mirror of https://github.com/vladmandic/human
full async operations
Vladimir Mandic
parent
0b07b548d1
commit
b65c824d88
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@ -48,6 +48,7 @@
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"promise/always-return": "off",
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"promise/catch-or-return": "off",
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"promise/no-nesting": "off",
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"no-async-promise-executor": "off",
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"import/no-absolute-path": "off",
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"import/no-extraneous-dependencies": "off",
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"node/no-unpublished-import": "off",
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@ -247,7 +247,7 @@ All configuration details can be changed in real-time!
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config = {
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backend: 'webgl', // select tfjs backend to use
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console: true, // enable debugging output to console
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async: false, // execute enabled models in parallel
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async: true, // execute enabled models in parallel
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// this disables per-model performance data but slightly increases performance
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// cannot be used if profiling is enabled
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profile: false, // enable tfjs profiling
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@ -434,8 +434,9 @@ result = {
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hand, // <array of string>
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}
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performance = { // performance data of last execution for each module measuredin miliseconds
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backend, // time to initialize tf backend, valid only during backend startup
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load, // time to load models, valid only during model load
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// note that per-model performance data is not available in async execution mode
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backend, // time to initialize tf backend, keeps longest value measured
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load, // time to load models, keeps longest value measured
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image, // time for image processing
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gesture, // gesture analysis time
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body, // model time
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@ -4,7 +4,7 @@
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export default {
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backend: 'webgl', // select tfjs backend to use
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console: true, // enable debugging output to console
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async: false, // execute enabled models in parallel
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async: true, // execute enabled models in parallel
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// this disables per-model performance data but slightly increases performance
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// cannot be used if profiling is enabled
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profile: false, // enable tfjs profiling
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@ -35,6 +35,7 @@ const config = {
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profile: false,
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deallocate: false,
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wasm: { path: '../assets' },
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async: true,
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filter: {
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enabled: true,
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width: 0,
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@ -324,6 +325,7 @@ function setupMenu() {
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menu.addHTML('<hr style="min-width: 200px; border-style: inset; border-color: dimgray">');
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menu.addList('Backend', ['cpu', 'webgl', 'wasm', 'webgpu'], config.backend, (val) => config.backend = val);
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menu.addBool('Async Operations', config, 'async');
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menu.addBool('Enable Profiler', config, 'profile');
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menu.addBool('Memory Shield', config, 'deallocate');
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menu.addBool('Use Web Worker', ui, 'useWorker');
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@ -27,7 +27,7 @@ async function drawFace(result, canvas, ui, triangulation) {
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}
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// silly hack since fillText does not suport new line
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const labels = [];
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if (face.agConfidence) labels.push(`${Math.trunc(100 * face.agConfidence)}% ${face.gender || ''}`);
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if (face.genderConfidence) labels.push(`${Math.trunc(100 * face.genderConfidence)}% ${face.gender || ''}`);
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if (face.age) labels.push(`age: ${face.age || ''}`);
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if (face.iris) labels.push(`iris: ${face.iris}`);
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if (face.emotion && face.emotion.length > 0) {
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@ -6,9 +6,11 @@
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- monitors specified filed and folders for changes
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- triggers library and application rebuild
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- any build errors are immediately displayed and can be corrected without need for restart
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- passthrough data compression
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*/
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const fs = require('fs');
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const zlib = require('zlib');
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const path = require('path');
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const http2 = require('http2');
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const chokidar = require('chokidar');
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@ -106,12 +108,12 @@ async function watch() {
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// get file content for a valid url request
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function content(url) {
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return new Promise((resolve) => {
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let obj = {};
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let obj = { ok: false };
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obj.file = url;
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if (!fs.existsSync(obj.file)) resolve(null);
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obj.stat = fs.statSync(obj.file);
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// should really use streams here instead of reading entire content in-memory, but this is micro-http2 not intended to serve huge files
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if (obj.stat.isFile()) obj.data = fs.readFileSync(obj.file);
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if (obj.stat.isFile()) obj.ok = true;
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if (obj.stat.isDirectory()) {
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obj.file = path.join(obj.file, options.default);
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obj = content(obj.file);
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@ -125,20 +127,32 @@ async function httpRequest(req, res) {
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content(path.join(__dirname, options.root, req.url)).then((result) => {
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const forwarded = (req.headers['forwarded'] || '').match(/for="\[(.*)\]:/);
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const ip = (Array.isArray(forwarded) ? forwarded[1] : null) || req.headers['x-forwarded-for'] || req.ip || req.socket.remoteAddress;
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if (!result || !result.data) {
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if (!result || !result.ok) {
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res.writeHead(404, { 'Content-Type': 'text/html' });
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res.end('Error 404: Not Found\n', 'utf-8');
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log.warn(`${req.method}/${req.httpVersion}`, res.statusCode, `${req.headers['host']}${req.url}`, ip);
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log.warn(`${req.method}/${req.httpVersion}`, res.statusCode, req.url, ip);
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} else {
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const ext = String(path.extname(result.file)).toLowerCase();
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const contentType = mime[ext] || 'application/octet-stream';
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const accept = req.headers['accept-encoding'] ? req.headers['accept-encoding'].includes('br') : false; // does target accept brotli compressed data
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res.writeHead(200, {
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'Content-Language': 'en', 'Content-Type': contentType, 'Content-Encoding': '', 'Content-Length': result.stat.size, 'Last-Modified': result.stat.mtime, 'Cache-Control': 'no-cache', 'X-Powered-By': `NodeJS/${process.version}`,
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'Content-Language': 'en', 'Content-Type': contentType, 'Content-Encoding': accept ? 'br' : '', 'Last-Modified': result.stat.mtime, 'Cache-Control': 'no-cache', 'X-Powered-By': `NodeJS/${process.version}`, // 'Content-Length': result.stat.size,
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});
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// ideally this should be passed through compress
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res.end(result.data);
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log.data(`${req.method}/${req.httpVersion}`, res.statusCode, contentType, result.stat.size, `${req.headers['host']}${req.url}`, ip);
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res.end();
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const compress = zlib.createBrotliCompress({ params: { [zlib.constants.BROTLI_PARAM_QUALITY]: 5 } }); // instance of brotli compression with level 5
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const stream = fs.createReadStream(result.file);
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if (!accept) stream.pipe(res); // don't compress data
