mirror of https://github.com/vladmandic/human
47 lines
1.7 KiB
TypeScript
47 lines
1.7 KiB
TypeScript
/**
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* Anti-spoofing model implementation
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*/
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import { log, join, now } from '../util/util';
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import type { Config } from '../config';
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import type { GraphModel, Tensor } from '../tfjs/types';
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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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let model: GraphModel | null;
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const cached: Array<number> = [];
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let skipped = Number.MAX_SAFE_INTEGER;
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let lastCount = 0;
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let lastTime = 0;
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export async function load(config: Config): Promise<GraphModel> {
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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.liveness?.modelPath || '')) as unknown as GraphModel;
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if (!model || !model['modelUrl']) log('load model failed:', config.face.liveness?.modelPath);
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else if (config.debug) log('load model:', model['modelUrl']);
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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: Tensor, config: Config, idx, count): Promise<number> {
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if (!model) return 0;
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const skipTime = (config.face.liveness?.skipTime || 0) > (now() - lastTime);
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const skipFrame = skipped < (config.face.liveness?.skipFrames || 0);
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if (config.skipAllowed && skipTime && skipFrame && (lastCount === count) && cached[idx]) {
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skipped++;
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return cached[idx];
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}
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skipped = 0;
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return new Promise(async (resolve) => {
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const resize = tf.image.resizeBilinear(image, [model?.inputs[0].shape ? model.inputs[0].shape[2] : 0, model?.inputs[0].shape ? model.inputs[0].shape[1] : 0], false);
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const res = model?.execute(resize) as Tensor;
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const num = (await res.data())[0];
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cached[idx] = Math.round(100 * num) / 100;
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lastCount = count;
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lastTime = now();
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tf.dispose([resize, res]);
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resolve(cached[idx]);
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});
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}
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