mirror of https://github.com/vladmandic/human
76 lines
2.5 KiB
JavaScript
76 lines
2.5 KiB
JavaScript
// @ts-nocheck
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const fs = require('fs');
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// eslint-disable-next-line import/no-extraneous-dependencies, node/no-unpublished-require
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const log = require('@vladmandic/pilogger');
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// workers actual import tfjs and faceapi modules
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// eslint-disable-next-line import/no-extraneous-dependencies, node/no-unpublished-require
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const tf = require('@tensorflow/tfjs-node');
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const Human = require('../dist/human.node.js').default; // or const Human = require('../dist/human.node-gpu.js').default;
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let human = null;
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const myConfig = {
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backend: 'tensorflow',
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modelBasePath: 'file://models/',
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debug: false,
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videoOptimized: false,
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async: true,
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face: {
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enabled: true,
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detector: { enabled: true, rotation: false },
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mesh: { enabled: true },
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iris: { enabled: false },
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description: { enabled: true },
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emotion: { enabled: true },
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},
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hand: {
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enabled: false,
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},
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// body: { modelPath: 'blazepose.json', enabled: true },
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body: { enabled: false },
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object: { enabled: false },
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};
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// read image from a file and create tensor to be used by faceapi
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// this way we don't need any monkey patches
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// you can add any pre-proocessing here such as resizing, etc.
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async function image(img) {
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const buffer = fs.readFileSync(img);
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const tensor = tf.tidy(() => tf.node.decodeImage(buffer).toFloat().expandDims());
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return tensor;
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}
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// actual faceapi detection
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async function detect(img) {
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const tensor = await image(img);
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const result = await human.detect(tensor);
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process.send({ image: img, detected: result }); // send results back to main
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process.send({ ready: true }); // send signal back to main that this worker is now idle and ready for next image
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tensor.dispose();
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}
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async function main() {
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// on worker start first initialize message handler so we don't miss any messages
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process.on('message', (msg) => {
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if (msg.exit) process.exit(); // if main told worker to exit
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if (msg.test) process.send({ test: true });
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if (msg.image) detect(msg.image); // if main told worker to process image
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log.data('Worker received message:', process.pid, msg); // generic log
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});
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// wait until tf is ready
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await tf.ready();
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// create instance of human
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human = new Human(myConfig);
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// pre-load models
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log.state('Worker: PID:', process.pid, `TensorFlow/JS ${human.tf.version_core} Human ${human.version} Backend: ${human.tf.getBackend()}`);
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await human.load();
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// now we're ready, so send message back to main that it knows it can use this worker
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process.send({ ready: true });
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}
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main();
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