mirror of https://github.com/vladmandic/human
96 lines
3.1 KiB
JavaScript
96 lines
3.1 KiB
JavaScript
/**
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* Human demo for NodeJS
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*/
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const fs = require('fs');
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const process = require('process');
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const log = require('@vladmandic/pilogger'); // eslint-disable-line node/no-unpublished-require
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// in nodejs environments tfjs-node is required to be loaded before human
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const tf = require('@tensorflow/tfjs-node'); // eslint-disable-line node/no-unpublished-require
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// const human = require('@vladmandic/human'); // use this when human is installed as module (majority of use cases)
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const Human = require('../../dist/human.node.js'); // use this when using human in dev mode
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let human = null;
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const myConfig = {
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modelBasePath: 'file://models/',
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debug: false,
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async: true,
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filter: { enabled: false },
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face: {
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enabled: true,
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detector: { enabled: true },
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mesh: { enabled: true },
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iris: { enabled: true },
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description: { enabled: true },
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emotion: { enabled: true },
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},
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hand: { enabled: true },
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body: { enabled: true },
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object: { enabled: true },
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};
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async function detect(input) {
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// read input image from file or url into buffer
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let buffer;
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log.info('Loading image:', input);
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if (input.startsWith('http:') || input.startsWith('https:')) {
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const res = await fetch(input);
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if (res && res.ok) buffer = Buffer.from(await res.arrayBuffer());
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else log.error('Invalid image URL:', input, res.status, res.statusText, res.headers.get('content-type'));
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} else {
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buffer = fs.readFileSync(input);
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}
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log.data('Image bytes:', buffer?.length, 'buffer:', buffer?.slice(0, 32));
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// decode image using tfjs-node so we don't need external depenencies
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if (!buffer) return;
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const tensor = human.tf.node.decodeImage(buffer, 3);
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// run detection
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await human.detect(tensor, myConfig);
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human.tf.dispose(tensor); // dispose image tensor as we no longer need it
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}
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async function main() {
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log.header();
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human = new Human.Human(myConfig);
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log.info('Human:', human.version, 'TF:', tf.version_core);
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if (human.events) {
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human.events.addEventListener('warmup', () => {
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log.info('Event Warmup');
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});
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human.events.addEventListener('load', () => {
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log.info('Event Loaded:', human.models.loaded(), human.tf.engine().memory());
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});
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human.events.addEventListener('image', () => {
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log.info('Event Image:', human.process.tensor.shape);
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});
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human.events.addEventListener('detect', () => {
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log.data('Event Detected:');
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const persons = human.result.persons;
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for (let i = 0; i < persons.length; i++) {
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const face = persons[i].face;
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const faceTxt = face ? `score:${face.score} age:${face.age} gender:${face.gender} iris:${face.distance}` : null;
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const body = persons[i].body;
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const bodyTxt = body ? `score:${body.score} keypoints:${body.keypoints?.length}` : null;
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log.data(` #${i}: Face:${faceTxt} Body:${bodyTxt} LeftHand:${persons[i].hands.left ? 'yes' : 'no'} RightHand:${persons[i].hands.right ? 'yes' : 'no'} Gestures:${persons[i].gestures.length}`);
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}
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});
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}
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await human.tf.ready(); // wait until tf is ready
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const input = process.argv[2]; // process input
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if (input) await detect(input);
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else log.error('Missing <input>');
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}
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main();
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