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else stream.pipe(compress).pipe(res); // compress data
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// alternative methods of sending data
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/// 2. read stream and send by chunk
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// const stream = fs.createReadStream(result.file);
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// stream.on('data', (chunk) => res.write(chunk));
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// stream.on('end', () => res.end());
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// 3. read entire file and send it as blob
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// const data = fs.readFileSync(result.file);
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// res.write(data);
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log.data(`${req.method}/${req.httpVersion}`, res.statusCode, contentType, result.stat.size, req.url, ip);
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}
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});
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}
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@ -1,7 +1,7 @@
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{
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"inputs": {
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"demo/browser.js": {
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"bytes": 17998,
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"bytes": 18066,
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"imports": [
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{
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"path": "dist/human.esm.js"
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@ -15,7 +15,7 @@
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]
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},
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"demo/draw.js": {
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"bytes": 7553,
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"bytes": 7561,
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"imports": []
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},
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"demo/menu.js": {
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@ -23,7 +23,7 @@
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"imports": []
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},
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"dist/human.esm.js": {
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"bytes": 1275163,
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"bytes": 1277557,
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"imports": []
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}
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},
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@ -31,28 +31,28 @@
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"dist/demo-browser-index.js.map": {
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"imports": [],
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"inputs": {},
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"bytes": 5522897
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"bytes": 5529553
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},
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"dist/demo-browser-index.js": {
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"imports": [],
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"inputs": {
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"dist/human.esm.js": {
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"bytesInOutput": 1660982
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"bytesInOutput": 1663845
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},
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"dist/human.esm.js": {
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"bytesInOutput": 8716
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},
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"demo/draw.js": {
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"bytesInOutput": 7443
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"bytesInOutput": 7451
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},
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"demo/menu.js": {
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"bytesInOutput": 12359
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},
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"demo/browser.js": {
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"bytesInOutput": 16212
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"bytesInOutput": 16281
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}
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},
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"bytes": 1705834
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"bytes": 1708774
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}
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}
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}
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@ -1,145 +1,267 @@
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{
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"inputs": {
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"config.js": {
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"bytes": 7320,
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"bytes": 7319,
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"imports": []
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},
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"package.json": {
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"bytes": 3260,
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"bytes": 3374,
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"imports": []
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},
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"src/emotion/emotion.js": {
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"bytes": 2596,
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"src/age/ssrnet.js": {
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"bytes": 1746,
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"imports": [
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{
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"path": "src/profile.js"
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}
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]
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},
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"src/facemesh/blazeface.js": {
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"src/body/buildParts.js": {
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"bytes": 2035,
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"imports": [
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{
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"path": "src/body/heapSort.js"
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}
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]
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},
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"src/body/decodeMultiple.js": {
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"bytes": 5605,
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"imports": [
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{
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"path": "src/body/buildParts.js"
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},
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{
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"path": "src/body/decodePose.js"
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},
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{
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"path": "src/body/vectors.js"
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}
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]
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},
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"src/body/decodePose.js": {
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"bytes": 4540,
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"imports": [
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{
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"path": "src/body/keypoints.js"
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},
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{
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"path": "src/body/vectors.js"
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}
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]
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},
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"src/body/heapSort.js": {
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"bytes": 1590,
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"imports": []
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},
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"src/body/keypoints.js": {
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"bytes": 2291,
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"imports": []
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},
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"src/body/modelBase.js": {
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"bytes": 1512,
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"imports": []
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},
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"src/body/modelMobileNet.js": {
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"bytes": 593,
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"imports": [
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{
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"path": "src/body/modelBase.js"
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}
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]
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},
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"src/body/modelPoseNet.js": {
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"bytes": 3539,
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"imports": [
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{
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"path": "src/body/modelMobileNet.js"
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},
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{
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"path": "src/body/decodeMultiple.js"
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},
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{
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"path": "src/body/util.js"
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}
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]
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},
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"src/body/posenet.js": {
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"bytes": 830,
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"imports": [
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{
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"path": "src/body/modelMobileNet.js"
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},
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{
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"path": "src/body/modelPoseNet.js"
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},
