mirror of https://github.com/vladmandic/human
added error handling
Vladimir Mandic
parent
b0da7fa5b6
commit
ee65aa7588
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@ -7,6 +7,7 @@ const ui = {
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baseLabel: 'rgba(255, 200, 255, 0.8)',
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baseFont: 'small-caps 1.2rem "Segoe UI"',
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baseLineWidth: 16,
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busy: false,
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};
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const config = {
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@ -16,13 +17,13 @@ const config = {
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enabled: true,
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detector: { maxFaces: 10, skipFrames: 10, minConfidence: 0.5, iouThreshold: 0.3, scoreThreshold: 0.7 },
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mesh: { enabled: true },
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iris: { enabled: true },
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iris: { enabled: false },
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age: { enabled: true, skipFrames: 10 },
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gender: { enabled: true },
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emotion: { enabled: true, minConfidence: 0.5, useGrayscale: true },
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},
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body: { enabled: true, maxDetections: 10, scoreThreshold: 0.7, nmsRadius: 20 },
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hand: { enabled: true, skipFrames: 10, minConfidence: 0.5, iouThreshold: 0.3, scoreThreshold: 0.7 },
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body: { enabled: false, maxDetections: 10, scoreThreshold: 0.7, nmsRadius: 20 },
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hand: { enabled: false, skipFrames: 10, minConfidence: 0.5, iouThreshold: 0.3, scoreThreshold: 0.7 },
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};
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let settings;
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let worker;
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@ -245,10 +246,16 @@ function webWorker(input, image, canvas) {
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}
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async function runHumanDetect(input, canvas) {
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const live = input.srcObject ? ((input.srcObject.getVideoTracks()[0].readyState === 'live') && (input.readyState > 2) && (!input.paused)) : false;
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timeStamp = performance.now();
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// perform detect if live video or not video at all
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if (live || !(input instanceof HTMLVideoElement)) {
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if (input.srcObject) {
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// if video not ready, just redo
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const live = (input.srcObject.getVideoTracks()[0].readyState === 'live') && (input.readyState > 2) && (!input.paused);
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if (!live) {
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if (!input.paused) log(`Video not ready: state: ${input.srcObject.getVideoTracks()[0].readyState} stream state: ${input.readyState}`);
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setTimeout(() => runHumanDetect(input, canvas), 500);
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return;
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}
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if (settings.getValue('Use Web Worker')) {
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// get image data from video as we cannot send html objects to webworker
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const offscreen = new OffscreenCanvas(canvas.width, canvas.height);
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@ -265,7 +272,8 @@ async function runHumanDetect(input, canvas) {
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} catch (err) {
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log('Error during execution:', err.message);
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}
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drawResults(input, result, canvas);
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if (result.error) log(result.error);
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else drawResults(input, result, canvas);
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}
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}
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}
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@ -333,7 +341,7 @@ function setupUI() {
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config.hand.iouThreshold = parseFloat(val);
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});
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settings.addHTML('title', 'UI Options'); settings.hideTitle('title');
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settings.addBoolean('Use Web Worker', true);
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settings.addBoolean('Use Web Worker', false);
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settings.addBoolean('Draw Boxes', true);
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settings.addBoolean('Draw Points', true);
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settings.addBoolean('Draw Polygons', true);
