mirror of https://github.com/vladmandic/human
77 lines
3.2 KiB
TypeScript
77 lines
3.2 KiB
TypeScript
/**
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* Gender model implementation
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*
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* Based on: [**SSR-Net**](https://github.com/shamangary/SSR-Net)
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*/
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import * as tf from 'dist/tfjs.esm.js';
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import { log, now } from '../util/util';
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import { loadModel } from '../tfjs/load';
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import { constants } from '../tfjs/constants';
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import type { Gender } from '../result';
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import type { Config } from '../config';
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import type { GraphModel, Tensor, Tensor4D } from '../tfjs/types';
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import { env } from '../util/env';
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let model: GraphModel | null;
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const last: { gender: Gender, genderScore: number }[] = [];
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let lastCount = 0;
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let lastTime = 0;
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let skipped = Number.MAX_SAFE_INTEGER;
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// tuning values
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const rgb = [0.2989, 0.5870, 0.1140]; // factors for red/green/blue colors when converting to grayscale
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export async function load(config: Config) {
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if (env.initial) model = null;
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if (!model) model = await loadModel(config.face['ssrnet']?.modelPathGender);
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else if (config.debug) log('cached model:', model['modelUrl']);
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return model;
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}
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export async function predict(image: Tensor4D, config: Config, idx, count): Promise<{ gender: Gender, genderScore: number }> {
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if (!model) return { gender: 'unknown', genderScore: 0 };
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const skipFrame = skipped < (config.face['ssrnet']?.skipFrames || 0);
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const skipTime = (config.face['ssrnet']?.skipTime || 0) > (now() - lastTime);
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if (config.skipAllowed && skipFrame && skipTime && (lastCount === count) && last[idx]?.gender && (last[idx]?.genderScore > 0)) {
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skipped++;
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return last[idx];
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}
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skipped = 0;
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return new Promise(async (resolve) => {
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if (!model?.inputs[0].shape) return;
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const t: Record<string, Tensor> = {};
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if (config.face['ssrnet']?.['crop'] > 0) { // optional crop
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const crop = config.face['ssrnet']?.['crop'];
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const box = [[crop, crop, 1 - crop, 1 - crop]];
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t.resize = tf.image.cropAndResize(image, box, [0], [model.inputs[0].shape[2], model.inputs[0].shape[1]]);
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} else {
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t.resize = tf.image.resizeBilinear(image, [model.inputs[0].shape[2], model.inputs[0].shape[1]], false);
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}
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t.enhance = tf.tidy(() => {
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let normalize: Tensor;
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if (model?.inputs?.[0].shape?.[3] === 1) {
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const [red, green, blue] = tf.split(t.resize, 3, 3);
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const redNorm = tf.mul(red, rgb[0]);
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const greenNorm = tf.mul(green, rgb[1]);
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const blueNorm = tf.mul(blue, rgb[2]);
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const grayscale = tf.addN([redNorm, greenNorm, blueNorm]);
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normalize = tf.mul(tf.sub(grayscale, constants.tf05), 2); // range grayscale:-1..1
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} else {
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normalize = tf.mul(tf.sub(t.resize, constants.tf05), 2); // range rgb:-1..1
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}
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return normalize;
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});
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const obj: { gender: Gender, genderScore: number } = { gender: 'unknown', genderScore: 0 };
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if (config.face['ssrnet']?.enabled) t.gender = model.execute(t.enhance) as Tensor;
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const data = await t.gender.data();
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obj.gender = data[0] > data[1] ? 'female' : 'male'; // returns two values 0..1, bigger one is prediction
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obj.genderScore = data[0] > data[1] ? (Math.trunc(100 * data[0]) / 100) : (Math.trunc(100 * data[1]) / 100);
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Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));
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last[idx] = obj;
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lastCount = count;
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lastTime = now();
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resolve(obj);
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});
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}
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