mirror of https://github.com/vladmandic/human
63 lines
1.9 KiB
TypeScript
63 lines
1.9 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.js';
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let model;
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let last = { age: 0 };
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let skipped = Number.MAX_SAFE_INTEGER;
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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.age.modelPath);
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log(`load model: ${config.face.age.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.age.skipFrames) && config.videoOptimized && last.age && (last.age > 0)) {
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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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/*
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const zoom = [0, 0]; // 0..1 meaning 0%..100%
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const box = [[
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(image.shape[1] * zoom[0]) / image.shape[1],
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(image.shape[2] * zoom[1]) / image.shape[2],
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(image.shape[1] - (image.shape[1] * zoom[0])) / image.shape[1],
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(image.shape[2] - (image.shape[2] * zoom[1])) / image.shape[2],
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]];
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const resize = tf.image.cropAndResize(image, box, [0], [config.face.age.inputSize, config.face.age.inputSize]);
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*/
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const resize = tf.image.resizeBilinear(image, [config.face.age.inputSize, config.face.age.inputSize], false);
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const enhance = tf.mul(resize, [255.0]);
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tf.dispose(resize);
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let ageT;
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const obj = { age: 0 };
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if (!config.profile) {
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if (config.face.age.enabled) ageT = await model.predict(enhance);
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} else {
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const profileAge = config.face.age.enabled ? await tf.profile(() => model.predict(enhance)) : {};
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ageT = profileAge.result.clone();
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profileAge.result.dispose();
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profile.run('age', profileAge);
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}
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enhance.dispose();
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if (ageT) {
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const data = ageT.dataSync();
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obj.age = Math.trunc(10 * data[0]) / 10;
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}
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ageT.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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