mirror of https://github.com/vladmandic/human
108 lines
4.1 KiB
TypeScript
108 lines
4.1 KiB
TypeScript
/**
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* Gender model implementation
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*
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* Based on: [**SSR-Net**](https://github.com/shamangary/SSR-Net)
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*
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* Obsolete and replaced by `faceres` that performs age/gender/descriptor analysis
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*/
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import { log, join, now } from '../util/util';
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import * as tf from '../../dist/tfjs.esm.js';
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import type { Config } from '../config';
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import type { GraphModel, Tensor } from '../tfjs/types';
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import { env } from '../util/env';
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let model: GraphModel | null;
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let last = { gender: '' };
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let lastTime = 0;
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let skipped = Number.MAX_SAFE_INTEGER;
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let alternative = false;
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// tuning values
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const rgb = [0.2989, 0.5870, 0.1140]; // factors for red/green/blue colors when converting to grayscale
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// eslint-disable-next-line @typescript-eslint/no-explicit-any
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export async function load(config: Config | any) {
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if (env.initial) model = null;
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if (!model) {
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model = await tf.loadGraphModel(join(config.modelBasePath, config.face.gender.modelPath)) as unknown as GraphModel;
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alternative = model.inputs[0].shape ? model.inputs[0]?.shape[3] === 1 : false;
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if (!model || !model['modelUrl']) log('load model failed:', config.face.gender.modelPath);
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else if (config.debug) log('load model:', model['modelUrl']);
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} else if (config.debug) log('cached model:', model['modelUrl']);
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return model;
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}
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// eslint-disable-next-line @typescript-eslint/no-explicit-any
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export async function predict(image: Tensor, config: Config | any) {
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if (!model) return null;
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if ((skipped < config.face.gender.skipFrames) && ((config.face.gender.skipTime || 0) <= (now() - lastTime)) && config.skipFrame && last.gender !== '') {
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skipped++;
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return last;
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}
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skipped = 0;
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return new Promise(async (resolve) => {
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if (!model?.inputs[0].shape) return;
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const resize = tf.image.resizeBilinear(image, [model.inputs[0].shape[2], model.inputs[0].shape[1]], false);
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let enhance;
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if (alternative) {
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enhance = tf.tidy(() => {
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const [red, green, blue] = tf.split(resize, 3, 3);
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const redNorm = tf.mul(red, rgb[0]);
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const greenNorm = tf.mul(green, rgb[1]);
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const blueNorm = tf.mul(blue, rgb[2]);
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const grayscale = tf.addN([redNorm, greenNorm, blueNorm]);
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const normalize = tf.mul(tf.sub(grayscale, 0.5), 2); // range grayscale:-1..1
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return normalize;
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});
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} else {
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enhance = tf.mul(resize, [255.0]); // range RGB:0..255
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}
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tf.dispose(resize);
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let genderT;
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const obj = { gender: '', confidence: 0 };
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if (config.face.gender.enabled) genderT = await model.predict(enhance);
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lastTime = now();
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tf.dispose(enhance);
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if (genderT) {
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if (!Array.isArray(genderT)) {
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const data = await genderT.data();
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if (alternative) {
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// returns two values 0..1, bigger one is prediction
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if (data[0] > config.face.gender.minConfidence || data[1] > config.face.gender.minConfidence) {
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obj.gender = data[0] > data[1] ? 'female' : 'male';
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obj.confidence = data[0] > data[1] ? (Math.trunc(100 * data[0]) / 100) : (Math.trunc(100 * data[1]) / 100);
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}
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} else {
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// returns one value 0..1, .5 is prediction threshold
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const confidence = Math.trunc(200 * Math.abs((data[0] - 0.5))) / 100;
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if (confidence > config.face.gender.minConfidence) {
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obj.gender = data[0] <= 0.5 ? 'female' : 'male';
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obj.confidence = Math.min(0.99, confidence);
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}
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}
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tf.dispose(genderT);
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} else {
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const gender = await genderT[0].data();
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const confidence = Math.trunc(200 * Math.abs((gender[0] - 0.5))) / 100;
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if (confidence > config.face.gender.minConfidence) {
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obj.gender = gender[0] <= 0.5 ? 'female' : 'male';
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obj.confidence = Math.min(0.99, confidence);
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}
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/*
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let age = (await genderT[1].argMax(1).data())[0];
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const all = await genderT[1].data();
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age = Math.round(all[age - 1] > all[age + 1] ? 10 * age - 100 * all[age - 1] : 10 * age + 100 * all[age + 1]) / 10;
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const descriptor = await genderT[1].data();
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*/
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genderT.forEach((t) => tf.dispose(t));
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}
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}
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last = obj;
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resolve(obj);
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});
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}
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