87 lines
3.1 KiB
JavaScript
87 lines
3.1 KiB
JavaScript
process.stderr.write = null; // silly hack to stock tfjs logging too much to stderr
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const fs = require('fs');
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const path = require('path');
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const fetch = require('node-fetch');
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const tf = require('@tensorflow/tfjs-node');
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const faceapi = require('../dist/face-api.node.js');
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// if you heve module installed, this would be
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// const faceapi = require('@vladmandic/face-api');
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// configuration options
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const modelPathRoot = '../model/'; // path to model folder that will be loaded using http
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const imgSize = 512; // maximum image size in pixels
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const minScore = 0.1; // minimum score
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const maxResults = 5; // maximum number of results to return
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const samples = ['sample (1).jpg', 'sample (2).jpg', 'sample (3).jpg', 'sample (4).jpg', 'sample (5).jpg', 'sample (6).jpg']; // sample images to be loaded using http
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// helper function to pretty-print json object to string
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function str(json) {
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const text = json ? JSON.stringify(json).replace(/{|}|"|\[|\]/g, '').replace(/,/g, ', ') : '';
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return text;
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}
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// helper function to print strings to html document as a log
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function log(...txt) {
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// eslint-disable-next-line no-console
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console.log(...txt);
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}
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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 main() {
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// initialize tfjs
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log('FaceAPI 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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faceapi.env.monkeyPatch({ fetch: fetch });
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// check version
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log(`Version: TensorFlow/JS ${str(faceapi.tf?.version_core || '(not loaded)')} FaceAPI ${str(faceapi?.version || '(not loaded)')} Backend: ${str(faceapi.tf?.getBackend() || '(not loaded)')}`);
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log(`Flags: ${JSON.stringify(faceapi.tf.ENV.flags)}`);
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// load face-api models
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log('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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const optionsSSDMobileNet = new faceapi.SsdMobilenetv1Options({ minConfidence: minScore, maxResults });
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// check tf engine state
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const engine = await faceapi.tf.engine();
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log(`TF Engine State: ${str(engine.state)}`);
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const dir = fs.readdirSync(__dirname);
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for (const img of dir) {
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if (!img.toLocaleLowerCase().endsWith('.jpg')) continue;
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// load image
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const tensor = await image(path.join(__dirname, img));
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// actual model execution
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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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log('Image:', img, 'Detected faces:', result.length);
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// you can access entire result object
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// console.log(result);
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tensor.dispose();
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}
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}
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main();
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