mirror of https://github.com/vladmandic/human
100 lines
3.7 KiB
TypeScript
100 lines
3.7 KiB
TypeScript
/**
|
|
* Module that analyzes person gender
|
|
* Obsolete
|
|
*/
|
|
|
|
import { log, join } from '../helpers';
|
|
import * as tf from '../../dist/tfjs.esm.js';
|
|
import { Config } from '../config';
|
|
import { GraphModel, Tensor } from '../tfjs/types';
|
|
|
|
let model: GraphModel;
|
|
let last = { gender: '' };
|
|
let skipped = Number.MAX_SAFE_INTEGER;
|
|
let alternative = false;
|
|
|
|
// tuning values
|
|
const rgb = [0.2989, 0.5870, 0.1140]; // factors for red/green/blue colors when converting to grayscale
|
|
|
|
// eslint-disable-next-line @typescript-eslint/no-explicit-any
|
|
export async function load(config: Config | any) {
|
|
if (!model) {
|
|
model = await tf.loadGraphModel(join(config.modelBasePath, config.face.gender.modelPath));
|
|
alternative = model.inputs[0].shape[3] === 1;
|
|
if (!model || !model.modelUrl) log('load model failed:', config.face.gender.modelPath);
|
|
else if (config.debug) log('load model:', model.modelUrl);
|
|
} else if (config.debug) log('cached model:', model.modelUrl);
|
|
return model;
|
|
}
|
|
|
|
// eslint-disable-next-line @typescript-eslint/no-explicit-any
|
|
export async function predict(image: Tensor, config: Config | any) {
|
|
if (!model) return null;
|
|
if ((skipped < config.face.gender.skipFrames) && config.skipFrame && last.gender !== '') {
|
|
skipped++;
|
|
return last;
|
|
}
|
|
skipped = 0;
|
|
return new Promise(async (resolve) => {
|
|
const resize = tf.image.resizeBilinear(image, [model.inputs[0].shape[2], model.inputs[0].shape[1]], false);
|
|
let enhance;
|
|
if (alternative) {
|
|
enhance = tf.tidy(() => {
|
|
const [red, green, blue] = tf.split(resize, 3, 3);
|
|
const redNorm = tf.mul(red, rgb[0]);
|
|
const greenNorm = tf.mul(green, rgb[1]);
|
|
const blueNorm = tf.mul(blue, rgb[2]);
|
|
const grayscale = tf.addN([redNorm, greenNorm, blueNorm]);
|
|
const normalize = grayscale.sub(0.5).mul(2); // range grayscale:-1..1
|
|
return normalize;
|
|
});
|
|
} else {
|
|
enhance = tf.mul(resize, [255.0]); // range RGB:0..255
|
|
}
|
|
tf.dispose(resize);
|
|
|
|
let genderT;
|
|
const obj = { gender: '', confidence: 0 };
|
|
|
|
if (config.face.gender.enabled) genderT = await model.predict(enhance);
|
|
enhance.dispose();
|
|
|
|
if (genderT) {
|
|
if (!Array.isArray(genderT)) {
|
|
const data = genderT.dataSync();
|
|
if (alternative) {
|
|
// returns two values 0..1, bigger one is prediction
|
|
if (data[0] > config.face.gender.minConfidence || data[1] > config.face.gender.minConfidence) {
|
|
obj.gender = data[0] > data[1] ? 'female' : 'male';
|
|
obj.confidence = data[0] > data[1] ? (Math.trunc(100 * data[0]) / 100) : (Math.trunc(100 * data[1]) / 100);
|
|
}
|
|
} else {
|
|
// returns one value 0..1, .5 is prediction threshold
|
|
const confidence = Math.trunc(200 * Math.abs((data[0] - 0.5))) / 100;
|
|
if (confidence > config.face.gender.minConfidence) {
|
|
obj.gender = data[0] <= 0.5 ? 'female' : 'male';
|
|
obj.confidence = Math.min(0.99, confidence);
|
|
}
|
|
}
|
|
genderT.dispose();
|
|
} else {
|
|
const gender = genderT[0].dataSync();
|
|
const confidence = Math.trunc(200 * Math.abs((gender[0] - 0.5))) / 100;
|
|
if (confidence > config.face.gender.minConfidence) {
|
|
obj.gender = gender[0] <= 0.5 ? 'female' : 'male';
|
|
obj.confidence = Math.min(0.99, confidence);
|
|
}
|
|
/*
|
|
let age = genderT[1].argMax(1).dataSync()[0];
|
|
const all = genderT[1].dataSync();
|
|
age = Math.round(all[age - 1] > all[age + 1] ? 10 * age - 100 * all[age - 1] : 10 * age + 100 * all[age + 1]) / 10;
|
|
const descriptor = genderT[1].dataSync();
|
|
*/
|
|
genderT.forEach((t) => tf.dispose(t));
|
|
}
|
|
}
|
|
last = obj;
|
|
resolve(obj);
|
|
});
|
|
}
|