88 lines
3.1 KiB
JavaScript
88 lines
3.1 KiB
JavaScript
// @ts-nocheck
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const fs = require('fs');
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const process = require('process');
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const path = require('path');
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// eslint-disable-next-line import/no-extraneous-dependencies, node/no-unpublished-require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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// eslint-disable-next-line import/no-extraneous-dependencies, node/no-unpublished-require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 faceapi = require('../dist/face-api.node.js'); // this is equivalent to '@vladmandic/faceapi'
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const modelPathRoot = '../model';
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const imgPathRoot = './demo'; // modify to include your sample images
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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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async function image(img) {
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const buffer = fs.readFileSync(img);
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const decoded = tf.node.decodeImage(buffer);
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const casted = decoded.toFloat();
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const result = casted.expandDims(0);
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decoded.dispose();
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casted.dispose();
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return result;
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}
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async function detect(tensor) {
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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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return result;
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}
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async function main() {
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log.header();
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log.info('FaceAPI single-process test');
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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(`Version: TensorFlow/JS ${faceapi.tf?.version_core} FaceAPI ${faceapi.version.faceapi} Backend: ${faceapi.tf?.getBackend()}`);
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log.info('Loading FaceAPI 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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if (process.argv.length !== 3) {
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const t0 = process.hrtime.bigint();
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const dir = fs.readdirSync(imgPathRoot);
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for (const img of dir) {
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if (!img.toLocaleLowerCase().endsWith('.jpg')) continue;
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const tensor = await image(path.join(imgPathRoot, img));
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const result = await detect(tensor);
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log.data('Image:', img, 'Detected faces:', result.length);
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for (const i of result) {
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log.data('Gender:', Math.round(100 * i.genderProbability), 'probability', i.gender, 'with age', Math.round(10 * i.age) / 10);
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}
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tensor.dispose();
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}
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const t1 = process.hrtime.bigint();
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log.info('Processed', dir.length, 'images in', Math.trunc(parseInt(t1 - t0) / 1000 / 1000), 'ms');
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} else {
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const param = process.argv[2];
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if (fs.existsSync(param)) {
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const tensor = await image(param);
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const result = await detect(tensor);
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log.data('Image:', param, 'Detected faces:', result.length);
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for (const i of result) {
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log.data('Gender:', Math.round(100 * i.genderProbability), 'probability', i.gender, 'with age', Math.round(10 * i.age) / 10);
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}
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tensor.dispose();
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}
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}
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}
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main();
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