human/src/embedding/embedding.ts

import { log } from '../log';
import * as tf from '../../dist/tfjs.esm.js';
import * as profile from '../profile';

// original: https://github.com/sirius-ai/MobileFaceNet_TF
// modified: https://github.com/sirius-ai/MobileFaceNet_TF/issues/46
// download: https://github.com/sirius-ai/MobileFaceNet_TF/files/3551493/FaceMobileNet192_train_false.zip

let model;

export async function load(config) {
  if (!model) {
    model = await tf.loadGraphModel(config.face.embedding.modelPath);
    if (config.debug) log(`load model: ${config.face.embedding.modelPath.match(/\/(.*)\./)[1]}`);
  }
  return model;
}

export function simmilarity(embedding1, embedding2) {
  if (!embedding1 || !embedding2) return 0;
  if (embedding1?.length === 0 || embedding2?.length === 0) return 0;
  if (embedding1?.length !== embedding2?.length) return 0;
  // general minkowski distance
  // euclidean distance is limited case where order is 2
  const order = 2;
  const distance = 10.0 * ((embedding1.map((val, i) => (val - embedding2[i])).reduce((dist, diff) => dist + (diff ** order), 0) ** (1 / order)));
  return (Math.trunc(1000 * (1 - distance)) / 1000);
}

export async function predict(image, config) {
  if (!model) return null;
  return new Promise(async (resolve) => {
    const resize = tf.image.resizeBilinear(image, [model.inputs[0].shape[2], model.inputs[0].shape[1]], false); // input is already normalized to 0..1
    // const mean = resize.mean();
    // const whiten = resize.sub(mean); // normalizes with mean value being at point 0
    let data: Array<[]> = [];
    if (config.face.embedding.enabled) {
      if (!config.profile) {
        const embeddingT = await model.predict({ img_inputs: resize });
        data = [...embeddingT.dataSync()]; // convert object array to standard array
        tf.dispose(embeddingT);
      } else {
        const profileData = await tf.profile(() => model.predict({ img_inputs: resize }));
        data = [...profileData.result.dataSync()];
        profileData.result.dispose();
        profile.run('emotion', profileData);
      }
    }
    resolve(data);
  });
}