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@ -342,21 +350,20 @@ function setupUI() {
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settings.addRange('FPS', 0, 100, 0, 1);
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}
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async function setupCanvas(input) {
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// setup canvas object to same size as input as camera resolution may change
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const canvas = document.getElementById('canvas');
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canvas.width = input.width;
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canvas.height = input.height;
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return canvas;
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}
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// eslint-disable-next-line no-unused-vars
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async function setupCamera() {
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log('Setting up camera');
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// setup webcam. note that navigator.mediaDevices requires that page is accessed via https
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if (ui.busy) return null;
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ui.busy = true;
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const video = document.getElementById('video');
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const canvas = document.getElementById('canvas');
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const output = document.getElementById('log');
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const live = video.srcObject ? ((video.srcObject.getVideoTracks()[0].readyState === 'live') && (video.readyState > 2) && (!video.paused)) : false;
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log('Setting up camera: live:', live);
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// setup webcam. note that navigator.mediaDevices requires that page is accessed via https
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if (!navigator.mediaDevices) {
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document.getElementById('log').innerText = 'Video not supported';
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const msg = 'Camera access not supported';
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output.innerText = msg;
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log(msg);
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return null;
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}
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const stream = await navigator.mediaDevices.getUserMedia({
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@ -365,11 +372,15 @@ async function setupCamera() {
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});
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video.srcObject = stream;
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return new Promise((resolve) => {
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video.onloadedmetadata = () => {
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video.onloadeddata = async () => {
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video.width = video.videoWidth;
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video.height = video.videoHeight;
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video.play();
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video.pause();
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canvas.width = video.videoWidth;
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canvas.height = video.videoHeight;
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if (live) video.play();
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ui.busy = false;
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// do once more because onresize events can be delayed or skipped
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if (video.width !== window.innerWidth) await setupCamera();
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resolve(video);
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};
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});
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@ -387,16 +398,15 @@ async function setupImage() {
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}
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async function main() {
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log('Human starting ...');
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log('Human demo starting ...');
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// setup ui control panel
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await setupUI();
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// setup webcam
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const input = await setupCamera();
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await setupCamera();
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// or setup image
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// const input = await setupImage();
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// setup output canvas from input object
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await setupCanvas(input);
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const msg = `Human ready: version: ${human.version} TensorFlow/JS version: ${human.tf.version_core}`;
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document.getElementById('log').innerText = msg;
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@ -407,4 +417,4 @@ async function main() {
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}
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window.onload = main;
