human/src/gender/ssrnet.js

64 lines
2.0 KiB
JavaScript

const tf = require('@tensorflow/tfjs');
const profile = require('../profile.js');
const models = {};
let last = { gender: '' };
let frame = Number.MAX_SAFE_INTEGER;
// tuning values
const zoom = [0, 0]; // 0..1 meaning 0%..100%
async function load(config) {
if (!models.gender) models.gender = await tf.loadGraphModel(config.face.gender.modelPath);
return models.gender;
}
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 genderT;
const obj = {};
if (!config.profile) {
if (config.face.gender.enabled) genderT = await models.gender.predict(enhance);
} else {
const profileGender = config.face.gender.enabled ? await tf.profile(() => models.gender.predict(enhance)) : {};
genderT = profileGender.result.clone();
profileGender.result.dispose();
profile.run('gender', profileGender);
}
enhance.dispose();
if (genderT) {
const data = genderT.dataSync();
const confidence = Math.trunc(Math.abs(1.9 * 100 * (data[0] - 0.5))) / 100;
if (confidence > config.face.gender.minConfidence) {
obj.gender = data[0] <= 0.5 ? 'female' : 'male';
obj.confidence = confidence;
}
}
genderT.dispose();
last = obj;
resolve(obj);
});
}
exports.predict = predict;
exports.load = load;