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{
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"path": "src/body/decodeMultiple.js"
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},
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{
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"path": "src/body/keypoints.js"
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},
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{
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"path": "src/body/util.js"
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}
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]
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},
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"src/body/util.js": {
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"bytes": 2260,
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"imports": [
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{
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"path": "src/body/keypoints.js"
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}
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]
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},
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"src/body/vectors.js": {
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"bytes": 1273,
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"imports": [
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{
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"path": "src/body/keypoints.js"
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}
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]
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},
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"src/emotion/emotion.js": {
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"bytes": 2767,
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"imports": [
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{
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"path": "src/profile.js"
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}
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]
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},
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"src/face/blazeface.js": {
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"bytes": 6991,
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"imports": []
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},
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"src/facemesh/box.js": {
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"src/face/box.js": {
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"bytes": 1924,
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"imports": []
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},
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"src/facemesh/facemesh.js": {
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"src/face/facemesh.js": {
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"bytes": 2572,
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"imports": [
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{
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"path": "src/facemesh/blazeface.js"
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"path": "src/face/blazeface.js"
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},
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{
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"path": "src/facemesh/keypoints.js"
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"path": "src/face/keypoints.js"
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},
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{
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"path": "src/facemesh/facepipeline.js"
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"path": "src/face/facepipeline.js"
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},
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{
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"path": "src/facemesh/uvcoords.js"
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"path": "src/face/uvcoords.js"
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},
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{
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"path": "src/facemesh/triangulation.js"
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"path": "src/face/triangulation.js"
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}
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]
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},
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"src/facemesh/facepipeline.js": {
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"bytes": 14296,
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"src/face/facepipeline.js": {
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"bytes": 14368,
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"imports": [
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{
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"path": "src/facemesh/box.js"
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"path": "src/face/box.js"
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},
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{
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"path": "src/facemesh/keypoints.js"
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"path": "src/face/keypoints.js"
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},
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{
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"path": "src/facemesh/util.js"
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"path": "src/face/util.js"
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}
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]
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},
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"src/facemesh/keypoints.js": {
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"src/face/keypoints.js": {
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"bytes": 2507,
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"imports": []
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},
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"src/facemesh/triangulation.js": {
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"src/face/triangulation.js": {
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"bytes": 12940,
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"imports": []
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},
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"src/facemesh/util.js": {
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"src/face/util.js": {
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"bytes": 3078,
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"imports": []
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},
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"src/facemesh/uvcoords.js": {
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"src/face/uvcoords.js": {
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"bytes": 19592,
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"imports": []
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},
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"src/gender/ssrnet.js": {
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"bytes": 2003,
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"imports": [
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{
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"path": "src/profile.js"
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}
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]
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},
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"src/gesture.js": {
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"bytes": 2175,
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"imports": []
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},
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"src/handpose/anchors.js": {
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"src/hand/anchors.js": {
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"bytes": 224151,
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"imports": []
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},
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"src/handpose/box.js": {
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"src/hand/box.js": {
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"bytes": 3192,
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"imports": []
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},
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"src/handpose/handdetector.js": {
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"src/hand/handdetector.js": {
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"bytes": 4313,
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"imports": [
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{
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"path": "src/handpose/box.js"
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"path": "src/hand/box.js"
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}
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]
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},
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"src/handpose/handpipeline.js": {
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"bytes": 8657,
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"src/hand/handpipeline.js": {
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"bytes": 8632,
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"imports": [
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{
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"path": "src/handpose/box.js"
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"path": "src/hand/box.js"
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},
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{
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"path": "src/handpose/util.js"
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"path": "src/hand/util.js"
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}
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]
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},
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"src/handpose/handpose.js": {
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"src/hand/handpose.js": {
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"bytes": 2839,
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"imports": [
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{
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"path": "src/handpose/handdetector.js"
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"path": "src/hand/handdetector.js"
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},
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{
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"path": "src/handpose/handpipeline.js"
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"path": "src/hand/handpipeline.js"
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},
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{
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"path": "src/handpose/anchors.js"
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||||
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||||
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|
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|
