human/src/gear/agegenderrace.ts

/**
 * Module that analyzes person age
 * Obsolete
 */

/*
  to enable, add to config.ts

    agegenderrace: {
      enabled: boolean,
      modelPath: string,
      skipFrames: number,
    }

    agegenderrace: {
      enabled: true,
      modelPath: 'gear.json',
      skipFrames: 1,
    },

  and enable model loading in models.ts
*/

import { log, join } from '../helpers';
import * as tf from '../../dist/tfjs.esm.js';
import { Config } from '../config';
import { GraphModel, Tensor } from '../tfjs/types';

let model: GraphModel;

let last = { age: 0 };
let skipped = Number.MAX_SAFE_INTEGER;

// eslint-disable-next-line @typescript-eslint/no-explicit-any
export async function load(config: Config | any) {
  if (!model) {
    // @ts-ignore type mismatch on GraphModel
    model = await tf.loadGraphModel(join(config.modelBasePath, config.face.agegenderrace.modelPath));
    if (!model || !model['modelUrl']) log('load model failed:', config.face.agegenderrace.modelPath);
    else if (config.debug) log('load model:', model['modelUrl']);
  } else if (config.debug) log('cached model:', model['modelUrl']);
  return model;
}

// eslint-disable-next-line @typescript-eslint/no-explicit-any
export async function predict(image: Tensor, config: Config) {
  if (!model) return null;
  // @ts-ignore config disabled
  if ((skipped < config.face.agegenderrace.skipFrames) && config.skipFrame && last.age && (last.age > 0)) {
    skipped++;
    return last;
  }
  skipped = 0;
  return new Promise(async (resolve) => {
    if (!model.inputs[0].shape) return;
    const resize = tf.image.resizeBilinear(image, [model.inputs[0].shape[2], model.inputs[0].shape[1]], false);
    // const enhance = tf.mul(resize, [255.0]);

    let ageT;
    let genderT;
    let raceT;
    const obj = { age: 0 };

    // @ts-ignore array definition unavailable at compile time
    if (config.face.agegenderrace.enabled) [ageT, genderT, raceT] = await model.execute(resize, ['age_output', 'gender_output', 'race_output']);
    tf.dispose(resize);
    // tf.dispose(enhance);

    if (ageT) {
      // const data = await ageT.data();
      // {0: 'below_20', 1: '21-25', 2: '26-30', 3: '31-40',4: '41-50', 5: '51-60', 6: 'Above60'}
    }
    if (genderT) {
      // const data = await genderT.data();
    }
    if (raceT) {
      // const data = await raceT.data();
      // {0: 'white', 1: 'black', 2: 'asian', 3: 'indian', 4: 'others'}
    }

    tf.dispose(ageT);
    tf.dispose(genderT);
    tf.dispose(raceT);

    last = obj;
    resolve(obj);
  });
}