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window.onresize = main;
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window.onresize = setupCamera;
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@ -1,7 +1,6 @@
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<head>
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<script src="https://cdn.jsdelivr.net/npm/quicksettings@latest/quicksettings.min.js"></script>
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<script src="../dist/human.js"></script>
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<script src="./demo-iife.js"></script>
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</head>
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<body style="margin: 0; background: black; color: white; font-family: 'Segoe UI'">
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<div id="main">
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@ -10,4 +9,414 @@
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<canvas id="canvas"></canvas>
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<div id="log">Starting Human library</div>
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</div>
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<script>
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/* global QuickSettings */
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const ui = {
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baseColor: 'rgba(255, 200, 255, 0.3)',
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baseLabel: 'rgba(255, 200, 255, 0.8)',
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baseFont: 'small-caps 1.2rem "Segoe UI"',
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baseLineWidth: 16,
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};
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const config = {
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backend: 'webgl',
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console: true,
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face: {
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enabled: true,
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detector: { maxFaces: 10, skipFrames: 10, minConfidence: 0.5, iouThreshold: 0.3, scoreThreshold: 0.7 },
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mesh: { enabled: true },
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iris: { enabled: true },
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age: { enabled: true, skipFrames: 10 },
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gender: { enabled: true },
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emotion: { enabled: true, minConfidence: 0.5, useGrayscale: true },
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},
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body: { enabled: true, maxDetections: 10, scoreThreshold: 0.7, nmsRadius: 20 },
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hand: { enabled: true, skipFrames: 10, minConfidence: 0.5, iouThreshold: 0.3, scoreThreshold: 0.7 },
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};
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let settings;
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let worker;
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let timeStamp;
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const fps = [];
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function str(...msg) {
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if (!Array.isArray(msg)) return msg;
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let line = '';
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for (const entry of msg) {
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if (typeof entry === 'object') line += JSON.stringify(entry).replace(/{|}|"|\[|\]/g, '').replace(/,/g, ', ');
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else line += entry;
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}
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return line;
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}
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const log = (...msg) => {
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// eslint-disable-next-line no-console
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if (config.console) console.log(...msg);
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};
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async function drawFace(result, canvas) {
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if (!result) return;
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const ctx = canvas.getContext('2d');
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ctx.strokeStyle = ui.baseColor;
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ctx.font = ui.baseFont;
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for (const face of result) {
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ctx.fillStyle = ui.baseColor;
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ctx.lineWidth = ui.baseLineWidth;
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ctx.beginPath();
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if (settings.getValue('Draw Boxes')) {
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ctx.rect(face.box[0], face.box[1], face.box[2], face.box[3]);
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}
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const labelAgeGender = `${face.gender || ''} ${face.age || ''}`;
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const labelIris = face.iris ? `iris: ${face.iris}` : '';
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const labelEmotion = face.emotion && face.emotion[0] ? `emotion: ${Math.trunc(100 * face.emotion[0].score)}% ${face.emotion[0].emotion}` : '';