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"bytesInOutput": 603
|
||||
},
|
||||
"src/posenet/util.js": {
|
||||
"src/body/util.js": {
|
||||
"bytesInOutput": 1053
|
||||
},
|
||||
"src/posenet/modelPoseNet.js": {
|
||||
"bytesInOutput": 841
|
||||
"src/body/modelPoseNet.js": {
|
||||
"bytesInOutput": 868
|
||||
},
|
||||
"src/posenet/posenet.js": {
|
||||
"src/body/posenet.js": {
|
||||
"bytesInOutput": 459
|
||||
},
|
||||
"src/handpose/box.js": {
|
||||
"src/hand/box.js": {
|
||||
"bytesInOutput": 1419
|
||||
},
|
||||
"src/handpose/handdetector.js": {
|
||||
"src/hand/handdetector.js": {
|
||||
"bytesInOutput": 1806
|
||||
},
|
||||
"src/handpose/util.js": {
|
||||
"src/hand/util.js": {
|
||||
"bytesInOutput": 996
|
||||
},
|
||||
"src/handpose/handpipeline.js": {
|
||||
"bytesInOutput": 3069
|
||||
"src/hand/handpipeline.js": {
|
||||
"bytesInOutput": 3057
|
||||
},
|
||||
"src/handpose/anchors.js": {
|
||||
"src/hand/anchors.js": {
|
||||
"bytesInOutput": 127000
|
||||
},
|
||||
"src/handpose/handpose.js": {
|
||||
"src/hand/handpose.js": {
|
||||
"bytesInOutput": 1127
|
||||
},
|
||||
"src/gesture.js": {
|
||||
|
|
@ -392,16 +406,16 @@
|
|||
"bytesInOutput": 1298
|
||||
},
|
||||
"package.json": {
|
||||
"bytesInOutput": 2898
|
||||
"bytesInOutput": 3004
|
||||
},
|
||||
"src/human.js": {
|
||||
"bytesInOutput": 28
|
||||
},
|
||||
"src/human.js": {
|
||||
"bytesInOutput": 5614
|
||||
"bytesInOutput": 7355
|
||||
}
|
||||
},
|
||||
"bytes": 213478
|
||||
"bytes": 215903
|
||||
}
|
||||
}
|
||||
}
|
||||
|
|
|
|||
|
|
@ -41,6 +41,7 @@
|
|||
"scripts": {
|
||||
"start": "node --trace-warnings --unhandled-rejections=strict --trace-uncaught --no-deprecation src/node.js",
|
||||
"lint": "eslint src/*.js demo/*.js",
|
||||
"dev": "node --trace-warnings --unhandled-rejections=strict --trace-uncaught --no-deprecation dev-server.js",
|
||||
"build-iife": "esbuild --bundle --minify --platform=browser --sourcemap --target=es2018 --format=iife --external:fs --global-name=Human --metafile=dist/human.json --outfile=dist/human.js src/human.js",
|
||||
"build-esm-bundle": "esbuild --bundle --minify --platform=browser --sourcemap --target=es2018 --format=esm --external:fs --metafile=dist/human.esm.json --outfile=dist/human.esm.js src/human.js",
|
||||
"build-esm-nobundle": "esbuild --bundle --minify --platform=browser --sourcemap --target=es2018 --format=esm --external:@tensorflow --external:fs --metafile=dist/human.esm-nobundle.json --outfile=dist/human.esm-nobundle.js src/human.js",
|
||||
|
|
|
|||
|
|
@ -0,0 +1,59 @@
|
|||
const tf = require('@tensorflow/tfjs');
|
||||
const profile = require('../profile.js');
|
||||
|
||||
const models = {};
|
||||
let last = { age: 0 };
|
||||
let frame = Number.MAX_SAFE_INTEGER;
|
||||
|
||||
// tuning values
|
||||
const zoom = [0, 0]; // 0..1 meaning 0%..100%
|
||||
|
||||
async function load(config) {
|
||||
if (!models.age) models.age = await tf.loadGraphModel(config.face.age.modelPath);
|
||||
return models.age;
|
||||
}
|
||||
|
||||
async function predict(image, config) {
|
||||
return new Promise(async (resolve) => {
|
||||
if (frame < config.face.age.skipFrames) {
|
||||
frame += 1;
|
||||
resolve(last);
|
||||
}
|
||||
frame = 0;
|
||||
const box = [[
|
||||
(image.shape[1] * zoom[0]) / image.shape[1],
|
||||
(image.shape[2] * zoom[1]) / image.shape[2],
|
||||
(image.shape[1] - (image.shape[1] * zoom[0])) / image.shape[1],
|
||||
(image.shape[2] - (image.shape[2] * zoom[1])) / image.shape[2],
|
||||
]];
|
||||
const resize = tf.image.cropAndResize(image, box, [0], [config.face.age.inputSize, config.face.age.inputSize]);
|
||||
// const resize = tf.image.resizeBilinear(image, [config.face.age.inputSize, config.face.age.inputSize], false);
|
||||
const enhance = tf.mul(resize, [255.0]);
|
||||
tf.dispose(resize);
|
||||
|
||||
let ageT;
|
||||
const obj = {};
|
||||
|
||||
if (!config.profile) {
|
||||
if (config.face.age.enabled) ageT = await models.age.predict(enhance);
|
||||
} else {
|
||||
const profileAge = config.face.age.enabled ? await tf.profile(() => models.age.predict(enhance)) : {};
|
||||
ageT = profileAge.result.clone();
|
||||
profileAge.result.dispose();
|
||||
profile.run('age', profileAge);
|
||||
}
|
||||
enhance.dispose();
|
||||
|
||||
if (ageT) {
|
||||
const data = ageT.dataSync();
|
||||
obj.age = Math.trunc(10 * data[0]) / 10;
|
||||
}
|
||||
ageT.dispose();
|
||||
|
||||
last = obj;
|
||||
resolve(obj);
|
||||
});
|
||||
}
|
||||
|
||||
exports.predict = predict;
|
||||
exports.load = load;
|
||||
|
|
@ -29,25 +29,27 @@ class PoseNet {
|
|||
* in the same scale as the original image
|
||||
*/
|
||||
async estimatePoses(input, config) {
|
||||
const outputStride = config.outputStride;
|
||||
// const inputResolution = config.inputResolution;
|
||||
const height = input.shape[1];
|
||||
const width = input.shape[2];
|
||||
const resized = util.resizeTo(input, [config.inputResolution, config.inputResolution]);
|
||||
const { heatmapScores, offsets, displacementFwd, displacementBwd } = this.baseModel.predict(resized);
|
||||
const allTensorBuffers = await util.toTensorBuffers3D([heatmapScores, offsets, displacementFwd, displacementBwd]);
|
||||
const scoresBuffer = allTensorBuffers[0];
|
||||
const offsetsBuffer = allTensorBuffers[1];
|
||||
const displacementsFwdBuffer = allTensorBuffers[2];
|
||||
const displacementsBwdBuffer = allTensorBuffers[3];
|
||||
const poses = await decodeMultiple.decodeMultiplePoses(scoresBuffer, offsetsBuffer, displacementsFwdBuffer, displacementsBwdBuffer, outputStride, config.maxDetections, config.scoreThreshold, config.nmsRadius);
|
||||
const resultPoses = util.scaleAndFlipPoses(poses, [height, width], [config.inputResolution, config.inputResolution]);
|
||||
heatmapScores.dispose();
|
||||
offsets.dispose();
|
||||
displacementFwd.dispose();
|
||||
displacementBwd.dispose();
|
||||
resized.dispose();
|
||||
return resultPoses;
|
||||
return new Promise(async (resolve) => {
|
||||
const outputStride = config.outputStride;
|
||||
// const inputResolution = config.inputResolution;
|
||||
const height = input.shape[1];
|
||||
const width = input.shape[2];
|
||||
const resized = util.resizeTo(input, [config.inputResolution, config.inputResolution]);
|
||||
const { heatmapScores, offsets, displacementFwd, displacementBwd } = this.baseModel.predict(resized);
|
||||
const allTensorBuffers = await util.toTensorBuffers3D([heatmapScores, offsets, displacementFwd, displacementBwd]);
|
||||
const scoresBuffer = allTensorBuffers[0];
|
||||
const offsetsBuffer = allTensorBuffers[1];
|
||||
const displacementsFwdBuffer = allTensorBuffers[2];
|
||||
const displacementsBwdBuffer = allTensorBuffers[3];
|
||||
const poses = await decodeMultiple.decodeMultiplePoses(scoresBuffer, offsetsBuffer, displacementsFwdBuffer, displacementsBwdBuffer, outputStride, config.maxDetections, config.scoreThreshold, config.nmsRadius);
|
||||
const resultPoses = util.scaleAndFlipPoses(poses, [height, width], [config.inputResolution, config.inputResolution]);
|
||||
heatmapScores.dispose();
|
||||
offsets.dispose();
|
||||
displacementFwd.dispose();
|
||||
displacementBwd.dispose();
|
||||
resized.dispose();
|
||||
resolve(resultPoses);
|
||||
});
|
||||
}
|
||||
|
||||
dispose() {
|
||||
|
|
@ -17,54 +17,57 @@ async function load(config) {
|
|||
}
|
||||
|
||||
async function predict(image, config) {
|
||||
if (frame < config.face.emotion.skipFrames) {
|
||||
frame += 1;
|
||||
return last;
|
||||
}
|
||||
frame = 0;
|
||||
const box = [[
|
||||
(image.shape[1] * zoom[0]) / image.shape[1],
|
||||
(image.shape[2] * zoom[1]) / image.shape[2],
|
||||
(image.shape[1] - (image.shape[1] * zoom[0])) / image.shape[1],
|
||||
(image.shape[2] - (image.shape[2] * zoom[1])) / image.shape[2],
|
||||
]];
|
||||
const resize = tf.image.cropAndResize(image, box, [0], [config.face.emotion.inputSize, config.face.emotion.inputSize]);
|
||||
// const resize = tf.image.resizeBilinear(image, [config.face.emotion.inputSize, config.face.emotion.inputSize], false);
|
||||
const [red, green, blue] = tf.split(resize, 3, 3);
|
||||
resize.dispose();
|
||||
// weighted rgb to grayscale: https://www.mathworks.com/help/matlab/ref/rgb2gray.html
|
||||
const redNorm = tf.mul(red, rgb[0]);
|
||||
const greenNorm = tf.mul(green, rgb[1]);
|
||||
const blueNorm = tf.mul(blue, rgb[2]);
|
||||
red.dispose();
|
||||
green.dispose();
|
||||
blue.dispose();
|
||||
const grayscale = tf.addN([redNorm, greenNorm, blueNorm]);
|
||||
const normalize = tf.tidy(() => grayscale.sub(0.5).mul(2));
|
||||
redNorm.dispose();
|
||||
greenNorm.dispose();
|
||||
blueNorm.dispose();
|
||||
const obj = [];
|
||||
if (config.face.emotion.enabled) {
|
||||
let data;
|
||||
if (!config.profile) {
|
||||
const emotionT = await models.emotion.predict(normalize);
|
||||
data = emotionT.dataSync();
|
||||
tf.dispose(emotionT);
|
||||
} else {
|
||||
const profileData = await tf.profile(() => models.emotion.predict(grayscale));
|
||||
data = profileData.result.dataSync();
|
||||
profileData.result.dispose();
|
||||
profile.run('emotion', profileData);
|
||||
return new Promise(async (resolve) => {
|
||||
if (frame < config.face.emotion.skipFrames) {
|
||||
frame += 1;
|
||||
resolve(last);
|
||||
}
|
||||
for (let i = 0; i < data.length; i++) {
|
||||
if (scale * data[i] > config.face.emotion.minConfidence) obj.push({ score: Math.min(0.99, Math.trunc(100 * scale * data[i]) / 100), emotion: annotations[i] });
|
||||
frame = 0;
|
||||
const box = [[
|
||||
(image.shape[1] * zoom[0]) / image.shape[1],
|
||||
(image.shape[2] * zoom[1]) / image.shape[2],
|
||||
(image.shape[1] - (image.shape[1] * zoom[0])) / image.shape[1],
|
||||
(image.shape[2] - (image.shape[2] * zoom[1])) / image.shape[2],
|
||||
]];
|
||||
const resize = tf.image.cropAndResize(image, box, [0], [config.face.emotion.inputSize, config.face.emotion.inputSize]);
|
||||
// const resize = tf.image.resizeBilinear(image, [config.face.emotion.inputSize, config.face.emotion.inputSize], false);
|
||||
const [red, green, blue] = tf.split(resize, 3, 3);
|
||||
resize.dispose();
|
||||
// weighted rgb to grayscale: https://www.mathworks.com/help/matlab/ref/rgb2gray.html
|
||||
const redNorm = tf.mul(red, rgb[0]);
|
||||
const greenNorm = tf.mul(green, rgb[1]);
|
||||
const blueNorm = tf.mul(blue, rgb[2]);
|
||||
red.dispose();
|
||||
green.dispose();
|
||||
blue.dispose();
|
||||
const grayscale = tf.addN([redNorm, greenNorm, blueNorm]);
|
||||
redNorm.dispose();
|
||||
greenNorm.dispose();
|
||||
blueNorm.dispose();
|
||||
const normalize = tf.tidy(() => grayscale.sub(0.5).mul(2));
|
||||
grayscale.dispose();
|
||||
const obj = [];
|
||||
if (config.face.emotion.enabled) {
|
||||
let data;
|
||||
if (!config.profile) {
|
||||
const emotionT = await models.emotion.predict(normalize);
|
||||
data = emotionT.dataSync();
|
||||
tf.dispose(emotionT);
|
||||
} else {
|
||||
const profileData = await tf.profile(() => models.emotion.predict(grayscale));
|
||||
data = profileData.result.dataSync();
|
||||
profileData.result.dispose();
|
||||
profile.run('emotion', profileData);
|
||||
}
|
||||
for (let i = 0; i < data.length; i++) {
|
||||
if (scale * data[i] > config.face.emotion.minConfidence) obj.push({ score: Math.min(0.99, Math.trunc(100 * scale * data[i]) / 100), emotion: annotations[i] });
|
||||
}
|
||||
obj.sort((a, b) => b.score - a.score);
|
||||
}
|
||||
obj.sort((a, b) => b.score - a.score);
|
||||
}
|
||||
tf.dispose(grayscale);
|
||||
last = obj;
|
||||
return obj;
|
||||
normalize.dispose();
|
||||
last = obj;
|
||||
resolve(obj);
|
||||
});
|
||||
}
|
||||
|
||||
exports.predict = predict;
|
||||
|
|
|
|||
|
|
@ -173,6 +173,7 @@ class Pipeline {
|
|||
let rotatedImage = input;
|
||||
let rotationMatrix = util.IDENTITY_MATRIX;
|
||||
if (angle !== 0) {
|
||||
// bug: input becomes disposed here when running in async mode!
