68 lines
2.6 KiB
JavaScript
68 lines
2.6 KiB
JavaScript
const fs = require('fs');
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const path = require('path');
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const log = require('@vladmandic/pilogger');
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// workers actual import tfjs and faceapi modules
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const tf = require('@tensorflow/tfjs-node');
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const faceapi = require('../dist/face-api.node.js'); // this is equivalent to '@vladmandic/faceapi'
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// options used by faceapi
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const modelPathRoot = '../model';
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const minScore = 0.1;
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const maxResults = 5;
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let optionsSSDMobileNet;
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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 faceapi
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.detectAllFaces(tensor, optionsSSDMobileNet)
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.withFaceLandmarks()
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.withFaceExpressions()
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.withFaceDescriptors()
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.withAgeAndGender();
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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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// then initialize tfjs
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await faceapi.tf.setBackend('tensorflow');
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await faceapi.tf.enableProdMode();
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await faceapi.tf.ENV.set('DEBUG', false);
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await faceapi.tf.ready();
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log.state('Worker: PID:', process.pid, `TensorFlow/JS ${faceapi.tf.version_core} FaceAPI ${faceapi.version.faceapi} Backend: ${faceapi.tf.getBackend()}`);
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// and load and initialize facepi models
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const modelPath = path.join(__dirname, modelPathRoot);
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await faceapi.nets.ssdMobilenetv1.loadFromDisk(modelPath);
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await faceapi.nets.ageGenderNet.loadFromDisk(modelPath);
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await faceapi.nets.faceLandmark68Net.loadFromDisk(modelPath);
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await faceapi.nets.faceRecognitionNet.loadFromDisk(modelPath);
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await faceapi.nets.faceExpressionNet.loadFromDisk(modelPath);
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optionsSSDMobileNet = new faceapi.SsdMobilenetv1Options({ minConfidence: minScore, maxResults });
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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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