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ctx.fillStyle = ui.baseLabel;
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ctx.fillText(`${Math.trunc(100 * face.confidence)}% face ${labelAgeGender} ${labelIris} ${labelEmotion}`, face.box[0] + 2, face.box[1] + 22);
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ctx.stroke();
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ctx.lineWidth = 1;
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if (face.mesh) {
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if (settings.getValue('Draw Points')) {
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for (const point of face.mesh) {
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ctx.fillStyle = `rgba(${127.5 + (2 * point[2])}, ${127.5 - (2 * point[2])}, 255, 0.5)`;
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ctx.beginPath();
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ctx.arc(point[0], point[1], 2, 0, 2 * Math.PI);
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ctx.fill();
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}
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}
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if (settings.getValue('Draw Polygons')) {
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for (let i = 0; i < human.facemesh.triangulation.length / 3; i++) {
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const points = [
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human.facemesh.triangulation[i * 3 + 0],
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human.facemesh.triangulation[i * 3 + 1],
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human.facemesh.triangulation[i * 3 + 2],
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].map((index) => face.mesh[index]);
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const path = new Path2D();
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path.moveTo(points[0][0], points[0][1]);
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for (const point of points) {
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path.lineTo(point[0], point[1]);
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}
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path.closePath();
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ctx.strokeStyle = `rgba(${127.5 + (2 * points[0][2])}, ${127.5 - (2 * points[0][2])}, 255, 0.3)`;
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ctx.stroke(path);
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if (settings.getValue('Fill Polygons')) {
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ctx.fillStyle = `rgba(${127.5 + (2 * points[0][2])}, ${127.5 - (2 * points[0][2])}, 255, 0.3)`;
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ctx.fill(path);
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}
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}
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}
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}
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}
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}
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async function drawBody(result, canvas) {
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if (!result) return;
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const ctx = canvas.getContext('2d');
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ctx.fillStyle = ui.baseColor;
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ctx.strokeStyle = ui.baseColor;
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ctx.font = ui.baseFont;
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ctx.lineWidth = ui.baseLineWidth;
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for (const pose of result) {
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if (settings.getValue('Draw Points')) {
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for (const point of pose.keypoints) {
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ctx.beginPath();
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ctx.arc(point.position.x, point.position.y, 2, 0, 2 * Math.PI);
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ctx.fill();
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}
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}
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if (settings.getValue('Draw Polygons')) {
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const path = new Path2D();
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let part;
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// torso
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part = pose.keypoints.find((a) => a.part === 'leftShoulder');
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path.moveTo(part.position.x, part.position.y);
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part = pose.keypoints.find((a) => a.part === 'rightShoulder');
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path.lineTo(part.position.x, part.position.y);
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part = pose.keypoints.find((a) => a.part === 'rightHip');
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path.lineTo(part.position.x, part.position.y);
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part = pose.keypoints.find((a) => a.part === 'leftHip');
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path.lineTo(part.position.x, part.position.y);
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part = pose.keypoints.find((a) => a.part === 'leftShoulder');
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path.lineTo(part.position.x, part.position.y);
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// legs
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part = pose.keypoints.find((a) => a.part === 'leftHip');
