const tf = require('@tensorflow/tfjs'); const profile = require('../profile.js'); const models = {}; let last = { age: 0 }; let frame = Number.MAX_SAFE_INTEGER; // tuning values const zoom = [0, 0]; // 0..1 meaning 0%..100% async function load(config) { if (!models.age) models.age = await tf.loadGraphModel(config.face.age.modelPath); return models.age; } async function predict(image, config) { return new Promise(async (resolve) => { if (frame < config.face.age.skipFrames) { frame += 1; resolve(last); } frame = 0; const box = [[ (image.shape[1] * zoom[0]) / image.shape[1], (image.shape[2] * zoom[1]) / image.shape[2], (image.shape[1] - (image.shape[1] * zoom[0])) / image.shape[1], (image.shape[2] - (image.shape[2] * zoom[1])) / image.shape[2], ]]; const resize = tf.image.cropAndResize(image, box, [0], [config.face.age.inputSize, config.face.age.inputSize]); // const resize = tf.image.resizeBilinear(image, [config.face.age.inputSize, config.face.age.inputSize], false); const enhance = tf.mul(resize, [255.0]); tf.dispose(resize); let ageT; const obj = {}; if (!config.profile) { if (config.face.age.enabled) ageT = await models.age.predict(enhance); } else { const profileAge = config.face.age.enabled ? await tf.profile(() => models.age.predict(enhance)) : {}; ageT = profileAge.result.clone(); profileAge.result.dispose(); profile.run('age', profileAge); } enhance.dispose(); if (ageT) { const data = ageT.dataSync(); obj.age = Math.trunc(10 * data[0]) / 10; } ageT.dispose(); last = obj; resolve(obj); }); } exports.predict = predict; exports.load = load;