|
||||
rotatedImage = tf.image.rotateWithOffset(input, angle, 0, faceCenterNormalized);
|
||||
rotationMatrix = util.buildRotationMatrix(-angle, faceCenter);
|
||||
}
|
||||
|
|
@ -0,0 +1,63 @@
|
|||
const tf = require('@tensorflow/tfjs');
|
||||
const profile = require('../profile.js');
|
||||
|
||||
const models = {};
|
||||
let last = { gender: '' };
|
||||
let frame = Number.MAX_SAFE_INTEGER;
|
||||
|
||||
// tuning values
|
||||
const zoom = [0, 0]; // 0..1 meaning 0%..100%
|
||||
|
||||
async function load(config) {
|
||||
if (!models.gender) models.gender = await tf.loadGraphModel(config.face.gender.modelPath);
|
||||
return models.gender;
|
||||
}
|
||||
|
||||
async function predict(image, config) {
|
||||
return new Promise(async (resolve) => {
|
||||
if (frame < config.face.age.skipFrames) {
|
||||
frame += 1;
|
||||
resolve(last);
|
||||
}
|
||||
frame = 0;
|
||||
const box = [[
|
||||
(image.shape[1] * zoom[0]) / image.shape[1],
|
||||
(image.shape[2] * zoom[1]) / image.shape[2],
|
||||
(image.shape[1] - (image.shape[1] * zoom[0])) / image.shape[1],
|
||||
(image.shape[2] - (image.shape[2] * zoom[1])) / image.shape[2],
|
||||
]];
|
||||
const resize = tf.image.cropAndResize(image, box, [0], [config.face.age.inputSize, config.face.age.inputSize]);
|
||||
// const resize = tf.image.resizeBilinear(image, [config.face.age.inputSize, config.face.age.inputSize], false);
|
||||
const enhance = tf.mul(resize, [255.0]);
|
||||
tf.dispose(resize);
|
||||
|
||||
let genderT;
|
||||
const obj = {};
|
||||
|
||||
if (!config.profile) {
|
||||
if (config.face.gender.enabled) genderT = await models.gender.predict(enhance);
|
||||
} else {
|
||||
const profileGender = config.face.gender.enabled ? await tf.profile(() => models.gender.predict(enhance)) : {};
|
||||
genderT = profileGender.result.clone();
|
||||
profileGender.result.dispose();
|
||||
profile.run('gender', profileGender);
|
||||
}
|
||||
enhance.dispose();
|
||||
|
||||
if (genderT) {
|
||||
const data = genderT.dataSync();
|
||||
const confidence = Math.trunc(Math.abs(1.9 * 100 * (data[0] - 0.5))) / 100;
|
||||
if (confidence > config.face.gender.minConfidence) {
|
||||
obj.gender = data[0] <= 0.5 ? 'female' : 'male';
|
||||
obj.confidence = confidence;
|
||||
}
|
||||
}
|
||||
genderT.dispose();
|
||||
|
||||
last = obj;
|
||||
resolve(obj);
|
||||
});
|
||||
}
|
||||
|
||||
exports.predict = predict;
|
||||
exports.load = load;
|
||||
|
|
@ -97,7 +97,6 @@ class HandPipeline {
|
|||
if (useFreshBox) {
|
||||
this.regionsOfInterest = [];
|
||||
if (!boundingBoxPredictions || boundingBoxPredictions.length === 0) {
|
||||
image.dispose();
|
||||
this.detectedHands = 0;
|
||||
return null;
|
||||
}
|
||||
275
src/human.js
275
src/human.js
|
|
@ -1,9 +1,10 @@
|
|||
const tf = require('@tensorflow/tfjs');
|
||||
const facemesh = require('./facemesh/facemesh.js');
|
||||
const ssrnet = require('./ssrnet/ssrnet.js');
|
||||
const facemesh = require('./face/facemesh.js');
|
||||
const age = require('./age/ssrnet.js');
|
||||
const gender = require('./gender/ssrnet.js');
|
||||
const emotion = require('./emotion/emotion.js');
|
||||
const posenet = require('./posenet/posenet.js');
|
||||
const handpose = require('./handpose/handpose.js');
|
||||
const posenet = require('./body/posenet.js');
|
||||
const handpose = require('./hand/handpose.js');
|
||||
const gesture = require('./gesture.js');
|
||||
const image = require('./image.js');
|
||||
const profile = require('./profile.js');
|
||||
|
|
@ -53,6 +54,7 @@ class Human {
|
|||
this.analyzeMemoryLeaks = false;
|
||||
this.checkSanity = false;
|
||||
this.firstRun = true;
|
||||
this.perf = {};
|
||||
// object that contains all initialized models
|
||||
this.models = {
|
||||
facemesh: null,
|
||||
|
|
@ -65,10 +67,11 @@ class Human {
|
|||
};
|
||||
// export raw access to underlying models
|
||||
this.facemesh = facemesh;
|
||||
this.ssrnet = ssrnet;
|
||||
this.age = age;
|
||||
this.gender = gender;
|
||||
this.emotion = emotion;
|
||||
this.posenet = posenet;
|
||||
this.handpose = handpose;
|
||||
this.body = posenet;
|
||||
this.hand = handpose;
|
||||
}
|
||||
|
||||
// helper function: wrapper around console output
|
||||
|
|
@ -109,6 +112,8 @@ class Human {
|
|||
|
||||
// preload models, not explicitly required as it's done automatically on first use
|
||||
async load(userConfig) {
|
||||
this.state = 'load';
|
||||
const timeStamp = now();
|
||||
if (userConfig) this.config = mergeDeep(defaults, userConfig);
|
||||
|
||||
if (this.firstRun) {
|
||||
|
|
@ -118,35 +123,55 @@ class Human {
|
|||
this.log('flags:', tf.ENV.flags);
|
||||
this.firstRun = false;
|
||||
}
|
||||
|
||||
if (this.config.face.enabled && !this.models.facemesh) {
|
||||
this.log('load model: face');
|
||||
this.models.facemesh = await facemesh.load(this.config.face);
|
||||
}
|
||||
if (this.config.body.enabled && !this.models.posenet) {
|
||||
this.log('load model: body');
|
||||
this.models.posenet = await posenet.load(this.config.body);
|
||||
}
|
||||
if (this.config.hand.enabled && !this.models.handpose) {
|
||||
this.log('load model: hand');
|
||||
this.models.handpose = await handpose.load(this.config.hand);
|
||||
}
|
||||
if (this.config.face.enabled && this.config.face.age.enabled && !this.models.age) {
|
||||