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path.moveTo(part.position.x, part.position.y);
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part = pose.keypoints.find((a) => a.part === 'leftKnee');
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path.lineTo(part.position.x, part.position.y);
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part = pose.keypoints.find((a) => a.part === 'leftAnkle');
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path.lineTo(part.position.x, part.position.y);
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part = pose.keypoints.find((a) => a.part === 'rightHip');
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path.moveTo(part.position.x, part.position.y);
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part = pose.keypoints.find((a) => a.part === 'rightKnee');
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path.lineTo(part.position.x, part.position.y);
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part = pose.keypoints.find((a) => a.part === 'rightAnkle');
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path.lineTo(part.position.x, part.position.y);
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// arms
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part = pose.keypoints.find((a) => a.part === 'leftShoulder');
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path.moveTo(part.position.x, part.position.y);
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part = pose.keypoints.find((a) => a.part === 'leftElbow');
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path.lineTo(part.position.x, part.position.y);
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part = pose.keypoints.find((a) => a.part === 'leftWrist');
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path.lineTo(part.position.x, part.position.y);
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// arms
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part = pose.keypoints.find((a) => a.part === 'rightShoulder');
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path.moveTo(part.position.x, part.position.y);
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part = pose.keypoints.find((a) => a.part === 'rightElbow');
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path.lineTo(part.position.x, part.position.y);
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part = pose.keypoints.find((a) => a.part === 'rightWrist');
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path.lineTo(part.position.x, part.position.y);
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// draw all
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ctx.stroke(path);
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}
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}
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}
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async function drawHand(result, canvas) {
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if (!result) return;
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const ctx = canvas.getContext('2d');
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ctx.font = ui.baseFont;
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ctx.lineWidth = ui.baseLineWidth;
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window.result = result;
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for (const hand of result) {
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if (settings.getValue('Draw Boxes')) {
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ctx.lineWidth = ui.baseLineWidth;
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ctx.beginPath();
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ctx.fillStyle = ui.baseColor;
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ctx.rect(hand.box[0], hand.box[1], hand.box[2], hand.box[3]);
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ctx.fillStyle = ui.baseLabel;
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ctx.fillText('hand', hand.box[0] + 2, hand.box[1] + 22, hand.box[2]);
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ctx.stroke();
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}
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if (settings.getValue('Draw Points')) {
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for (const point of hand.landmarks) {
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ctx.fillStyle = `rgba(${127.5 + (2 * point[2])}, ${127.5 - (2 * point[2])}, 255, 0.5)`;
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ctx.beginPath();
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ctx.arc(point[0], point[1], 2, 0, 2 * Math.PI);
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ctx.fill();
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}
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}
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if (settings.getValue('Draw Polygons')) {
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const addPart = (part) => {
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for (let i = 1; i < part.length; i++) {
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ctx.lineWidth = ui.baseLineWidth;
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ctx.beginPath();
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ctx.strokeStyle = `rgba(${127.5 + (2 * part[i][2])}, ${127.5 - (2 * part[i][2])}, 255, 0.5)`;
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ctx.moveTo(part[i - 1][0], part[i - 1][1]);
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ctx.lineTo(part[i][0], part[i][1]);
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ctx.stroke();
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}
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};
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addPart(hand.annotations.indexFinger);
