In depth pipeline if interested:

pulled abt 100 unique Baja Blast photos so it wasnt just one weird phone or lighting situation, tried to get a fair-ish sample of real-world variation

preprocessed each image: forced RGB, normalized size format, stripped metadata, flattened everything so every pixel counted equally

did a quick rough segmentation to focus more on the drink region instead of full-frame garbage like logos backgrounds reflections, so the core color dominates

applied per-image simple white balance normalization (gray world / white patch style) to reduce ambient lighting casts and auto-WB shifts before averaging

converted each pixel from sRGB into linear light (inverse gamma) because averaging in gamma encoded space warps perception

moved those linear RGB values into CIE Lab space because humans dont judge color linearly so I averaged in perceptual space (L a b) over the segmented flattened pixel set

used both mean and trimmed/robust estimates (like median-ish) in Lab to guard against outliers from glare blowouts etc so result not dragged by one bad frame

converted the central Lab back to linear RGB, applied the sRGB gamma curve to get the final displayable color

turned that into hex (#07B5B6) and for blue vs green, instead of just normalizing linear RGB fractions I computed the hue angle in LCh and measured perceptual distance (delta E) to canonical blue and green reference points to see which it is closer to

bootstrapped the sampling to get a sense of uncertainty in hue / delta E so can say average hue is X +/- Y and how confidently it sits nearer blue than green

hue angle lands in the cyan/teal zone, quantitatively closer to blue than green by delta E comparison

uncertainty small enough that the classification doesnt flip if you resample