mirror of https://github.com/vladmandic/human
84 lines
2.9 KiB
TypeScript
84 lines
2.9 KiB
TypeScript
import { log } from '../log';
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import * as tf from '../../dist/tfjs.esm.js';
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import * as profile from '../profile';
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let model;
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let last = { gender: '' };
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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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export async function load(config) {
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if (!model) {
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model = await tf.loadGraphModel(config.face.gender.modelPath);
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alternative = model.inputs[0].shape[3] === 1;
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if (config.debug) log(`load model: ${config.face.gender.modelPath.match(/\/(.*)\./)[1]}`);
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}
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return model;
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}
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export async function predict(image, config) {
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if (!model) return null;
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if ((skipped < config.face.gender.skipFrames) && config.videoOptimized && last.gender !== '') {
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skipped++;
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return last;
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}
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if (config.videoOptimized) skipped = 0;
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else skipped = Number.MAX_SAFE_INTEGER;
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return new Promise(async (resolve) => {
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const resize = tf.image.resizeBilinear(image, [config.face.gender.inputSize, config.face.gender.inputSize], 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 = grayscale.sub(0.5).mul(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.profile) {
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if (config.face.gender.enabled) genderT = await model.predict(enhance);
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} else {
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const profileGender = config.face.gender.enabled ? await tf.profile(() => model.predict(enhance)) : {};
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genderT = profileGender.result.clone();
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profileGender.result.dispose();
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profile.run('gender', profileGender);
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}
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enhance.dispose();
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if (genderT) {
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const data = genderT.dataSync();
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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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}
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genderT.dispose();
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last = obj;
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resolve(obj);
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});
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}
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