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addPart(hand.annotations.middleFinger);
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addPart(hand.annotations.ringFinger);
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addPart(hand.annotations.pinky);
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addPart(hand.annotations.thumb);
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addPart(hand.annotations.palmBase);
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}
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}
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}
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async function drawResults(input, result, canvas) {
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// update fps
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settings.setValue('FPS', Math.round(1000 / (performance.now() - timeStamp)));
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fps.push(1000 / (performance.now() - timeStamp));
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if (fps.length > 20) fps.shift();
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settings.setValue('FPS', Math.round(10 * fps.reduce((a, b) => a + b) / fps.length) / 10);
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// eslint-disable-next-line no-use-before-define
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requestAnimationFrame(() => runHumanDetect(input, canvas)); // immediate loop
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// draw image from video
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const ctx = canvas.getContext('2d');
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ctx.drawImage(input, 0, 0, input.width, input.height, 0, 0, canvas.width, canvas.height);
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// draw all results
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drawFace(result.face, canvas);
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drawBody(result.body, canvas);
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drawHand(result.hand, canvas);
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// update log
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const engine = await human.tf.engine();
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const memory = `${engine.state.numBytes.toLocaleString()} bytes ${engine.state.numDataBuffers.toLocaleString()} buffers ${engine.state.numTensors.toLocaleString()} tensors`;
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const gpu = engine.backendInstance ? `GPU: ${engine.backendInstance.numBytesInGPU.toLocaleString()} bytes` : '';
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document.getElementById('log').innerText = `
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TFJS Version: ${human.tf.version_core} | Backend: ${human.tf.getBackend()} | Memory: ${memory} ${gpu}
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Performance: ${str(result.performance)} | Object size: ${(str(result)).length.toLocaleString()} bytes
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`;
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}
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// simple wrapper for worker.postmessage that creates worker if one does not exist
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function webWorker(input, image, canvas) {
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if (!worker) {
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// create new webworker and add event handler only once
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log('Creating worker thread');
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worker = new Worker('demo-esm-webworker.js', { type: 'module' });
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// after receiving message from webworker, parse&draw results and send new frame for processing
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worker.addEventListener('message', (msg) => drawResults(input, msg.data, canvas));
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}
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// pass image data as arraybuffer to worker by reference to avoid copy
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worker.postMessage({ image: image.data.buffer, width: canvas.width, height: canvas.height, config }, [image.data.buffer]);
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}
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async function runHumanDetect(input, canvas) {
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const live = input.srcObject ? ((input.srcObject.getVideoTracks()[0].readyState === 'live') && (input.readyState > 2) && (!input.paused)) : false;
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timeStamp = performance.now();
|
||||
// perform detect if live video or not video at all
|
||||
if (live || !(input instanceof HTMLVideoElement)) {
|
||||
if (settings.getValue('Use Web Worker')) {
|
||||
// get image data from video as we cannot send html objects to webworker
|
||||
const offscreen = new OffscreenCanvas(canvas.width, canvas.height);
|
||||
const ctx = offscreen.getContext('2d');
|
||||
ctx.drawImage(input, 0, 0, input.width, input.height, 0, 0, canvas.width, canvas.height);
|
||||
const data = ctx.getImageData(0, 0, canvas.width, canvas.height);
|
||||
// perform detection in worker
|
||||
webWorker(input, data, canvas);
|
||||
} else {
|
||||
let result = {};
|
||||
try {
|
||||
// perform detection
|
||||
result = await human.detect(input, config);
|
||||
} catch (err) {
|
||||
log('Error during execution:', err.message);
|
||||
}
|
||||
drawResults(input, result, canvas);
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
function setupUI() {
|
||||
// add all variables to ui control panel
