mirror of https://github.com/vladmandic/human
61 lines
2.2 KiB
TypeScript
61 lines
2.2 KiB
TypeScript
/**
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* Age 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 { log, join, now } from '../util/util';
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import * as tf from '../../dist/tfjs.esm.js';
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import { env } from '../util/env';
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import { constants } from '../tfjs/constants';
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import type { Config } from '../config';
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import type { GraphModel, Tensor } from '../tfjs/types';
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let model: GraphModel | null;
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const last: Array<{ age: 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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// eslint-disable-next-line @typescript-eslint/no-explicit-any
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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) {
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model = await tf.loadGraphModel(join(config.modelBasePath, config.face['ssrnet'].modelPathAge)) as unknown as GraphModel;
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if (!model || !model['modelUrl']) log('load model failed:', config.face['ssrnet'].modelPathAge);
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else if (config.debug) log('load model:', model['modelUrl']);
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} else {
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if (config.debug) log('cached model:', model['modelUrl']);
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}
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return model;
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}
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// eslint-disable-next-line @typescript-eslint/no-explicit-any
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export async function predict(image: Tensor, config: Config, idx, count): Promise<{ age: number }> {
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if (!model) return { age: 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]?.age && (last[idx]?.age > 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 || !model.inputs[0] || !model.inputs[0].shape) return;
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const t: Record<string, Tensor> = {};
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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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t.enhance = tf.mul(t.resize, constants.tf255);
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const obj = { age: 0 };
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if (config.face['ssrnet'].enabled) t.age = model.execute(t.enhance) as Tensor;
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if (t.age) {
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const data = await t.age.data();
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obj.age = Math.trunc(10 * data[0]) / 10;
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}
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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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