study/ipadWhite/ISO100/Selection/crops',
        is.neutral     = TRUE,
        map.to.rgb     = cm.set,
        use.camera.tc  = use.camera.tc,
        target.tc      = target.tc ,
        rgb.scale      = dest.scale
    )
    
    # Append the result of this set to the whole collection
    vvm.rgb.all$append.from(vvm.set.rgb)
  }
  # The result is the whole set of the good samples digested as RGB
  vvm.rgb.all;
}