Color Channels

import * as tf from '@tensorflow/tfjs';

// An RGB image has 3 channels stacked in depth
const image = tf.browser.fromPixels(canvas); // [224, 224, 3]

// Split into individual channels
const [red, green, blue] = tf.split(image, 3, 2);
console.log(red.shape);   // [224, 224, 1] — red intensity map
console.log(green.shape); // [224, 224, 1] — green intensity map
console.log(blue.shape);  // [224, 224, 1] — blue intensity map

// Each channel is a grayscale image showing that color's contribution
// High values = strong presence of that color
// Low values = weak presence

// Grayscale: collapse channels into one
const gray = image.mean(2, true); // [224, 224, 1]

// RGB to HSV conversion (manual)
function rgbToHsv(rgb: tf.Tensor3D): tf.Tensor3D {
const normalized = rgb.toFloat().div(255);
const [r, g, b] = tf.split(normalized, 3, 2) as tf.Tensor3D[];

const max = tf.maximum(tf.maximum(r, g), b);
const min = tf.minimum(tf.minimum(r, g), b);
const delta = max.sub(min);

// Value = max channel
const v = max;
// Saturation = delta / max (0 when max is 0)
const s = tf.where(max.greater(0), delta.div(max), tf.zerosLike(max));

return tf.concat([s, s, v], 2) as tf.Tensor3D; // simplified
}

// Why HSV matters for vision:
// H (hue) = what color — invariant to lighting
// S (saturation) = how vivid — separates color from gray
// V (value) = how bright — separates content from illumination
// A camera in shadow and sunlight has different V but same H

// Surveillance application: analyzing individual channels

function analyzeChannels(image: tf.Tensor3D): void {
const [r, g, b] = tf.split(image, 3, 2);

// Mean intensity per channel
const rMean = r.mean().dataSync()[0];
const gMean = g.mean().dataSync()[0];
const bMean = b.mean().dataSync()[0];

console.log('Channel means — R:', rMean.toFixed(1),
            'G:', gMean.toFixed(1), 'B:', bMean.toFixed(1));

// Nighttime camera feeds: high blue bias from IR LEDs
// Daylight feeds: balanced channels
// Meridian hidden data: anomalous blue channel variance

// Detect hidden data: compare channel variances
const rVar = r.toFloat().sub(rMean).square().mean().dataSync()[0];
const gVar = g.toFloat().sub(gMean).square().mean().dataSync()[0];
const bVar = b.toFloat().sub(bMean).square().mean().dataSync()[0];

console.log('Channel variance — R:', rVar.toFixed(1),
            'G:', gVar.toFixed(1), 'B:', bVar.toFixed(1));

// Unusually high blue variance could indicate embedded data
if (bVar > rVar * 1.5 && bVar > gVar * 1.5) {
  console.warn('Anomalous blue channel variance — possible steganography');
}

[r, g, b].forEach(t => t.dispose());
}