this.log('load model: age');
|
||||
this.models.age = await ssrnet.loadAge(this.config);
|
||||
}
|
||||
if (this.config.face.enabled && this.config.face.gender.enabled && !this.models.gender) {
|
||||
this.log('load model: gender');
|
||||
this.models.gender = await ssrnet.loadGender(this.config);
|
||||
}
|
||||
if (this.config.face.enabled && this.config.face.emotion.enabled && !this.models.emotion) {
|
||||
this.log('load model: emotion');
|
||||
this.models.emotion = await emotion.load(this.config);
|
||||
if (this.config.async) {
|
||||
[
|
||||
this.models.age,
|
||||
this.models.gender,
|
||||
this.models.emotion,
|
||||
this.models.facemesh,
|
||||
this.models.posenet,
|
||||
this.models.handpose,
|
||||
] = await Promise.all([
|
||||
this.models.age || age.load(this.config),
|
||||
this.models.gender || gender.load(this.config),
|
||||
this.models.emotion || emotion.load(this.config),
|
||||
this.models.facemesh || facemesh.load(this.config.face),
|
||||
this.models.posenet || posenet.load(this.config.body),
|
||||
this.models.handpose || handpose.load(this.config.hand),
|
||||
]);
|
||||
} else {
|
||||
if (this.config.face.enabled && !this.models.facemesh) {
|
||||
this.log('load model: face');
|
||||
this.models.facemesh = await facemesh.load(this.config.face);
|
||||
}
|
||||
if (this.config.body.enabled && !this.models.posenet) {
|
||||
this.log('load model: body');
|
||||
this.models.posenet = await posenet.load(this.config.body);
|
||||
}
|
||||
if (this.config.hand.enabled && !this.models.handpose) {
|
||||
this.log('load model: hand');
|
||||
this.models.handpose = await handpose.load(this.config.hand);
|
||||
}
|
||||
if (this.config.face.enabled && this.config.face.age.enabled && !this.models.age) {
|
||||
this.log('load model: age');
|
||||
this.models.age = await age.load(this.config);
|
||||
}
|
||||
if (this.config.face.enabled && this.config.face.gender.enabled && !this.models.gender) {
|
||||
this.log('load model: gender');
|
||||
this.models.gender = await gender.load(this.config);
|
||||
}
|
||||
if (this.config.face.enabled && this.config.face.emotion.enabled && !this.models.emotion) {
|
||||
this.log('load model: emotion');
|
||||
this.models.emotion = await emotion.load(this.config);
|
||||
}
|
||||
}
|
||||
const current = Math.trunc(now() - timeStamp);
|
||||
if (current > (this.perf.load || 0)) this.perf.load = current;
|
||||
}
|
||||
|
||||
// check if backend needs initialization if it changed
|
||||
async checkBackend(force) {
|
||||
const timeStamp = now();
|
||||
if (force || (tf.getBackend() !== this.config.backend)) {
|
||||
this.state = 'backend';
|
||||
/* force backend reload
|
||||
|
|
@ -174,12 +199,104 @@ class Human {
|
|||
tf.ENV.set('WEBGL_PACK_DEPTHWISECONV', true);
|
||||
await tf.ready();
|
||||
}
|
||||
const current = Math.trunc(now() - timeStamp);
|
||||
if (current > (this.perf.backend || 0)) this.perf.backend = current;
|
||||
}
|
||||
|
||||
async detectFace(input) {
|
||||
// run facemesh, includes blazeface and iris
|
||||
// eslint-disable-next-line no-async-promise-executor
|
||||
let timeStamp;
|
||||
let ageRes;
|
||||
let genderRes;
|
||||
let emotionRes;
|
||||
const faceRes = [];
|
||||
this.state = 'run:face';
|
||||
timeStamp = now();
|
||||
const faces = await this.models.facemesh.estimateFaces(input, this.config.face);
|
||||
this.perf.face = Math.trunc(now() - timeStamp);
|
||||
for (const face of faces) {
|
||||
this.analyze('Get Face');
|
||||
// is something went wrong, skip the face
|
||||
if (!face.image || face.image.isDisposedInternal) {
|
||||
this.log('Face object is disposed:', face.image);
|
||||
continue;
|
||||
}
|
||||
// run age, inherits face from blazeface
|
||||
this.analyze('Start Age:');
|
||||
if (this.config.async) {
|
||||
ageRes = this.config.face.age.enabled ? age.predict(face.image, this.config) : {};
|
||||
} else {
|
||||
this.state = 'run:age';
|
||||
timeStamp = now();
|
||||
ageRes = this.config.face.age.enabled ? await age.predict(face.image, this.config) : {};
|
||||
this.perf.age = Math.trunc(now() - timeStamp);
|
||||
}
|
||||
|
||||
// run gender, inherits face from blazeface
|
||||
this.analyze('Start Gender:');
|
||||
if (this.config.async) {
|
||||
genderRes = this.config.face.gender.enabled ? gender.predict(face.image, this.config) : {};
|
||||
} else {
|
||||
this.state = 'run:gender';
|
||||
timeStamp = now();
|
||||
genderRes = this.config.face.gender.enabled ? await gender.predict(face.image, this.config) : {};
|
||||
this.perf.gender = Math.trunc(now() - timeStamp);
|
||||
}
|
||||
// run emotion, inherits face from blazeface
|
||||
this.analyze('Start Emotion:');
|
||||
if (this.config.async) {
|
||||
emotionRes = this.config.face.emotion.enabled ? emotion.predict(face.image, this.config) : {};
|
||||
} else {
|
||||
this.state = 'run:emotion';
|
||||
timeStamp = now();
|
||||
emotionRes = this.config.face.emotion.enabled ? await emotion.predict(face.image, this.config) : {};
|
||||
this.perf.emotion = Math.trunc(now() - timeStamp);
|
||||
}
|
||||
this.analyze('End Emotion:');
|
||||
|
||||
// if async wait for results
|
||||
if (this.config.async) {
|
||||
[ageRes, genderRes, emotionRes] = await Promise.all([ageRes, genderRes, emotionRes]);
|
||||
}
|
||||
|
||||
this.analyze('Finish Face:');
|
||||