|
||||
settings = QuickSettings.create(10, 10, 'Settings', document.getElementById('main'));
|
||||
const style = document.createElement('style');
|
||||
// style.type = 'text/css';
|
||||
style.innerHTML = `
|
||||
.qs_main { font: 1rem "Segoe UI"; }
|
||||
.qs_label { font: 0.8rem "Segoe UI"; }
|
||||
.qs_title_bar { display: none; }
|
||||
.qs_content { background: darkslategray; }
|
||||
.qs_container { background: transparent; color: white; margin: 6px; padding: 6px; }
|
||||
.qs_checkbox_label { top: 2px; }
|
||||
.qs_button { width: -webkit-fill-available; font: 1rem "Segoe UI"; cursor: pointer; }
|
||||
`;
|
||||
document.getElementsByTagName('head')[0].appendChild(style);
|
||||
settings.addButton('Play/Pause', () => {
|
||||
const video = document.getElementById('video');
|
||||
const canvas = document.getElementById('canvas');
|
||||
if (!video.paused) {
|
||||
document.getElementById('log').innerText = 'Paused ...';
|
||||
video.pause();
|
||||
} else {
|
||||
document.getElementById('log').innerText = 'Starting Human Library ...';
|
||||
video.play();
|
||||
}
|
||||
runHumanDetect(video, canvas);
|
||||
});
|
||||
settings.addDropDown('Backend', ['webgl', 'wasm', 'cpu'], async (val) => config.backend = val.value);
|
||||
settings.addHTML('title', 'Enabled Models'); settings.hideTitle('title');
|
||||
settings.addBoolean('Face Detect', config.face.enabled, (val) => config.face.enabled = val);
|
||||
settings.addBoolean('Face Mesh', config.face.mesh.enabled, (val) => config.face.mesh.enabled = val);
|
||||
settings.addBoolean('Face Iris', config.face.iris.enabled, (val) => config.face.iris.enabled = val);
|
||||
settings.addBoolean('Face Age', config.face.age.enabled, (val) => config.face.age.enabled = val);
|
||||
settings.addBoolean('Face Gender', config.face.gender.enabled, (val) => config.face.gender.enabled = val);
|
||||
settings.addBoolean('Face Emotion', config.face.emotion.enabled, (val) => config.face.emotion.enabled = val);
|
||||
settings.addBoolean('Body Pose', config.body.enabled, (val) => config.body.enabled = val);
|
||||
settings.addBoolean('Hand Pose', config.hand.enabled, (val) => config.hand.enabled = val);
|
||||
settings.addHTML('title', 'Model Parameters'); settings.hideTitle('title');
|
||||
settings.addRange('Max Objects', 1, 20, 5, 1, (val) => {
|
||||
config.face.detector.maxFaces = parseInt(val);
|
||||
config.body.maxDetections = parseInt(val);
|
||||
});
|
||||
settings.addRange('Skip Frames', 1, 20, config.face.detector.skipFrames, 1, (val) => {
|
||||
config.face.detector.skipFrames = parseInt(val);
|
||||
config.face.emotion.skipFrames = parseInt(val);
|
||||
config.face.age.skipFrames = parseInt(val);
|
||||
config.hand.skipFrames = parseInt(val);
|
||||
});
|
||||
settings.addRange('Min Confidence', 0.1, 1.0, config.face.detector.minConfidence, 0.05, (val) => {
|
||||
config.face.detector.minConfidence = parseFloat(val);
|
||||
config.face.emotion.minConfidence = parseFloat(val);
|
||||
config.hand.minConfidence = parseFloat(val);
|
||||
});
|
||||
settings.addRange('Score Threshold', 0.1, 1.0, config.face.detector.scoreThreshold, 0.05, (val) => {
|
||||
config.face.detector.scoreThreshold = parseFloat(val);
|
||||
config.hand.scoreThreshold = parseFloat(val);
|
||||
config.body.scoreThreshold = parseFloat(val);
|
||||
});
|
||||
settings.addRange('IOU Threshold', 0.1, 1.0, config.face.detector.iouThreshold, 0.05, (val) => {
|
||||
config.face.detector.iouThreshold = parseFloat(val);
|
||||
config.hand.iouThreshold = parseFloat(val);
|
||||
});
|
||||
settings.addHTML('title', 'UI Options'); settings.hideTitle('title');
|
||||
settings.addBoolean('Use Web Worker', false);
|
||||
settings.addBoolean('Draw Boxes', true);
|
||||
settings.addBoolean('Draw Points', true);
|
||||
settings.addBoolean('Draw Polygons', true);
|
||||
settings.addBoolean('Fill Polygons', true);
|
||||
settings.addHTML('line1', '<hr>'); settings.hideTitle('line1');
|
||||
settings.addRange('FPS', 0, 100, 0, 1);
|
||||
}
|
||||
|
||||
async function setupCanvas(input) {
|
||||
// setup canvas object to same size as input as camera resolution may change
|
||||
const canvas = document.getElementById('canvas');
|
||||
canvas.width = input.width;
|
||||
canvas.height = input.height;
|
||||
return canvas;
|
||||
}
|
||||
|
||||
// eslint-disable-next-line no-unused-vars
|
||||
async function setupCamera() {
|
||||
log('Setting up camera');
|
||||
// setup webcam. note that navigator.mediaDevices requires that page is accessed via https
|
||||
const video = document.getElementById('video');
|
||||
if (!navigator.mediaDevices) {
|
||||
document.getElementById('log').innerText = 'Video not supported';
|
||||
return null;
|
||||
}
|
||||
const stream = await navigator.mediaDevices.getUserMedia({
|
||||
audio: false,
|
||||
video: { facingMode: 'user', width: window.innerWidth, height: window.innerHeight },
|
||||
});
|
||||
video.srcObject = stream;
|
||||
return new Promise((resolve) => {
|
||||
video.onloadedmetadata = () => {
|
||||
video.width = video.videoWidth;
|
||||
video.height = video.videoHeight;
|
||||
video.play();
|
||||
video.pause();
|
||||
resolve(video);
|
||||
};
|
||||
});
|
||||
}
|
||||
|
||||
// eslint-disable-next-line no-unused-vars
|
||||
async function setupImage() {
|
||||
const image = document.getElementById('image');
|
||||
image.width = window.innerWidth;
|
||||
image.height = window.innerHeight;
|
||||
return new Promise((resolve) => {
|
||||
image.onload = () => resolve(image);
|
||||
image.src = 'sample.jpg';
|
||||
});
|
||||
}
|
||||
|
||||
async function main() {
|
||||
log('Human starting ...');
|
||||
|
||||
// setup ui control panel
|
||||
await setupUI();
|
||||
// setup webcam
|
||||
const input = await setupCamera();
|
||||
// or setup image
|
||||
// const input = await setupImage();
|
||||
// setup output canvas from input object
