mirror of https://github.com/vladmandic/human
99 lines
3.5 KiB
JavaScript
99 lines
3.5 KiB
JavaScript
require('@tensorflow/tfjs-node');
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const fs = require('fs');
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const path = require('path');
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const Human = require('../dist/human.node.js').default;
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const log = (status, ...data) => {
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if (typeof process.send !== 'undefined') process.send([status, data]); // send to parent process over ipc
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else console.log(status, ...data); // eslint-disable-line no-console
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};
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process.env.TF_CPP_MIN_LOG_LEVEL = '2';
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const humanConfig = {
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backend: 'tensorflow',
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debug: false,
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cacheSensitivity: 0,
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modelBasePath: 'https://vladmandic.github.io/human-models/models/',
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face: {
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detector: { enabled: true, modelPath: 'blazeface-back.json', cropFactor: 1.6 },
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mesh: { enabled: true },
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iris: { enabled: false },
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description: { enabled: true, modelPath: 'faceres.json' },
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gear: { enabled: true, modelPath: 'gear.json' },
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ssrnet: { enabled: true, modelPathAge: 'age.json', modelPathGender: 'gender.json' },
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emotion: { enabled: false },
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},
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body: { enabled: false },
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hand: { enabled: false },
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object: { enabled: false },
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gestures: { enabled: false },
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};
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const human = new Human(humanConfig);
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function getImageTensor(imageFile) {
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let tensor;
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try {
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const buffer = fs.readFileSync(imageFile);
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tensor = human.tf.node.decodeImage(buffer, 3);
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} catch (e) {
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log('error', `failed: loading image ${imageFile}: ${e.message}`);
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}
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return tensor;
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}
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function printResult(obj) {
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if (!obj || !obj.res || !obj.res.face || obj.res.face.length === 0) log('warn', 'failed: no faces detected');
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else obj.res.face.forEach((face, i) => log('data', 'results', { face: i, model: obj.model, image: obj.image, age: face.age, gender: face.gender, genderScore: face.genderScore, race: face.race }));
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}
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async function main() {
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const inputs = process.argv.length === 3 ? process.argv[2] : 'samples/in/ai-face.jpg';
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if (!fs.existsSync(inputs)) throw new Error(`file not found: ${inputs}`);
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const stat = fs.statSync(inputs);
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const files = [];
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if (stat.isFile()) files.push(inputs);
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else if (stat.isDirectory()) fs.readdirSync(inputs).forEach((f) => files.push(path.join(inputs, f)));
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log('data', 'input:', files);
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await human.load();
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let res;
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for (const f of files) {
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const tensor = getImageTensor(f);
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if (!tensor) continue;
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let msg = {};
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human.config.face.description.enabled = true;
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human.config.face.gear.enabled = false;
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human.config.face.ssrnet.enabled = false;
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res = await human.detect(tensor);
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msg = { model: 'faceres', image: f, res };
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if (res?.face?.[0].age > 20 && res?.face?.[0].age < 30) log('state', 'passed: gear', msg.model, msg.image);
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else log('error', 'failed: gear', msg);
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printResult(msg);
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human.config.face.description.enabled = false;
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human.config.face.gear.enabled = true;
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human.config.face.ssrnet.enabled = false;
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res = await human.detect(tensor);
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msg = { model: 'gear', image: f, res };
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if (res?.face?.[0].age > 20 && res?.face?.[0].age < 30) log('state', 'passed: gear', msg.model, msg.image);
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else log('error', 'failed: gear', msg);
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printResult(msg);
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human.config.face.description.enabled = false;
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human.config.face.gear.enabled = false;
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human.config.face.ssrnet.enabled = true;
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res = await human.detect(tensor);
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msg = { model: 'ssrnet', image: f, res };
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if (res?.face?.[0].age > 20 && res?.face?.[0].age < 30) log('state', 'passed: gear', msg.model, msg.image);
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else log('error', 'failed: gear', msg);
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printResult(msg);
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human.tf.dispose(tensor);
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}
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}
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exports.test = main;
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if (require.main === module) main();
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