// dont need face anymore
|
||||
face.image.dispose();
|
||||
|
||||
// calculate iris distance
|
||||
// iris: array[ bottom, left, top, right, center ]
|
||||
const iris = (face.annotations.leftEyeIris && face.annotations.rightEyeIris)
|
||||
? Math.max(face.annotations.leftEyeIris[3][0] - face.annotations.leftEyeIris[1][0], face.annotations.rightEyeIris[3][0] - face.annotations.rightEyeIris[1][0])
|
||||
: 0;
|
||||
|
||||
// combine results
|
||||
faceRes.push({
|
||||
confidence: face.confidence,
|
||||
box: face.box,
|
||||
mesh: face.mesh,
|
||||
annotations: face.annotations,
|
||||
age: ageRes.age,
|
||||
gender: genderRes.gender,
|
||||
genderConfidence: genderRes.confidence,
|
||||
emotion: emotionRes,
|
||||
iris: (iris !== 0) ? Math.trunc(100 * 11.7 /* human iris size in mm */ / iris) / 100 : 0,
|
||||
});
|
||||
this.analyze('End Face');
|
||||
}
|
||||
this.analyze('End FaceMesh:');
|
||||
if (this.config.async) {
|
||||
if (this.perf.face) delete this.perf.face;
|
||||
if (this.perf.age) delete this.perf.age;
|
||||
if (this.perf.gender) delete this.perf.gender;
|
||||
if (this.perf.emotion) delete this.perf.emotion;
|
||||
}
|
||||
return faceRes;
|
||||
}
|
||||
|
||||
// main detect function
|
||||
async detect(input, userConfig = {}) {
|
||||
this.state = 'config';
|
||||
const perf = {};
|
||||
let timeStamp;
|
||||
|
||||
// update configuration
|
||||
|
|
@ -195,109 +312,69 @@ class Human {
|
|||
}
|
||||
|
||||
// detection happens inside a promise
|
||||
// eslint-disable-next-line no-async-promise-executor
|
||||
return new Promise(async (resolve) => {
|
||||
let poseRes;
|
||||
let handRes;
|
||||
let ssrRes;
|
||||
let emotionRes;
|
||||
let faceRes;
|
||||
|
||||
const timeStart = now();
|
||||
|
||||
// configure backend
|
||||
timeStamp = now();
|
||||
await this.checkBackend();
|
||||
perf.backend = Math.trunc(now() - timeStamp);
|
||||
|
||||
// load models if enabled
|
||||
timeStamp = now();
|
||||
this.state = 'load';
|
||||
await this.load();
|
||||
perf.load = Math.trunc(now() - timeStamp);
|
||||
|
||||
if (this.config.scoped) tf.engine().startScope();
|
||||
|
||||
this.analyze('Start Detect:');
|
||||
this.analyze('Start Scope:');
|
||||
|
||||
timeStamp = now();
|
||||
const process = image.process(input, this.config);
|
||||
perf.image = Math.trunc(now() - timeStamp);
|
||||
this.perf.image = Math.trunc(now() - timeStamp);
|
||||
this.analyze('Get Image:');
|
||||
|
||||
// run facemesh, includes blazeface and iris
|
||||
const faceRes = [];
|
||||
if (this.config.face.enabled) {
|
||||
// run face detection followed by all models that rely on face bounding box: face mesh, age, gender, emotion
|
||||
if (this.config.async) {
|
||||
faceRes = this.config.face.enabled ? this.detectFace(process.tensor) : [];
|
||||
if (this.perf.face) delete this.perf.face;
|
||||
} else {
|
||||
this.state = 'run:face';
|
||||
timeStamp = now();
|
||||
this.analyze('Start FaceMesh:');
|
||||
const faces = await this.models.facemesh.estimateFaces(process.tensor, this.config.face);
|
||||
perf.face = Math.trunc(now() - timeStamp);
|
||||
for (const face of faces) {
|
||||
// is something went wrong, skip the face
|
||||
if (!face.image || face.image.isDisposedInternal) {
|
||||
this.log('Face object is disposed:', face.image);
|
||||
continue;
|
||||
}
|
||||
// run ssr-net age & gender, inherits face from blazeface
|
||||
this.state = 'run:agegender';
|
||||
timeStamp = now();
|
||||
ssrRes = (this.config.face.age.enabled || this.config.face.gender.enabled) ? await ssrnet.predict(face.image, this.config) : {};
|
||||
perf.agegender = Math.trunc(now() - timeStamp);
|
||||
// run emotion, inherits face from blazeface
|
||||
this.state = 'run:emotion';
|
||||
timeStamp = now();
|
||||
emotionRes = this.config.face.emotion.enabled ? await emotion.predict(face.image, this.config) : {};
|
||||
perf.emotion = Math.trunc(now() - timeStamp);
|
||||
|
||||
// dont need face anymore
|
||||
face.image.dispose();
|
||||
// calculate iris distance
|
||||
// iris: array[ bottom, left, top, right, center ]
|
||||
const iris = (face.annotations.leftEyeIris && face.annotations.rightEyeIris)
|
||||
? Math.max(face.annotations.leftEyeIris[3][0] - face.annotations.leftEyeIris[1][0], face.annotations.rightEyeIris[3][0] - face.annotations.rightEyeIris[1][0])
|
||||
: 0;
|
||||
faceRes.push({
|
||||
confidence: face.confidence,
|
||||
box: face.box,
|
||||
mesh: face.mesh,
|
||||
annotations: face.annotations,
|
||||
age: ssrRes.age,
|
||||
gender: ssrRes.gender,
|
||||
agConfidence: ssrRes.confidence,
|
||||
emotion: emotionRes,
|
||||
iris: (iris !== 0) ? Math.trunc(100 * 11.7 /* human iris size in mm */ / iris) / 100 : 0,
|
||||
});
|
||||
this.analyze('End FaceMesh:');
|
||||
}
|
||||
faceRes = this.config.face.enabled ? await this.detectFace(process.tensor) : [];
|
||||
this.perf.face = Math.trunc(now() - timeStamp);
|
||||
}
|
||||
|
||||
// run posenet
|
||||
this.analyze('Start Body:');
|
||||
if (this.config.async) {
|
||||
poseRes = this.config.body.enabled ? this.models.posenet.estimatePoses(process.tensor, this.config.body) : [];
|
||||
if (this.perf.body) delete this.perf.body;
|
||||
} else {
|
||||
this.state = 'run:body';
|
||||
timeStamp = now();
|
||||
this.analyze('Start PoseNet');
|
||||
