human/demo/segmentation/index.js

97 lines
4.4 KiB
JavaScript

/**
* Human demo for browsers
* @default Human Library
* @summary <https://github.com/vladmandic/human>
* @author <https://github.com/vladmandic>
* @copyright <https://github.com/vladmandic>
* @license MIT
*/
import * as H from '../../dist/human.esm.js'; // equivalent of @vladmandic/Human
const humanConfig = { // user configuration for human, used to fine-tune behavior
modelBasePath: 'https://vladmandic.github.io/human-models/models/',
filter: { enabled: true, equalization: false, flip: false },
face: { enabled: false },
body: { enabled: false },
hand: { enabled: false },
object: { enabled: false },
gesture: { enabled: false },
segmentation: {
enabled: true,
modelPath: 'rvm.json', // can use rvm, selfie or meet
ratio: 0.5,
mode: 'default',
},
};
const human = new H.Human(humanConfig); // create instance of human with overrides from user configuration
const log = (...msg) => console.log(...msg); // eslint-disable-line no-console
async function main() {
// gather dom elements
const dom = {
video: document.getElementById('video'),
webcam: document.getElementById('webcam'),
output: document.getElementById('output'),
merge: document.getElementById('merge'),
mode: document.getElementById('mode'),
composite: document.getElementById('composite'),
ratio: document.getElementById('ratio'),
fps: document.getElementById('fps'),
};
// set defaults
dom.fps.innerText = 'initializing';
dom.ratio.valueAsNumber = human.config.segmentation.ratio;
dom.video.src = '../assets/rijeka.mp4';
dom.composite.innerHTML = ['source-atop', 'color', 'color-burn', 'color-dodge', 'copy', 'darken', 'destination-atop', 'destination-in', 'destination-out', 'destination-over', 'difference', 'exclusion', 'hard-light', 'hue', 'lighten', 'lighter', 'luminosity', 'multiply', 'overlay', 'saturation', 'screen', 'soft-light', 'source-in', 'source-out', 'source-over', 'xor'].map((gco) => `<option value="${gco}">${gco}</option>`).join(''); // eslint-disable-line max-len
const ctxMerge = dom.merge.getContext('2d');
log('human version:', human.version, '| tfjs version:', human.tf.version['tfjs-core']);
log('platform:', human.env.platform, '| agent:', human.env.agent);
await human.load(); // preload all models
log('backend:', human.tf.getBackend(), '| available:', human.env.backends);
log('models stats:', human.getModelStats());
log('models loaded:', Object.values(human.models).filter((model) => model !== null).length);
await human.warmup(); // warmup function to initialize backend for future faster detection
const numTensors = human.tf.engine().state.numTensors;
// initialize webcam
dom.webcam.onplay = () => { // start processing on video play
log('start processing');
dom.output.width = human.webcam.width;
dom.output.height = human.webcam.height;
dom.merge.width = human.webcam.width;
dom.merge.height = human.webcam.height;
loop(); // eslint-disable-line no-use-before-define
};
await human.webcam.start({ element: dom.webcam, crop: true, width: 960, height: 720 }); // use human webcam helper methods and associate webcam stream with a dom element
if (!human.webcam.track) dom.fps.innerText = 'webcam error';
// processing loop
async function loop() {
if (!human.webcam.element || human.webcam.paused) return; // check if webcam is valid and playing
human.config.segmentation.mode = dom.mode.value; // get segmentation mode from ui
human.config.segmentation.ratio = dom.ratio.valueAsNumber; // get segmentation downsample ratio from ui
const t0 = Date.now();
const rgba = await human.segmentation(human.webcam.element, human.config); // run model and process results
const t1 = Date.now();
if (!rgba) {
dom.fps.innerText = 'error';
return;
}
dom.fps.innerText = `fps: ${Math.round(10000 / (t1 - t0)) / 10}`; // mark performance
human.tf.browser.toPixels(rgba, dom.output); // draw raw output
human.tf.dispose(rgba); // dispose tensors
ctxMerge.globalCompositeOperation = 'source-over';
ctxMerge.drawImage(dom.video, 0, 0); // draw original video to first stacked canvas
ctxMerge.globalCompositeOperation = dom.composite.value;
ctxMerge.drawImage(dom.output, 0, 0); // draw processed output to second stacked canvas
if (numTensors !== human.tf.engine().state.numTensors) log({ leak: human.tf.engine().state.numTensors - numTensors }); // check for memory leaks
requestAnimationFrame(loop);
}
}
window.onload = main;