mirror of https://github.com/vladmandic/human
53 lines
2.0 KiB
TypeScript
53 lines
2.0 KiB
TypeScript
import { log } from '../log';
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import * as tf from '../../dist/tfjs.esm.js';
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import * as profile from '../profile';
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// original: https://github.com/sirius-ai/MobileFaceNet_TF
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// modified: https://github.com/sirius-ai/MobileFaceNet_TF/issues/46
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// download: https://github.com/sirius-ai/MobileFaceNet_TF/files/3551493/FaceMobileNet192_train_false.zip
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let model;
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export async function load(config) {
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if (!model) {
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model = await tf.loadGraphModel(config.face.embedding.modelPath);
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if (config.debug) log(`load model: ${config.face.embedding.modelPath.match(/\/(.*)\./)[1]}`);
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}
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return model;
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}
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export function simmilarity(embedding1, embedding2) {
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if (!embedding1 || !embedding2) return 0;
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if (embedding1?.length === 0 || embedding2?.length === 0) return 0;
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if (embedding1?.length !== embedding2?.length) return 0;
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// general minkowski distance
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// euclidean distance is limited case where order is 2
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const order = 2;
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const distance = 10.0 * ((embedding1.map((val, i) => (val - embedding2[i])).reduce((dist, diff) => dist + (diff ** order), 0) ** (1 / order)));
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return (Math.trunc(1000 * (1 - distance)) / 1000);
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}
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export async function predict(image, config) {
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if (!model) return null;
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return new Promise(async (resolve) => {
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const resize = tf.image.resizeBilinear(image, [model.inputs[0].shape[2], model.inputs[0].shape[1]], false);
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// const normalize = tf.tidy(() => resize.div(127.5).sub(0.5)); // this is -0.5...0.5 ???
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let data: Array<[]> = [];
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if (config.face.embedding.enabled) {
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if (!config.profile) {
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const embeddingT = await model.predict({ img_inputs: resize });
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data = [...embeddingT.dataSync()]; // convert object array to standard array
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tf.dispose(embeddingT);
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} else {
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const profileData = await tf.profile(() => model.predict({ img_inputs: resize }));
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data = [...profileData.result.dataSync()];
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profileData.result.dispose();
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profile.run('emotion', profileData);
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}
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}
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resize.dispose();
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// normalize.dispose();
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resolve(data);
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});
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}
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