poseRes = this.config.body.enabled ? await this.models.posenet.estimatePoses(process.tensor, this.config.body) : [];
|
||||
this.analyze('End PoseNet:');
|
||||
perf.body = Math.trunc(now() - timeStamp);
|
||||
this.perf.body = Math.trunc(now() - timeStamp);
|
||||
}
|
||||
this.analyze('End Body:');
|
||||
|
||||
// run handpose
|
||||
this.analyze('Start Hand:');
|
||||
if (this.config.async) {
|
||||
handRes = this.config.hand.enabled ? this.models.handpose.estimateHands(process.tensor, this.config.hand) : [];
|
||||
if (this.perf.hand) delete this.perf.hand;
|
||||
} else {
|
||||
this.state = 'run:hand';
|
||||
timeStamp = now();
|
||||
this.analyze('Start HandPose:');
|
||||
handRes = this.config.hand.enabled ? await this.models.handpose.estimateHands(process.tensor, this.config.hand) : [];
|
||||
this.analyze('End HandPose:');
|
||||
perf.hand = Math.trunc(now() - timeStamp);
|
||||
this.perf.hand = Math.trunc(now() - timeStamp);
|
||||
}
|
||||
// this.analyze('End Hand:');
|
||||
|
||||
if (this.config.async) [poseRes, handRes] = await Promise.all([poseRes, handRes]);
|
||||
|
||||
// if async wait for results
|
||||
if (this.config.async) {
|
||||
[faceRes, poseRes, handRes] = await Promise.all([faceRes, poseRes, handRes]);
|
||||
}
|
||||
process.tensor.dispose();
|
||||
this.state = 'idle';
|
||||
|
||||
if (this.config.scoped) tf.engine().endScope();
|
||||
this.analyze('End Scope:');
|
||||
|
|
@ -306,11 +383,13 @@ class Human {
|
|||
if (this.config.gesture.enabled) {
|
||||
timeStamp = now();
|
||||
gestureRes = { body: gesture.body(poseRes), hand: gesture.hand(handRes), face: gesture.face(faceRes) };
|
||||
perf.gesture = Math.trunc(now() - timeStamp);
|
||||
if (!this.config.async) this.perf.gesture = Math.trunc(now() - timeStamp);
|
||||
else if (this.perf.gesture) delete this.perf.gesture;
|
||||
}
|
||||
|
||||
perf.total = Math.trunc(now() - timeStart);
|
||||
resolve({ face: faceRes, body: poseRes, hand: handRes, gesture: gestureRes, performance: perf, canvas: process.canvas });
|
||||
this.perf.total = Math.trunc(now() - timeStart);
|
||||
this.state = 'idle';
|
||||
resolve({ face: faceRes, body: poseRes, hand: handRes, gesture: gestureRes, performance: this.perf, canvas: process.canvas });
|
||||
});
|
||||
}
|
||||
}
|
||||
|
|
|
|||
|
|
@ -1,80 +0,0 @@
|
|||
const tf = require('@tensorflow/tfjs');
|
||||
const profile = require('../profile.js');
|
||||
|
||||
const models = {};
|
||||
let last = { age: 0, gender: '' };
|
||||
let frame = Number.MAX_SAFE_INTEGER;
|
||||
|
||||
// tuning values
|
||||
const zoom = [0, 0]; // 0..1 meaning 0%..100%
|
||||
|
||||
async function loadAge(config) {
|
||||
if (!models.age) models.age = await tf.loadGraphModel(config.face.age.modelPath);
|
||||
return models.age;
|
||||
}
|
||||
|
||||
async function loadGender(config) {
|
||||
if (!models.gender) models.gender = await tf.loadGraphModel(config.face.gender.modelPath);
|
||||
return models.gender;
|
||||
}
|
||||
|
||||
async function predict(image, config) {
|
||||
if (frame < config.face.age.skipFrames) {
|
||||
frame += 1;
|
||||
return last;
|
||||
}
|
||||
frame = 0;
|
||||
const box = [[
|
||||
(image.shape[1] * zoom[0]) / image.shape[1],
|
||||
(image.shape[2] * zoom[1]) / image.shape[2],
|
||||
(image.shape[1] - (image.shape[1] * zoom[0])) / image.shape[1],
|
||||
(image.shape[2] - (image.shape[2] * zoom[1])) / image.shape[2],
|
||||
]];
|
||||
const resize = tf.image.cropAndResize(image, box, [0], [config.face.age.inputSize, config.face.age.inputSize]);
|
||||
// const resize = tf.image.resizeBilinear(image, [config.face.age.inputSize, config.face.age.inputSize], false);
|
||||
const enhance = tf.mul(resize, [255.0]);
|
||||
tf.dispose(resize);
|
||||
|
||||
const promises = [];
|
||||
let ageT;
|
||||
let genderT;
|
||||
const obj = {};
|
||||
|
||||
if (!config.profile || config.async) {
|
||||
if (config.face.age.enabled) promises.push(ageT = models.age.predict(enhance));
|
||||
if (config.face.gender.enabled) promises.push(genderT = models.gender.predict(enhance));
|
||||
await Promise.all(promises);
|
||||
} else {
|
||||
const profileAge = config.face.age.enabled ? await tf.profile(() => models.age.predict(enhance)) : {};
|
||||
ageT = profileAge.result.clone();
|
||||
profileAge.result.dispose();
|
||||
profile.run('age', profileAge);
|
||||
const profileGender = config.face.gender.enabled ? await tf.profile(() => models.gender.predict(enhance)) : {};
|
||||
genderT = profileGender.result.clone();
|
||||
profileGender.result.dispose();
|
||||
profile.run('gender', profileGender);
|
||||
}
|
||||
|
||||
if (ageT) {
|
||||
const data = ageT.dataSync();
|
||||
obj.age = Math.trunc(10 * data[0]) / 10;
|
||||
tf.dispose(ageT);
|
||||
}
|
||||
if (genderT) {
|
||||
const data = genderT.dataSync();
|
||||
const confidence = Math.trunc(Math.abs(1.9 * 100 * (data[0] - 0.5))) / 100;
|
||||
if (confidence > config.face.gender.minConfidence) {
|
||||
obj.gender = data[0] <= 0.5 ? 'female' : 'male';
|
||||
obj.confidence = confidence;
|
||||
}
|
||||
tf.dispose(genderT);
|
||||
}
|
||||
|
||||
tf.dispose(enhance);
|
||||
last = obj;
|
||||
return obj;
|
||||
}
|
||||
|
||||
exports.predict = predict;
|
||||
exports.loadAge = loadAge;
|
||||
exports.loadGender = loadGender;
|
||||
Loading…
Reference in New Issue