human/src/image/enhance.ts

/**
 * Image enhancements
 */

import * as tf from 'dist/tfjs.esm.js';
import type { Tensor } from '../exports';

export async function histogramEqualization(inputImage: Tensor): Promise<Tensor> {
  // const maxValue = 254; // using 255 results in values slightly larger than 1 due to math rounding errors
  const squeeze = inputImage.shape.length === 4 ? tf.squeeze(inputImage) : inputImage;
  const channels = tf.split(squeeze, 3, 2);
  const min: Tensor[] = [tf.min(channels[0]), tf.min(channels[1]), tf.min(channels[2])];
  const max: Tensor[] = [tf.max(channels[0]), tf.max(channels[1]), tf.max(channels[2])];
  const absMax = await Promise.all(max.map((channel) => channel.data()));
  const maxValue = 0.99 * Math.max(absMax[0][0], absMax[1][0], absMax[2][0]);
  const sub = [tf.sub(channels[0], min[0]), tf.sub(channels[1], min[1]), tf.sub(channels[2], min[2])];
  const range = [tf.sub(max[0], min[0]), tf.sub(max[1], min[1]), tf.sub(max[2], min[2])];
  const fact = [tf.div(maxValue, range[0]), tf.div(maxValue, range[1]), tf.div(maxValue, range[2])];
  const enh = [tf.mul(sub[0], fact[0]), tf.mul(sub[1], fact[1]), tf.mul(sub[2], fact[2])];
  const rgb = tf.stack([enh[0], enh[1], enh[2]], 2);
  const reshape = tf.reshape(rgb, [1, squeeze.shape[0] || 0, squeeze.shape[1] || 0, 3]);
  tf.dispose([...channels, ...min, ...max, ...sub, ...range, ...fact, ...enh, rgb, squeeze]);
  return reshape; // output shape is [1, height, width, 3]
}