mirror of https://github.com/vladmandic/human
59 lines
3.4 KiB
HTML
59 lines
3.4 KiB
HTML
<!DOCTYPE html>
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<html lang="en">
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<head>
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<meta charset="utf-8">
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<title>Human</title>
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<meta name="viewport" content="width=device-width" id="viewport">
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<meta name="keywords" content="Human">
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<meta name="description" content="Human: Demo; Author: Vladimir Mandic <https://github.com/vladmandic>">
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<link rel="manifest" href="../manifest.webmanifest">
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<link rel="shortcut icon" href="../../favicon.ico" type="image/x-icon">
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<style>
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@font-face { font-family: 'Lato'; font-display: swap; font-style: normal; font-weight: 100; src: local('Lato'), url('../../assets/lato-light.woff2') }
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body { font-family: 'Lato', 'Segoe UI'; font-size: 16px; font-variant: small-caps; margin: 0; background: black; color: white; overflow: hidden; width: 100vw; height: 100vh; }
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</style>
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</head>
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<body>
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<canvas id="canvas" style="margin: 0 auto; width: 100%"></canvas>
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<pre id="log" style="padding: 8px; position: fixed; bottom: 0"></pre>
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<script type="module">
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import * as H from '../../dist/human.esm.js'; // equivalent of import @vladmandic/Human
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const humanConfig = { // user configuration for human, used to fine-tune behavior
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modelBasePath: '../../models', // models can be loaded directly from cdn as well
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filter: { enabled: true, equalization: true, flip: false },
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face: { enabled: true, detector: { rotation: false }, mesh: { enabled: true }, attention: { enabled: false }, iris: { enabled: true }, description: { enabled: true }, emotion: { enabled: true } },
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body: { enabled: true },
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hand: { enabled: true },
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gesture: { enabled: true },
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object: { enabled: false },
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segmentation: { enabled: false },
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};
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const human = new H.Human(humanConfig); // create instance of human with overrides from user configuration
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const canvas = document.getElementById('canvas'); // output canvas to draw both webcam and detection results
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async function drawLoop() { // main screen refresh loop
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const interpolated = human.next(); // get smoothened result using last-known results which are continously updated based on input webcam video
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human.draw.canvas(human.webcam.element, canvas); // draw webcam video to screen canvas // better than using procesed image as this loop happens faster than processing loop
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await human.draw.all(canvas, interpolated); // draw labels, boxes, lines, etc.
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setTimeout(drawLoop, 30); // use to slow down refresh from max refresh rate to target of 1000/30 ~ 30 fps
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}
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async function main() { // main entry point
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document.getElementById('log').innerHTML = `human version: ${human.version} | tfjs version: ${human.tf.version['tfjs-core']}<br>platform: ${human.env.platform} | agent ${human.env.agent}`;
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await human.webcam.start({ crop: true }); // find webcam and start it
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human.video(human.webcam.element); // instruct human to continously detect video frames
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canvas.width = human.webcam.width; // set canvas resolution to input webcam native resolution
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canvas.height = human.webcam.height;
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canvas.onclick = async () => { // pause when clicked on screen and resume on next click
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if (human.webcam.paused) await human.webcam.play();
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else human.webcam.pause();
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};
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await drawLoop(); // start draw loop
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}
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window.onload = main;
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</script>
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</body>
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</html>
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