|
||||
await setupCanvas(input);
|
||||
|
||||
const msg = `Human ready: version: ${human.version} TensorFlow/JS version: ${human.tf.version_core}`;
|
||||
document.getElementById('log').innerText = msg;
|
||||
log(msg);
|
||||
|
||||
// run actual detection. if input is video, it will run in a loop else it will run only once
|
||||
// runHumanDetect(video, canvas);
|
||||
}
|
||||
|
||||
window.onload = main;
|
||||
window.onresize = main;
|
||||
</script>
|
||||
</body>
|
||||
|
|
|
|||
|
|
@ -2,7 +2,7 @@ const tf = require('@tensorflow/tfjs-node');
|
|||
const fs = require('fs');
|
||||
const process = require('process');
|
||||
const console = require('console');
|
||||
const human = require('..'); // this would be '@vladmandic/human'
|
||||
const human = require('..'); // this resolves to project root which is '@vladmandic/human'
|
||||
|
||||
const logger = new console.Console({
|
||||
stdout: process.stdout,
|
||||
|
|
@ -24,6 +24,8 @@ const logger = new console.Console({
|
|||
});
|
||||
|
||||
const config = {
|
||||
backend: 'tensorflow',
|
||||
console: true,
|
||||
face: {
|
||||
enabled: false,
|
||||
detector: { modelPath: 'file://models/blazeface/model.json', inputSize: 128, maxFaces: 10, skipFrames: 10, minConfidence: 0.8, iouThreshold: 0.3, scoreThreshold: 0.75 },
|
||||
|
|
|
|||
|
|
@ -3,7 +3,7 @@
|
|||
"version": "0.3.3",
|
||||
"description": "human: 3D Face Detection, Iris Tracking and Age & Gender Prediction",
|
||||
"sideEffects": false,
|
||||
"main": "dist/human.cjs",
|
||||
"main": "dist/human-nobundle.cjs",
|
||||
"module": "dist/human.esm.js",
|
||||
"browser": "dist/human.esm.js",
|
||||
"author": "Vladimir Mandic <mandic00@live.com>",
|
||||
|
|
|
|||
|
|
@ -1,4 +1,6 @@
|
|||
export default {
|
||||
backend: 'webgl',
|
||||
console: true,
|
||||
face: {
|
||||
enabled: true, // refers to detector, but since all other face modules rely on detector, it should be a global
|
||||
detector: {
|
||||
|
|
|
|||
53
src/index.js
53
src/index.js
|
|
@ -19,6 +19,10 @@ const models = {
|
|||
gender: null,
|
||||
emotion: null,
|
||||
};
|
||||
const now = () => {
|
||||
if (typeof performance !== 'undefined') return performance.now();
|
||||
return parseInt(Number(process.hrtime.bigint()) / 1000 / 1000);
|
||||
};
|
||||
|
||||
const log = (...msg) => {
|
||||
// eslint-disable-next-line no-console
|
||||
|
|
@ -44,11 +48,31 @@ function mergeDeep(...objects) {
|
|||
}, {});
|
||||
}
|
||||
|
||||
function sanity(input) {
|
||||
if (!input) return 'input is not defined';
|
||||
const width = input.naturalWidth || input.videoWidth || input.width || (input.shape && (input.shape[1] > 0));
|
||||
if (!width || (width === 0)) return 'input is empty';
|
||||
if (input.readyState && (input.readyState <= 2)) return 'input is not ready';
|
||||
try {
|
||||
tf.getBackend();
|
||||
} catch {
|
||||
return 'backend not loaded';
|
||||
}
|
||||
return null;
|
||||
}
|
||||
|
||||
async function detect(input, userConfig) {
|
||||
config = mergeDeep(defaults, userConfig);
|
||||
|
||||
// sanity checks
|
||||
const error = sanity(input);
|
||||
if (error) {
|
||||
log(error, input);
|
||||
return { error };
|
||||
}
|
||||
|
||||
// eslint-disable-next-line no-async-promise-executor
|
||||
return new Promise(async (resolve) => {
|
||||
config = mergeDeep(defaults, userConfig);
|
||||
|
||||
// check number of loaded models
|
||||
const loadedModels = Object.values(models).filter((a) => a).length;
|
||||
if (loadedModels === 0) log('Human library starting');
|
||||
|
|
@ -78,35 +102,40 @@ async function detect(input, userConfig) {
|
|||
let timeStamp;
|
||||
|
||||
// run posenet
|
||||
timeStamp = performance.now();
|
||||
timeStamp = now();
|
||||
tf.engine().startScope();
|
||||
const poseRes = config.body.enabled ? await models.posenet.estimatePoses(input, config.body) : [];
|
||||
tf.engine().endScope();
|
||||
perf.body = Math.trunc(performance.now() - timeStamp);
|
||||
perf.body = Math.trunc(now() - timeStamp);
|
||||
|
||||
// run handpose
|
||||
timeStamp = performance.now();
|
||||
timeStamp = now();
|
||||
tf.engine().startScope();
|
||||
const handRes = config.hand.enabled ? await models.handpose.estimateHands(input, config.hand) : [];
|
||||
tf.engine().endScope();
|
||||
perf.hand = Math.trunc(performance.now() - timeStamp);
|
||||
perf.hand = Math.trunc(now() - timeStamp);
|
||||
|
||||
// run facemesh, includes blazeface and iris
|
||||
const faceRes = [];
|
||||
if (config.face.enabled) {
|
||||
timeStamp = performance.now();
|
||||
timeStamp = now();
|
||||
tf.engine().startScope();
|
||||
const faces = await models.facemesh.estimateFaces(input, config.face);
|
||||
perf.face = Math.trunc(performance.now() - timeStamp);
|
||||
perf.face = Math.trunc(now() - timeStamp);
|
||||
for (const face of faces) {
|
||||
// is something went wrong, skip the face
|
||||
if (!face.image || face.image.isDisposedInternal) {
|
||||
log('face object is disposed:', face.image);
|
||||
continue;
|
||||
}
|
||||
// run ssr-net age & gender, inherits face from blazeface
|
||||
timeStamp = performance.now();
|
||||
timeStamp = now();
|
||||
const ssrData = (config.face.age.enabled || config.face.gender.enabled) ? await ssrnet.predict(face.image, config) : {};
|
||||
perf.agegender = Math.trunc(performance.now() - timeStamp);
|
||||
perf.agegender = Math.trunc(now() - timeStamp);
|
||||
// run emotion, inherits face from blazeface
|
||||
timeStamp = performance.now();
|
||||
timeStamp = now();
|
||||
const emotionData = config.face.emotion.enabled ? await emotion.predict(face.image, config) : {};
|
||||
perf.emotion = Math.trunc(performance.now() - timeStamp);
|
||||
perf.emotion = Math.trunc(now() - timeStamp);
|
||||
face.image.dispose();
|
||||
// calculate iris distance
|
||||
// iris: array[ bottom, left, top, right, center ]
|
||||
|
|
|
|||
Loading…
Reference in New Issue